CMSA

Non-invertible Symmetries in Nature
2022-09-13 09:30 - 11:00 (America/New_York)
Quantum Matter in Mathematics and Physics

Speaker: Yichul Cho (SUNY Stony Brook)
Title: Non-invertible Symmetries in Nature
Abstract: In this talk, I will discuss non-invertible symmetries in
familiar 3+1d quantum field theories describing our Nature. In
massless QED, the classical U(1) axial symmetry is not completely
broken by the ABJ anomaly. Instead, it turns into a discrete,
non-invertible symmetry. The non-invertible symmetry operator is
obtained by dressing the naïve U(1) axial symmetry operator with a
fractional quantum Hall state. We also find a similar non-invertible
symmetry in the massless limit of QCD, which provides an alternative
explanation for the neutral pion decay. In the latter part of the
talk, I will discuss non-invertible time-reversal symmetries in 3+1d
gauge theories. In particular, I will show that in free Maxwell
theory, there exists a non-invertible time-reversal symmetry at every
rational value of the theta angle.
Based on https://arxiv.org/abs/2205.05086 and https://arxiv.org/abs/2208.04331.

Duality in Einstein’s Gravity
2022-09-09 12:00 - 13:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
Title: Duality in Einstein’s Gravity
Abstract: Electric-Magnetic duality has been a key feature behind our understanding of Quantum Field Theory for over a century. In this talk I will describe a similar property in Einstein’s gravity. The gravitational duality reveals, in turn, a wide range of new IR phenomena, including aspects of the double copy for scattering amplitudes, asymptotic symmetries and more.

The second law of black hole mechanics in effective field theory
2022-09-08 10:30 - 11:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
General Relativity Seminar
Speaker: Professor Harvey Reall (University of Cambridge)
Title: The second law of black hole mechanics in effective field theory
Abstract: I shall discuss the second law of black hole mechanics in gravitational theories with higher derivative terms in the action. Wall has described a method for defining an entropy that satisfies the second law to linear order in perturbations around a stationary black hole. I shall explain how this can be extended to define an entropy that satisfies the second law to quadratic order in perturbations, provided that one treats the higher derivative terms in the sense of effective field theory. This talk is based on work with Stefan Hollands and Aron Kovacs.
Video

Gifts from anomalies: new results on quantum critical transport in non- Fermi liquids
2022-09-07 09:00 - 10:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
Quantum Matter in Mathematics and Physics Seminar
Speaker: Zhengyan Darius Shi (MIT)

Title: Gifts from anomalies: new results on quantum critical transport in non-Fermi liquids
Abstract: Non-Fermi liquid phenomena arise naturally near Landau ordering transitions in metallic systems. Here, we leverage quantum anomalies as a powerful nonperturbative tool to calculate optical transport in these models in the infrared limit. While the simplest such models with a single boson flavor (N=1) have zero incoherent conductivity, a recently proposed large N deformation involving flavor-random Yukawa couplings between N flavors of bosons and fermions admits a nontrivial incoherent conductivity (z is the boson dynamical exponent) when the order parameter is odd under inversion. The presence of incoherent conductivity in the random flavor model is a consequence of its unusual anomaly structure. From this we conclude that the large N deformation does not share important nonperturbative features with the physical N = 1 model, though it remains an interesting theory in its own right. Going beyond the IR fixed point, we also consider the effects of irrelevant operators and show, within the scope of the RPA expansion, that the old result due to Kim et al. is incorrect for inversion-odd order parameters.

State Diagram of Cancer Cell Unjamming Predicts Metastatic Risk
2022-09-06 13:00 - 14:00
Speaker: Josef Käs, Leipzig University
Title: State Diagram of Cancer Cell Unjamming Predicts Metastatic Risk
Abstract: Distant metastasis is probably the most lethal hallmark of cancer. Due to a lack of suitable markers, cancer cell motility only has a negligible impact on current diagnosis. Based on cell unjamming we derive a cell motility marker for static histological images. This enables us to sample huge numbers of breast cancer patient data to derive a comprehensive state diagram of unjamming as a collective transition in cell clusters of solid tumors. As recently discovered, cell unjamming transitions occur in embryonic development and as pathological changes in diseases such as cancer. No consensus has been achieved on the variables and the parameter space that describe this transition. Cell shapes or densities based on different unjamming models have been separately used to describe the unjamming transition under different experimental conditions. Moreover, the role of the nucleus is not considered in the current unjamming models. Mechanical stress propagating through the tissue mechanically couples the cell nuclei mediated by the cell’s cytoplasm, which strongly impacts jamming.
Based on our exploratory retrospective clinical study with N=1,380 breast cancer patients and vital cell tracking in patient-derived tumor explants, we find that the unjamming state diagram depends on cell and nucleus shapes as one variable and the nucleus number density as the other that measures the cytoplasmic spacing between the nuclei. Our approach unifies previously controversial results into one state diagram. It spans a broad range of states that cancer cell clusters can assume in a solid tumor. We can use an empirical decision boundary to show that the unjammed regions in the diagram correlate with the patient’s risk for metastasis.
We conclude that unjamming within primary tumors is part of the metastatic cascade, which significantly advances the understanding of the early metastatic events. With the histological slides of two independent breast cancer patients’ collectives, we train (N=688) and validate (N=692) our quantitative prognostic index based on unjamming regarding metastatic risk. Our index corrects for false high- and low-risk predictions based on the invasion of nearby lymph nodes, the current gold standard. Combining information derived from the nodal status with unjamming may reduce over- and under-treatment.
Video (Youtube)

Big Data Conference 2022
2022-08-26 09:00 - 13:00 | Virtual
On August 26, 2022 the CMSA hosted our eighth annual Conference on Big Data. The Big Data Conference features speakers from the Harvard community as well as scholars from across the globe, with talks focusing on computer science, statistics, math and physics, and economics.
The 2022 Big Data Conference took place virtually on Zoom.
Organizers:

Scott Duke Kominers, MBA Class of 1960 Associate Professor, Harvard Business
Horng-Tzer Yau, Professor of Mathematics, Harvard University
Sergiy Verstyuk, CMSA, Harvard University

Speakers:

Xiaohong Chen, Yale
Miles Cranmer, Princeton
Jessica Jeffers, University of Chicago
Dan Roberts, MIT

Schedule

9:00 am
Conference Organizers
Introduction and Welcome

9:10 am – 9:55 am
Xiaohong Chen
Title: On ANN optimal estimation and inference for policy functionals of nonparametric conditional moment restrictions
Abstract: Many causal/policy parameters of interest are expectation functionals of unknown infinite-dimensional structural functions identified via conditional moment restrictions. Artificial Neural Networks (ANNs) can be viewed as nonlinear sieves that can approximate complex functions of high dimensional covariates more effectively than linear sieves. In this talk we present ANN optimal estimation and inference on policy functionals, such as average elasticities or value functions, of unknown structural functions of endogenous covariates. We provide ANN efficient estimation and optimal t based confidence interval for regular policy functionals such as average derivatives in nonparametric instrumental variables regressions. We also present ANN quasi likelihood ratio based inference for possibly irregular policy functionals of general nonparametric conditional moment restrictions (such as quantile instrumental variables models or Bellman equations) for time series data. We conduct intensive Monte Carlo studies to investigate computational issues with ANN based optimal estimation and inference in economic structural models with endogeneity. For economic data sets that do not have very high signal to noise ratios, there are current gaps between theoretical advantage of ANN approximation theory vs inferential performance in finite samples.
Some of the results are applied to efficient estimation and optimal inference for average price elasticity in consumer demand and BLP type demand.
The talk is based on two co-authored papers:
(1) Efficient Estimation of Average Derivatives in NPIV Models: Simulation Comparisons of Neural Network Estimators
(Authors: Jiafeng Chen, Xiaohong Chen and Elie Tamer)
https://arxiv.org/abs/2110.06763
(2) Neural network Inference on Nonparametric conditional moment restrictions with weakly dependent data
(Authors: Xiaohong Chen, Yuan Liao and Weichen Wang).
View/Download Lecture Slides (pdf)

10:00 am – 10:45 am
Jessica Jeffers
Title: Labor Reactions to Credit Deterioration: Evidence from LinkedIn Activity
Abstract: We analyze worker reactions to their firms’ credit deterioration. Using weekly networking activity on LinkedIn, we show workers initiate more connections immediately following a negative credit event, even at firms far from bankruptcy. Our results suggest that workers are driven by concerns about both unemployment and future prospects at their firm. Heightened networking activity is associated with contemporaneous and future departures, especially at financially healthy firms. Other negative events like missed earnings and equity downgrades do not trigger similar reactions. Overall, our results indicate that the build-up of connections triggered by credit deterioration represents a source of fragility for firms.

10:50 am – 11:35 am
Miles Cranmer
Title: Interpretable Machine Learning for Physics
Abstract: Would Kepler have discovered his laws if machine learning had been around in 1609? Or would he have been satisfied with the accuracy of some black box regression model, leaving Newton without the inspiration to discover the law of gravitation? In this talk I will explore the compatibility of industry-oriented machine learning algorithms with discovery in the natural sciences. I will describe recent approaches developed with collaborators for addressing this, based on a strategy of “translating” neural networks into symbolic models via evolutionary algorithms. I will discuss the inner workings of the open-source symbolic regression library PySR (github.com/MilesCranmer/PySR), which forms a central part of this interpretable learning toolkit. Finally, I will present examples of how these methods have been used in the past two years in scientific discovery, and outline some current efforts.
View/Download Lecture Slides (pdf)

11:40 am – 12:25 pm
Dan Roberts
Title: A Statistical Model of Neural Scaling Laws
Abstract: Large language models of a huge number of parameters and trained on near internet-sized number of tokens have been empirically shown to obey “neural scaling laws” for which their performance behaves predictably as a power law in either parameters or dataset size until bottlenecked by the other resource. To understand this better, we first identify the necessary properties allowing such scaling laws to arise and then propose a statistical model — a joint generative data model and random feature model — that captures this neural scaling phenomenology. By solving this model using tools from random matrix theory, we gain insight into (i) the statistical structure of datasets and tasks that lead to scaling laws (ii) how nonlinear feature maps, i.e the role played by the deep neural network, enable scaling laws when trained on these datasets, and (iii) how such scaling laws can break down, and what their behavior is when they do. A key feature is the manner in which the power laws that occur in the statistics of natural datasets are translated into power law scalings of the test loss, and how the finite extent of such power laws leads to both bottlenecks and breakdowns.
View/Download Lecture Slides (pdf)

12:30 pm
Conference Organizers
Closing Remarks

Information about last year’s conference can be found here.

Scalable Dynamic Graph Algorithms
2022-08-18 10:00 - 10:30 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
CMSA Interdisciplinary Science Seminar
Speaker: Quanquan Liu, Northwestern University
Title: Scalable Dynamic Graph Algorithms
Abstract: The field of dynamic graph algorithms seeks to understand and compute statistics on real-world networks that undergo changes with time. Some of these networks could have up to millions of edge insertions and deletions per second. In light of these highly dynamic networks, we propose various scalable and accurate graph algorithms for a variety of problems. In this talk, I will discuss new algorithms for various graph problems in the batch-dynamic model in shared-memory architectures where updates to the graph arrive in multiple batches of one or more updates. I’ll also briefly discuss my work in other dynamic models such as distributed dynamic models where the communication topology of the network also changes with time (ITCS 2022). In these models, I will present efficient algorithms for graph problems including k-core decomposition, low out-degree orientation, matching, triangle counting, and coloring.
Specifically, in the batch-dynamic model where we are given a batch of B updates, I’ll discuss an efficient O(B log^2 n) amortized work and O(log^2 n log log n) depth algorithm that gives a (2+epsilon)-approximation on the k-core decomposition after each batch of updates (SPAA 2022). We also obtain new batch-dynamic algorithms for matching, triangle counting, and coloring using techniques and data structures developed in our k-core decomposition algorithm. In addition to our theoretical results, we implemented and experimentally evaluated our k-core decomposition algorithm on a 30-core machine with two-way hyper-threading on 11 graphs of varying densities and sizes. Our experiments show improvements over state-of-the-art algorithms even on machines with only 4 cores (your standard laptop). I’ll conclude with a discussion of some open questions and potential future work that these lines of research inspire.
Bio: Quanquan C. Liu is a postdoctoral scholar at Northwestern University under the mentorship of Prof. Samir Khuller. She completed her PhD in Computer Science at MIT where she was advised by Prof. Erik Demaine and Prof. Julian Shun. Before that, she obtained her dual bachelor’s degree in computer science and math also at MIT. She has worked on a number of problems in algorithms and the intersection between theory and practice. Her most recent work focuses on scalable dynamic and static graph algorithms as well as differentially private graph algorithms for problems including k-core decomposition, densest subgraphs, subgraph counting, matching, maximal independent set and coloring. She has earned the Best Paper Award at SPAA 2022, a NSF Graduate Research Fellowship, and participated in the 2021 EECS Rising Stars workshop. Outside of research, she is extensively involved in programming outreach as a coach for the USA Computing Olympiad (USACO) and as a trainer for the North America Programming Camp (NAPC).

Transport in large-N critical Fermi surface
2022-08-16 10:00 - 11:30 | Virtual
Speaker: Haoyu Guo (Harvard)
Title: Transport in large-N critical Fermi surface

Abstract: A Fermi surface coupled to a scalar field can be described in a 1/N expansion by choosing the fermion-scalar Yukawa coupling to be random in the N-dimensional flavor space, but invariant under translations. We compute the conductivity of such a theory in two spatial dimensions for a critical scalar. We find a Drude contribution, and show that a previously proposed omega^{-2/3} contribution to the optical conductivity at frequency omega has vanishing co-efficient. We also describe the influence of impurity scattering of the fermions, and find that while the self energy resembles a marginal Fermi liquid, the resistivity behaves like a Fermi liquid. Arxiv references: 2203.04990, 2207.08841

Exploring and Exploiting the Universality Phenomena in High-Dimensional Estimation and Learning
2022-08-11 09:00 - 10:00 | Hybrid
Interdisciplinary Science Seminar
Speaker: Yue M. Lu, Harvard University
Title: Exploring and Exploiting the Universality Phenomena in High-Dimensional Estimation and Learning
Abstract: Universality is a fascinating high-dimensional phenomenon. It points to the existence of universal laws that govern the macroscopic behavior of wide classes of large and complex systems, despite their differences in microscopic details. The notion of universality originated in statistical mechanics, especially in the study of phase transitions. Similar phenomena have been observed in probability theory, dynamical systems, random matrix theory, and number theory.
In this talk, I will present some recent progresses in rigorously understanding and exploiting the universality phenomena in the context of statistical estimation and learning on high-dimensional data. Examples include spectral methods for high-dimensional projection pursuit, statistical learning based on kernel and random feature models, and approximate message passing algorithms on highly structured, strongly correlated, and even (nearly) deterministic data matrices. Together, they demonstrate the robustness and wide applicability of the universality phenomena.
Bio: Yue M. Lu attended the University of Illinois at Urbana-Champaign, where he received the M.Sc. degree in mathematics and the Ph.D. degree in electrical engineering, both in 2007. He is currently Gordon McKay Professor of Electrical Engineering and of Applied Mathematics at Harvard University. He is also fortunate to have held visiting appointments at Duke University in 2016 and at the École Normale Supérieure (ENS) in 2019. His research interests include the mathematical foundations of statistical signal processing and machine learning in high dimensions.

Recent Advances on Maximum Flows and Minimum-Cost Flows
2022-08-10 09:00 - 10:00 | Virtual
Interdisciplinary Science Seminar

Speaker: Yang P. Liu

Title: Recent Advances on Maximum Flows and Minimum-Cost Flows

Abstract: We survey recent advances on computing flows in graphs, culminating in an almost linear time algorithm for solving minimum-cost flow and several other problems to high accuracy on directed graphs. Along the way, we will discuss intuitions from linear programming, graph theory, and data structures that influence these works, and the resulting natural open problems.
Bio: Yang P. Liu is a final-year graduate student at Stanford University. He is broadly interested in the efficient design of algorithms, particularly flows, convex optimization, and online algorithms. For his work, he has been awarded STOC and ITCS best student papers.

Phase Transitions and Topological Defects in the Early Universe
2022-08-02 09:00 - 2022-08-05 17:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
On August 2–5, the CMSA hosted a workshop on Phase Transitions and Topological Defects in the Early Universe.
The workshop was held in room G10 of the CMSA, located at 20 Garden Street, Cambridge, MA and online via Zoom webinar.
The next decade will see a wealth of new cosmological data, which can lead to new insights into fundamental physics. Upcoming facilities (such as LISA) will be able to probe signals of fascinating phenomena in the early universe. These include signals from “Phase Transitions and Topological Defects,” which are ubiquitously given rise to in well-motivated UV models. In-depth studies of such signals requires cross-talks between experts from a wide spectrum of fields.
The workshop aims to provide a platform for efficient exchange of new ideas related to these topics. It will start with an overview of some of the past and future experimental efforts. Next, there will be a substantial number of talks probing different aspects of phenomenology of phase transitions and topological defects in the early universe. It will finally close with discussions on recent formal development in the field.
Scientific Advisory: Julian B. Muñoz, Lisa Randall, Matthew Reece, Tracy Slatyer, Shing-Tung Yau
Organizers:
Harvard: Nick DePorzio, Katie Fraser, Sam Homiller, Rashmish Mishra, & Aditya Parikh
MIT: Pouya Asadi, Marianne Moore, & Yitian Sun
Schedule/Format
There will be 20+ 10 minute talks, ample discussion time, and lightning chalkboard talks.
Speakers:

Nancy Aggarwal (Northwestern)
Jae Hyeok Chang (UMD – JHU)
Yanou Cui (UC Riverside)
David Dunsky (UC Berkeley)
Isabel Garcia-Garcia (KITP – UCSB)
Oliver Gould (Nottingham)
Yann Gouttenoire (Tel Aviv)
Eleanor Hall (UC Berkeley)
Sungwoo Hong (Chicago)
Anson Hook (UMD)
Jessica Howard (UC Irvine)
Seth Koren (Chicago)
Mrunal Korwar (Wisconsin)
Soubhik Kumar (UC Berkeley)
Vuk Mandic (Minnesota)
Yuto Minami (Osaka)
Michael Nee (Oxford)
Kai Schmitz (CERN)
Stephen R. Taylor (Vanderbilt)
Ofri Telem (UC Berkeley)
Juven Wang (Harvard)
Yikun Wang (Caltech)

Participants:

Manuel Buen Abad (UMD)
Pouya Asadi (MIT)
Sean Benevedes (MIT)
Sandipan Bhattacherjee (Birla Institute of Technology Mesra Ranchi India)
Xingang Chen (Harvard University)
Nicholas DePorzio (Harvard University)
Peizhi Du (Stony Brook University)
Nicolas Fernandez (University of Illinois Urbana-Champaign)
Joshua Foster (MIT)
Katherine Fraser (Harvard University)
Sarah Geller (MIT)
Aurora Ireland (University of Chicago)
Marius Kongsore (New York University)
Ho Tat Lam (Massachusetts Institute of Technology)
Lingfeng Li (Brown University)
Yingying Li (Fermilab)
Gustavo Marques-Tavares (UMD)
Rashmish Mishra (Harvard University)
Siddharth Mishra-Sharma (MIT/Harvard University)
Toby Opferkuch (UC Berkeley)
Tong Ou (University of Chicago)
Aditya Parikh (Harvard University)
Yitian Sun (MIT)
Juan Sebastian Valbuena-Bermudez (Ludwig Maximilian University of Munich and Max Planck Institute for Physics)
Isaac Wang (Rutgers)
Wei Xue (University of Florida)
Winston Yin (UC Berkeley)
Quratulain Zahoor (The Islamia University of Bahwalpur Punjab (Pakistan)

Schedule
Tuesday, August 2, 2022

9:00–9:20 am
Breakfast

9:20–9:30 am
Rashmish Mishra
Opening Remarks

9:30–10:00 am
Vuk Mandic
Title: Searching for the Stochastic Gravitational Wave Background with LISA
Abstract: The upcoming space-borne gravitational wave detector Laser Interferometer Space Antenna (LISA) will open a window into the milliHertz band of the gravitational wave spectrum. Among the many sources in this band is the stochastic gravitational wave background (SGWB), arising as an incoherent superposition of many uncorrelated gravitational wave sources. The SGWB could be of cosmological origin, carrying unique information about the physical processes that took place within the first minute after the big bang, including possible phase transitions and topological defects. LISA therefore has the potential to illuminate particle physics at very high energy scales that may be inaccessible in laboratories. I will discuss how LISA can be used to search for the SGWB, highlighting a new pipeline developed for this purpose as well as several challenges and limitations that such a search will encounter.

10:00–10:30 am
Nancy Aggarwal
Title: Gravitational waves at frequencies above 10 kHz
Abstract: Gravitational waves (GWs) at frequencies higher than the LIGO band can bring us completely new information about the universe. Besides being the most-interesting frequency region for looking at cosmological phenomena, they can also convey signatures of ultralight bosons through blackhole superradiance and light primordial blackholes (PBHs). I will introduce a new global initiative to study GW sources and detectors at ultra-high-frequencies (MHz-GHz), as well as a new experiment at Northwestern University to look for GWs in the frequency band of 10 kHz to 300 kHz using levitated optomechanical sensors. I will summarize the design, the current experimental progress, as well as a path forward for future improvements.

10:30–11:00 am
Yuto Minami
Title: New measurements of the cosmic birefringence
Abstract: Polarised light of the cosmic microwave background, the remnant light of the Big Bang, is sensitive to parity-violating physics, cosmic birefringence. In this presentation I report on a new measurement of cosmic birefringence from polarisation data of the European Space Agency (ESA)’s Planck satellite released in 2018. The statistical significance of the measured signal is 2.4 sigma. Recently, we found a signal with 3.3 sigma statistical significance when we use the latest Planck data and consider an effect of polarised foreground emission. If confirmed with higher statistical significance in future, it would have important implications for the elusive nature of dark matter and dark energy.

11:00–1:30 pm
Break

1:30–3:00 pm
Lighting Talks 1
Lingfeng Li
Winston Yin
Marius Kongsore
Nick DePorzio

3:00–3:30 pm
Jae Hyeok Chang
Title: Correlating gravitational wave and gamma-ray signals from primordial black holes
Abstract: Asteroid-mass primordial black holes (PBHs) can explain the observed dark matter abundance while being consistent with the current indirect detection constraints. These PBHs can produce gamma-ray signals from Hawking radiation that are within the sensitivity of future measurements by the AMEGO and e-ASTROGAM experiments. PBHs which give rise to such observable gamma-ray signals have a cosmic origin from large primordial curvature fluctuations. There must then be a companion, stochastic gravitational wave (GW) background produced by the same curvature fluctuations. I will demonstrate that the resulting GW signals will be well within the sensitivity of future detectors such as LISA, DECIGO, BBO, and the Einstein Telescope. The multimessenger signal from the observed gamma-rays and GWs will allow a precise measurement of the primordial curvature perturbation that produces the PBH. I will also argue that the resulting correlation between the two types of observations can provide a smoking-gun signal of PBHs.

3:30–4:00 pm
Anson Hook
(Virtual via Zoom)
Title: Early Universe Cosmology from Stochastic Gravitational Waves
Abstract: The causal tail of stochastic gravitational waves can be used to probe the energy density in free streaming relativistic species as well as measure gstar and beta functions as a function of temperature. In the event of the discovery of loud stochastic gravitational waves, we demonstrate that LISA can measure the free streaming fraction of the universe down to the 10^-3 level, 100 times more sensitive than current constraints. Additionally, it would be sensitive to O(1) deviations of gstar and the QCD beta function from their Standard Model value at temperatures ~ 10^5 GeV. In this case, many motivated models such as split SUSY and other solutions to the Electroweak Hierarchy problem would be tested. Future detectors, such as DECIGO, would be 100 times more sensitive than LISA to these effects and be capable of testing other motivated scenarios such as WIMPs and axions. The amazing prospect of using precision gravitational wave measurements to test such well motivated theories provides a benchmark to aim for when developing a precise understanding of the gravitational wave spectrum both experimentally and theoretically.

Wednesday, August 3, 2022

9:00–9:30 am
Breakfast

9:30–10:00 am
Kai Schmitz
(Virtual via Zoom)
Title: Gravitational waves from metastable cosmic strings
Abstract: Cosmic strings are predicted by many Standard Model extensions involving the cosmological breaking of an Abelian symmetry and represent a potential source of primordial gravitational waves (GWs). In many Grand Unified Theories (GUTs), cosmic strings especially turn out to be metastable, as the nucleation of GUT monopoles along strings after a finite lifetime eventually leads to the collapse of the entire string network. In this talk, I will discuss the theoretical description of such a network and its individual components as well as the consequences for the emitted GW spectrum. Remarkably, the GW signal from metastable strings may well explain the common-spectrum process recently observed in pulsar timing data, while at the same time and in contrast to stable cosmic strings predicting a signal at higher frequencies that is still within the reach of current-generation ground-based interferometers. On their way to design sensitivity, existing GW experiments will thus have a realistic chance to probe particle physics processes at energies close to the GUT scale via the observation of GWs from metastable strings. This talk is based on 2107.04578 in collaboration with Wilfried Buchmüller and Valerie Domcke.

10:00–10:30 am
Oliver Gould
(Virtual via Zoom)
Title: Effective field theory for cosmological phase transitions
Abstract: Phase transitions are driven by thermal loop fluctuations, which modify background fields at leading order. This breaks the loop expansion and leads to large theoretical uncertainties in typical calculations, especially for gravitational wave predictions. I will give an overview of our present understanding of these uncertainties, and of the tools that have been developed to overcome them. Effective field theory has been at the forefront of this development, and I will outline how it can be used to solve a number of decades-long-standing theoretical problems.

10:30–11:00 am
Isabel Garcia-Garcia
Title: The Rocket Science of Expanding Bubbles

11:00–1:30 pm
Break

1:30–3:00 pm
Lightning Talks 2
Sarah Geller
Peizhi Du
Tong Ou
Isaac Wang
Katie Fraser

3:00–3:30 pm
David Dunsky
(Virtual via Zoom)
Title: Gravitational Wave Gastronomy
Abstract: The symmetry breaking of grand unified gauge groups in the early universe often leaves behind relic topological defects such as cosmic strings, domain walls, or monopoles. For some symmetry breaking chains, hybrid defects can form where cosmic strings attach to domain walls or monopoles attach to strings. In general, such hybrid defects are unstable and can leave behind unique gravitational wave fingerprints. In this talk, I will discuss the gravitational wave spectrum from 1) the destruction of a cosmic string network by the nucleation of monopoles which cut up and “eat” the strings, 2) the collapse and decay of a monopole-string network by strings that “eat” the monopoles, 3) the destruction of a domain wall network by the nucleation of string-bounded holes on the wall that expand and “eat” the wall, and 4) the collapse and decay of a string-bounded wall network by walls that “eat” the strings. We call the gravitational wave signals produced from the “eating” of one topological defect by another “gravitational wave gastronomy”. The gravitational wave gastronomy signals considered yield unique spectra that can be used to narrow down the SO(10) symmetry breaking chain to the Standard Model and the scales of symmetry breaking associated with the consumed topological defects.

3:30–4:00 pm
Yanou Cui
(Virtual via Zoom)
Title: Cosmic Archaeology with gravitational waves from (axion) cosmic strings
Abstract: In this talk I will discuss important aspects of cosmology and particle physics that can be probed with GW signals from cosmic strings: probing the pre-BBN primordial dark age and axion physics. Gravitational waves (GWs) originating from the dynamics of a cosmic string network have the ability to probe many otherwise inaccessible properties of the early universe. In particular, I will discuss how the frequency spectrum of a stochastic GW background (SGWB) from a cosmic string network can be used to probe Hubble expansion rate of the early universe prior to Big Bang Nucleosynthesis (BBN), during the “primordial dark age”. Furthermore I will show that in contrast to the standard expectation, cosmic strings formed before inflation could regrow back into the horizon and leave imprints, with GW bursts potentially being the leading signal. In relation to axion physics I will also demonstrate the detection prospect for SGWB from global/axion strings which may provide a new probe for axion-like dark matter models, considering various scenarios of cosmic history.

4:00–4:30 pm
Michael Nee
Title: The Boring Monopole
Abstract: First order phase transitions play an important role in the cosmology of many theories of BSM physics. In this talk I will discuss how a population of magnetic monopoles present in the early universe can seed first order phase transitions, causing them to proceed much more rapidly than in the usual case. The field profiles describing the decay do not have the typically assumed O(3)/O(4) symmetry, thus requiring an extension of the usual decay rate calculation. To numerically determine the saddle point solutions which describe the decay we use a new algorithm based on the mountain pass theorem. Our results show that monopole-catalysed tunnelling can dominate over the homogeneous decay for a wide range of parameters.

Thursday, August 4, 2022

9:00–9:30 am
Breakfast

9:30–10:00 am
Yikun Wang
Title: A New Approach to Electroweak Symmetry Non-Restoration
Abstract: Electroweak symmetry non-restoration up to high temperatures well above the electroweak scale has intriguing implications for (electroweak) baryogenesis and early universe thermal histories. In this talk, I will discuss such a possible fate of the electroweak symmetry in the early universe and a new approach to realize it, via an inert Higgs sector that couples to the Standard Model Higgs as well as an extended scalar singlet sector. Examples of benchmark scenarios that allow for electroweak symmetry non-restoration all the way up to hundreds of TeV temperatures, at the same time featuring suppressed sphaleron washout factors down to the electroweak scale, will be presented. Renormalization group improvements and thermal resummation, necessary to evaluate the effective potential spanning over a broad range of energy scales and temperatures, have been implemented calculating the thermal history. This method for transmitting the Standard Model broken electroweak symmetry to an inert Higgs sector can be scrutinized through Higgs physics phenomenology and electroweak precision measurements at the HL-LHC.

10:00–10:30 am
Soubhik Kumar
Title: Probing primordial fluctuations through stochastic gravitational wave background anisotropies
Abstract: Stochastic gravitational wave backgrounds are expected to be anisotropic. While such anisotropies can be of astrophysical origin, a cosmological component of such anisotropies can carry rich information about primordial perturbations. Focusing on the case of a cosmological phase transition, I will talk about how such anisotropies can give us a powerful probe of primordial non-Gaussianities, complementary to current and future CMB and LSS searches. In the scenario where astrophysical foregrounds are also present, I will then discuss some strategies using which we can extract the cosmological signal, focusing on the case of LISA, Taiji and BBO, in particular.

10:30–11:00 am
Jessica Howard
(Virtual via Zoom)
Title: Dark Matter Freeze-out during SU(2)_L Confinement
Abstract: We explore the possibility that dark matter is a pair of SU(2)_L doublets and propose a novel mechanism of dark matter production that proceeds through the confinement of the weak sector of the Standard Model. This phase of confinement causes the Standard Model doublets and dark matter to confine into pion-like objects. Before the weak sector deconfines, the dark pions freezeout and generate a relic abundance of dark matter. We solve the Boltzmann equations for this scenario to determine the scale of confinement and constituent dark matter mass required to produce the observed relic density. We determine which regions of this parameter space evade direct detection and collider bounds.

11:00–11:30 am
Juven Wang
Title: Quantum Matter Adventure to Beyond the Standard Model Prediction
Abstract: Ideas developed from the quantum matter and quantum field theory frontier may guide us to explore new physics beyond the 4d Standard Model. I propose a few such ideas. First, new physics for neutrinos: right-handed neutrinos carry a Z_{16} class mixed gauge-gravitational global anomaly index, which could be replaced by 4d or 5d topological quantum field theory, or 4d interacting conformal field theory. These theories provide possible new neutrino mass mechanisms [arXiv:2012.15860]. Second, deconfined quantum criticality between Grand Unified Theories: dictated by a Z_2 class global anomaly, a gapless quantum critical region can happen between Georgi-Glashow and Pati-Salam models as deformation of the Standard Model, where Beyond the Standard Model physics and Dark Gauge sector occur as neighbor phases [arXiv:2106.16248, arXiv:2112.14765, arXiv:2204.08393]. Third, the Strong CP problem can be solved by a new solution involving Symmetric Mass Generation [arXiv:2204.14271].

11:30–1:30 pm
Break

1:30–4:00 pm
Stephen R. Taylor
Title: Pulsar Timing Arrays: The Next Window onto the Low-frequency Gravitational-wave Universe
Abstract: The nanohertz-frequency band of gravitational waves should be awash with signals from supermassive black-hole binaries, as well as cosmological signatures of phase transitions, cosmic strings, and other relics of the early Universe. Pulsar-timing arrays (PTAs) like the North American Nanohertz Observatory for Gravitational waves (NANOGrav) and the International Pulsar Timing Array are poised to chart this new frontier of gravitational wave discovery within the next several years. I will present exciting new results from recent cutting-edge searches, discuss some milestones on the road to the next decade of PTA discovery, and take workshop attendees through a guided tutorial of how the broader community can use our production-level analysis pipeline to extract new science with ease.

Friday, August 5, 2022

9:00–9:30 am
Breakfast

9:30–10:00 am
Ofri Telem
Title: Charge-Monopole Pairwise Phases from Dressed Quantum States

10:00–10:30 am
Sungwoo Hong
Title: Coupling a Cosmic String to a TQFT
Abstract: In the last few years, the notion of symmetry has been enlarged to “generalized symmetry” or “higher-form symmetry” and these more generalized symmetries have played a critical role in deepening our understanding of QFT, notably IR phases of QFT. In this talk, I will discuss a various ways of coupling the axion-Maxwell theory to a topological field theory (TQFT). Contrary to a common wisdom, I will show that such topological modifications can lead to direct changes in the local physics with possible observable consequences. This surprise can be realized by a dimensional reduction, namely, a coupling to a TQFT in 4d leads to a non-trivial and local impact on the 2d string world-sheet QFT. There also exists a topological modification of the theory, i.e. gauging a discrete subgroup of 0-form shift symmetry, and this time it results in a alteration of spectrum of cosmic strings. If time permits, I will also discuss generalized symmetries and associated higher-groups of these theories.

10:30–11:00 am
Eleanor Hall
(Virtual via Zoom)
Title: Non-perturbative methods for false vacuum decay
Abstract: Gravitational waves from phase transitions in the early universe are one of our most promising signal channels of BSM physics; however, existing methods for predicting these signals are limited to weakly-coupled theories. In this talk, I present the quasi-stationary effective action, a new non-perturbative formalism for false vacuum decay that integrates over local fluctuations in field space using the functional renormalization group. This method opens the door to reliable calculation of gravitational wave signals and false vacuum decay rates for strongly-interacting theories. I will also discuss recent developments and ongoing extensions of the QSEA.

11:00–1:30 pm
Break

1:30–2:00 pm
Mrunal Korwar
Title: Electroweak Symmetric Balls
Abstract: Electroweak symmetric balls are macroscopic objects with electroweak symmetry restored inside. Such an object can arise in models where dark sectors contain monopole or non-topological soliton with a Higgs portal interaction to the Standard Model. It could be produced in the early universe via phase transition or parametric resonance, accounting for all dark matter. In a scenario where the balls are allowed to evaporate, the observed baryon asymmetry in our universe could be explained by a mechanism of “catalyzed baryogenesis.” In this mechanism, the motion of a ball-like catalyst provides the necessary out-of-equilibrium condition, its outer wall has CP-violating interactions with the Standard Model particles, and its interior has baryon number violating interactions via electroweak Sphaleron. Because of electroweak symmetric cores, such objects have a large geometric cross-section off a nucleus, generating a multi-hit signature in large volume detectors. These objects could radiatively capture a nucleus and release GeV-scale energy for each interaction. The IceCube detector can probe dark matter balls with masses up to a gram.

2:00–2:30 pm
Seth Koren
Title: Discrete Gauged Baryon Minus Lepton Number and the Cosmological Lithium Problem
Abstract: We study the baryon minus lepton number gauge theory broken by a scalar with charge six. The infrared discrete vestige of the gauge symmetry demands the existence of cosmic string solutions, and their production as dynamical objects in the early universe is guaranteed by causality. These topological defects can support interactions which convert three protons into three positrons, and we argue an `electric’-`magnetic’ interplay can lead to an amplified, strong-scale cross-section in an analogue of the Callan-Rubakov effect.
The cosmological lithium problem—that theory predicts a primordial abundance thrice as high as that observed—has resisted decades of attempts by cosmologists, nuclear physicists, and astronomers alike to root out systematics. We suggest cosmic strings have disintegrated O(1) of the primordial lithium nuclei and estimate the rate in a benchmark scenario. To our knowledge this is the first new physics mechanism with microphysical justification for the abundance of lithium uniquely to be modified after Big Bang Nucleosynthesis.

2:30–3:00 pm
Yann Gouttenoire
Title: Supercool Composite Dark Matter beyond 100 TeV

Advances in Mathematical Physics
2022-07-30 09:00 - 2022-08-01 13:45 | Harvard Science Center, 1 Oxford Street, Cambridge, MA, 02138
A Conference in Honor of Elliott H. Lieb on his 90th Birthday
On July 30 – Aug 1, 2022 the Harvard Mathematics Department and the CMSA co-hosted a birthday conference in honor of Elliott Lieb.
This meeting highlights Elliott’s vast contribution to math and physics. Additionally, this meeting features Prof. Lieb’s more recent impact in strong subadditivity of entropy and integrable systems (ice model, Temperley-Lieb algebra etc.).
Venue:
July 30–31, 2022: Hall B, Science Center, 1 Oxford Street, Cambridge, MA, 02138
August 1, 2022: Hall C, Science Center, 1 Oxford Street, Cambridge, MA, 02138
Schedule (pdf)
Organizers:
Michael Aizenman, Princeton University
Joel Lebowitz, Rutgers University
Ruedi Seiler, Technische Universität Berlin
Herbert Spohn, Technical University of Munich
Horng-Tzer Yau, Harvard University
Shing-Tung Yau, Harvard University
Jakob Yngvason, University of Vienna
SPEAKERS:
Rafael Benguria, Pontificia Universidad Catolica de Chile
Eric Carlen, Rutgers University
Philippe Di Francesco, University of Illinois
Hugo Duminil-Copin, IHES
László Erdös, Institute of Science and Technology Austria
Rupert Frank, Ludwig Maximilian University of Munich
Jürg Fröhlich, ETH Zurich
Alessandro Giuliani, Università degli Studi Roma Tre
Bertrand Halperin, Harvard University
Klaus Hepp, Institute for Theoretical Physics, ETH Zurich
Sabine Jansen, Ludwig Maximilian University of Munich
Mathieu Lewin, Université Paris-Dauphine
Bruno Nachtergaele, The University of California, Davis
Yoshiko Ogata, University of Tokyo
Ron Peled, Tel Aviv University
Benjamin Schlein, University of Zurich
Robert Seiringer, Institute of Science and Technology Austria
Jan Philip Solovej, University of Copenhagen
Hal Tasaki, Gakushuin University
Simone Warzel, Technical University of Munich
Jun Yin, The University of California, Los Angeles

Statistical Mechanical theory for spatio-temporal evolution of Intra-tumor heterogeneity in cancers: Analysis of Multiregion sequencing data
2022-07-28 09:00 - 10:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
CMSA Interdisciplinary Science Seminar
Speaker: Sumit Sinha, Harvard University
Title: Statistical Mechanical theory for spatio-temporal evolution of Intra-tumor heterogeneity in cancers: Analysis of Multiregion sequencing data (https://arxiv.org/abs/2202.10595)
Abstract: Variations in characteristics from one region (sub-population) to another are commonly observed in complex systems, such as glasses and a collection of cells. Such variations are manifestations of heterogeneity, whose spatial and temporal behavior is hard to describe theoretically. In the context of cancer, intra-tumor heterogeneity (ITH), characterized by cells with genetic and phenotypic variability that co-exist within a single tumor, is often the cause of ineffective therapy and recurrence of cancer. Next-generation sequencing, obtained by sampling multiple regions of a single tumor (multi-region sequencing, M-Seq), has vividly demonstrated the pervasive nature of ITH, raising the need for a theory that accounts for evolution of tumor heterogeneity. Here, we develop a statistical mechanical theory to quantify ITH, using the Hamming distance, between genetic mutations in distinct regions within a single tumor. An analytic expression for ITH, expressed in terms of cell division probability (α) and mutation probability (p), is validated using cellular-automaton type simulations. Application of the theory successfully captures ITH extracted from M-seq data in patients with exogenous cancers (melanoma and lung). The theory, based on punctuated evolution at the early stages of the tumor followed by neutral evolution, is accurate provided the spatial variation in the tumor mutation burden is not large. We show that there are substantial variations in ITH in distinct regions of a single solid tumor, which supports the notion that distinct subclones could co-exist. The simulations show that there are substantial variations in the sub-populations, with the ITH increasing as the distance between the regions increases. The analytical and simulation framework developed here could be used in the quantitative analyses of the experimental (M-Seq) data. More broadly, our theory is likely to be useful in analyzing dynamic heterogeneity in complex systems such as supercooled liquids.
Bio: I am a postdoctoral fellow in Harvard SEAS (Applied Mathematics) and Dana Farber Cancer Institute (Data Science) beginning Feb 2022. I finished my PhD in Physics (Theoretical Biophysics) from UT Austin (Jan 2022) on “Theoretical and computational studies of growing tissue”. I pursued my undergraduate degree in Physics from the Indian Institute of Technology, Kanpur in India (2015). Boradly, I am interested in developing theoretical models, inspired from many-body statistical physics, for biological processes at different length and time scales.

7/22/2020 Quantum Matter Seminar
2022-07-22 09:30 - 11:00

Infants’ sensory-motor cortices undergo microstructural tissue growth coupled with myelination
2022-07-21 09:00 - 10:00
Abstract: The establishment of neural circuitry during early infancy is critical for developing visual, auditory, and motor functions. However, how cortical tissue develops postnatally is largely unknown. By combining T1 relaxation time from quantitative MRI and mean diffusivity (MD) from diffusion MRI, we tracked cortical tissue development in infants across three timepoints (newborn, 3 months, and 6 months). Lower T1 and MD indicate higher microstructural tissue density and more developed cortex. Our data reveal three main findings: First, primary sensory-motor areas (V1: visual, A1: auditory, S1: somatosensory, M1: motor) have lower T1 and MD at birth than higher-level cortical areas. However, all primary areas show significant reductions in T1 and MD in the first six months of life, illustrating profound tissue growth after birth. Second, significant reductions in T1 and MD from newborns to 6-month-olds occur in all visual areas of the ventral and dorsal visual streams. Strikingly, this development was heterogenous across the visual hierarchies: Earlier areas are more developed with denser tissue at birth than higher-order areas, but higher-order areas had faster rates of development. Finally, analysis of transcriptomic gene data that compares gene expression in postnatal vs. prenatal tissue samples showed strong postnatal expression of genes associated with myelination, synaptic signaling, and dendritic processes. Our results indicate that these cellular processes may contribute to profound postnatal tissue growth in sensory cortices observed in our in-vivo measurements. We propose a novel principle of postnatal maturation of sensory systems: development of cortical tissue proceeds in a hierarchical manner, enabling the lower-level areas to develop first to provide scaffolding for higher-order areas, which begin to develop more rapidly following birth to perform complex computations for vision and audition.
This work is published here: https://www.nature.com/articles/s42003-021-02706-w

Topological and geometrical aspects of spinors in insulating crystals
2022-07-14 09:00 - 10:00
Abstract: Introducing internal degrees of freedom in the description of crystalline insulators has led to a myriad of theoretical and experimental advances. Of particular interest are the effects of periodic perturbations, either in time or space, as they considerably enrich the variety of electronic responses. Here, we present a semiclassical approach to transport and accumulation of general spinor degrees of freedom in adiabatically driven, weakly inhomogeneous crystals of dimensions one, two and three under external electromagnetic fields. Our approach shows that spatio-temporal modulations of the system induce a spinor current and density that is related to geometrical and topological objects — the spinor-Chern fluxes and numbers — defined over the higher-dimensional phase-space of the system, i.e., its combined momentum-position-time coordinates.
The results are available here: https://arxiv.org/abs/2203.14902
Bio: Ioannis Petrides is a postdoctoral fellow at the School of Engineering and Applied Sciences at Harvard University. He received his Ph.D. from the Institute for Theoretical Physics at ETH Zurich. His research focuses on the topological and geometrical aspects of condensed matter systems.

Anomalies, dynamics and phases in strongly-coupled chiral gauge theories: Recent developments
2022-07-07 10:30 - 12:30 | Virtual
Speaker: Kenichi Konishi (UNIPI.IT)
Title: Anomalies, dynamics and phases in strongly-coupled chiral gauge theories: Recent developments
Abstract: After many years of efforts, still very little is known today about the physics of strongly-coupled chiral gauge theories in four dimensions, in spite of an important role they might play in the physics of fundamental interactions beyond the standard SU(3)xSU(2)xU(1) model. This is in stark contrast with the vectorlike gauge theories for which we have many solid results, thanks to some exact theorems, to the lattice simulation studies, to the Seiberg-Witten exact solution of N=2 supersymmetric gauge theories, and last, but not the least, to the real-world strong-interaction phenomenology and experimental tests of Quantum Chromodynamics.
The purpose of this seminar is to discuss the results of our recent efforts to improve the understanding of the strongly-coupled chiral gauge theories. Among the main tools of analysis are the consideration of anomalies. We use both the conventional ’t Hooft anomaly-matching ideas, and new, more stringent constraints coming from the generalized anomalies involving some higher-form symmetries. Also, the so-called strong anomalies, little considered in the context of chiral gage theories, are found to carry significant implications.
As the playground we study several classes of SU(N) gauge theories, the so-called Bars-Yankielowicz models, the generalized Georgi-Glashow models, as well as a few other simple theories with the fermions in complex, anomaly-free representations of the color SU(N).
Color-flavor-locked dynamical Higgs phase and dynamical Abelianization, emerge, among others, as two particularly interesting possible phases the system can flow into in the infrared, depending on the matter fermion content of the model.

The phenotype of the last universal common ancestor and the evolution of complexity
2022-07-07 09:00 - 10:00
Interdisciplinary Science Seminar

Speaker: Fouad El Baidouri, Broad Institute
Title: The phenotype of the last universal common ancestor and the evolution of complexity
Abstract: A fundamental concept in evolutionary theory is the last universal common ancestor (LUCA) from which all living organisms originated. While some authors have suggested a relatively complex LUCA it is still widely assumed that LUCA must have been a very simple cell and that life has subsequently increased in complexity through time. However, while current thought does tend towards a general increase in complexity through time in Eukaryotes, there is increasing evidence that bacteria and archaea have undergone considerable genome reduction during their evolution. This raises the surprising possibility that LUCA, as the ancestor of bacteria and archaea may have been a considerably complex cell. While hypotheses regarding the phenotype of LUCA do exist, all are founded on gene presence/absence. Yet, despite recent attempts to link genes and phenotypic traits in prokaryotes, it is still inherently difficult to predict phenotype based on the presence or absence of genes alone. In response to this, we used Bayesian phylogenetic comparative methods to predict ancestral traits. Testing for robustness to horizontal gene transfer (HGT) we inferred the phenotypic traits of LUCA using two robust published phylogenetic trees and a dataset of 3,128 bacterial and archaeal species.
Our results depict LUCA as a far more complex cell than has previously been proposed, challenging the evolutionary model of increased complexity through time in prokaryotes. Given current estimates for the emergence of LUCA we suggest that early life very rapidly evolved cellular complexity.

Entanglement and its key role in quantum information
2022-06-30 16:23 - 17:23
Abstract: Entanglement is a type of correlation found in composite quantum systems, connected with various non-classical phenomena. Currently, entanglement plays a key role in quantum information applications such as quantum computing, quantum communication, and quantum sensing. In this talk the concept of entanglement will be introduced along with various methods that have been proposed to detect and quantify it. The fundamental role of entanglement in both quantum theory and quantum technology will also be discussed.
Bio: Spyros Tserkis is a postdoctoral researcher at Harvard University, working on quantum information theory. Before joining Harvard in Fall 2021, he was a postdoctoral researcher at MIT and the Australian National University. He received his PhD from the University of Queensland.

Some new algorithms in statistical genomics
2022-06-23 09:00 - 10:00
Abstract: The statistical analysis of genomic data has incubated many innovations for computational method development. This talk will discuss some simple algorithms that may be useful in analyzing such data. Examples include algorithms for efficient resampling-based hypothesis testing, minimizing the sum of truncated convex functions, and fitting equality-constrained lasso problems. These algorithms have the potential to be used in other applications beyond statistical genomics.
Bio: Hui Jiang is an Associate Professor in the Department of Biostatistics at the University of Michigan. He received his Ph.D. in Computational and Mathematical Engineering from Stanford University. Before joining the University of Michigan, he was a postdoc in the Department of Statistics and Stanford Genome Technology Center at Stanford University. He is interested in developing statistical and computational methods for analyzing large-scale biological data generated using modern high-throughput technologies.

Joint BHI/CMSA Conference on Flat Holography
2022-06-21 09:00 - 2022-06-24 17:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
On June 21–24, 2022, the Harvard Black Hole Initiative and the CMSA hosted the Joint BHI/CMSA Conference on Flat Holography (and related topics).
The recent discovery of infinitely-many soft symmetries for all quantum theories of gravity in asymptotically flat space has provided a promising starting point for a bottom-up construction of a holographic dual for the real world. Recent developments have brought together previously disparate studies of soft theorems, asymptotic symmetries, twistor theory, asymptotically flat black holes and their microscopic duals, self-dual gravity, and celestial scattering amplitudes, and link directly to AdS/CFT.
The conference was held in room G10 of the CMSA, 20 Garden Street, Cambridge, MA.
Organizers:

Daniel Kapec, CMSA
Andrew Strominger, BHI
Shing-Tung Yau, Harvard & Tsinghua

Confirmed Speakers:

Nima Arkani-Hamed, IAS
Shamik Banerjee, Bhubaneswar, Inst. Phys.
Miguel Campiglia, Republica U., Montevido
Geoffrey Compere, Brussels
Laura Donnay, Vienna
Netta Engelhardt, MIT
Laurent Freidel, Perimeter
Alex Lupsasca, Princeton
Juan Maldacena, IAS
Lionel Mason, Oxford
Natalie Paquette, U. Washington
Sabrina Pasterski, Princeton/Perimeter
Andrea Puhm, Ecole Polytechnique
Ana-Maria Raclariu, Perimeter
Marcus Spradlin, Brown
Tomasz Taylor, Northeastern
Herman Verlinde, Princeton
Anastasia Volovich, Brown
Bin Zhu, Northeastern

Short talks by: Gonçalo Araujo-Regado (Cambridge), Adam Ball (Harvard), Eduardo Casali (Harvard), Jordan Cotler (Harvard), Erin Crawley (Harvard), Stéphane Detournay (Brussels), Alfredo Guevara (Harvard), Temple He (UC Davis), Elizabeth Himwich (Harvard), Yangrui Hu (Brown), Daniel Kapec (Harvard), Rifath Khan (Cambridge), Albert Law (Harvard), Luke Lippstreu (Brown), Noah Miller (Harvard), Sruthi Narayanan (Harvard), Lecheng Ren (Brown), Francisco Rojas (UAI), Romain Ruzziconi (Vienna), Andrew Strominger (Harvard), Adam Tropper (Harvard), Tianli Wang (Harvard), Walker Melton (Harvard)

Schedule
Monday, June 20, 2022

Arrival

7:00–9:00 pm
Welcome Reception at Andy’s residence

Tuesday, June 21, 2022

9:00–9:30 am
Breakfast
light breakfast provided

Morning Session
Chair: Dan Kapec

9:30–10:00 am
Herman Verlinde
Title: Comments on Celestial Dynamics

10:00–10:30 am
Juan Maldacena
Title: What happens when you spend too much time looking at supersymmetric
black holes?

10:30–11:00
Coffee break

11:00–11:30 am
Miguel Campiglia
Title: Asymptotic symmetries and loop corrections to soft theorems

11:30–12:00 pm
Geoffrey Compere
Title: Metric reconstruction from $Lw_{1+infty}$ multipoles
Abstract: The most general vacuum solution to Einstein’s field equations with no incoming radiation can be constructed perturbatively from two infinite sets of canonical multipole moments, which are found to be exchanged under gravitational electric-magnetic duality at the non-linear level. We demonstrate that in non-radiative regions such spacetimes are completely determined by a set of conserved celestial charges, which uniquely label transitions among non-radiative regions caused by radiative processes. The algebra of the conserved celestial charges is derived from the real $Lw_{1+infty}$ algebra. The celestial charges are expressed in terms of multipole moments, which allows to holographically reconstruct the metric in de Donder, Newman-Unti or Bondi gauge outside of sources.

12:00–2:00 pm
Lunch break

Afternoon Session
Chair: Eduardo Casali

2:00–2:30 pm
Natalie Paquette
Title: New thoughts on old gauge amplitudes

2:30–3:00 pm
Lionel Mason
Title: An open sigma model for celestial gravity
Abstract: A global twistor construction for conformally self-dual split signature metrics on $S2times S2$ was developed 15 years ago by Claude LeBrun and the speaker. This encodes the conformal metric into the location of a finite deformation of the real twistor space inside the flat complex twistor space, $mathbb{CP}3$. This talk adapts the construction to construct global SD Einstein metrics from conformal boundary data and perturbations around the self-dual sector. The construction entails determining a family of holomorphic discs in $mathbb{CP}3$ whose boundaries lie on the deformed real slice and the (chiral) sigma model controls these discs in the Einstein case and provides amplitude formulae.

3:00–3:30 pm
Coffee break

3:30–4:30 pm
Short Talks
Daniel Kapec: Soft Scalars and the Geometry of the Space of Celestial CFTs
Albert Law: Soft Scalars and the Geometry of the Space of Celestial CFTs
Sruthi Narayanan: Soft Scalars and the Geometry of the Space of Celestial CFTs
Stéphane Detournay: Non-conformal symmetries and near-extremal black holes
Francisco Rojas: Celestial string amplitudes beyond tree level
Temple He: An effective description of energy transport from holography

4:30–5:00 pm
Nima Arkani-Hamed
(Dual) surfacehedra and flow particles know about strings

Wednesday, June 22, 2022

9:00–9:30 am
Breakfast
light breakfast provided

Morning Session
Chair: Alfredo Guevara

9:30–10:00 am
Laurent Freidel
Title: Higher spin symmetry in gravity
Abstract: In this talk, I will review how the gravitational conservation laws at infinity reveal a tower of symmetry charges in an asymptotically flat spacetime.
I will show how the conservation laws, at spacelike infinity, give a tower of soft theorems that connect to the ones revealed by celestial holography.
I’ll present the expression for the symmetry charges in the radiative phase space, which opens the way to reveal the structure of the algebra beyond the positive helicity sector. Then, if time permits I’ll browse through many questions that these results raise:
such as the nature of the spacetime symmetry these charges represent, the nature of the relationship with multipole moments, and the insights their presence provides for quantum gravity.

10:00–10:30 am
Ana-Maria Raclariu
Title: Eikonal approximation in celestial CFT

10:30–11:00 am
Coffee break

11:00–11:30 am
Anastasia Volovich
Title: Effective Field Theories with Celestial Duals

11:30–12:00 pm
Marcus Spradlin
Title: Loop level gluon OPE’s in celestial holography

12:00–2:00 pm
Lunch break

Afternoon Session
Chair: Chiara Toldo

2:00–2:30 pm
Netta Engelhardt
Title: Wormholes from entanglement: true or false?

2:30–3:00 pm
Short Talks
Luke Lippstreu: Loop corrections to the OPE of celestial gluons
Yangrui Hu: Light transforms of celestial amplitudes
Lecheng Ren: All-order OPE expansion of celestial gluon and graviton primaries from MHV amplitudes

3:00–3:30 pm
Coffee break

3:30–4:30 pm
Short Talks
Noah Miller: C Metric Thermodynamics
Erin Crawley: Kleinian black holes
Rifath Khan: Cauchy Slice Holography: A New AdS/CFT Dictionary
Gonçalo Araujo-Regado: Cauchy Slice Holography: A New AdS/CFT Dictionary
Tianli Wang: Soft Theorem in the BFSS Matrix Model
Adam Tropper: Soft Theorem in the BFSS Matrix Model

7:00–9:00 pm
Banquet
Maharaja Restaurant, 57 JFK Street, Cambridge, MA

Thursday, June 23, 2022

9:00–9:30 am
Breakfast
light breakfast provided

Morning Session
Chair: Jordan Cotler

9:30–10:00 am
Laura Donnay
Title: A Carrollian road to flat space holography

10:00–10:30 am
Andrea Puhm
Title: Celestial wave scattering on Kerr-Schild backgrounds

10:30–11:00 am
Coffee break

11:00–11:30 am
Sabrina Pasterski
Title: Mining Celestial Symmetries
Abstract: The aim of this talk is to delve into the common thread that ties together recent work with H. Verlinde, L. Donnay, A. Puhm, and S. Banerjee exploring, explaining, and exploiting the symmetries encoded in the conformally soft sector.
Come prepared to debate the central charge, loop corrections, contour prescriptions, and orders of limits!

11:30–12:00 pm
Shamik Banerjee
Title: Virasoro and other symmetries in CCFT
Abstract: In this talk I will briefly describe my ongoing work with Sabrina Pasterski. In this work we revisit the standard construction of the celestial stress tensor as a shadow of the subleading conformally soft graviton. In its original formulation, we find that there is an obstruction to reproducing the expected $TT$ OPE in the double soft limit. This obstruction is related to the existence of the $SL_2$ current algebra symmetry of the CCFT. We propose a modification to the definition of the stress tensor which circumvents this obstruction and also discuss its implications for the existence of other current algebra (w_{1+infty}) symmetries in CCFT.

12:00–2:00 pm
Lunch break

Afternoon Session
Chair: Albert Law

2:00–2:30 pm
Tomasz Taylor
Title: Celestial Yang-Mills amplitudes and D=4 conformal blocks

2:30–3:00 pm
Bin Zhu
Title: Single-valued correlators and Banerjee-Ghosh equations
Abstract: Low-point celestial amplitudes are plagued with singularities resulting from spacetime translation. We consider a marginal deformation of the celestial CFT which is realized by coupling Yang-Mills theory to a background dilaton field, with the (complex) dilaton source localized on the celestial sphere. This picture emerges from the physical interpretation of the solutions of the system of differential equations discovered by Banerjee and Ghosh. We show that the solutions can be written as Mellin transforms of the amplitudes evaluated in such a dilaton background. The resultant three-gluon and four-gluon amplitudes are single-valued functions of celestial coordinates enjoying crossing symmetry and all other properties expected from standard CFT correlators.

3:00–3:30 pm
Coffee break

3:30–4:00 pm
Alex Lupsasca
Title: Holography of the Photon Ring

4:00–5:30 pm
Short Talks
Elizabeth Himwich: Celestial OPEs and w(1+infinity) symmetry of massless and massive amplitudes
Adam Ball: Perturbatively exact $w_{1+infty}$ asymptotic symmetry of quantum self-dual gravity
Romain Ruzziconi: A Carrollian Perspective on Celestial Holography
Jordan Cotler: Soft Gravitons in 3D
Alfredo Guevara: Comments on w_1+inf
Andrew Strominger: Top-down celestial holograms
Eduardo Casali: Celestial amplitudes as AdS-Witten diagrams
Walker Melton: Top-down celestial holograms

Friday, June 24, 2022

9:00–9:30 am
Breakfast

9:30–12:30 pm
Open Discussion

12:30–2:30 pm
Lunch provided at the BHI

Departure

Surface hopping algorithms for non-adiabatic quantum systems
2022-06-16 09:00 - 10:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
Interdisciplinary Science Seminar

Speaker: Jianfeng Lu, Duke UniversityTitle: Surface hopping algorithms for non-adiabatic quantum systems
Abstract: Surface hopping algorithm is widely used in chemistry for mixed quantum-classical dynamics. In this talk, we will discuss some of our recent works in mathematical understanding and algorithm development for surface hopping methods. These methods are based on stochastic approximations of semiclassical path-integral representation to the solution of multi-level Schrodinger equations; such methodology also extends to other high-dimensional transport systems.

Symposium on Foundations of Responsible Computing (FORC)
2022-06-06 09:00 - 2022-06-08 17:00 | CMSA Room G10, CMSA, 20 Garden Street, Cambridge, MA, 02138, United States
On June 6-8, 2022, the CMSA hosted the 3rd annual Symposium on Foundations of Responsible Computing (FORC).
The Symposium on Foundations of Responsible Computing (FORC) is a forum for mathematical research in computation and society writ large. The Symposium aims to catalyze the formation of a community supportive of the application of theoretical computer science, statistics, economics and other relevant analytical fields to problems of pressing and anticipated societal concern.
Organizers: Cynthia Dwork, Harvard SEAS | Omer Reingold, Stanford | Elisa Celis, Yale
Schedule
June 6, 2022

9:15 am–10:15 am
Opening Remarks
Keynote Speaker: Caroline Nobo, Yale University
Title: From Theory to Impact: Why Better Data Systems are Necessary for Criminal Legal Reform
Abstract: This talk will dive into the messy, archaic, and siloed world of local criminal justice data in America. We will start with a 30,000 foot discussion about the current state of criminal legal data systems, then transition to the challenges of this broken paradigm, and conclude with a call to measure new things – and to measure them better! This talk will leave you with an understanding of criminal justice data infrastructure and transparency in the US, and will discuss how expensive case management software and other technology are built on outdated normative values which impede efforts to reform the system. The result is an infuriating paradox: an abundance of tech products built without theoretical grounding, in a space rich with research and evidence.

10:15 am–10:45 am
Coffee Break

10:45 am–12:15 pm
Paper Session 1
Session Chair: Ruth Urner

Georgy Noarov, University of Pennsylvania
Title: Online Minimax Multiobjective Optimization
Abstract: We introduce a simple but general online learning framework in which a learner plays against an adversary in a vector-valued game that changes every round. The learner’s objective is to minimize the maximum cumulative loss over all coordinates. We give a simple algorithm that lets the learner do almost as well as if she knew the adversary’s actions in advance. We demonstrate the power of our framework by using it to (re)derive optimal bounds and efficient algorithms across a variety of domains, ranging from multicalibration to a large set of no-regret algorithms, to a variant of Blackwell’s approachability theorem for polytopes with fast convergence rates. As a new application, we show how to “(multi)calibeat” an arbitrary collection of forecasters — achieving an exponentially improved dependence on the number of models we are competing against, compared to prior work.

Matthew Eichhorn, Cornell University
Title: Mind your Ps and Qs: Allocation with Priorities and Quotas
Abstract: In many settings, such as university admissions, the rationing of medical supplies, and the assignment of public housing, decision-makers use normative criteria (ethical, financial, legal, etc.) to justify who gets an allocation. These criteria can often be translated into quotas for the number of units available to particular demographics and priorities over agents who qualify in each demographic. Each agent may qualify in multiple categories at different priority levels, so many allocations may conform to a given set of quotas and priorities. Which of these allocations should be chosen? In this talk, I’ll formalize this reserve allocation problem and motivate Pareto efficiency as a natural desideratum. I’ll present an algorithm to locate efficient allocations that conform to the quota and priority constraints. This algorithm relies on beautiful techniques from integer and linear programming, and it is both faster and more straightforward than existing techniques in this space. Moreover, its clean formulation allows for further refinement, such as the secondary optimization of some heuristics for fairness.

Haewon Jeong, Harvard University
Title: Fairness without Imputation: A Decision Tree Approach for Fair Prediction with Missing Values
Abstract: We investigate the fairness concerns of training a machine learning model using data with missing values. Even though there are a number of fairness intervention methods in the literature, most of them require a complete training set as input. In practice, data can have missing values, and data missing patterns can depend on group attributes (e.g. gender or race). Simply applying off-the-shelf fair learning algorithms to an imputed dataset may lead to an unfair model. In this paper, we first theoretically analyze different sources of discrimination risks when training with an imputed dataset. Then, we propose an integrated approach based on decision trees that does not require a separate process of imputation and learning. Instead, we train a tree with missing incorporated as attribute (MIA), which does not require explicit imputation, and we optimize a fairness-regularized objective function. We demonstrate that our approach outperforms existing fairness intervention methods applied to an imputed dataset, through several experiments on real-world datasets.

Emily Diana, University of Pennsylvania
Title: Multiaccurate Proxies for Downstream Fairness
Abstract: We study the problem of training a model that must obey demographic fairness conditions when the sensitive features are not available at training time — in other words, how can we train a model to be fair by race when we don’t have data about race? We adopt a fairness pipeline perspective, in which an “upstream” learner that does have access to the sensitive features will learn a proxy model for these features from the other attributes. The goal of the proxy is to allow a general “downstream” learner — with minimal assumptions on their prediction task — to be able to use the proxy to train a model that is fair with respect to the true sensitive features. We show that obeying multiaccuracy constraints with respect to the downstream model class suffices for this purpose, provide sample- and oracle efficient-algorithms and generalization bounds for learning such proxies, and conduct an experimental evaluation. In general, multiaccuracy is much easier to satisfy than classification accuracy, and can be satisfied even when the sensitive features are hard to predict.

12:15 pm–1:45 pm
Lunch Break

1:45–3:15 pm
Paper Session 2
Session Chair: Guy Rothblum

Elbert Du, Harvard University
Title: Improved Generalization Guarantees in Restricted Data Models
Abstract: Differential privacy is known to protect against threats to validity incurred due to adaptive, or exploratory, data analysis — even when the analyst adversarially searches for a statistical estimate that diverges from the true value of the quantity of interest on the underlying population. The cost of this protection is the accuracy loss incurred by differential privacy. In this work, inspired by standard models in the genomics literature, we consider data models in which individuals are represented by a sequence of attributes with the property that where distant attributes are only weakly correlated. We show that, under this assumption, it is possible to “re-use” privacy budget on different portions of the data, significantly improving accuracy without increasing the risk of overfitting.

Ruth Urner, York University
Title: Robustness Should not be at Odds with Accuracy
Abstract: The phenomenon of adversarial examples in deep learning models has caused substantial concern over their reliability and trustworthiness: in many instances an imperceptible perturbation can falsely flip a neural network’s prediction. Applied research in this area has mostly focused on developing novel adversarial attack strategies or building better defenses against such. It has repeatedly been pointed out that adversarial robustness may be in conflict with requirements for high accuracy. In this work, we take a more principled look at modeling the phenomenon of adversarial examples. We argue that deciding whether a model’s label change under a small perturbation is justified, should be done in compliance with the underlying data-generating process. Through a series of formal constructions, systematically analyzing the the relation between standard Bayes classifiers and robust-Bayes classifiers, we make the case for adversarial robustness as a locally adaptive measure. We propose a novel way defining such a locally adaptive robust loss, show that it has a natural empirical counterpart, and develop resulting algorithmic guidance in form of data-informed adaptive robustness radius. We prove that our adaptive robust data-augmentation maintains consistency of 1-nearest neighbor classification under deterministic labels and thereby argue that robustness should not be at odds with accuracy.

Sushant Agarwal, University of Waterloo
Title: Towards the Unification and Robustness of Perturbation and Gradient Based Explanations
Abstract: As machine learning black boxes are increasingly being deployed in critical domains such as healthcare and criminal justice, there has been a growing emphasis on developing techniques for explaining these black boxes in a post hoc manner. In this work, we analyze two popular post hoc interpretation techniques: SmoothGrad which is a gradient based method, and a variant of LIME which is a perturbation based method. More specifically, we derive explicit closed form expressions for the explanations output by these two methods and show that they both converge to the same explanation in expectation, i.e., when the number of perturbed samples used by these methods is large. We then leverage this connection to establish other desirable properties, such as robustness and linearity, for these techniques. We also derive finite sample complexity bounds for the number of perturbations required for these methods to converge to their expected explanation. Finally, we empirically validate our theory using extensive experimentation on both synthetic and real world datasets.

Tijana Zrnic, University of California, Berkeley
Title: Regret Minimization with Performative Feedback
Abstract: In performative prediction, the deployment of a predictive model triggers a shift in the data distribution. As these shifts are typically unknown ahead of time, the learner needs to deploy a model to get feedback about the distribution it induces. We study the problem of finding near-optimal models under performativity while maintaining low regret. On the surface, this problem might seem equivalent to a bandit problem. However, it exhibits a fundamentally richer feedback structure that we refer to as performative feedback: after every deployment, the learner receives samples from the shifted distribution rather than only bandit feedback about the reward. Our main contribution is regret bounds that scale only with the complexity of the distribution shifts and not that of the reward function. The key algorithmic idea is careful exploration of the distribution shifts that informs a novel construction of confidence bounds on the risk of unexplored models. The construction only relies on smoothness of the shifts and does not assume convexity. More broadly, our work establishes a conceptual approach for leveraging tools from the bandits literature for the purpose of regret minimization with performative feedback.

3:15 pm–3:45 pm
Coffee Break

3:45 pm–5:00 pm
Panel Discussion
Title: What is Responsible Computing?
Panelists: Jiahao Chen, Cynthia Dwork, Kobbi Nissim, Ruth Urner
Moderator: Elisa Celis

June 7, 2022

9:15 am–10:15 am
Keynote Speaker: Isaac Kohane, Harvard Medical School
Title: What’s in a label? The case for and against monolithic group/ethnic/race labeling for machine learning
Abstract: Populations and group labels have been used and abused for thousands of years. The scale at which AI can incorporate such labels into its models and the ways in which such models can be misused are cause for significant concern. I will describe, with examples drawn from experiments in precision medicine, the task dependence of how underserved and oppressed populations can be both harmed and helped by the use of group labels. The source of the labels and the utility models underlying their use will be particularly emphasized.

10:15 am–10:45 am
Coffee Break

10:45 am–12:15 pm
Paper Session 3
Session Chair: Ruth Urner

Rojin Rezvan, University of Texas at Austin
Title: Individually-Fair Auctions for Multi-Slot Sponsored Search
Abstract: We design fair-sponsored search auctions that achieve a near-optimal tradeoff between fairness and quality. Our work builds upon the model and auction design of Chawla and Jagadeesan, who considered the special case of a single slot. We consider sponsored search settings with multiple slots and the standard model of click-through rates that are multiplicatively separable into an advertiser-specific component and a slot-specific component. When similar users have similar advertiser-specific click-through rates, our auctions achieve the same near-optimal tradeoff between fairness and quality. When similar users can have different advertiser-specific preferences, we show that a preference-based fairness guarantee holds. Finally, we provide a computationally efficient algorithm for computing payments for our auctions as well as those in previous work, resolving another open direction from Chawla and Jagadeesan.

Judy Hanwen Shen, Stanford
Title: Leximax Approximations and Representative Cohort Selection
Abstract: Finding a representative cohort from a broad pool of candidates is a goal that arises in many contexts such as choosing governing committees and consumer panels. While there are many ways to define the degree to which a cohort represents a population, a very appealing solution concept is lexicographic maximality (leximax) which offers a natural (pareto-optimal like) interpretation that the utility of no population can be increased without decreasing the utility of a population that is already worse off. However, finding a leximax solution can be highly dependent on small variations in the utility of certain groups. In this work, we explore new notions of approximate leximax solutions with three distinct motivations: better algorithmic efficiency, exploiting significant utility improvements, and robustness to noise. Among other definitional contributions, we give a new notion of an approximate leximax that satisfies a similarly appealing semantic interpretation and relate it to algorithmically-feasible approximate leximax notions. When group utilities are linear over cohort candidates, we give an efficient polynomial-time algorithm for finding a leximax distribution over cohort candidates in the exact as well as in the approximate setting. Furthermore, we show that finding an integer solution to leximax cohort selection with linear utilities is NP-Hard.

Jiayuan Ye,
National University of Singapore
Title: Differentially Private Learning Needs Hidden State (or Much Faster Convergence)
Abstract: Differential privacy analysis of randomized learning algorithms typically relies on composition theorems, where the implicit assumption is that the internal state of the iterative algorithm is revealed to the adversary. However, by assuming hidden states for DP algorithms (when only the last-iterate is observable), recent works prove a converging privacy bound for noisy gradient descent (on strongly convex smooth loss function) that is significantly smaller than composition bounds after a few epochs. In this talk, we extend this hidden-state analysis to various stochastic minibatch gradient descent schemes (such as under “shuffle and partition” and “sample without replacement”), by deriving novel bounds for the privacy amplification by random post-processing and subsampling. We prove that, in these settings, our privacy bound is much smaller than composition for training with a large number of iterations (which is the case for learning from high-dimensional data). Our converging privacy analysis, thus, shows that differentially private learning, with a tight bound, needs hidden state privacy analysis or a fast convergence. To complement our theoretical results, we present experiments for training classification models on MNIST, FMNIST and CIFAR-10 datasets, and observe a better accuracy given fixed privacy budgets, under the hidden-state analysis.

Mahbod Majid, University of Waterloo
Title: Efficient Mean Estimation with Pure Differential Privacy via a Sum-of-Squares Exponential Mechanism
Abstract: We give the first polynomial-time algorithm to estimate the mean of a d-variate probability distribution from O(d) independent samples (up to logarithmic factors) subject to pure differential privacy.
Our main technique is a new approach to use the powerful Sum of Squares method (SoS) to design differentially private algorithms. SoS proofs to algorithms is a key theme in numerous recent works in high-dimensional algorithmic statistics – estimators which apparently require exponential running time but whose analysis can be captured by low-degree Sum of Squares proofs can be automatically turned into polynomial-time algorithms with the same provable guarantees. We demonstrate a similar proofs to private algorithms phenomenon: instances of the workhorse exponential mechanism which apparently require exponential time but which can be analyzed with low-degree SoS proofs can be automatically turned into polynomial-time differentially private algorithms. We prove a meta-theorem capturing this phenomenon, which we expect to be of broad use in private algorithm design.

12:15 pm–1:45 pm
Lunch Break

1:45–3:15 pm
Paper Session 4
Session Chair: Kunal Talwar

Kunal Talwar,
Apple
Title: Differential Secrecy for Distributed Data and Applications to Robust Differentially Secure Vector Summation
Abstract: Computing the noisy sum of real-valued vectors is an important primitive in differentially private learning and statistics. In private federated learning applications, these vectors are held by client devices, leading to a distributed summation problem. Standard Secure Multiparty Computation (SMC) protocols for this problem are susceptible to poisoning attacks, where a client may have a large influence on the sum, without being detected.
In this work, we propose a poisoning-robust private summation protocol in the multiple-server setting, recently studied in PRIO. We present a protocol for vector summation that verifies that the Euclidean norm of each contribution is approximately bounded. We show that by relaxing the security constraint in SMC to a differential privacy like guarantee, one can improve over PRIO in terms of communication requirements as well as the client-side computation. Unlike SMC algorithms that inevitably cast integers to elements of a large finite field, our algorithms work over integers/reals, which may allow for additional efficiencies.

Giuseppe Vietri, University of Minnesota
Title: Improved Regret for Differentially Private Exploration in Linear MDP
Abstract: We study privacy-preserving exploration in sequential decision-making for environments that rely on sensitive data such as medical records. In particular, we focus on solving the problem of reinforcement learning (RL) subject to the constraint of (joint) differential privacy in the linear MDP setting, where both dynamics and rewards are given by linear functions. Prior work on this problem due to Luyo et al. (2021) achieves a regret rate that has a dependence of O(K^{3/5}) on the number of episodes K. We provide a private algorithm with an improved regret rate with an optimal dependence of O(K^{1/2}) on the number of episodes. The key recipe for our stronger regret guarantee is the adaptivity in the policy update schedule, in which an update only occurs when sufficient changes in the data are detected. As a result, our algorithm benefits from low switching cost and only performs O(log(K)) updates, which greatly reduces the amount of privacy noise. Finally, in the most prevalent privacy regimes where the privacy parameter ? is a constant, our algorithm incurs negligible privacy cost — in comparison with the existing non-private regret bounds, the additional regret due to privacy appears in lower-order terms.

Mingxun Zhou,
Carnegie Mellon University
Title: The Power of the Differentially Oblivious Shuffle in Distributed Privacy MechanismsAbstract: The shuffle model has been extensively investigated in the distributed differential privacy (DP) literature. For a class of useful computational tasks, the shuffle model allows us to achieve privacy-utility tradeoff similar to those in the central model, while shifting the trust from a central data curator to a “trusted shuffle” which can be implemented through either trusted hardware or cryptography. Very recently, several works explored cryptographic instantiations of a new type of shuffle with relaxed security, called differentially oblivious (DO) shuffles. These works demonstrate that by relaxing the shuffler’s security from simulation-style secrecy to differential privacy, we can achieve asymptotical efficiency improvements. A natural question arises, can we replace the shuffler in distributed DP mechanisms with a DO-shuffle while retaining a similar privacy-utility tradeoff?
In this paper, we prove an optimal privacy amplification theorem by composing any locally differentially private (LDP) mechanism with a DO-shuffler, achieving parameters that tightly match the shuffle model. Moreover, we explore multi-message protocols in the DO-shuffle model, and construct mechanisms for the real summation and histograph problems. Our error bounds approximate the best known results in the multi-message shuffle-model up to sub-logarithmic factors. Our results also suggest that just like in the shuffle model, allowing each client to send multiple messages is fundamentally more powerful than restricting to a single message.

Badih Ghazi,
Google Research
Title: Differentially Private Ad Conversion Measurement
Abstract: In this work, we study conversion measurement, a central functionality in the digital advertising space, where an advertiser seeks to estimate advertiser site conversions attributed to ad impressions that users have interacted with on various publisher sites. We consider differential privacy (DP), a notion that has gained in popularity due to its strong and rigorous guarantees, and suggest a formal framework for DP conversion measurement, uncovering a subtle interplay between attribution and privacy. We define the notion of an operationally valid configuration of the attribution logic, DP adjacency relation, privacy
budget scope and enforcement point, and provide, for a natural space of configurations, a complete characterization.

3:15 pm–3:45 pm
Coffee Break

3:45 pm–5:00 pm
Open Poster Session

June 8, 2022

9:15 am–10:15 am
Keynote Speaker: Nuria Oliver, Data-Pop Alliance
Title: Data Science against COVID-19
Abstract: In my talk, I will describe the work that I have been doing since March 2020, leading a multi-disciplinary team of 20+ volunteer scientists working very closely with the Presidency of the Valencian Government in Spain on 4 large areas: (1) human mobility modeling; (2) computational epidemiological models (both metapopulation, individual and LSTM-based models); (3) predictive models; and (4) citizen surveys via the COVID19impactsurvey with over 600,000 answers worldwide.
I will describe the results that we have produced in each of these areas, including winning the 500K XPRIZE Pandemic Response Challenge and best paper award at ECML-PKDD 2021. I will share the lessons learned in this very special initiative of collaboration between the civil society at large (through the survey), the scientific community (through the Expert Group) and a public administration (through the Commissioner at the Presidency level). WIRED magazine just published an article describing our story.

10:15 am–10:45 am
Coffee Break

10:45 am–12:15 pm
Paper Session 5
Session Chair: Kunal Talwar

Shengyuan Hu, Carnegie Mellon University
Title: Private Multi-Task Learning: Formulation and Applications to Federated Learning
Abstract: Many problems in machine learning rely on multi-task learning (MTL), in which the goal is to solve multiple related machine learning tasks simultaneously. MTL is particularly relevant for privacy-sensitive applications in areas such as healthcare, finance, and IoT computing, where sensitive data from multiple, varied sources are shared for the purpose of learning. In this work, we formalize notions of task-level privacy for MTL via joint differential privacy (JDP), a relaxation of differential privacy for mechanism design and distributed optimization. We then propose an algorithm for mean-regularized MTL, an objective commonly used for applications in personalized federated learning, subject to JDP. We analyze our objective and solver, providing certifiable guarantees on both privacy and utility. Empirically, our method allows for improved privacy/utility trade-offs relative to global baselines across common federated learning benchmarks

Christina Yu,
Cornell University
Title: Sequential Fair Allocation: Achieving the Optimal Envy-Efficiency Tradeoff Curve
Abstract: We consider the problem of dividing limited resources to individuals arriving over T rounds with a goal of achieving fairness across individuals. In general there may be multiple resources and multiple types of individuals with different utilities. A standard definition of `fairness’ requires an allocation to simultaneously satisfy envy-freeness and Pareto efficiency. However, in the online sequential setting, the social planner must decide on a current allocation before the downstream demand is realized, such that no policy can guarantee these desiderata simultaneously with probability 1, requiring a modified metric of measuring fairness for online policies. We show that in the online setting, the two desired properties (envy-freeness and efficiency) are in direct contention, in that any algorithm achieving additive counterfactual envy-freeness up to L_T necessarily suffers an efficiency loss of at least 1 / L_T. We complement this uncertainty principle with a simple algorithm, HopeGuardrail, which allocates resources based on an adaptive threshold policy and is able to achieve any fairness-efficiency point on this frontier. Our result is the first to provide guarantees for fair online resource allocation with high probability for multiple resource and multiple type settings. In simulation results, our algorithm provides allocations close to the optimal fair solution in hindsight, motivating its use in practical applications as the algorithm is able to adapt to any desired fairness efficiency trade-off.

Hedyeh Beyhaghi, Carnegie Mellon University
Title: On classification of strategic agents who can both game and improve
Abstract: In this work, we consider classification of agents who can both game and improve. For example, people wishing to get a loan may be able to take some actions that increase their perceived credit-worthiness and others that also increase their true credit-worthiness. A decision-maker would like to define a classification rule with few false-positives (does not give out many bad loans) while yielding many true positives (giving out many good loans), which includes encouraging agents to improve to become true positives if possible. We consider two models for this problem, a general discrete model and a linear model, and prove algorithmic, learning, and hardness results for each. For the general discrete model, we give an efficient algorithm for the problem of maximizing the number of true positives subject to no false positives, and show how to extend this to a partial-information learning setting. We also show hardness for the problem of maximizing the number of true positives subject to a nonzero bound on the number of false positives, and that this hardness holds even for a finite-point version of our linear model. We also show that maximizing the number of true positives subject to no false positive is NP-hard in our full linear model. We additionally provide an algorithm that determines whether there exists a linear classifier that classifies all agents accurately and causes all improvable agents to become qualified, and give additional results for low-dimensional data.

Keegan Harris, Carnegie Mellon University
Title: Bayesian Persuasion for Algorithmic Recourse
Abstract: When subjected to automated decision-making, decision subjects may strategically modify their observable features in ways they believe will maximize their chances of receiving a favorable decision. In many practical situations, the underlying assessment rule is deliberately kept secret to avoid gaming and maintain competitive advantage. The resulting opacity forces the decision subjects to rely on incomplete information when making strategic feature modifications. We capture such settings as a game of Bayesian persuasion, in which the decision maker offers a form of recourse to the decision subject by providing them with an action recommendation (or signal) to incentivize them to modify their features in desirable ways. We show that when using persuasion, both the decision maker and decision subject are never worse off in expectation, while the decision maker can be significantly better off. While the decision maker’s problem of finding the optimal Bayesian incentive-compatible (BIC) signaling policy takes the form of optimization over infinitely-many variables, we show that this optimization can be cast as a linear program over finitely-many regions of the space of possible assessment rules. While this reformulation simplifies the problem dramatically, solving the linear program requires reasoning about exponentially-many variables, even under relatively simple settings. Motivated by this observation, we provide a polynomial-time approximation scheme that recovers a near-optimal signaling policy. Finally, our numerical simulations on semi-synthetic data empirically illustrate the benefits of using persuasion in the algorithmic recourse setting.

12:15 pm–1:45 pm
Lunch Break

1:45–3:15 pm
Paper Session 6
Session Chair: Elisa Celis

Mark Bun, Boston University
Title: Controlling Privacy Loss in Sampling Schemes: An Analysis of Stratified and Cluster Sampling
Abstract: Sampling schemes are fundamental tools in statistics, survey design, and algorithm design. A fundamental result in differential privacy is that a differentially private mechanism run on a simple random sample of a population provides stronger privacy guarantees than the same algorithm run on the entire population. However, in practice, sampling designs are often more complex than the simple, data-independent sampling schemes that are addressed in prior work. In this work, we extend the study of privacy amplification results to more complex, data-dependent sampling schemes. We find that not only do these sampling schemes often fail to amplify privacy, they can actually result in privacy degradation. We analyze the privacy implications of the pervasive cluster sampling and stratified sampling paradigms, as well as provide some insight into the study of more general sampling designs.

Samson Zhou, Carnegie Mellon University
Title: Private Data Stream Analysis for Universal Symmetric Norm Estimation
Abstract: We study how to release summary statistics on a data stream subject to the constraint of differential privacy. In particular, we focus on releasing the family of symmetric norms, which are invariant under sign-flips and coordinate-wise permutations on an input data stream and include L_p norms, k-support norms, top-k norms, and the box norm as special cases. Although it may be possible to design and analyze a separate mechanism for each symmetric norm, we propose a general parametrizable framework that differentially privately releases a number of sufficient statistics from which the approximation of all symmetric norms can be simultaneously computed. Our framework partitions the coordinates of the underlying frequency vector into different levels based on their magnitude and releases approximate frequencies for the “heavy” coordinates in important levels and releases approximate level sizes for the “light” coordinates in important levels. Surprisingly, our mechanism allows for the release of an arbitrary number of symmetric norm approximations without any overhead or additional loss in privacy. Moreover, our mechanism permits (1+alpha)-approximation to each of the symmetric norms and can be implemented using sublinear space in the streaming model for many regimes of the accuracy and privacy parameters.

Aloni Cohen, University of Chicago
Title: Attacks on Deidentification’s Defenses
Abstract: Quasi-identifier-based deidentification techniques (QI-deidentification) are widely used in practice, including k-anonymity, ?-diversity, and t-closeness. We present three new attacks on QI-deidentification: two theoretical attacks and one practical attack on a real dataset. In contrast to prior work, our theoretical attacks work even if every attribute is a quasi-identifier. Hence, they apply to k-anonymity, ?-diversity, t-closeness, and most other QI-deidentification techniques.
First, we introduce a new class of privacy attacks called downcoding attacks, and prove that every QI-deidentification scheme is vulnerable to downcoding attacks if it is minimal and hierarchical. Second, we convert the downcoding attacks into powerful predicate singling-out (PSO) attacks, which were recently proposed as a way to demonstrate that a privacy mechanism fails to legally anonymize under Europe’s General Data Protection Regulation. Third, we use LinkedIn.com to reidentify 3 students in a k-anonymized dataset published by EdX (and show thousands are potentially vulnerable), undermining EdX’s claimed compliance with the Family Educational Rights and Privacy Act.
The significance of this work is both scientific and political. Our theoretical attacks demonstrate that QI-deidentification may offer no protection even if every attribute is treated as a quasi-identifier. Our practical attack demonstrates that even deidentification experts acting in accordance with strict privacy regulations fail to prevent real-world reidentification. Together, they rebut a foundational tenet of QI-deidentification and challenge the actual arguments made to justify the continued use of k-anonymity and other QI-deidentification techniques.

Steven Wu,
Carnegie Mellon University
Title: Fully Adaptive Composition in Differential Privacy
Abstract: Composition is a key feature of differential privacy. Well-known advanced composition theorems allow one to query a private database quadratically more times than basic privacy composition would permit. However, these results require that the privacy parameters of all algorithms be fixed before interacting with the data. To address this, Rogers et al. introduced fully adaptive composition, wherein both algorithms and their privacy parameters can be selected adaptively. The authors introduce two probabilistic objects to measure privacy in adaptive composition: privacy filters, which provide differential privacy guarantees for composed interactions, and privacy odometers, time-uniform bounds on privacy loss. There are substantial gaps between advanced composition and existing filters and odometers. First, existing filters place stronger assumptions on the algorithms being composed. Second, these odometers and filters suffer from large constants, making them impractical. We construct filters that match the tightness of advanced composition, including constants, despite allowing for adaptively chosen privacy parameters. We also construct several general families of odometers. These odometers can match the tightness of advanced composition at an arbitrary, preselected point in time, or at all points in time simultaneously, up to a doubly-logarithmic factor. We obtain our results by leveraging recent advances in time-uniform martingale concentration. In sum, we show that fully adaptive privacy is obtainable at almost no loss, and conjecture that our results are essentially not improvable (even in constants) in general.

3:15 pm–3:45 pm
FORC Reception

3:45 pm–5:00 pm
Social Hour

Fast Point Transformer
2022-06-02 16:13 - 17:13
Abstract: The recent success of neural networks enables a better interpretation of 3D point clouds, but processing a large-scale 3D scene remains a challenging problem. Most current approaches divide a large-scale scene into small regions and combine the local predictions together. However, this scheme inevitably involves additional stages for pre- and post-processing and may also degrade the final output due to predictions in a local perspective. This talk introduces Fast Point Transformer that consists of a new lightweight self-attention layer. Our approach encodes continuous 3D coordinates, and the voxel hashing-based architecture boosts computational efficiency. The proposed method is demonstrated with 3D semantic segmentation and 3D detection. The accuracy of our approach is competitive to the best voxel-based method, and our network achieves 129 times faster inference time than the state-of-the-art, Point Transformer, with a reasonable accuracy trade-off in 3D semantic segmentation on S3DIS dataset.
Bio: Jaesik Park is an Assistant Professor at POSTECH. He received his Bachelor’s degree from Hanyang University in 2009, and he received his Master’s degree and Ph.D. degree from KAIST in 2011 and 2015, respectively. Before joining POSTECH, He worked at Intel as a research scientist, where he co-created the Open3D library. His research interests include image synthesis, scene understanding, and 3D reconstruction. He serves as a program committee at prestigious computer vision conferences, such as Area Chair for ICCV, CVPR, and ECCV.

Extinction and coexistence for reaction-diffusion systems on metric graphs
2022-05-26 09:00 - 10:00
Abstract: In spatial population genetics, it is important to understand the probability of extinction in multi-species interactions such as growing bacterial colonies, cancer tumor evolution and human migration. This is because extinction probabilities are instrumental in determining the probability of coexistence and the genealogies of populations. A key challenge is the complication due to spatial effect and different sources of stochasticity. In this talk, I will discuss about methods to compute the probability of extinction and other long-time behaviors for stochastic reaction-diffusion equations on metric graphs that flexibly parametrizes the underlying space. Based on recent joint work with Adrian Gonzalez-Casanova and Yifan (Johnny) Yang.

Oblique Lessons from the W Mass Measurement at CDF II
2022-05-25 10:30 - 12:00 | Hybrid
Speaker: Seth Koren (University of Chicago)
Title: Baryon Minus Lepton Number BF Theory for the Cosmological Lithium Problem
Abstract: The cosmological lithium problem—that the observed primordial abundance is lower than theoretical expectations by order one—is perhaps the most statistically significant anomaly of SM+ ΛCDM, and has resisted decades of attempts by cosmologists, nuclear physicists, and astronomers alike to root out systematics. We upgrade a discrete subgroup of the anomaly-free global symmetry of the SM to an infrared gauge symmetry, and UV complete this at a scale Λ as the familiar U(1)_{B-N_cL} Abelian Higgs theory. The early universe phase transition forms cosmic strings which are charged under the emergent higher-form symmetry of the baryon minus lepton BF theory. These topological defects catalyze interactions which turn N_g baryons into N_g leptons at strong scale rates in an analogue of the Callan-Rubakov effect, where N_g=3 is the number of SM generations. We write down a model in which baryon minus lepton strings superconduct bosonic global baryon plus lepton number currents and catalyze solely 3p^+ → 3e^+. We suggest that such cosmic strings have disintegrated O(1) of the lithium nuclei formed during Big Bang Nucleosynthesis and estimate the rate, with our benchmark model finding Λ ~ 10^8 GeV gives the right number density of strings.

2022 Summer Introduction to Mathematical Research
2022-05-21 09:00 - 2022-06-12 17:00 | Virtual
The Math Department and Harvard’s Center of Mathematical Sciences and Applications (CMSA) will be running a math program/course for mathematically minded undergraduates this summer. The course will be run by Dr. Yingying Wu from CMSA. Here is a description:
Summer Introduction to Mathematical Research (sponsored by CMSA and the Harvard Math Department)
In this course, we will start with an introduction to computer programming, algorithms, and scientific computing. Then we will discuss topics in topology, classical geometry, projective geometry, and differential geometry, and see how they can be applied to machine learning. We will go on to discuss fundamental concepts of deep learning, different deep neural network models, and mathematical interpretations of why deep neural networks are effective from a calculus viewpoint. We will conclude the course with a gentle introduction to cryptography, introducing some of the iconic topics: Yao’s Millionaires’ problem, zero-knowledge proof, the multi-party computation algorithm, and its proof.
The program hopes to provide several research mentors from various disciplines who will give some of the course lectures. Students will have the opportunity to work with one of the research mentors offered by the program.
Prerequisites: Basic coding ability in some programming language (C/Python/Matlab or CS50 experience). Some background in calculus and linear algebra is needed too. If you wish to work with a research mentor on differential geometry, more background in geometry such as from Math 132 or 136 will be useful. If you wish to work with a research mentor on computer science, coding experience mentioned above will be very useful. If you wish to work with a medical scientist, some background in life science or basic organic chemistry is recommended.
The course will meet 3 hours per week for 7 weeks via Zoom on days and times that will be scheduled for the convenience of the participants. There may be other times to be arranged for special events.
This program is only open to current Harvard undergraduates; both Mathematics concentrators and non-math concentrators are invited to apply. People already enrolled in a Math Department summer tutorial are welcome to partake in this program also. As with the summer tutorials, there is no association with the Harvard Summer School; and neither Math concentration credit nor Harvard College credit will be given for completing this course. This course has no official Harvard status and enrollment does not qualify you for any Harvard-related perks (such as a place to live if you are in Boston over the summer.)
However: As with the summer tutorials, those enrolled are eligible* to receive a stipend of $700, and if you are a Mathematics concentrator, any written paper for the course can be submitted to fulfill the Math Concentration third-year paper requirement. (*The stipend is not available for people already receiving a stipend via the Math Department’s summer tutorial program, nor is it available for PRISE participants or participants in the Herchel Smith program.)
If you wish to join this program, please email Cliff Taubes (chtaubes@math.harvard.edu). The enrollment is limited, so don’t wait too long to apply.

The geometry of conditional independence models with hidden variables
2022-05-19 09:00 - 10:00
Abstract: Conditional independence (CI) is an important tool instatistical modeling, as, for example, it gives a statistical interpretation to graphical models. In general, given a list of dependencies among random variables, it is difficult to say which constraints are implied by them. Moreover, it is important to know what constraints on the random variables are caused by hidden variables. On the other hand, such constraints are corresponding to some determinantal conditions on the tensor of joint probabilities of the observed random variables. Hence, the inference question in statistics relates to understanding the algebraic and geometric properties of determinantal varieties such as their irreducible decompositions or determining their defining equations. I will explain some recent progress that arises by uncovering the link to point configurations in matroid theory and incidence geometry. This connection, in particular, leads to effective computational approaches for (1) giving a decomposition for each CI variety; (2) identifying each component in the decomposition as a matroid variety; (3) determining whether the variety has a real point or equivalently there is a statistical model satisfying a given collection of dependencies. The talk is based on joint works with Oliver Clarke, Kevin Grace, and Harshit Motwani.
The papers are available on arxiv: https://arxiv.org/pdf/2011.02450
and https://arxiv.org/pdf/2103.16550.pdf

Boundary conditions and LSM anomalies of conformal field theories in 1+1 dimensions
2022-05-18 16:00 - 17:30 | Virtual
Speaker: Linhao Li (ISSP, U Tokyo)
Title: Boundary conditions and LSM anomalies of conformal field theories in 1+1 dimensions
Abstract: In this talk, we will study a relationship between conformally invariant boundary conditions and anomalies of conformal field theories (CFTs) in 1+1 dimensions. For a given CFT with a global symmetry, we consider symmetric gapping potentials which are relevant perturbations to the CFT. If a gapping potential is introduced only in a subregion of the system, it provides a certain boundary condition to the CFT. From this equivalence, if there exists a Cardy boundary state which is invariant under a symmetry, then the CFT can be gapped with a unique ground state by adding the corresponding gapping potential. This means that the symmetry of the CFT is anomaly free. Using this approach, we will systematically deduce the anomaly-free conditions for various types of CFTs with several different symmetries. When the symmetry of the CFT is anomalous, it implies a Lieb-Schultz-Mattis type ingappability of the system. Our results are consistent with, where available, known results in the literature. Moreover, we extend the discussion to other symmetries including spin groups and generalized time-reversal symmetries. As an application, we propose 1d LSM theorem involving magnetic space group symmetries on the lattice. The extended LSM theorems apply to systems with a broader class of spin interactions, such as Dzyaloshinskii-Moriya interactions and chiral three-spin interactions.

The Generalized Landau Paradigm (a review of generalized symmetries in condensed matter)
2022-05-18 16:00 - 17:30 | Virtual
Abstract: Recent advances in our understanding of symmetry in quantum many-body systems offer the possibility of a generalized Landau paradigm that encompasses all equilibrium phases of matter. This talk will be an elementary review of some of these developments, based on: https://arxiv.org/abs/2204.03045
