• August 18, 2022 10:00 AM
Title: Scalable Dynamic Graph Algorithms
Venue: CMSA Room G10

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…

  • August 11, 2022 09:00 AM
Title: Exploring and Exploiting the Universality Phenomena in High-Dimensional Estimation and Learning
Venue: Virtual

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…

  • August 10, 2022 09:00 AM
Title: Recent Advances on Maximum Flows and Minimum-Cost Flows
Venue: 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.

  • July 28, 2022 09:00 AM
Title: Statistical Mechanical theory for spatio-temporal evolution of Intra-tumor heterogeneity in cancers: Analysis of Multiregion sequencing data
Venue: CMSA Room G10

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…

  • July 21, 2022 09:00 AM
Speaker: Vaidehi S. Natu, Stanford University
Title: Infants’ sensory-motor cortices undergo microstructural tissue growth coupled with myelination
Venue: Virtual

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….

  • July 14, 2022 09:00 AM
Speaker: Ioannis Petrides, Harvard University
Title: Topological and geometrical aspects of spinors in insulating crystals
Venue: Virtual

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…

  • July 07, 2022 09:00 AM
Title: The phenotype of the last universal common ancestor and the evolution of complexity
Venue: Virtual

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…

  • June 30, 2022 04:23 PM
Speaker: Spyros Tserkis, Harvard
Title: Entanglement and its key role in quantum information
Venue: Virtual

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.

  • June 23, 2022 09:00 AM
Speaker: Hui Jiang, University of Michigan
Title: Some new algorithms in statistical genomics
Venue: Virtual

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…

  • June 16, 2022 09:00 AM
Title: Surface hopping algorithms for non-adiabatic quantum systems
Venue: CMSA Room G10

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.

  • June 02, 2022 04:13 PM
Speaker: Jaesik Park, Pohang University of Science and Technology
Title: Fast Point Transformer
Venue: Virtual

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…

  • May 26, 2022 09:00 AM
Speaker: Wai-Tong Louis Fan, Indiana University
Title: Extinction and coexistence for reaction-diffusion systems on metric graphs
Venue: Virtual

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.

  • May 19, 2022 09:00 AM
Speaker: Fatemeh Mohammadi, Ghent University
Title: The geometry of conditional independence models with hidden variables
Venue: Virtual

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…

  • May 12, 2022 03:38 PM
Speaker: Justin Solomon, MIT
Title: Geometric Models for Sets of Probability Measures
Venue: Virtual

Abstract: Many statistical and computational tasks boil down to comparing probability measures expressed as density functions, clouds of data points, or generative models.  In this setting, we often are unable to match individual data points but rather need to deduce relationships between entire weighted and unweighted point sets. In this talk, I will summarize our team’s recent efforts to apply geometric techniques to problems in this space, using tools from optimal transport and spectral geometry. Motivated by applications in dataset comparison, time series analysis, and robust learning, our work reveals how to apply geometric reasoning to data expressed as probability measures without sacrificing computational efficiency.

  • May 05, 2022 03:36 PM
Speaker: Yang Yuan, Tsinghua University, IIIS
Title: Qianfang: a type-safe and data-driven healthcare system starting from Traditional Chinese Medicine
Venue: Virtual

Abstract: Although everyone talks about AI + healthcare, many people were unaware of the fact that there are two possible outcomes of the collaboration, due to the inherent dissimilarity between the two giant subjects. The first possibility is healthcare-leads, and AI is for building new tools to make steps in healthcare easier, better, more effective or more accurate. The other possibility is AI-leads, and therefore the protocols of healthcare can be redesigned or redefined to make sure that the whole infrastructure and pipelines are ideal for running AI algorithms. Our system Qianfang belongs to the second category. We have designed a new kind of clinic for the doctors and patients, so that it will be able to collect high…

  • April 28, 2022 03:33 PM
Title: Intersection number and systole on hyperbolic surfaces
Venue: Virtual

Abstract: Let X be a compact hyperbolic surface. We can see that there is a constant C(X) such that the intersection number of the closed geodesics is  \leq C(X) times the product of their lengths. Consider the optimum constant C(X). In this talk, we describe its asymptotic behavior in terms of systole,  length of the shortest closed geodesic on X.

  • April 21, 2022 09:00 AM
Speaker: Peihan Miao, University of Illinois Chicago
Title: Secure Multi-Party Computation: from Theory to Practice
Venue: Virtual

Abstract: Encryption is the backbone of cybersecurity. While encryption can secure data both in transit and at rest, in the new era of ubiquitous computing, modern cryptography also aims to protect data during computation. Secure multi-party computation (MPC) is a powerful technology to tackle this problem, which enables distrustful parties to jointly perform computation over their private data without revealing their data to each other. Although it is theoretically feasible and provably secure, the adoption of MPC in real industry is still very much limited as of today, the biggest obstacle of which boils down to its efficiency. My research goal is to bridge the gap between the theoretical feasibility and practical efficiency of MPC. Towards this goal,…

  • April 14, 2022 09:00 AM
Speaker: Songpeng Zu, University of California, San Diego
Title: SIMPLEs: a single-cell RNA sequencing imputation strategy preserving gene modules and cell clusters variation
Venue: Virtual

Abstract: A main challenge in analyzing single-cell RNA sequencing (scRNA-seq) data is to reduce technical variations yet retain cell heterogeneity. Due to low mRNAs content per cell and molecule losses during the experiment (called ‘dropout’), the gene expression matrix has a substantial amount of zero read counts. Existing imputation methods treat either each cell or each gene as independently and identically distributed, which oversimplifies the gene correlation and cell type structure. We propose a statistical model-based approach, called SIMPLEs (SIngle-cell RNA-seq iMPutation and celL clustErings), which iteratively identifies correlated gene modules and cell clusters and imputes dropouts customized for individual gene module and cell type. Simultaneously, it quantifies the uncertainty of imputation and cell clustering via multiple imputations. In…

  • April 07, 2022 03:22 PM
Speaker: Ravi Vakil, Stanford University
Title: The space of vector bundles on spheres: algebra, geometry, topology
Venue: Virtual

Abstract: Bott periodicity relates vector bundles on a topological space X to vector bundles on X “times a sphere”.   I’m not a topologist, so I will try to explain an algebraic or geometric incarnation, in terms of vector bundles on the Riemann sphere.   I will attempt to make the talk introductory, and (for the most part) accessible to those in all fields, at the expense of speaking informally and not getting far.   This relates to recent work of Hannah Larson, as well as joint work with (separately) Larson and Jim Bryan.

  • March 31, 2022 03:20 PM
Speaker: Christopher Eur, Harvard University
Title: Compactification of an embedded vector space and its combinatorics
Venue: Virtual

Abstract: Matroids are combinatorial abstractions of vector spaces embedded in a coordinate space.  Many fundamental questions have been open for these classical objects.  We highlight some recent progress that arise from the interaction between matroid theory and algebraic geometry.  Key objects involve compactifications of embedded vector spaces, and an exceptional Hirzebruch-Riemann-Roch isomorphism between the K-ring of vector bundles and the cohomology ring of stellahedral varieties.