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DTSTART;TZID=America/New_York:20220728T090000
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SUMMARY:Statistical Mechanical theory for spatio-temporal evolution of Intra-tumor heterogeneity in cancers: Analysis of Multiregion sequencing data
DESCRIPTION:CMSA Interdisciplinary Science Seminar \nSpeaker: Sumit Sinha\, Harvard University \nTitle: Statistical Mechanical theory for spatio-temporal evolution of Intra-tumor heterogeneity in cancers: Analysis of Multiregion sequencing data (https://arxiv.org/abs/2202.10595) \nAbstract: 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. \nBio: 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. \n 
URL:https://cmsa.fas.harvard.edu/event/iss_72822/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Interdisciplinary Science Seminar
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DTSTART;TZID=America/New_York:20220810T090000
DTEND;TZID=America/New_York:20220810T100000
DTSTAMP:20240229T090234Z
CREATED:20240215T095253Z
LAST-MODIFIED:20240229T090234Z
UID:10002731-1660122000-1660125600@cmsa.fas.harvard.edu
SUMMARY:Recent Advances on Maximum Flows and Minimum-Cost Flows
DESCRIPTION:Interdisciplinary Science Seminar\n\n\n\n\n\n\nSpeaker: Yang P. Liu\n\n\nTitle: Recent Advances on Maximum Flows and Minimum-Cost Flows\n\nAbstract: 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. \nBio: 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.
URL:https://cmsa.fas.harvard.edu/event/iss_81022/
LOCATION:Virtual
CATEGORIES:Interdisciplinary Science Seminar
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DTSTART;TZID=America/New_York:20220811T090000
DTEND;TZID=America/New_York:20220811T100000
DTSTAMP:20240229T085717Z
CREATED:20240215T095012Z
LAST-MODIFIED:20240229T085717Z
UID:10002730-1660208400-1660212000@cmsa.fas.harvard.edu
SUMMARY:Exploring and Exploiting the Universality Phenomena in High-Dimensional Estimation and Learning
DESCRIPTION:Interdisciplinary Science Seminar \nSpeaker: Yue M. Lu\, Harvard University \nTitle: Exploring and Exploiting the Universality Phenomena in High-Dimensional Estimation and Learning \nAbstract: 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.\nIn 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. \nBio: 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.
URL:https://cmsa.fas.harvard.edu/event/iss_81122/
LOCATION:Hybrid
CATEGORIES:Interdisciplinary Science Seminar
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DTSTART;TZID=America/New_York:20220818T100000
DTEND;TZID=America/New_York:20220818T103000
DTSTAMP:20240229T085424Z
CREATED:20240215T094804Z
LAST-MODIFIED:20240229T085424Z
UID:10002728-1660816800-1660818600@cmsa.fas.harvard.edu
SUMMARY:Scalable Dynamic Graph Algorithms
DESCRIPTION:CMSA Interdisciplinary Science Seminar \nSpeaker: Quanquan Liu\, Northwestern University \nTitle: Scalable Dynamic Graph Algorithms \nAbstract: 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. \nSpecifically\, 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. \nBio: 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).
URL:https://cmsa.fas.harvard.edu/event/iss_81822/
LOCATION:CMSA Room G10\, CMSA\, 20 Garden Street\, Cambridge\, MA\, 02138\, United States
CATEGORIES:Interdisciplinary Science Seminar
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