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DTSTART;TZID=America/New_York:20220810T090000
DTEND;TZID=America/New_York:20220810T100000
DTSTAMP:20240229T090234Z
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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
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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
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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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