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yin tat lee cv

Math. National Culture and Decoupling. Courtesy of University of Washington; Courtesy of the College of Computing at Georgia Tech “This is amazing,” Bubeck remembers saying when Lee told him of the result. @inproceedings{BCLLM18, author = {S{\'{e}}bastien Bubeck and Michael B. Cohen and Full version. with Distinction { Dean’s Honours List (Highest Honours) Majors: computer science, combinatorics & optimization, and jointly pure math Graduate-level course highlights: algorithms, convex optimization, probabilistic ), A new approach to computing maximum flows using electrical flows (STOC 2013), Assistant Professor in University of Washington, Visiting Researcher in Microsoft Research, Ph.D. in Mathematics, Massachusetts Institute of Technology, B.S. Interests. Yin Tat Lee. CSE 599: Sketching Algorithms. First theoretic improvement on the running time of linear programming since 1986. A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path (arXiv), Solving Tall Dense Linear Programs in Nearly Linear Time (STOC 2020), Solving Linear Programs in the Current Matrix Multiplication Time (STOC 2019), Optimal Algorithms for Non-Smooth Distributed Optimization in Networks (NeurIPS 2018), k-server via multiscale entropic regularization (STOC 2018), Convergence rate of Riemannian Hamiltonian Monte Carlo and faster polytope volume computation (STOC 2018), Eldan's Stochastic Localization and the {KLS} Hyperplane Conjecture: An Improved Lower Bound for Expansion (FOCS 2017), Kernel-based methods for bandit convex optimization (STOC 2017), A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization (FOCS 2015), Path Finding Methods for Linear Programming: Solving Linear Programs in $\tilde{O}(\sqrt{\mathrm{rank}})$ Iterations and Faster Algorithms for Maximum Flow (FOCS 2014), An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations (SODA 2014), An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications (STOC 2020), Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent (STOC 2020), Strong Self-Concordance and Sampling (STOC 2020), Computing Circle Packing Representations of Planar Graphs (SODA 2020), Differentially Private Release of Synthetic Graphs (SODA 2020), Chasing Nested Convex Bodies Nearly Optimally (SODA 2020), A Generalized Central Limit Conjecture for Convex Bodies (GAFA Seminar Notes), Complexity of Highly Parallel Non-Smooth Convex Optimization (NeurIPS 19), The Randomized Midpoint Method for Log-Concave Sampling (NeurIPS 2019), Competitively chasing convex bodies (STOC 2019), A near-optimal algorithm for approximating the John Ellipsoid (COLT 2019), Near Optimal Methods for Minimizing Convex Functions with Lipschitz p-th Derivatives (COLT 2019), Solving Empirical Risk Minimization in the Current Matrix Multiplication Time (COLT 2019), Adversarial examples from computational constraints (ICML 2019), Metrical task systems on trees via mirror descent and unfair gluing (SODA 2019), A Nearly-Linear Bound for Chasing Nested Convex Bodies (SODA 2019), Efficient Convex Optimization with Membership Oracles (COLT 2018), Universal Barrier is n-Self-Concordant (arXiv), The Kannan-Lovász-Simonovits Conjecture (arXiv), The Paulsen problem, continuous operator scaling, and smoothed analysis (STOC 2018), A matrix expander Chernoff bound (STOC 2018), Stochastic localization + Stieltjes barrier = tight bound for log-Sobolev (STOC 2018), An homotopy method for lp regression provably beyond self-concordance and in input-sparsity time (STOC 2018), Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks (ICML 2017), An SDP-based algorithm for linear-sized spectral sparsification (STOC 2017), Subquadratic submodular function minimization (STOC 2017), Faster Algorithms for Convex and Combinatorial Optimization (), Black-box Optimization with a Politician (ICML 2016), Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver (SODA 2016), Improved Cheeger's Inequality and Analysis of Local Graph Partitioning using Vertex Expansion and Expansion Profile (SODA 2016), Geometric median in nearly linear time (STOC 2016), Sparsified Cholesky and multigrid solvers for connection laplacians (STOC 2016), Landmark-Matching Transformation with Large Deformation Via n-dimensional Quasi-conformal Maps (J. Sci. A Faster Algorithm for Linear Programming. Yin Tat Lee. Comput. Yin Tat Lee. The design of algorithms is traditionally a discrete endeavor. The team includes Kevin Jamieson and Yin Tat Lee, assistant professors in the Paul G. Allen School of Computer Science & Engineering, along with their newest member, Abel Rodriguez, professor and chair of the Statistics department, who comes to the UW from UC-Santa Cruz and serves as the diversity liaison for the Institute. 46 No. Yin Tat Lee is a Paul G. Allen endowed assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. ... Anupam Gupta, and Yin Tat Lee, SODA 2019. Guanghao Ye (叶光昊) I’m a fourth-year BS/MS student at Paul G. Allen School of Computer Science & Engineering at the University of Washington, where I am very fortunate to be advised by Yin Tat Lee.. Breaking the Quadratic Barrier for Matroid Intersection with Joakim Blikstad, Sagnik Mukhopadhyay and Danupon Nanongkai. His research interests are primarily in algorithms and they Papers/Manuscripts. Awarded Papers [ bib] Path Finding Methods for Linear Programming: Solving Linear Programs in $\tilde{O}(\sqrt{\mathrm{rank}})$ Iterations and Faster Algorithms for Maximum Flow with Aaron Sidford on FOCS 2014 . In SIAM Journal on Computing (SICOMP), Vol. Presentations. Paul G. Allen School of Computer Science & Engineering, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, 2013 IEEE 54th Annual Symposium on Foundations of Computer Science, 147-156, Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete …, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, K Scaman, F Bach, S Bubeck, YT Lee, L Massoulié, international conference on machine learning, 3027-3036, MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford, Proceedings of the 2015 Conference on Innovations in Theoretical Computer …, M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford, SIAM Journal on Computing 46 (1), 456-477, International Conference on Machine Learning, 831-840, R Kyng, YT Lee, R Peng, S Sachdeva, DA Spielman, Proceedings of the forty-eighth annual ACM symposium on Theory of Computing …, Proceedings of the 49th Annual ACM SIGACT Symposium on Theory of Computing …, Proceedings of the forty-fifth annual ACM symposium on Theory of computing …, MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford, SIAM Journal on Computing 47 (6), 2315-2336, Proceedings of the 49th annual acm sigact symposium on theory of computing …, Proceedings of the twenty-seventh annual ACM-SIAM symposium on Discrete …, New articles related to this author's research, Sr Principal Research Manager, ML Foundations group, Microsoft Research, Assistant Professor, University of Massachusetts Amherst, Graduate Student in Computer Science, MIT, Professor of Computer Science, Yale University, Associate Professor, Georgia Institute of Technology, Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow, Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems, An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations, A faster cutting plane method and its implications for combinatorial and convex optimization, Optimal algorithms for smooth and strongly convex distributed optimization in networks, Uniform sampling for matrix approximation, A geometric alternative to Nesterov's accelerated gradient descent, Single pass spectral sparsification in dynamic streams, Adversarial examples from computational constraints, Sparsified cholesky and multigrid solvers for connection laplacians, Optimal algorithms for non-smooth distributed optimization in networks, Solving linear programs in the current matrix multiplication time, Kernel-based methods for bandit convex optimization, Efficient Inverse Maintenance and Faster Algorithms for Linear Programming, A new approach to computing maximum flows using electrical flows, Constructing linear-sized spectral sparsification in almost-linear time, An sdp-based algorithm for linear-sized spectral sparsification, Eldan's Stochastic Localization and the KLS Hyperplane Conjecture: An Improved Lower Bound for Expansion, Using optimization to obtain a width-independent, parallel, simpler, and faster positive SDP solver. Jung, J., & Lee, Y. Partitioning Well-Clustered Graphs: Spectral Clustering Works! Convex Optimization, Spectral Graph, and Online Algorithms. STOC 2021. Comput. His research interests are primarily in algorithms and they span a wide range of topics such as convex optimization, convex geometry, spectral graph … He completed his PhD at Massachusetts Institute of Technology and his undergraduate studies at the Chinese University of Hong Kong. Sketching algorithms are powerful techniques to compress data in a way that lets you answer various queries. With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. Positive Semidefinite Programming: Mixed, Parallel, and Width-Independent with Arun Jambulapati, Yin Tat Lee… with Ron Aharoni, European J. Combin. Areas of interest: Algorithms, convex optimization, convex geometry, spectral graph theory. Unifying Matrix Data Structures: Simplifying and Speeding up Iterative Algorithms SOSA 2021, Best Paper. ), Uniform Sampling for Matrix Approximation (ITCS 2015), Single Pass Spectral Sparsification in Dynamic Streams (FOCS 2014, SIAM J. Yin Tat Lee Lee, who joined the Allen School faculty in 2017 and is also a visiting researcher at Microsoft Research AI, combines ideas from continuous and discrete mathematics to produce state-of-the-art algorithms for solving optimization problems that underpin the theory and practice of computing. In this course, we will cover various algorithms that make use of sketching techniques. Paul G. Allen School of Computer Science & Engineering, University of … with Haotian Jiang, Tarun Kathuria, Yin Tat Lee, Zhao Song FOCS 2020. with Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford. An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations With Jonathan A. Kelner, Yin Tat Lee, and Lorenzo Orecchia. and the Maximum Flow Problem I. Yin Tat Lee (MIT, Simons) Joint work with Aaron Sidford Yin Tat Lee is an assistant professor in the Paul G. Allen School of Computer Science and Engineering at the University of Washington, and a visiting researcher in Microsoft Research AI. in Mathematics, Chinese University of Hong Kong, Best Student Paper by my PhD student Haotian Jiang, Symposium on Discrete Algorithms, Best Paper Award, Neural Information Processing Systems, Best Student Paper, Symposium on Foundations of Computer Science, Notable article in computing in 2014 by Computing Reviews, Best Paper Award, Symposium on Foundations of Computer Science, Best Paper Award, Symposium on Discrete Algorithms, Charles W. and Jennifer C. Johnson Prize, MIT, CSE 535 Theory of Optimization and Continuous Algorithms, CSE 599 Interplay between Convex Optimization and Geometry, Co-organizer of a data science workshop in the University of Washington, Program Committee of Foundations of Computer Science (FOCS 2018), Co-organizer of a data science workshop in the University of Wisconsin, Program Committee of Symposium on Discrete Algorithms (SODA 2017), Co-organizer of a workshop in The 49th Annual ACM Symposium on the Theory of Computing (STOC 2017), Program Committee of International Workshop on Randomization and Computation (RANDOM 2017), Co-organizer of three sessions in The fifth International Conference on Continuous Optimization (ICCOPT 2016). Covers in Partitioned Intersecting Hypergraphs. with Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song and Di Wang. Since 2005, Microsoft has used its Faculty Fellowship program to recognize promising, early-career researchers whose exceptional research talent makes them emerging leaders in their fields. Finalizing before submission. The system can't perform the operation now. 2016. “In my feeling it was a big deal, worthy of the highest praise,” he said. Read Yin Tat Lee's latest research, browse their coauthor's research, and play around with their algorithms ), Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems (FOCS 2013), Improved Cheeger's inequality: analysis of spectral partitioning algorithms (STOC 2013, SIAM J. I am broadly interested in theoretical computer science. Research I am broadly interested in online algorithms and other information theoretic questions in algorithms, convex geometry, convex optimization, and linear algebra. https://news.cs.washington.edu/2016/05/24/yin-tat-lee-to-join-the-uw-cse-faculty He completed his Ph.D. at Massachusetts Institute of Technology and his undergraduate studies at the Chinese University of Hong Kong. Comput. STOC 2021. An O(m/epsilon^3.5) Algorithm for Semidefinite Programs with Diagonal Constraints with Yin Tat Lee COLT 2020 . Upcoming West Coast Optimization Meeting Apr 29, 2019 Yin Lee Page 2 WORK IN PROGRESS Lee, Y., & Kramer, A. Writing stage. With an extremely fun team of co-authors (Yin Tat Lee, Yuanzhi Li, Mark Sellke) we finally managed to obtain a competitive algorithm for chasing convex bodies (after a couple of years of infructuous attempts), see also this youtube video. 2, pp 710-743, 2017. ), A geometric alternative to Nesterov's accelerated gradient descent (arXiv), Efficient Inverse Maintenance and Faster Algorithms for Linear Programming (FOCS 2015), Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time (FOCS 2015, SIAM J. Comput. Convex Optimization, Spectral Graph, and Online Algorithms. The ones marked. The following articles are merged in Scholar. Theory of Computation. However, many advances have come from a continuous viewpoint. Yin Tat Lee is an assistant professor in the Paul G. Allen School of Computer Science & Engineering at the University of Washington. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Yin Tat Lee. https://www.engr.washington.edu/facresearch/newfaculty/2017/Yin-TatLee CV. Convex Optimization, Spectral Graph, and Online Algorithms. Working paper targeted for submission to Journal of International Business Studies. Advisor: Yin Tat Lee University of Waterloo June 2018 B. Yin Tat Lee, Microsoft Research and University of Washington Computational Challenges in Machine Learning https://simons.berkeley.edu/talks/yin-tat-lee-2017-5-2 CSE 562. yintat cs.washington.edu. Yin Tat Lee. Yin-Tat Lee, left, and Santosh Vempala made a significant improvement to the KLS bound in 2016. Symbolic but Consequential: Securities Analysts’ Forecasts and Corporate Downsizing Decisions. Try again later. with He Sun and Luca Zanetti. ADSI coPI, Yin Tat Lee, has been named a 2019 Microsoft Research Faculty Fellow. Yin Tat Lee and coauthors win a best paper award at NeurIPS 2018 for their work on algorithms for distributed optimization. We also obtained a … Their, This "Cited by" count includes citations to the following articles in Scholar. Best paper and best student paper in FOCS 2014. I am a Postdoc at Microsoft Research and will be joining Computer Science & Engineering at the University of Washington in Fall 2017. In 55th Annual Symposium on Foundations of Computer Science (FOCS 2014). Extended abstract appeared in Proceedings of the ACM-SIAM Symposium on Discrete Algorithms (SODA) …

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