aaron sidford cv

Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Prior to that, I received an MPhil in Scientific Computing at the University of Cambridge on a Churchill Scholarship where I was advised by Sergio Bacallado. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ With Yair Carmon, John C. Duchi, and Oliver Hinder. publications | Daogao Liu CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat Articles Cited by Public access. the Operations Research group. /N 3 Accelerated Methods for NonConvex Optimization | Semantic Scholar Thesis, 2016. pdf. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Contact. ?_l) [PDF] Faster Algorithms for Computing the Stationary Distribution Aaron Sidford - Home - Author DO Series " Geometric median in nearly linear time ." In Proceedings of the 48th Annual ACM SIGACT Symposium on Theory of Computing, STOC 2016, Cambridge, MA, USA, June 18-21, 2016, Pp. small tool to obtain upper bounds of such algebraic algorithms. in Mathematics and B.A. 2022 - Learning and Games Program, Simons Institute, Sept. 2021 - Young Researcher Workshop, Cornell ORIE, Sept. 2021 - ACO Student Seminar, Georgia Tech, Dec. 2019 - NeurIPS Spotlight presentation. SODA 2023: 4667-4767. in math and computer science from Swarthmore College in 2008. Yin Tat Lee and Aaron Sidford. aaron sidford cv I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. /Creator (Apache FOP Version 1.0) With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Faculty and Staff Intranet. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization [pdf] [poster] ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. The paper, Efficient Convex Optimization Requires Superlinear Memory, was co-authored with Stanford professor Gregory Valiant as well as current Stanford student Annie Marsden and alumnus Vatsal Sharan. with Yair Carmon, Aaron Sidford and Kevin Tian ! To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. Journal of Machine Learning Research, 2017 (arXiv). [pdf] [talk] [poster] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). sidford@stanford.edu. Aaron Sidford (sidford@stanford.edu) Welcome This page has informatoin and lecture notes from the course "Introduction to Optimization Theory" (MS&E213 / CS 269O) which I taught in Fall 2019. Allen Liu. with Aaron Sidford . /CreationDate (D:20230304061109-08'00') Stanford, CA 94305 July 8, 2022. with Kevin Tian and Aaron Sidford I am broadly interested in mathematics and theoretical computer science. 4026. BayLearn, 2019, "Computing stationary solution for multi-agent RL is hard: Indeed, CCE for simultaneous games and NE for turn-based games are both PPAD-hard. Yujia Jin - Stanford University Google Scholar Digital Library; Russell Lyons and Yuval Peres. It was released on november 10, 2017. what is a blind trust for lottery winnings; ithaca college park school scholarships; July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Email: sidford@stanford.edu. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian Google Scholar, The Complexity of Infinite-Horizon General-Sum Stochastic Games, The Complexity of Optimizing Single and Multi-player Games, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions, On the Sample Complexity for Average-reward Markov Decision Processes, Stochastic Methods for Matrix Games and its Applications, Acceleration with a Ball Optimization Oracle, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, The Complexity of Infinite-Horizon General-Sum Stochastic Games Call (225) 687-7590 or park nicollet dermatology wayzata today! Management Science & Engineering A nearly matching upper and lower bound for constant error here! Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Aaron Sidford. 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. This improves upon previous best known running times of O (nr1.5T-ind) due to Cunningham in 1986 and (n2T-ind+n3) due to Lee, Sidford, and Wong in 2015. << In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. I received a B.S. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University van vu professor, yale Verified email at yale.edu. [pdf] Yang P. Liu - GitHub Pages I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. endobj Aaron Sidford. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in Efficient Convex Optimization Requires Superlinear Memory. MS&E welcomes new faculty member, Aaron Sidford ! With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. If you see any typos or issues, feel free to email me. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods KTH in Stockholm, Sweden, and my BSc + MSc at the Student Intranet. [pdf] [slides] Information about your use of this site is shared with Google. Computer Science. Alcatel flip phones are also ready to purchase with consumer cellular. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. The ones marked, 2014 IEEE 55th Annual Symposium on Foundations of Computer Science, 424-433, SIAM Journal on Optimization 28 (2), 1751-1772, Proceedings of the twenty-fifth annual ACM-SIAM symposium on Discrete, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 1049-1065, 2013 ieee 54th annual symposium on foundations of computer science, 147-156, Proceedings of the forty-fifth annual ACM symposium on Theory of computing, MB Cohen, YT Lee, C Musco, C Musco, R Peng, A Sidford, Proceedings of the 2015 Conference on Innovations in Theoretical Computer, Advances in Neural Information Processing Systems 31, M Kapralov, YT Lee, CN Musco, CP Musco, A Sidford, SIAM Journal on Computing 46 (1), 456-477, P Jain, S Kakade, R Kidambi, P Netrapalli, A Sidford, MB Cohen, YT Lee, G Miller, J Pachocki, A Sidford, Proceedings of the forty-eighth annual ACM symposium on Theory of Computing, International Conference on Machine Learning, 2540-2548, P Jain, SM Kakade, R Kidambi, P Netrapalli, A Sidford, 2015 IEEE 56th Annual Symposium on Foundations of Computer Science, 230-249, Mathematical Programming 184 (1-2), 71-120, P Jain, C Jin, SM Kakade, P Netrapalli, A Sidford, International conference on machine learning, 654-663, Proceedings of the Twenty-Ninth Annual ACM-SIAM Symposium on Discrete, D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford, New articles related to this author's research, Path finding methods for linear programming: Solving linear programs in o (vrank) iterations and faster algorithms for maximum flow, Accelerated methods for nonconvex optimization, 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, Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems, A simple, combinatorial algorithm for solving SDD systems in nearly-linear time, Uniform sampling for matrix approximation, Near-optimal time and sample complexities for solving Markov decision processes with a generative model, Single pass spectral sparsification in dynamic streams, Parallelizing stochastic gradient descent for least squares regression: mini-batching, averaging, and model misspecification, Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, Accelerating stochastic gradient descent for least squares regression, Efficient inverse maintenance and faster algorithms for linear programming, Lower bounds for finding stationary points I, Streaming pca: Matching matrix bernstein and near-optimal finite sample guarantees for ojas algorithm, Convex Until Proven Guilty: Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, Competing with the empirical risk minimizer in a single pass, Variance reduced value iteration and faster algorithms for solving Markov decision processes, Robust shift-and-invert preconditioning: Faster and more sample efficient algorithms for eigenvector computation. Some I am still actively improving and all of them I am happy to continue polishing. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. Adam Bouland - Stanford University F+s9H My CV. [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time << Articles 1-20. Before attending Stanford, I graduated from MIT in May 2018. SHUFE, where I was fortunate Yujia Jin. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). with Arun Jambulapati, Aaron Sidford and Kevin Tian In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . We forward in this generation, Triumphantly. About - Annie Marsden Advanced Data Structures (6.851) - Massachusetts Institute of Technology Summer 2022: I am currently a research scientist intern at DeepMind in London. Optimization and Algorithmic Paradigms (CS 261): Winter '23, Optimization Algorithms (CS 369O / CME 334 / MS&E 312): Fall '22, Discrete Mathematics and Algorithms (CME 305 / MS&E 315): Winter '22, '21, '20, '19, '18, Introduction to Optimization Theory (CS 269O / MS&E 213): Fall '20, '19, Spring '19, '18, '17, Almost Linear Time Graph Algorithms (CS 269G / MS&E 313): Fall '18, Winter '17. Our method improves upon the convergence rate of previous state-of-the-art linear programming . Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . This is the academic homepage of Yang Liu (I publish under Yang P. Liu). If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Aaron Sidford - Stanford University Before Stanford, I worked with John Lafferty at the University of Chicago. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Iterative methods, combinatorial optimization, and linear programming In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. I was fortunate to work with Prof. Zhongzhi Zhang. ", Applied Math at Fudan United States. theses are protected by copyright. This work presents an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second derivatives that is Hessian free, i.e., it only requires gradient computations, and is therefore suitable for large-scale applications. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions Before attending Stanford, I graduated from MIT in May 2018. 9-21. Aaron's research interests lie in optimization, the theory of computation, and the . With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. Aaron Sidford is an Assistant Professor of Management Science and Engineering at Stanford University, where he also has a courtesy appointment in Computer Science and an affiliation with the Institute for Computational and Mathematical Engineering (ICME). They will share a $10,000 prize, with financial sponsorship provided by Google Inc. (ACM Doctoral Dissertation Award, Honorable Mention.) I have the great privilege and good fortune of advising the following PhD students: I have also had the great privilege and good fortune of advising the following PhD students who have now graduated: Kirankumar Shiragur (co-advised with Moses Charikar) - PhD 2022, AmirMahdi Ahmadinejad (co-advised with Amin Saberi) - PhD 2020, Yair Carmon (co-advised with John Duchi) - PhD 2020. Their, This "Cited by" count includes citations to the following articles in Scholar. % (, In Symposium on Foundations of Computer Science (FOCS 2015) (, In Conference on Learning Theory (COLT 2015) (, In International Conference on Machine Learning (ICML 2015) (, In Innovations in Theoretical Computer Science (ITCS 2015) (, In Symposium on Fondations of Computer Science (FOCS 2013) (, In Symposium on the Theory of Computing (STOC 2013) (, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (, Journal of Machine Learning Research, 2017 (. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Another research focus are optimization algorithms. 22nd Max Planck Advanced Course on the Foundations of Computer Science Aaron Sidford - Teaching with Yair Carmon, Arun Jambulapati and Aaron Sidford Mary Wootters - Google 4 0 obj Selected recent papers . Done under the mentorship of M. Malliaris. 2023. . About Me. Interior Point Methods for Nearly Linear Time Algorithms | ISL Associate Professor of . I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Cameron Musco - Manning College of Information & Computer Sciences ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. with Yair Carmon, Kevin Tian and Aaron Sidford . Try again later. Yair Carmon. Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& He received his PhD from the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he was advised by Jonathan Kelner. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. I regularly advise Stanford students from a variety of departments. In Symposium on Discrete Algorithms (SODA 2018) (arXiv), Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes, Efficient (n/) Spectral Sketches for the Laplacian and its Pseudoinverse, Stability of the Lanczos Method for Matrix Function Approximation. 2016. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). My research was supported by the National Defense Science and Engineering Graduate (NDSEG) Fellowship from 2018-2021, and by a Google PhD Fellowship from 2022-2023. Nearly Optimal Communication and Query Complexity of Bipartite Matching . [name] = yangpliu, Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, Online Edge Coloring via Tree Recurrences and Correlation Decay, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, Discrepancy Minimization via a Self-Balancing Walk, Faster Divergence Maximization for Faster Maximum Flow. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Lower bounds for finding stationary points II: first-order methods. If you see any typos or issues, feel free to email me. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs 2013. I am broadly interested in optimization problems, sometimes in the intersection with machine learning However, even restarting can be a hard task here. Aaron Sidford receives best paper award at COLT 2022 Aviv Tamar - Reinforcement Learning Research Labs - Technion Goethe University in Frankfurt, Germany. Etude for the Park City Math Institute Undergraduate Summer School. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games ", "Streaming matching (and optimal transport) in $\tilde{O}(1/\epsilon)$ passes and $O(n)$ space. Google Scholar; Probability on trees and . 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! Office: 380-T 5 0 obj With Cameron Musco and Christopher Musco. 2017. Some I am still actively improving and all of them I am happy to continue polishing. The site facilitates research and collaboration in academic endeavors. 2016. arXiv | conference pdf (alphabetical authorship) Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan, Big-Step-Little-Step: Gradient Methods for Objectives with . My broad research interest is in theoretical computer science and my focus is on fundamental mathematical problems in data science at the intersection of computer science, statistics, optimization, biology and economics. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. Sampling random spanning trees faster than matrix multiplication View Full Stanford Profile. to be advised by Prof. Dongdong Ge. O! dblp: Yin Tat Lee Li Chen, Rasmus Kyng, Yang P. Liu, Richard Peng, Maximilian Probst Gutenberg, Sushant Sachdeva, Online Edge Coloring via Tree Recurrences and Correlation Decay, STOC 2022 Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss DOI: 10.1109/FOCS.2016.69 Corpus ID: 3311; Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More @article{Cohen2016FasterAF, title={Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More}, author={Michael B. Cohen and Jonathan A. Kelner and John Peebles and Richard Peng and Aaron Sidford and Adrian Vladu}, journal . Many of my results use fast matrix multiplication Yang P. Liu, Aaron Sidford, Department of Mathematics Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 /Length 11 0 R The paper, Efficient Convex Optimization Requires Superlinear Memory, was co-authored with Stanford professor Gregory Valiant as well as current Stanford student Annie Marsden and alumnus Vatsal Sharan. I am broadly interested in mathematics and theoretical computer science. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Neural Information Processing Systems (NeurIPS), 2014. Enrichment of Network Diagrams for Potential Surfaces. Outdated CV [as of Dec'19] Students I am very lucky to advise the following Ph.D. students: Siddartha Devic (co-advised with Aleksandra Korolova . My long term goal is to bring robots into human-centered domains such as homes and hospitals. Honorable Mention for the 2015 ACM Doctoral Dissertation Award went to Aaron Sidford of the Massachusetts Institute of Technology, and Siavash Mirarab of the University of Texas at Austin. with Yair Carmon, Arun Jambulapati and Aaron Sidford Parallelizing Stochastic Gradient Descent for Least Squares Regression Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Faster Matroid Intersection Princeton University with Yair Carmon, Aaron Sidford and Kevin Tian Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Publications | Jakub Pachocki - Harvard University SODA 2023: 5068-5089. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Allen Liu - GitHub Pages Here are some lecture notes that I have written over the years. how . Secured intranet portal for faculty, staff and students. Stanford University. Alcatel One Touch Flip Phone - New Product Recommendations, Promotions Email / Mail Code. Links. . [pdf] [pdf] [talk] In this talk, I will present a new algorithm for solving linear programs. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford

aaron sidford cv 2023