aaron sidford cv

Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. with Yair Carmon, Arun Jambulapati and Aaron Sidford {{{;}#q8?\. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Journal of Machine Learning Research, 2017 (arXiv). This is the academic homepage of Yang Liu (I publish under Yang P. Liu). theory and graph applications. Conference on Learning Theory (COLT), 2015. Our algorithm combines the derandomized square graph operation (Rozenman and Vadhan, 2005), which we recently used for solving Laplacian systems in nearly logarithmic space (Murtagh, Reingold, Sidford, and Vadhan, 2017), with ideas from (Cheng, Cheng, Liu, Peng, and Teng, 2015), which gave an algorithm that is time-efficient (while ours is . with Yang P. Liu and Aaron Sidford. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Information about your use of this site is shared with Google. [pdf] This site uses cookies from Google to deliver its services and to analyze traffic. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Annie Marsden. The design of algorithms is traditionally a discrete endeavor. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. 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. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. [pdf] My research focuses on AI and machine learning, with an emphasis on robotics applications. I maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . 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. 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. Sequential Matrix Completion. Associate Professor of . July 8, 2022. Aaron Sidford's 143 research works with 2,861 citations and 1,915 reads, including: Singular Value Approximation and Reducing Directed to Undirected Graph Sparsification Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions SODA 2023: 4667-4767. Faculty Spotlight: Aaron Sidford. Applying this technique, we prove that any deterministic SFM algorithm . Faculty and Staff Intranet. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA publications by categories in reversed chronological order. 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). Articles Cited by Public access. Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Np%p `a!2D4! My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). 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. I graduated with a PhD from Princeton University in 2018. In submission. One research focus are dynamic algorithms (i.e. We will start with a primer week to learn the very basics of continuous optimization (July 26 - July 30), followed by two weeks of talks by the speakers on more advanced . 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. ! sidford@stanford.edu. Lower bounds for finding stationary points II: first-order methods. Computer Science. Many of my results use fast matrix multiplication Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. what is a blind trust for lottery winnings; ithaca college park school scholarships; with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian >> In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. 4 0 obj Before attending Stanford, I graduated from MIT in May 2018. I received a B.S. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Title. when do tulips bloom in maryland; indo pacific region upsc Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford IEEE, 147-156. In Symposium on Foundations of Computer Science (FOCS 2017) (arXiv), "Convex Until Proven Guilty": Dimension-Free Acceleration of Gradient Descent on Non-Convex Functions, With Yair Carmon, John C. Duchi, and Oliver Hinder, In International Conference on Machine Learning (ICML 2017) (arXiv), Almost-Linear-Time Algorithms for Markov Chains and New Spectral Primitives for Directed Graphs, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, Anup B. Rao, and, Adrian Vladu, In Symposium on Theory of Computing (STOC 2017), Subquadratic Submodular Function Minimization, With Deeparnab Chakrabarty, Yin Tat Lee, and Sam Chiu-wai Wong, In Symposium on Theory of Computing (STOC 2017) (arXiv), Faster Algorithms for Computing the Stationary Distribution, Simulating Random Walks, and More, With Michael B. Cohen, Jonathan A. Kelner, John Peebles, Richard Peng, and Adrian Vladu, In Symposium on Foundations of Computer Science (FOCS 2016) (arXiv), With Michael B. Cohen, Yin Tat Lee, Gary L. Miller, and Jakub Pachocki, In Symposium on Theory of Computing (STOC 2016) (arXiv), With Alina Ene, Gary L. Miller, and Jakub Pachocki, Streaming PCA: Matching Matrix Bernstein and Near-Optimal Finite Sample Guarantees for Oja's Algorithm, With Prateek Jain, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli, In Conference on Learning Theory (COLT 2016) (arXiv), Principal Component Projection Without Principal Component Analysis, With Roy Frostig, Cameron Musco, and Christopher Musco, In International Conference on Machine Learning (ICML 2016) (arXiv), Faster Eigenvector Computation via Shift-and-Invert Preconditioning, With Dan Garber, Elad Hazan, Chi Jin, Sham M. Kakade, Cameron Musco, and Praneeth Netrapalli, Efficient Algorithms for Large-scale Generalized Eigenvector Computation and Canonical Correlation Analysis. with Aaron Sidford Slides from my talk at ITCS. van vu professor, yale Verified email at yale.edu. % MS&E welcomes new faculty member, Aaron Sidford ! From 2016 to 2018, I also worked in ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. with Yair Carmon, Aaron Sidford and Kevin Tian I was fortunate to work with Prof. Zhongzhi Zhang. Two months later, he was found lying in a creek, dead from . Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. I hope you enjoy the content as much as I enjoyed teaching the class and if you have questions or feedback on the note, feel free to email me. By using this site, you agree to its use of cookies. with Aaron Sidford [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. 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. with Aaron Sidford Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . /Filter /FlateDecode >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. with Kevin Tian and Aaron Sidford Best Paper Award. Stanford University Before attending Stanford, I graduated from MIT in May 2018. ", Applied Math at Fudan I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in pdf, Sequential Matrix Completion. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. I maintain a mailing list for my graduate students and the broader Stanford community that it is interested in the work of my research group. Semantic parsing on Freebase from question-answer pairs. Office: 380-T Here is a slightly more formal third-person biography, and here is a recent-ish CV. [pdf] Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Towards this goal, some fundamental questions need to be solved, such as how can machines learn models of their environments that are useful for performing tasks . %PDF-1.4 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. The following articles are merged in Scholar. which is why I created a About Me. CoRR abs/2101.05719 ( 2021 ) with Vidya Muthukumar and Aaron Sidford 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 . Roy Frostig, Sida Wang, Percy Liang, Chris Manning. I received my PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where I was advised by Professor Jonathan Kelner. with Yair Carmon, Kevin Tian and Aaron Sidford with Yair Carmon, Arun Jambulapati and Aaron Sidford Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. [pdf] [talk] [poster] 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. Authors: Michael B. Cohen, Jonathan Kelner, Rasmus Kyng, John Peebles, Richard Peng, Anup B. Rao, Aaron Sidford Download PDF Abstract: We show how to solve directed Laplacian systems in nearly-linear time. 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. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . 2021. >> Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Email / Simple MAP inference via low-rank relaxations. Conference Publications 2023 The Complexity of Infinite-Horizon General-Sum Stochastic Games With Yujia Jin, Vidya Muthukumar, Aaron Sidford To appear in Innovations in Theoretical Computer Science (ITCS 2023) (arXiv) 2022 Optimal and Adaptive Monteiro-Svaiter Acceleration With Yair Carmon, I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. endobj " 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. of practical importance. University of Cambridge MPhil. Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Another research focus are optimization algorithms. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. "t a","H ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Some I am still actively improving and all of them I am happy to continue polishing. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. 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. Thesis, 2016. pdf. en_US: dc.format.extent: 266 pages: en_US: dc.language.iso: eng: en_US: dc.publisher: Massachusetts Institute of Technology: en_US: dc.rights: M.I.T. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 I am broadly interested in mathematics and theoretical computer science. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Efficient Convex Optimization Requires Superlinear Memory. Page 1 of 5 Aaron Sidford Assistant Professor of Management Science and Engineering and of Computer Science CONTACT INFORMATION Administrative Contact Jackie Nguyen - Administrative Associate Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Email: [name]@stanford.edu Goethe University in Frankfurt, Germany. It was released on november 10, 2017. Call (225) 687-7590 or park nicollet dermatology wayzata today! 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 [pdf] with Yair Carmon, Aaron Sidford and Kevin Tian 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. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. We also provide two . Some I am still actively improving and all of them I am happy to continue polishing. Source: www.ebay.ie [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. in Chemistry at the University of Chicago. I completed my PhD at BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Allen Liu. to be advised by Prof. Dongdong Ge. /Producer (Apache FOP Version 1.0) 5 0 obj My long term goal is to bring robots into human-centered domains such as homes and hospitals. Alcatel flip phones are also ready to purchase with consumer cellular. Selected recent papers . This is the academic homepage of Yang Liu (I publish under Yang P. Liu). aaron sidford cvis sea bass a bony fish to eat. stream Stanford University. (ACM Doctoral Dissertation Award, Honorable Mention.) Abstract. Neural Information Processing Systems (NeurIPS), 2014. Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, Aaron Sidford, Zhao Song, Di Wang: Minimum Cost Flows, MDPs, and 1 -Regression in Nearly Linear Time for Dense Instances. [pdf] [poster] Before Stanford, I worked with John Lafferty at the University of Chicago. ICML Workshop on Reinforcement Learning Theory, 2021, Variance Reduction for Matrix Games Unlike previous ADFOCS, this year the event will take place over the span of three weeks. I often do not respond to emails about applications. Google Scholar Digital Library; Russell Lyons and Yuval Peres. arXiv preprint arXiv:2301.00457, 2023 arXiv. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Yin Tat Lee and Aaron Sidford. View Full Stanford Profile. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. . International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle missouri noodling association president cnn. with Arun Jambulapati, Aaron Sidford and Kevin Tian Secured intranet portal for faculty, staff and students. how . he Complexity of Infinite-Horizon General-Sum Stochastic Games, Yujia Jin, Vidya Muthukumar, Aaron Sidford, Innovations in Theoretical Computer Science (ITCS 202, air Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, Advances in Neural Information Processing Systems (NeurIPS 2022), Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Advances in Neural Information Processing Systems (NeurIPS 202, n Symposium on Foundations of Computer Science (FOCS 2022) (, International Conference on Machine Learning (ICML 2022) (, Conference on Learning Theory (COLT 2022) (, International Colloquium on Automata, Languages and Programming (ICALP 2022) (, In Symposium on Theory of Computing (STOC 2022) (, In Symposium on Discrete Algorithms (SODA 2022) (, In Advances in Neural Information Processing Systems (NeurIPS 2021) (, In Conference on Learning Theory (COLT 2021) (, In International Conference on Machine Learning (ICML 2021) (, In Symposium on Theory of Computing (STOC 2021) (, In Symposium on Discrete Algorithms (SODA 2021) (, In Innovations in Theoretical Computer Science (ITCS 2021) (, In Conference on Neural Information Processing Systems (NeurIPS 2020) (, In Symposium on Foundations of Computer Science (FOCS 2020) (, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (, In International Conference on Machine Learning (ICML 2020) (, In Conference on Learning Theory (COLT 2020) (, In Symposium on Theory of Computing (STOC 2020) (, In International Conference on Algorithmic Learning Theory (ALT 2020) (, In Symposium on Discrete Algorithms (SODA 2020) (, In Conference on Neural Information Processing Systems (NeurIPS 2019) (, In Symposium on Foundations of Computer Science (FOCS 2019) (, In Conference on Learning Theory (COLT 2019) (, In Symposium on Theory of Computing (STOC 2019) (, In Symposium on Discrete Algorithms (SODA 2019) (, In Conference on Neural Information Processing Systems (NeurIPS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2018) (, In Conference on Learning Theory (COLT 2018) (, In Symposium on Discrete Algorithms (SODA 2018) (, In Innovations in Theoretical Computer Science (ITCS 2018) (, In Symposium on Foundations of Computer Science (FOCS 2017) (, In International Conference on Machine Learning (ICML 2017) (, In Symposium on Theory of Computing (STOC 2017) (, In Symposium on Foundations of Computer Science (FOCS 2016) (, In Symposium on Theory of Computing (STOC 2016) (, In Conference on Learning Theory (COLT 2016) (, In International Conference on Machine Learning (ICML 2016) (, In International Conference on Machine Learning (ICML 2016). Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Summer 2022: I am currently a research scientist intern at DeepMind in London. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f /Length 11 0 R University, Research Institute for Interdisciplinary Sciences (RIIS) at [pdf] I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. << I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. . with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. AISTATS, 2021. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. 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 . In each setting we provide faster exact and approximate algorithms. University, where D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. [pdf] [talk] [poster] My CV. Group Resources. Secured intranet portal for faculty, staff and students. Aaron's research interests lie in optimization, the theory of computation, and the . However, many advances have come from a continuous viewpoint. rl1 We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. I am an Assistant Professor in the School of Computer Science at Georgia Tech. 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 Multiple Scales. In International Conference on Machine Learning (ICML 2016). Publications and Preprints. 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. 2016. If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University Google Scholar; Probability on trees and . Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. ", "Sample complexity for average-reward MDPs? Try again later. I enjoy understanding the theoretical ground of many algorithms that are ICML, 2016. with Yair Carmon, Aaron Sidford and Kevin Tian I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). [pdf] Stanford, CA 94305 the Operations Research group. Personal Website. . Prof. Erik Demaine TAs: Timothy Kaler, Aaron Sidford [Home] [Assignments] [Open Problems] [Accessibility] sample frame from lecture videos Data structures play a central role in modern computer science. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. [pdf] [poster] Student Intranet. Here are some lecture notes that I have written over the years. I am broadly interested in mathematics and theoretical computer science. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. I am broadly interested in optimization problems, sometimes in the intersection with machine learning 2023. . 4026. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& Yang P. Liu, Aaron Sidford, Department of Mathematics We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. 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.

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