what is a blind trust for lottery winnings; ithaca college park school scholarships; We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Some I am still actively improving and all of them I am happy to continue polishing. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization With Yair Carmon, John C. Duchi, and Oliver Hinder. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Student Intranet. Personal Website. " 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. Call (225) 687-7590 or park nicollet dermatology wayzata today! Try again later. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. 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. Np%p `a!2D4! From 2016 to 2018, I also worked in "t a","H I often do not respond to emails about applications. [pdf] << Email / [pdf] [slides] The site facilitates research and collaboration in academic endeavors. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Yin Tat Lee and Aaron Sidford. [pdf] 2013. Yujia Jin. Lower bounds for finding stationary points II: first-order methods. Try again later. Given a linear program with n variables, m > n constraints, and bit complexity L, our algorithm runs in (sqrt(n) L) iterations each consisting of solving (1) linear systems and additional nearly linear time computation. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. van vu professor, yale Verified email at yale.edu. >> ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. Follow. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). . >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? 4026. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian Mail Code. However, even restarting can be a hard task here. Secured intranet portal for faculty, staff and students. I regularly advise Stanford students from a variety of departments. rl1 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Some I am still actively improving and all of them I am happy to continue polishing. Our method improves upon the convergence rate of previous state-of-the-art linear programming . Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford 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 Google Scholar; Probability on trees and . [pdf] [poster] In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. << F+s9H [pdf] [poster] 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 . with Yair Carmon, Arun Jambulapati and Aaron Sidford I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. STOC 2023. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Full CV is available here. University of Cambridge MPhil. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Assistant Professor of Management Science and Engineering and of Computer Science. We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . SHUFE, where I was fortunate Etude for the Park City Math Institute Undergraduate Summer School. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Links. /CreationDate (D:20230304061109-08'00') We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. ICML, 2016. /Filter /FlateDecode Aaron's research interests lie in optimization, the theory of computation, and the . Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions 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 Allen Liu. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Research Institute for Interdisciplinary Sciences (RIIS) at to be advised by Prof. Dongdong Ge. 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 . With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! with Yair Carmon, Kevin Tian and Aaron Sidford Articles Cited by Public access. 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. We forward in this generation, Triumphantly. /Length 11 0 R theses are protected by copyright. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). /N 3 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. Navajo Math Circles Instructor. 113 * 2016: The system can't perform the operation now. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Best Paper Award. Google Scholar Digital Library; Russell Lyons and Yuval Peres. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Verified email at stanford.edu - Homepage. Simple MAP inference via low-rank relaxations. If you see any typos or issues, feel free to email me. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. 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. . 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, (, 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 (. 2016. Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 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. 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. Email: sidford@stanford.edu. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. arXiv | conference pdf, Annie Marsden, Sergio Bacallado. Nearly Optimal Communication and Query Complexity of Bipartite Matching . Stanford, CA 94305 I am fortunate to be advised by Aaron Sidford . [pdf] [pdf] [talk] [poster] 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. View Full Stanford Profile. Improves the stochas-tic convex optimization problem in parallel and DP setting. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. with Yair Carmon, Aaron Sidford and Kevin Tian Goethe University in Frankfurt, Germany. I also completed my undergraduate degree (in mathematics) at MIT. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. I was fortunate to work with Prof. Zhongzhi Zhang. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. I am broadly interested in mathematics and theoretical computer science. Anup B. Rao. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. 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 . COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. when do tulips bloom in maryland; indo pacific region upsc Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. I enjoy understanding the theoretical ground of many algorithms that are The design of algorithms is traditionally a discrete endeavor. O! With Cameron Musco and Christopher Musco. in Mathematics and B.A. Title. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Sidford received his PhD from the department of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology where he was advised by Professor Jonathan Kelner. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. David P. Woodruff . 4 0 obj with Yair Carmon, Aaron Sidford and Kevin Tian United States. Alcatel flip phones are also ready to purchase with consumer cellular. 2023. . Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Sequential Matrix Completion. By using this site, you agree to its use of cookies. In Sidford's dissertation, Iterative Methods, Combinatorial . arXiv preprint arXiv:2301.00457, 2023 arXiv. in Chemistry at the University of Chicago. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. [pdf] [talk] [poster] ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods My CV. 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. /Producer (Apache FOP Version 1.0) Computer Science. with Aaron Sidford with Aaron Sidford Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. COLT, 2022. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration with Yang P. Liu and Aaron Sidford. small tool to obtain upper bounds of such algebraic algorithms. 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 . They will share a $10,000 prize, with financial sponsorship provided by Google Inc. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. Information about your use of this site is shared with Google. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! Faculty and Staff Intranet. MS&E welcomes new faculty member, Aaron Sidford ! "I am excited to push the theory of optimization and algorithm design to new heights!" Assistant Professor Aaron Sidford speaks at ICME's Xpo event. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan [pdf] [talk] [poster] Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games how . Annie Marsden. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. 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. AISTATS, 2021. 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 research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. /Creator (Apache FOP Version 1.0) Their, This "Cited by" count includes citations to the following articles in Scholar. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs 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. with Arun Jambulapati, Aaron Sidford and Kevin Tian If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . 2017. The following articles are merged in Scholar. [pdf] [poster] Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. endobj Journal of Machine Learning Research, 2017 (arXiv). Yair Carmon. Contact. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Before attending Stanford, I graduated from MIT in May 2018. 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. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. In each setting we provide faster exact and approximate algorithms. 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. One research focus are dynamic algorithms (i.e. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Before Stanford, I worked with John Lafferty at the University of Chicago. In International Conference on Machine Learning (ICML 2016). My long term goal is to bring robots into human-centered domains such as homes and hospitals. 5 0 obj Summer 2022: I am currently a research scientist intern at DeepMind in London. About Me. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Before attending Stanford, I graduated from MIT in May 2018. I completed my PhD at [pdf] 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. Secured intranet portal for faculty, staff and students. Source: appliancesonline.com.au. with Aaron Sidford Abstract. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods I graduated with a PhD from Princeton University in 2018. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). {{{;}#q8?\. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Huang Engineering Center The authors of most papers are ordered alphabetically. Source: www.ebay.ie Efficient Convex Optimization Requires Superlinear Memory. 2021 - 2022 Postdoc, Simons Institute & UC . (arXiv), A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization, In Symposium on Foundations of Computer Science (FOCS 2015), Machtey Award for Best Student Paper (arXiv), Efficient Inverse Maintenance and Faster Algorithms for Linear Programming, In Symposium on Foundations of Computer Science (FOCS 2015) (arXiv), Competing with the Empirical Risk Minimizer in a Single Pass, With Roy Frostig, Rong Ge, and Sham Kakade, In Conference on Learning Theory (COLT 2015) (arXiv), Un-regularizing: approximate proximal point and faster stochastic algorithms for empirical risk minimization, In International Conference on Machine Learning (ICML 2015) (arXiv), Uniform Sampling for Matrix Approximation, With Michael B. Cohen, Yin Tat Lee, Cameron Musco, Christopher Musco, and Richard Peng, In Innovations in Theoretical Computer Science (ITCS 2015) (arXiv), Path-Finding Methods for Linear Programming : Solving Linear Programs in (rank) Iterations and Faster Algorithms for Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2014), Best Paper Award and Machtey Award for Best Student Paper (arXiv), Single Pass Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco, 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, In Symposium on Discrete Algorithms (SODA 2014), Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems, In Symposium on Fondations of Computer Science (FOCS 2013) (arXiv), A Simple, Combinatorial Algorithm for Solving SDD Systems in Nearly-Linear Time, With Jonathan A. Kelner, Lorenzo Orecchia, and Zeyuan Allen Zhu, In Symposium on the Theory of Computing (STOC 2013) (arXiv), SIAM Journal on Computing (arXiv before merge), Derandomization beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space, With Jack Murtagh, Omer Reingold, and Salil Vadhan, Book chapter in Building Bridges II: Mathematics of Laszlo Lovasz, 2020 (arXiv), Lower Bounds for Finding Stationary Points II: First-Order Methods. the Operations Research group. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Email: [name]@stanford.edu In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. aaron sidford cvis sea bass a bony fish to eat. Improved Lower Bounds for Submodular Function Minimization.
