aaron sidford cv

Google Scholar; Probability on trees and . Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford I regularly advise Stanford students from a variety of departments. Enrichment of Network Diagrams for Potential Surfaces. Secured intranet portal for faculty, staff and students. 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. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) >> We prove that deterministic first-order methods, even applied to arbitrarily smooth functions, cannot achieve convergence rates in $$ better than $^{-8/5}$, which is within $^{-1/15}\\log\\frac{1}$ of the best known rate for such . ICML, 2016. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . In Foundations of Computer Science (FOCS), 2013 IEEE 54th Annual Symposium on. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Research Institute for Interdisciplinary Sciences (RIIS) at 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. Google Scholar Digital Library; Russell Lyons and Yuval Peres. Secured intranet portal for faculty, staff and students. We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Neural Information Processing Systems (NeurIPS), 2014. I am fortunate to be advised by Aaron Sidford. I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Some I am still actively improving and all of them I am happy to continue polishing. I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. Before joining Stanford in Fall 2016, I was an NSF post-doctoral fellow at Carnegie Mellon University ; I received a Ph.D. in mathematics from the University of Michigan in 2014, and a B.A. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs 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. Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. Navajo Math Circles Instructor. 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. Main Menu. 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. Call (225) 687-7590 or park nicollet dermatology wayzata today! International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG ", "Streaming matching (and optimal transport) in $\tilde{O}(1/\epsilon)$ passes and $O(n)$ space. 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. Articles Cited by Public access. 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. The site facilitates research and collaboration in academic endeavors. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. AISTATS, 2021. Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 IEEE, 147-156. 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. I am broadly interested in mathematics and theoretical computer science. aaron sidford cvis sea bass a bony fish to eat. Allen Liu. (, 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 (. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. [pdf] United States. endobj Lower bounds for finding stationary points I, Accelerated Methods for NonConvex Optimization, SIAM Journal on Optimization, 2018 (arXiv), Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Np%p `a!2D4! However, many advances have come from a continuous viewpoint. CV (last updated 01-2022): PDF Contact. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Links. Verified email at stanford.edu - Homepage. << The system can't perform the operation now. 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 Operations Research group. Nearly Optimal Communication and Query Complexity of Bipartite Matching . ", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games About Me. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 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). I am broadly interested in optimization problems, sometimes in the intersection with machine learning David P. Woodruff . The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. In Sidford's dissertation, Iterative Methods, Combinatorial . %PDF-1.4 pdf, Sequential Matrix Completion. COLT, 2022. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 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 . Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. Improves the stochas-tic convex optimization problem in parallel and DP setting. with Yair Carmon, Kevin Tian and Aaron Sidford With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. ", "How many $\epsilon$-length segments do you need to look at for finding an $\epsilon$-optimal minimizer of convex function on a line? This is the academic homepage of Yang Liu (I publish under Yang P. Liu). 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. SHUFE, where I was fortunate [pdf] [talk] [poster] Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . 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 Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Before Stanford, I worked with John Lafferty at the University of Chicago. /CreationDate (D:20230304061109-08'00') Here is a slightly more formal third-person biography, and here is a recent-ish CV. Their, This "Cited by" count includes citations to the following articles in Scholar. 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. View Full Stanford Profile. This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). small tool to obtain upper bounds of such algebraic algorithms. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent [pdf] Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. with Yair Carmon, Aaron Sidford and Kevin Tian with Aaron Sidford Abstract. ", "Sample complexity for average-reward MDPs? Unlike previous ADFOCS, this year the event will take place over the span of three weeks. . with Yair Carmon, Aaron Sidford and Kevin Tian Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& The following articles are merged in Scholar. ", "A short version of the conference publication under the same title. >> I am an Assistant Professor in the School of Computer Science at Georgia Tech. I received a B.S. with Aaron Sidford Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss 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. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. resume/cv; publications. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Two months later, he was found lying in a creek, dead from . 4026. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods with Yair Carmon, Arun Jambulapati and Aaron Sidford of practical importance. My research focuses on AI and machine learning, with an emphasis on robotics applications. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). 4 0 obj which is why I created a Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. arXiv | conference pdf (alphabetical authorship) Jonathan Kelner, Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Honglin Yuan, Big-Step-Little-Step: Gradient Methods for Objectives with . to be advised by Prof. Dongdong Ge. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. rl1 [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 [pdf] Applying this technique, we prove that any deterministic SFM algorithm . . Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. /Producer (Apache FOP Version 1.0) 2017. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f (ACM Doctoral Dissertation Award, Honorable Mention.) 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. Best Paper Award. " 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. Management Science & Engineering Articles 1-20. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. with Arun Jambulapati, Aaron Sidford and Kevin Tian in math and computer science from Swarthmore College in 2008. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford ?_l) % It was released on november 10, 2017. This site uses cookies from Google to deliver its services and to analyze traffic. In International Conference on Machine Learning (ICML 2016). Semantic parsing on Freebase from question-answer pairs. 2023. . [last name]@stanford.edu where [last name]=sidford. publications by categories in reversed chronological order. Another research focus are optimization algorithms. Intranet Web Portal. 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 . Mail Code. University, where Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Many of my results use fast matrix multiplication Before attending Stanford, I graduated from MIT in May 2018. A nearly matching upper and lower bound for constant error here!