aaron sidford cv

", "A new Catalyst framework with relaxed error condition for faster finite-sum and minimax solvers. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. [pdf] [talk] July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Lower bounds for finding stationary points II: first-order methods. Office: 380-T I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. 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. KTH in Stockholm, Sweden, and my BSc + MSc at the Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . They will share a $10,000 prize, with financial sponsorship provided by Google Inc. ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. She was 19 years old and looking - freewareppc.com AISTATS, 2021. Faculty Spotlight: Aaron Sidford - Management Science and Engineering In this talk, I will present a new algorithm for solving linear programs. STOC 2023. with Aaron Sidford rl1 I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). My CV. 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. Before Stanford, I worked with John Lafferty at the University of Chicago. [pdf] 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& 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. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle MS&E welcomes new faculty member, Aaron Sidford ! Computer Science. Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. 2021 - 2022 Postdoc, Simons Institute & UC . Before attending Stanford, I graduated from MIT in May 2018. . Yin Tat Lee and Aaron Sidford. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Done under the mentorship of M. Malliaris. Full CV is available here. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Aaron Sidford | Management Science and Engineering missouri noodling association president cnn. Selected recent papers . Articles Cited by Public access. SHUFE, where I was fortunate Information about your use of this site is shared with Google. [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. [pdf] [poster] I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). 2023. . 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. Roy Frostig - Stanford University Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. with Yair Carmon, Kevin Tian and Aaron Sidford Student Intranet. Alcatel flip phones are also ready to purchase with consumer cellular. Abstract. 2016. Yujia Jin. Here are some lecture notes that I have written over the years. Email: [name]@stanford.edu % With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. F+s9H 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. I graduated with a PhD from Princeton University in 2018. The authors of most papers are ordered alphabetically. by Aaron Sidford. Kirankumar Shiragur | Data Science with Vidya Muthukumar and Aaron Sidford 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. Publications and Preprints. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper 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 am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in [pdf] I am broadly interested in mathematics and theoretical computer science. I regularly advise Stanford students from a variety of departments. I was fortunate to work with Prof. Zhongzhi Zhang. "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. Enrichment of Network Diagrams for Potential Surfaces. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. 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, [1811.10722] Solving Directed Laplacian Systems in Nearly-Linear Time Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Stanford University. ", Applied Math at Fudan 4 0 obj Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games pdf, Sequential Matrix Completion. endobj 475 Via Ortega We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games [pdf] [poster] NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games 2015 Doctoral Dissertation Award - Association for Computing Machinery I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Iterative methods, combinatorial optimization, and linear programming We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). Vatsal Sharan - GitHub Pages with Yair Carmon, Aaron Sidford and Kevin Tian I received a B.S. In Symposium on Theory of Computing (STOC 2020) (arXiv), Constant Girth Approximation for Directed Graphs in Subquadratic Time, With Shiri Chechik, Yang P. Liu, and Omer Rotem, Leverage Score Sampling for Faster Accelerated Regression and ERM, With Naman Agarwal, Sham Kakade, Rahul Kidambi, Yin Tat Lee, and Praneeth Netrapalli, In International Conference on Algorithmic Learning Theory (ALT 2020) (arXiv), Near-optimal Approximate Discrete and Continuous Submodular Function Minimization, In Symposium on Discrete Algorithms (SODA 2020) (arXiv), Fast and Space Efficient Spectral Sparsification in Dynamic Streams, With Michael Kapralov, Aida Mousavifar, Cameron Musco, Christopher Musco, Navid Nouri, and Jakab Tardos, In Conference on Neural Information Processing Systems (NeurIPS 2019), Complexity of Highly Parallel Non-Smooth Convex Optimization, With Sbastien Bubeck, Qijia Jiang, Yin Tat Lee, and Yuanzhi Li, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, A Direct (1/) Iteration Parallel Algorithm for Optimal Transport, In Conference on Neural Information Processing Systems (NeurIPS 2019) (arXiv), A General Framework for Efficient Symmetric Property Estimation, With Moses Charikar and Kirankumar Shiragur, Parallel Reachability in Almost Linear Work and Square Root Depth, In Symposium on Foundations of Computer Science (FOCS 2019) (arXiv), With Deeparnab Chakrabarty, Yin Tat Lee, Sahil Singla, and Sam Chiu-wai Wong, Deterministic Approximation of Random Walks in Small Space, With Jack Murtagh, Omer Reingold, and Salil P. Vadhan, In International Workshop on Randomization and Computation (RANDOM 2019), A Rank-1 Sketch for Matrix Multiplicative Weights, With Yair Carmon, John C. Duchi, and Kevin Tian, In Conference on Learning Theory (COLT 2019) (arXiv), Near-optimal method for highly smooth convex optimization, Efficient profile maximum likelihood for universal symmetric property estimation, In Symposium on Theory of Computing (STOC 2019) (arXiv), Memory-sample tradeoffs for linear regression with small error, Perron-Frobenius Theory in Nearly Linear Time: Positive Eigenvectors, M-matrices, Graph Kernels, and Other Applications, With AmirMahdi Ahmadinejad, Arun Jambulapati, and Amin Saberi, In Symposium on Discrete Algorithms (SODA 2019) (arXiv), Exploiting Numerical Sparsity for Efficient Learning: Faster Eigenvector Computation and Regression, In Conference on Neural Information Processing Systems (NeurIPS 2018) (arXiv), Near-Optimal Time and Sample Complexities for Solving Discounted Markov Decision Process with a Generative Model, With Mengdi Wang, Xian Wu, Lin F. Yang, and Yinyu Ye, Coordinate Methods for Accelerating Regression and Faster Approximate Maximum Flow, In Symposium on Foundations of Computer Science (FOCS 2018), Solving Directed Laplacian Systems in Nearly-Linear Time through Sparse LU Factorizations, With Michael B. Cohen, Jonathan A. Kelner, Rasmus Kyng, John Peebles, Richard Peng, and Anup B. Rao, In Symposium on Foundations of Computer Science (FOCS 2018) (arXiv), Efficient Convex Optimization with Membership Oracles, In Conference on Learning Theory (COLT 2018) (arXiv), Accelerating Stochastic Gradient Descent for Least Squares Regression, With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli, Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners. Some I am still actively improving and all of them I am happy to continue polishing. Two months later, he was found lying in a creek, dead from . With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization 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 . ReSQueing Parallel and Private Stochastic Convex Optimization. Anup B. Rao - Google Scholar Thesis, 2016. pdf. 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. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space In Sidford's dissertation, Iterative Methods, Combinatorial . My research is on the design and theoretical analysis of efficient algorithms and data structures. Aaron Sidford | Stanford Online >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Sampling random spanning trees faster than matrix multiplication Selected for oral presentation. Anup B. Rao. 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. To appear as a contributed talk at QIP 2023 ; Quantum Pseudoentanglement. aaron sidford cv natural fibrin removal - libiot.kku.ac.th 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. with Yair Carmon, Arun Jambulapati and Aaron Sidford 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 . SODA 2023: 5068-5089. In Symposium on Foundations of Computer Science (FOCS 2020) Invited to the special issue ( arXiv) Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Navajo Math Circles Instructor. [pdf] in Mathematics and B.A. Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . Alcatel One Touch Flip Phone - New Product Recommendations, Promotions with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford AISTATS, 2021. Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Associate Professor of . Aaron Sidford - All Publications Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Faculty and Staff Intranet. 2013. I am fortunate to be advised by Aaron Sidford. aaron sidford cvnatural fibrin removalnatural fibrin removal View Full Stanford Profile. A nearly matching upper and lower bound for constant error here! I am an Assistant Professor in the School of Computer Science at Georgia Tech. The system can't perform the operation now. I also completed my undergraduate degree (in mathematics) at MIT. Aaron Sidford - Selected Publications I am generally interested in algorithms and learning theory, particularly developing algorithms for machine learning with provable guarantees. [pdf] Main Menu. theory and graph applications. Aaron Sidford - live-simons-institute.pantheon.berkeley.edu Stanford, CA 94305 With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Improves the stochas-tic convex optimization problem in parallel and DP setting. Summer 2022: I am currently a research scientist intern at DeepMind in London. O! Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory ( COLT 2022 )! Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! David P. Woodruff - Carnegie Mellon University Many of my results use fast matrix multiplication the Operations Research group. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . Improved Lower Bounds for Submodular Function Minimization. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). Our method improves upon the convergence rate of previous state-of-the-art linear programming . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Eigenvalues of the laplacian and their relationship to the connectedness of a graph. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. About Me. IEEE, 147-156. Parallelizing Stochastic Gradient Descent for Least Squares Regression with Aaron Sidford In submission. [PDF] Faster Algorithms for Computing the Stationary Distribution Neural Information Processing Systems (NeurIPS), 2014. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. resume/cv; publications. CME 305/MS&E 316: Discrete Mathematics and Algorithms Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching 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 . With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). /Filter /FlateDecode Aaron Sidford's Homepage - Stanford University Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. 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. Faster Matroid Intersection Princeton University If you have been admitted to Stanford, please reach out to discuss the possibility of rotating or working together. Source: appliancesonline.com.au. . xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y Yang P. Liu - GitHub Pages when do tulips bloom in maryland; indo pacific region upsc The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Personal Website. 172 Gates Computer Science Building 353 Jane Stanford Way Stanford University We present an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second . We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. 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. Here is a slightly more formal third-person biography, and here is a recent-ish CV. 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).

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