aaron sidford cv
Instructor: Aaron Sidford Winter 2018 Time: Tuesdays and Thursdays, 10:30 AM - 11:50 AM Room: Education Building, Room 128 Here is the course syllabus. Etude for the Park City Math Institute Undergraduate Summer School. what is a blind trust for lottery winnings; ithaca college park school scholarships; I am fortunate to be advised by Aaron Sidford . I am broadly interested in optimization problems, sometimes in the intersection with machine learning View Full Stanford Profile. << 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. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. 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, Faculty and Staff Intranet. Source: www.ebay.ie Some I am still actively improving and all of them I am happy to continue polishing. >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. van vu professor, yale Verified email at yale.edu. Secured intranet portal for faculty, staff and students. 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. Email / I am a senior researcher in the Algorithms group at Microsoft Research Redmond. with Arun Jambulapati, Aaron Sidford and Kevin Tian Abstract. rl1 I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). [pdf] 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. with Aaron Sidford ", "How many \(\epsilon\)-length segments do you need to look at for finding an \(\epsilon\)-optimal minimizer of convex function on a line? Lower bounds for finding stationary points II: first-order methods. My CV. MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f [pdf] [talk] Nearly Optimal Communication and Query Complexity of Bipartite Matching . My broad research interest is in theoretical computer science and my focus is on fundamental mathematical problems in data science at the intersection of computer science, statistics, optimization, biology and economics. (ACM Doctoral Dissertation Award, Honorable Mention.) "I am excited to push the theory of optimization and algorithm design to new heights!" Assistant Professor Aaron Sidford speaks at ICME's Xpo event. Faster energy maximization for faster maximum flow. I often do not respond to emails about applications. Given a linear program with n variables, m > n constraints, and bit complexity L, our algorithm runs in (sqrt(n) L) iterations each consisting of solving (1) linear systems and additional nearly linear time computation. [pdf] [poster] United States. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . Stanford University It was released on november 10, 2017. Allen Liu. with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian I was fortunate to work with Prof. Zhongzhi Zhang. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). /N 3 Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization Algorithms which I created. Semantic parsing on Freebase from question-answer pairs. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. when do tulips bloom in maryland; indo pacific region upsc Try again later. Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, FOCS 2022 I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. . 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). theory and graph applications. 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. Research Institute for Interdisciplinary Sciences (RIIS) at [pdf] [talk] [poster] I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. 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. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). 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 . Mail Code. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games A nearly matching upper and lower bound for constant error here! 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. Anup B. Rao. [pdf] [talk] [poster] 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. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Aaron Sidford. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Algorithms Optimization and Numerical Analysis. Conference on Learning Theory (COLT), 2015. About Me. Call (225) 687-7590 or park nicollet dermatology wayzata today! Thesis, 2016. pdf. 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. 2016. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan The following articles are merged in Scholar. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. I graduated with a PhD from Princeton University in 2018. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. In Sidford's dissertation, Iterative Methods, Combinatorial . in Chemistry at the University of Chicago. with Yang P. Liu and Aaron Sidford. Contact. with Kevin Tian and Aaron Sidford with Aaron Sidford Slides from my talk at ITCS. Some I am still actively improving and all of them I am happy to continue polishing. 2017. Before Stanford, I worked with John Lafferty at the University of Chicago. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Aaron Sidford Stanford University Verified email at stanford.edu. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 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. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . by Aaron Sidford. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. ?_l) Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. resume/cv; publications. 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. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! SODA 2023: 4667-4767. in Mathematics and B.A. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Full CV is available here. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. missouri noodling association president cnn. Their, This "Cited by" count includes citations to the following articles in Scholar. [pdf] I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 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. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions with Yair Carmon, Kevin Tian and Aaron Sidford Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Faculty Spotlight: Aaron Sidford. STOC 2023. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Sequential Matrix Completion. 2013. Google Scholar Digital Library; Russell Lyons and Yuval Peres. with Yair Carmon, Arun Jambulapati and Aaron Sidford endobj Email: [name]@stanford.edu CV (last updated 01-2022): PDF Contact. I am a fourth year PhD student at Stanford co-advised by Moses Charikar and Aaron Sidford. Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. 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. . "t a","H Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) If you see any typos or issues, feel free to email me. Yujia Jin. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Aaron Sidford. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 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. [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. 2023. . I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y SHUFE, where I was fortunate Student Intranet. Here is a slightly more formal third-person biography, and here is a recent-ish CV. [pdf] [poster] Selected recent papers . Email: sidford@stanford.edu. I am fortunate to be advised by Aaron Sidford. CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. [pdf] With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data.