Stanford University. SODA 2023: 5068-5089. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Stanford University /N 3 Yujia Jin. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Here is a slightly more formal third-person biography, and here is a recent-ish CV. Computer Science. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA Np%p `a!2D4! SODA 2023: 4667-4767. Before attending Stanford, I graduated from MIT in May 2018. Applying this technique, we prove that any deterministic SFM algorithm . Some I am still actively improving and all of them I am happy to continue polishing. In each setting we provide faster exact and approximate 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, I regularly advise Stanford students from a variety of departments. Conference on Learning Theory (COLT), 2015. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! Publications and Preprints. I am broadly interested in mathematics and theoretical computer science. ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. AISTATS, 2021. 2017. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Contact: dwoodruf (at) cs (dot) cmu (dot) edu or dpwoodru (at) gmail (dot) com CV (updated July, 2021) Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Many of my results use fast matrix multiplication /Length 11 0 R pdf, Sequential Matrix Completion. by Aaron Sidford. 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. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). van vu professor, yale Verified email at yale.edu. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Links. The site facilitates research and collaboration in academic endeavors. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. Verified email at stanford.edu - Homepage. ", "We characterize when solving the max \(\min_{x}\max_{i\in[n]}f_i(x)\) is (not) harder than solving the average \(\min_{x}\frac{1}{n}\sum_{i\in[n]}f_i(x)\). %PDF-1.4 to be advised by Prof. Dongdong Ge. Congratulations to Prof. Aaron Sidford for receiving the Best Paper Award at the 2022 Conference on Learning Theory (COLT 2022)! 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. 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. missouri noodling association president cnn. 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 . Yujia Jin. My long term goal is to bring robots into human-centered domains such as homes and hospitals. sidford@stanford.edu. publications by categories in reversed chronological order. United States. 4 0 obj ", "Sample complexity for average-reward MDPs? CV; Theory Group; Data Science; CSE 535: Theory of Optimization and Continuous Algorithms. CoRR abs/2101.05719 ( 2021 ) My interests are in the intersection of algorithms, statistics, optimization, and machine learning. 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. [pdf] Contact. of practical importance. Stanford, CA 94305 Abstract. Selected recent papers . when do tulips bloom in maryland; indo pacific region upsc February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. With Jack Murtagh, Omer Reingold, and Salil P. Vadhan. [pdf] [talk] [poster] Intranet Web Portal. With Cameron Musco and Christopher Musco. [pdf] Annie Marsden. [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. 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 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. My research is on the design and theoretical analysis of efficient algorithms and data structures. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. [c7] Sivakanth Gopi, Yin Tat Lee, Daogao Liu, Ruoqi Shen, Kevin Tian: Private Convex Optimization in General Norms. Yu Gao, Yang P. Liu, Richard Peng, Faster Divergence Maximization for Faster Maximum Flow, FOCS 2020 They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. . arXiv preprint arXiv:2301.00457, 2023 arXiv. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss Journal of Machine Learning Research, 2017 (arXiv). In this talk, I will present a new algorithm for solving linear programs. Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . In Sidford's dissertation, Iterative Methods, Combinatorial . 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 . Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. From 2016 to 2018, I also worked in 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. 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. Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. Try again later. If you see any typos or issues, feel free to email me. (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. I am fortunate to be advised by Aaron Sidford. Navajo Math Circles Instructor. I also completed my undergraduate degree (in mathematics) at MIT. Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Etude for the Park City Math Institute Undergraduate Summer School. F+s9H >> [pdf] [talk] [poster] With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). David P. Woodruff . My CV. Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Aaron's research interests lie in optimization, the theory of computation, and the . I am [pdf] [slides] riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. [last name]@stanford.edu where [last name]=sidford. Huang Engineering Center ! Eigenvalues of the laplacian and their relationship to the connectedness of a graph. Before attending Stanford, I graduated from MIT in May 2018. 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 . aaron sidford cvnatural fibrin removalnatural fibrin removal Previously, I was a visiting researcher at the Max Planck Institute for Informatics and a Simons-Berkeley Postdoctoral Researcher. Two months later, he was found lying in a creek, dead from . 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. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. He received his PhD from the Electrical Engineering and Computer Science Department at the Massachusetts Institute of Technology, where he was advised by Jonathan Kelner. "I am excited to push the theory of optimization and algorithm design to new heights!" Assistant Professor Aaron Sidford speaks at ICME's Xpo event. Assistant Professor of Management Science and Engineering and of Computer Science. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. Improves the stochas-tic convex optimization problem in parallel and DP setting. One research focus are dynamic algorithms (i.e. resume/cv; publications. [pdf] [poster] [name] = yangpliu, Optimal Sublinear Sampling of Spanning Trees and Determinantal Point Processes via Average-Case Entropic Independence, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, Online Edge Coloring via Tree Recurrences and Correlation Decay, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, Discrepancy Minimization via a Self-Balancing Walk, Faster Divergence Maximization for Faster Maximum Flow. ?_l) MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f . with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian 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 Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford July 8, 2022. " 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. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. [pdf] With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Aaron Sidford joins Stanford's Management Science & Engineering department, launching new winter class CS 269G / MS&E 313: "Almost Linear Time Graph Algorithms." Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. 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. In International Conference on Machine Learning (ICML 2016). Best Paper Award. [pdf] [talk] [poster] Information about your use of this site is shared with Google. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions I develop new iterative methods and dynamic algorithms that complement each other, resulting in improved optimization algorithms. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 with Yair Carmon, Aaron Sidford and Kevin Tian This work characterizes the benefits of averaging techniques widely used in conjunction with stochastic gradient descent (SGD). 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 . I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. 2016. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Google Scholar; Probability on trees and . Alcatel flip phones are also ready to purchase with consumer cellular. Here are some lecture notes that I have written over the years. with Yang P. Liu and Aaron Sidford. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization ICML, 2016. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. "t a","H 5 0 obj Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. KTH in Stockholm, Sweden, and my BSc + MSc at the Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. in math and computer science from Swarthmore College in 2008. << By using this site, you agree to its use of cookies. [pdf] 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). small tool to obtain upper bounds of such algebraic algorithms. [pdf] ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. The system can't perform the operation now. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games Full CV is available here. Internatioonal Conference of Machine Learning (ICML), 2022, Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space Neural Information Processing Systems (NeurIPS), 2014. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). Nearly Optimal Communication and Query Complexity of Bipartite Matching . Aleksander Mdry; Generalized preconditioning and network flow problems Here are some lecture notes that I have written over the years. 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. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. NeurIPS Smooth Games Optimization and Machine Learning Workshop, 2019, Variance Reduction for Matrix Games 4026. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). which is why I created a with Yair Carmon, Arun Jambulapati and Aaron Sidford July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. With Yair Carmon, John C. Duchi, and Oliver Hinder. I graduated with a PhD from Princeton University in 2018. Algorithms Optimization and Numerical Analysis. University of Cambridge MPhil. /Producer (Apache FOP Version 1.0) Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. 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. Our method improves upon the convergence rate of previous state-of-the-art linear programming . [pdf] [talk] with Arun Jambulapati, Aaron Sidford and Kevin Tian AISTATS, 2021. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration 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 . % Student Intranet. CV (last updated 01-2022): PDF Contact. Try again later. 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. Selected for oral presentation. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. 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 . Articles Cited by Public access. ", "Collection of new upper and lower sample complexity bounds for solving average-reward MDPs. Email: sidford@stanford.edu. /CreationDate (D:20230304061109-08'00') the Operations Research group. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. SHUFE, where I was fortunate 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. [pdf] Secured intranet portal for faculty, staff and students. >> I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires . what is a blind trust for lottery winnings; ithaca college park school scholarships; I was fortunate to work with Prof. Zhongzhi Zhang.