I am broadly interested in mathematics and theoretical computer science. International Colloquium on Automata, Languages, and Programming (ICALP), 2022, Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Methods Some I am still actively improving and all of them I am happy to continue polishing. COLT, 2022. arXiv | code | conference pdf (alphabetical authorship), Annie Marsden, John Duchi and Gregory Valiant, Misspecification in Prediction Problems and Robustness via Improper Learning. Title. Publications and Preprints. 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 . STOC 2023. Allen Liu. Anup B. Rao. United States. [pdf] [talk] [poster] With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. With Cameron Musco and Christopher Musco. Follow. ! Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper arXiv preprint arXiv:2301.00457, 2023 arXiv. 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. ", "Team-convex-optimization for solving discounted and average-reward MDPs! The system can't perform the operation now. Office: 380-T [pdf] [talk] F+s9H With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. Efficient accelerated coordinate descent methods and faster algorithms for solving linear systems. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). 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. 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 . Before attending Stanford, I graduated from MIT in May 2018. 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 [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. I often do not respond to emails about applications. riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration If you see any typos or issues, feel free to email me. Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. 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. We are excited to have Professor Sidford join the Management Science & Engineering faculty starting Fall 2016. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Slides from my talk at ITCS. We forward in this generation, Triumphantly. Np%p `a!2D4! with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford missouri noodling association president cnn. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Abstract. 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. with Yair Carmon, Danielle Hausler, Arun Jambulapati and Aaron Sidford Navajo Math Circles Instructor. IEEE, 147-156. Mail Code. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. Before attending Stanford, I graduated from MIT in May 2018. Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. A nearly matching upper and lower bound for constant error here! Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. My research focuses on AI and machine learning, with an emphasis on robotics applications. 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. Thesis, 2016. pdf. Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 with Arun Jambulapati, Aaron Sidford and Kevin Tian Personal Website. Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. ICML, 2016. 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. Here are some lecture notes that I have written over the years. ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! when do tulips bloom in maryland; indo pacific region upsc SHUFE, where I was fortunate . My CV. July 8, 2022. In Innovations in Theoretical Computer Science (ITCS 2018) (arXiv), Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space. Here are some lecture notes that I have written over the years. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. We establish lower bounds on the complexity of finding $$-stationary points of smooth, non-convex high-dimensional functions using first-order methods. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Articles Cited by Public access. I enjoy understanding the theoretical ground of many algorithms that are van vu professor, yale Verified email at yale.edu. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Discrete Mathematics and Algorithms: An Introduction to Combinatorial Optimization: I used these notes to accompany the course Discrete Mathematics and Algorithms. [pdf] with Yair Carmon, Aaron Sidford and Kevin Tian I completed my PhD at I graduated with a PhD from Princeton University in 2018. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. View Full Stanford Profile. with Kevin Tian and Aaron Sidford In each setting we provide faster exact and approximate algorithms. 4026. in math and computer science from Swarthmore College in 2008. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games with Aaron Sidford Assistant Professor of Management Science and Engineering and of Computer Science. Annie Marsden. 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 Google Scholar Digital Library; Russell Lyons and Yuval Peres. In Symposium on Discrete Algorithms (SODA 2018) (arXiv), Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes, Efficient (n/) Spectral Sketches for the Laplacian and its Pseudoinverse, Stability of the Lanczos Method for Matrix Function Approximation. Simple MAP inference via low-rank relaxations. This work presents an accelerated gradient method for nonconvex optimization problems with Lipschitz continuous first and second derivatives that is Hessian free, i.e., it only requires gradient computations, and is therefore suitable for large-scale applications. Alcatel flip phones are also ready to purchase with consumer cellular. 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. My research focuses on the design of efficient algorithms based on graph theory, convex optimization, and high dimensional geometry (CV). endobj Improved Lower Bounds for Submodular Function Minimization. I was fortunate to work with Prof. Zhongzhi Zhang. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs } 4(JR!$AkRf[(t Bw!hz#0 )l`/8p.7p|O~ [pdf] [poster] I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. /Producer (Apache FOP Version 1.0) [pdf] [poster] In submission. Done under the mentorship of M. Malliaris. with Yair Carmon, Kevin Tian and Aaron Sidford Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations. Student Intranet. [pdf] [talk] ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. Sequential Matrix Completion. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. ", "Sample complexity for average-reward MDPs? With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang. University of Cambridge MPhil. with Yair Carmon, Arun Jambulapati and Aaron Sidford Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . in Chemistry at the University of Chicago. with Vidya Muthukumar and Aaron Sidford Oral Presentation for Misspecification in Prediction Problems and Robustness via Improper Learning. Main Menu. SHUFE, Oct. 2022 - Algorithm Seminar, Google Research, Oct. 2022 - Young Researcher Workshop, Cornell ORIE, Apr. 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 . Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA SODA 2023: 4667-4767. 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, There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! theory and graph applications. O! ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. of practical importance. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. by Aaron Sidford. Email / International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG [pdf] [poster] Conference on Learning Theory (COLT), 2015. Michael B. Cohen, Yin Tat Lee, Gary L. Miller, Jakub Pachocki, and Aaron Sidford. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). 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. Yin Tat Lee and Aaron Sidford. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). /Filter /FlateDecode COLT, 2022. I also completed my undergraduate degree (in mathematics) at MIT. 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. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. Neural Information Processing Systems (NeurIPS), 2021, Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss >CV >code >contact; My PhD dissertation, Algorithmic Approaches to Statistical Questions, 2012. Here is a slightly more formal third-person biography, and here is a recent-ish CV. 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), Optimal and Adaptive Monteiro-Svaiter Acceleration, With Yair Carmon, Danielle Hausler, Arun Jambulapati, and Yujia Jin, To appear in Advances in Neural Information Processing Systems (NeurIPS 2022) (arXiv), On the Efficient Implementation of High Accuracy Optimality of Profile Maximum Likelihood, With Moses Charikar, Zhihao Jiang, and Kirankumar Shiragur, Improved Lower Bounds for Submodular Function Minimization, With Deeparnab Chakrabarty, Andrei Graur, and Haotian Jiang, In Symposium on Foundations of Computer Science (FOCS 2022) (arXiv), RECAPP: Crafting a More Efficient Catalyst for Convex Optimization, With Yair Carmon, Arun Jambulapati, and Yujia Jin, International Conference on Machine Learning (ICML 2022) (arXiv), Efficient Convex Optimization Requires Superlinear Memory, With Annie Marsden, Vatsal Sharan, and Gregory Valiant, Conference on Learning Theory (COLT 2022), Sharper Rates for Separable Minimax and Finite Sum Optimization via Primal-Dual Extragradient Method, Conference on Learning Theory (COLT 2022) (arXiv), Big-Step-Little-Step: Efficient Gradient Methods for Objectives with Multiple Scales, With Jonathan A. Kelner, Annie Marsden, Vatsal Sharan, Gregory Valiant, and Honglin Yuan, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching, With Arun Jambulapati, Yujia Jin, and Kevin Tian, International Colloquium on Automata, Languages and Programming (ICALP 2022) (arXiv), Fully-Dynamic Graph Sparsifiers Against an Adaptive Adversary, With Aaron Bernstein, Jan van den Brand, Maximilian Probst, Danupon Nanongkai, Thatchaphol Saranurak, and He Sun, Faster Maxflow via Improved Dynamic Spectral Vertex Sparsifiers, With Jan van den Brand, Yu Gao, Arun Jambulapati, Yin Tat Lee, Yang P. Liu, and Richard Peng, In Symposium on Theory of Computing (STOC 2022) (arXiv), Semi-Streaming Bipartite Matching in Fewer Passes and Optimal Space, With Sepehr Assadi, Arun Jambulapati, Yujia Jin, and Kevin Tian, In Symposium on Discrete Algorithms (SODA 2022) (arXiv), Algorithmic trade-offs for girth approximation in undirected graphs, With Avi Kadria, Liam Roditty, Virginia Vassilevska Williams, and Uri Zwick, In Symposium on Discrete Algorithms (SODA 2022), Computing Lewis Weights to High Precision, With Maryam Fazel, Yin Tat Lee, and Swati Padmanabhan, With Hilal Asi, Yair Carmon, Arun Jambulapati, and Yujia Jin, In Advances in Neural Information Processing Systems (NeurIPS 2021) (arXiv), Thinking Inside the Ball: Near-Optimal Minimization of the Maximal Loss, In Conference on Learning Theory (COLT 2021) (arXiv), The Bethe and Sinkhorn Permanents of Low Rank Matrices and Implications for Profile Maximum Likelihood, With Nima Anari, Moses Charikar, and Kirankumar Shiragur, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs, In International Conference on Machine Learning (ICML 2021) (arXiv), Minimum cost flows, MDPs, and 1-regression in nearly linear time for dense instances, With Jan van den Brand, Yin Tat Lee, Yang P. Liu, Thatchaphol Saranurak, and Zhao Song, Di Wang, In Symposium on Theory of Computing (STOC 2021) (arXiv), Ultrasparse Ultrasparsifiers and Faster Laplacian System Solvers, In Symposium on Discrete Algorithms (SODA 2021) (arXiv), Relative Lipschitzness in Extragradient Methods and a Direct Recipe for Acceleration, In Innovations in Theoretical Computer Science (ITCS 2021) (arXiv), Acceleration with a Ball Optimization Oracle, With Yair Carmon, Arun Jambulapati, Qijia Jiang, Yujia Jin, Yin Tat Lee, and Kevin Tian, In Conference on Neural Information Processing Systems (NeurIPS 2020), Instance Based Approximations to Profile Maximum Likelihood, In Conference on Neural Information Processing Systems (NeurIPS 2020) (arXiv), Large-Scale Methods for Distributionally Robust Optimization, With Daniel Levy*, Yair Carmon*, and John C. Duch (* denotes equal contribution), High-precision Estimation of Random Walks in Small Space, With AmirMahdi Ahmadinejad, Jonathan A. Kelner, Jack Murtagh, John Peebles, and Salil P. Vadhan, In Symposium on Foundations of Computer Science (FOCS 2020) (arXiv), Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs, With Jan van den Brand, Yin Tat Lee, Danupon Nanongkai, Richard Peng, Thatchaphol Saranurak, Zhao Song, and Di Wang, In Symposium on Foundations of Computer Science (FOCS 2020), With Yair Carmon, Yujia Jin, and Kevin Tian, Unit Capacity Maxflow in Almost $O(m^{4/3})$ Time, Invited to the special issue (arXiv before merge)), Solving Discounted Stochastic Two-Player Games with Near-Optimal Time and Sample Complexity, In International Conference on Artificial Intelligence and Statistics (AISTATS 2020) (arXiv), Efficiently Solving MDPs with Stochastic Mirror Descent, In International Conference on Machine Learning (ICML 2020) (arXiv), Near-Optimal Methods for Minimizing Star-Convex Functions and Beyond, With Oliver Hinder and Nimit Sharad Sohoni, In Conference on Learning Theory (COLT 2020) (arXiv), Solving Tall Dense Linear Programs in Nearly Linear Time, With Jan van den Brand, Yin Tat Lee, and Zhao Song, In Symposium on Theory of Computing (STOC 2020). By using this site, you agree to its use of cookies. Try again later. 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 D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Etude for the Park City Math Institute Undergraduate Summer School. Best Paper Award. [pdf] [talk] [poster] CV (last updated 01-2022): PDF Contact. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . We make safe shipping arrangements for your convenience from Baton Rouge, Louisiana. Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. 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 Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. However, many advances have come from a continuous viewpoint. I am a fifth year Ph.D. student in Computer Science at Stanford University co-advised by Gregory Valiant and John Duchi. 2016. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. with Yair Carmon, Arun Jambulapati and Aaron Sidford Efficient Convex Optimization Requires Superlinear Memory. Management Science & Engineering 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. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in ", "Improved upper and lower bounds on first-order queries for solving \(\min_{x}\max_{i\in[n]}\ell_i(x)\). I am fortunate to be advised by Aaron Sidford. It was released on november 10, 2017. 2022 - current Assistant Professor, Georgia Institute of Technology (Georgia Tech) 2022 Visiting researcher, Max Planck Institute for Informatics. In particular, it achieves nearly linear time for DP-SCO in low-dimension settings. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. Associate Professor of . Secured intranet portal for faculty, staff and students. Many of my results use fast matrix multiplication Research Institute for Interdisciplinary Sciences (RIIS) at << Aaron Sidford, Introduction to Optimization Theory; Lap Chi Lau, Convexity and Optimization; Nisheeth Vishnoi, Algorithms for . They will share a $10,000 prize, with financial sponsorship provided by Google Inc. . I am broadly interested in optimization problems, sometimes in the intersection with machine learning 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. /N 3 Aleksander Mdry; Generalized preconditioning and network flow problems with Yair Carmon, Aaron Sidford and Kevin Tian 2021. Aaron Sidford. ?_l) Aaron's research interests lie in optimization, the theory of computation, and the . 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. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows.