with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian 2017. ", "About how and why coordinate (variance-reduced) methods are a good idea for exploiting (numerical) sparsity of data. [pdf] [talk] [poster] Here is a slightly more formal third-person biography, and here is a recent-ish CV. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in . riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries I am a senior researcher in the Algorithms group at Microsoft Research Redmond. ", "Team-convex-optimization for solving discounted and average-reward MDPs! Before attending Stanford, I graduated from MIT in May 2018. I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. 5 0 obj STOC 2023. /Producer (Apache FOP Version 1.0) ICML, 2016. With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. [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. Semantic parsing on Freebase from question-answer pairs. Assistant Professor of Management Science and Engineering and of Computer Science. Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. rl1 Alcatel One Touch Flip Phone - New Product Recommendations, Promotions My long term goal is to bring robots into human-centered domains such as homes and hospitals. In International Conference on Machine Learning (ICML 2016). A Faster Algorithm for Linear Programming and the Maximum Flow Problem II Neural Information Processing Systems (NeurIPS, Spotlight), 2019, Variance Reduction for Matrix Games F+s9H Aviv Tamar - Reinforcement Learning Research Labs - Technion what is a blind trust for lottery winnings; ithaca college park school scholarships; to be advised by Prof. Dongdong Ge. SHUFE, where I was fortunate Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. Computer Science. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Stanford University [pdf] [poster] University of Cambridge MPhil. Advanced Data Structures (6.851) - Massachusetts Institute of Technology Optimization Algorithms: I used variants of these notes to accompany the courses Introduction to Optimization Theory and Optimization . >> MI #~__ Q$.R$sg%f,a6GTLEQ!/B)EogEA?l kJ^- \?l{ P&d\EAt{6~/fJq2bFn6g0O"yD|TyED0Ok-\~[`|4P,w\A8vD$+)%@P4 0L ` ,\@2R 4f Annie Marsden, Vatsal Sharan, Aaron Sidford, and Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. However, many advances have come from a continuous viewpoint. Emphasis will be on providing mathematical tools for combinatorial optimization, i.e. I completed my PhD at Fall'22 8803 - Dynamic Algebraic Algorithms, small tool to obtain upper bounds of such algebraic algorithms. Mail Code. Jan van den Brand dblp: Yin Tat Lee Group Resources. [pdf] [poster] endobj Aaron Sidford - Teaching "t a","H Gary L. Miller Carnegie Mellon University Verified email at cs.cmu.edu. 475 Via Ortega [pdf] [poster] Neural Information Processing Systems (NeurIPS), 2014. 9-21. In Symposium on Discrete Algorithms (SODA 2018) (arXiv), Variance Reduced Value Iteration and Faster Algorithms for Solving Markov Decision Processes, Efficient (n/) Spectral Sketches for the Laplacian and its Pseudoinverse, Stability of the Lanczos Method for Matrix Function Approximation. aaron sidford cv I was fortunate to work with Prof. Zhongzhi Zhang. Neural Information Processing Systems (NeurIPS, Oral), 2019, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions xwXSsN`$!l{@ $@TR)XZ( RZD|y L0V@(#q `= nnWXX0+; R1{Ol (Lx\/V'LKP0RX~@9k(8u?yBOr y with Yair Carmon, Aaron Sidford and Kevin Tian 4026. Prof. Sidford's paper was chosen from more than 150 accepted papers at the conference. Another research focus are optimization algorithms. Stanford University. 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. dblp: Daogao Liu [5] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian. In particular, this work presents a sharp analysis of: (1) mini-batching, a method of averaging many . 2015 Doctoral Dissertation Award - Association for Computing Machinery Research Interests: My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. 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). with Yang P. Liu and Aaron Sidford. I received a B.S. 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. I regularly advise Stanford students from a variety of departments. /Length 11 0 R aaron sidford cvnatural fibrin removalnatural fibrin removal [i14] Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, Kevin Tian: ReSQueing Parallel and Private Stochastic Convex Optimization. I graduated with a PhD from Princeton University in 2018. Faculty Spotlight: Aaron Sidford - Management Science and Engineering Prateek Jain, Sham M. Kakade, Rahul Kidambi, Praneeth Netrapalli, Aaron Sidford; 18(223):142, 2018. Aaron Sidford - live-simons-institute.pantheon.berkeley.edu 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 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 currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. Conference on Learning Theory (COLT), 2015. [pdf] [talk] [poster] Microsoft Research Faculty Fellowship 2020: Researchers in academia at 4 0 obj Annie Marsden, Vatsal Sharan, Aaron Sidford, Gregory Valiant, Efficient Convex Optimization Requires Superlinear Memory. A nearly matching upper and lower bound for constant error here! 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 Done under the mentorship of M. Malliaris. Thesis, 2016. pdf. Yang P. Liu, Aaron Sidford, Department of Mathematics with Sepehr Assadi, Arun Jambulapati, Aaron Sidford and Kevin Tian [pdf] [talk] [poster] 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. Nima Anari, Yang P. Liu, Thuy-Duong Vuong, Maximum Flow and Minimum-Cost Flow in Almost Linear Time, FOCS 2022, Best Paper Roy Frostig, Sida Wang, Percy Liang, Chris Manning. publications | Daogao Liu ReSQueing Parallel and Private Stochastic Convex Optimization. You interact with data structures even more often than with algorithms (think Google, your mail server, and even your network routers). This is the academic homepage of Yang Liu (I publish under Yang P. Liu). If you see any typos or issues, feel free to email me. International Conference on Machine Learning (ICML), 2020, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG Given an independence oracle, we provide an exact O (nr log rT-ind) time algorithm. Two months later, he was found lying in a creek, dead from . Google Scholar, The Complexity of Infinite-Horizon General-Sum Stochastic Games, The Complexity of Optimizing Single and Multi-player Games, A Near-Optimal Method for Minimizing the Maximum of N Convex Loss Functions, On the Sample Complexity for Average-reward Markov Decision Processes, Stochastic Methods for Matrix Games and its Applications, Acceleration with a Ball Optimization Oracle, Principal Component Projection and Regression in Nearly Linear Time through Asymmetric SVRG, The Complexity of Infinite-Horizon General-Sum Stochastic Games The system can't perform the operation now. 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. pdf, Sequential Matrix Completion. Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. with Yair Carmon, Arun Jambulapati and Aaron Sidford 2016. The Journal of Physical Chemsitry, 2015. pdf, Annie Marsden. . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Our method improves upon the convergence rate of previous state-of-the-art linear programming . 2013. pdf, Fourier Transformation at a Representation, Annie Marsden. Faster energy maximization for faster maximum flow. 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 . 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. in math and computer science from Swarthmore College in 2008. Aaron Sidford is part of Stanford Profiles, official site for faculty, postdocs, students and staff information (Expertise, Bio, Research, Publications, and more). [pdf] [talk] with Kevin Tian and Aaron Sidford With Michael Kapralov, Yin Tat Lee, Cameron Musco, and Christopher Musco. It was released on november 10, 2017. I am fortunate to be advised by Aaron Sidford . Aaron Sidford is an assistant professor in the departments of Management Science and Engineering and Computer Science at Stanford University. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission . We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). The site facilitates research and collaboration in academic endeavors. to appear in Innovations in Theoretical Computer Science (ITCS), 2022, Optimal and Adaptive Monteiro-Svaiter Acceleration Some I am still actively improving and all of them I am happy to continue polishing. Associate Professor of . Links. Np%p `a!2D4! Simple MAP inference via low-rank relaxations. theses are protected by copyright. Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA with Yair Carmon, Arun Jambulapati and Aaron Sidford in Chemistry at the University of Chicago. I enjoy understanding the theoretical ground of many algorithms that are 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. when do tulips bloom in maryland; indo pacific region upsc Before Stanford, I worked with John Lafferty at the University of Chicago. 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. Improves the stochas-tic convex optimization problem in parallel and DP setting. Aaron Sidford | Management Science and Engineering Stanford, CA 94305 Aaron Sidford - Stanford University There will be a talk every day from 16:00-18:00 CEST from July 26 to August 13. Allen Liu - GitHub Pages Multicalibrated Partitions for Importance Weights Parikshit Gopalan, Omer Reingold, Vatsal Sharan, Udi Wieder ALT, 2022 arXiv . AISTATS, 2021. My interests are in the intersection of algorithms, statistics, optimization, and machine learning. with Yair Carmon, Aaron Sidford and Kevin Tian Fresh Faculty: Theoretical computer scientist Aaron Sidford joins MS&E ", "Sample complexity for average-reward MDPs? In this talk, I will present a new algorithm for solving linear programs. ", "A special case where variance reduction can be used to nonconvex optimization (monotone operators). Aaron's research interests lie in optimization, the theory of computation, and the . to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. Full CV is available here. (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. Try again later. Follow. ?_l) My research focuses on AI and machine learning, with an emphasis on robotics applications. Neural Information Processing Systems (NeurIPS, Oral), 2020, Coordinate Methods for Matrix Games 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. Secured intranet portal for faculty, staff and students. 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. UGTCS the Operations Research group. Overview This class will introduce the theoretical foundations of discrete mathematics and algorithms. %PDF-1.4 Prior to coming to Stanford, in 2018 I received my Bachelor's degree in Applied Math at Fudan The design of algorithms is traditionally a discrete endeavor. % Here are some lecture notes that I have written over the years. 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. 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 . Google Scholar; Probability on trees and . 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 . February 16, 2022 aaron sidford cv on alcatel kaios flip phone manual. Articles Cited by Public access. 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 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. Source: appliancesonline.com.au. arXiv preprint arXiv:2301.00457, 2023 arXiv. Aaron Sidford - All Publications Aaron Sidford is an Assistant Professor in the departments of Management Science and Engineering and Computer Science at Stanford University. My research interests lie broadly in optimization, the theory of computation, and the design and analysis of algorithms. " 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. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. with Arun Jambulapati, Aaron Sidford and Kevin Tian [last name]@stanford.edu where [last name]=sidford. About Me. [pdf] 2023. . [pdf] [talk] July 2015. pdf, Szemerdi Regularity Lemma and Arthimetic Progressions, Annie Marsden. Accelerated Methods for NonConvex Optimization | Semantic Scholar United States. 2016. With Jakub Pachocki, Liam Roditty, Roei Tov, and Virginia Vassilevska Williams. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! In submission. missouri noodling association president cnn. From 2016 to 2018, I also worked in CSE 535: Theory of Optimization and Continuous Algorithms - Yin Tat Parallelizing Stochastic Gradient Descent for Least Squares Regression Allen Liu. This is the academic homepage of Yang Liu (I publish under Yang P. Liu). Research interests : Data streams, machine learning, numerical linear algebra, sketching, and sparse recovery.. Etude for the Park City Math Institute Undergraduate Summer School. 2013. International Conference on Machine Learning (ICML), 2021, Acceleration with a Ball Optimization Oracle Annie Marsden. Try again later. [pdf] van vu professor, yale Verified email at yale.edu. I am broadly interested in mathematics and theoretical computer science. Email: sidford@stanford.edu. 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. >> 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. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Faculty Spotlight: Aaron Sidford. Deeparnab Chakrabarty, Andrei Graur, Haotian Jiang, Aaron Sidford. Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. Before attending Stanford, I graduated from MIT in May 2018. Aleksander Mdry; Generalized preconditioning and network flow problems COLT, 2022. Aaron Sidford . CoRR abs/2101.05719 ( 2021 )
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