Daniel Spielman Professor of Computer Science, Yale University Verified email at yale.edu. With Bill Fefferman, Soumik Ghosh, Umesh Vazirani, and Zixin Zhou (2022). Many of my results use fast matrix multiplication Thesis, 2016. pdf. They will share a $10,000 prize, with financial sponsorship provided by Google Inc. Selected recent papers . % riba architectural drawing numbering system; fort wayne police department gun permit; how long does chambord last unopened; wayne county news wv obituaries which is why I created a CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019. About Me. {{{;}#q8?\. Email: sidford@stanford.edu. Here are some lecture notes that I have written over the years. with Hilal Asi, Yair Carmon, Arun Jambulapati and Aaron Sidford Before Stanford, I worked with John Lafferty at the University of Chicago. She was 19 years old and looking forward to the start of classes and reuniting with her college pals. [pdf] One research focus are dynamic algorithms (i.e. We will start with a primer week to learn the very basics of continuous optimization (July 26 - July 30), followed by two weeks of talks by the speakers on more advanced . I am an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. stream University of Cambridge MPhil. with Yair Carmon, Arun Jambulapati and Aaron Sidford (arXiv pre-print) arXiv | pdf, Annie Marsden, R. Stephen Berry. data structures) that maintain properties of dynamically changing graphs and matrices -- such as distances in a graph, or the solution of a linear system. In Sidford's dissertation, Iterative Methods, Combinatorial . I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures. [pdf] Yair Carmon. Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, David P. Woodruff Innovations in Theoretical Computer Science (ITCS) 2018. ReSQueing Parallel and Private Stochastic Convex Optimization. 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. 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 Their, This "Cited by" count includes citations to the following articles in Scholar. aaron sidford cvnatural fibrin removalnatural fibrin removal Management Science & Engineering in Chemistry at the University of Chicago. ", Applied Math at Fudan with Yair Carmon, Aaron Sidford and Kevin Tian Aaron Sidford, Gregory Valiant, Honglin Yuan COLT, 2022 arXiv | pdf. 2019 (and hopefully 2022 onwards Covid permitting) For more information please watch this and please consider donating here! pdf, Sequential Matrix Completion. Another research focus are optimization algorithms. in math and computer science from Swarthmore College in 2008. Slides from my talk at ITCS. With Cameron Musco, Praneeth Netrapalli, Aaron Sidford, Shashanka Ubaru, and David P. Woodruff. 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. 2021. I am a senior researcher in the Algorithms group at Microsoft Research Redmond. 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. KTH in Stockholm, Sweden, and my BSc + MSc at the . Aleksander Mdry; Generalized preconditioning and network flow problems to appear in Neural Information Processing Systems (NeurIPS), 2022, Regularized Box-Simplex Games and Dynamic Decremental Bipartite Matching Janardhan Kulkarni, Yang P. Liu, Ashwin Sah, Mehtaab Sawhney, Jakub Tarnawski, Fully Dynamic Electrical Flows: Sparse Maxflow Faster Than Goldberg-Rao, FOCS 2021 Lower bounds for finding stationary points II: first-order methods. Verified email at stanford.edu - Homepage. with Yang P. Liu and Aaron Sidford. << ", "Faster algorithms for separable minimax, finite-sum and separable finite-sum minimax. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). with Yair Carmon, Aaron Sidford and Kevin Tian ", "General variance reduction framework for solving saddle-point problems & Improved runtimes for matrix games. ", "A general continuous optimization framework for better dynamic (decremental) matching algorithms. Associate Professor of . Computer Science. "FV %H"Hr ![EE1PL* rP+PPT/j5&uVhWt :G+MvY c0 L& 9cX& ?_l) ", "Streaming matching (and optimal transport) in \(\tilde{O}(1/\epsilon)\) passes and \(O(n)\) space. With Rong Ge, Chi Jin, Sham M. Kakade, and Praneeth Netrapalli. I am affiliated with the Stanford Theory Group and Stanford Operations Research Group. van vu professor, yale Verified email at yale.edu. COLT, 2022. Before joining Stanford in Fall 2016, I was an NSF post-doctoral fellow at Carnegie Mellon University ; I received a Ph.D. in mathematics from the University of Michigan in 2014, and a B.A. Improved Lower Bounds for Submodular Function Minimization. University, where I am broadly interested in optimization problems, sometimes in the intersection with machine learning theory and graph applications. Source: www.ebay.ie [pdf] ", "An attempt to make Monteiro-Svaiter acceleration practical: no binary search and no need to know smoothness parameter! My interests are in the intersection of algorithms, statistics, optimization, and machine learning. The site facilitates research and collaboration in academic endeavors. Yang P. Liu, Aaron Sidford, Department of Mathematics resume/cv; publications. ", "A low-bias low-cost estimator of subproblem solution suffices for acceleration! 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. With Yosheb Getachew, Yujia Jin, Aaron Sidford, and Kevin Tian (2023). what is a blind trust for lottery winnings; ithaca college park school scholarships; 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. BayLearn, 2021, On the Sample Complexity of Average-reward MDPs Roy Frostig, Rong Ge, Sham M. Kakade, Aaron Sidford. Conference of Learning Theory (COLT), 2022, RECAPP: Crafting a More Efficient Catalyst for Convex Optimization A nearly matching upper and lower bound for constant error here! Spectrum Approximation Beyond Fast Matrix Multiplication: Algorithms and Hardness. I am broadly interested in mathematics and theoretical computer science. Office: 380-T Contact. /Filter /FlateDecode ", "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] [talk] [poster] Follow. ", "Collection of variance-reduced / coordinate methods for solving matrix games, with simplex or Euclidean ball domains. Aaron Sidford. In this talk, I will present a new algorithm for solving linear programs. Assistant Professor of Management Science and Engineering and of Computer Science. Enrichment of Network Diagrams for Potential Surfaces. D Garber, E Hazan, C Jin, SM Kakade, C Musco, P Netrapalli, A Sidford. Alcatel flip phones are also ready to purchase with consumer cellular. theory and graph applications. Links. However, many advances have come from a continuous viewpoint. Best Paper Award. [pdf] [poster] Yin Tat Lee and Aaron Sidford. MS&E welcomes new faculty member, Aaron Sidford ! Title. Done under the mentorship of M. Malliaris. theses are protected by copyright. with Aaron Sidford missouri noodling association president cnn. Before attending Stanford, I graduated from MIT in May 2018. We also provide two . 5 0 obj 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 . %PDF-1.4 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. In September 2018, I started a PhD at Stanford University in mathematics, and am advised by Aaron Sidford. I am a fifth-and-final-year PhD student in the Department of Management Science and Engineering at Stanford in the Operations Research group. Email: [name]@stanford.edu 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 . Call (225) 687-7590 or park nicollet dermatology wayzata today! In each setting we provide faster exact and approximate algorithms. The design of algorithms is traditionally a discrete endeavor. 2013. 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. We organize regular talks and if you are interested and are Stanford affiliated, feel free to reach out (from a Stanford email). with Yair Carmon, Arun Jambulapati and Aaron Sidford (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. Group Resources. I am currently a third-year graduate student in EECS at MIT working under the wonderful supervision of Ankur Moitra. 9-21. /Producer (Apache FOP Version 1.0) We provide a generic technique for constructing families of submodular functions to obtain lower bounds for submodular function minimization (SFM). Unlike previous ADFOCS, this year the event will take place over the span of three weeks. Annie Marsden. By using this site, you agree to its use of cookies. [pdf] Summer 2022: I am currently a research scientist intern at DeepMind in London. rl1 However, even restarting can be a hard task here. ACM-SIAM Symposium on Discrete Algorithms (SODA), 2022, Stochastic Bias-Reduced Gradient Methods Symposium on Foundations of Computer Science (FOCS), 2020, Efficiently Solving MDPs with Stochastic Mirror Descent Personal Website. . Winter 2020 Teaching assistant for EE364a: Convex Optimization I taught by John Duchi, Fall 2018 Teaching assitant for CS265/CME309: Randomized Algorithms and Probabilistic Analysis, Fall 2019 taught by Greg Valiant. Allen Liu. I am fortunate to be advised by Aaron Sidford . Many of these algorithms are iterative and solve a sequence of smaller subproblems, whose solution can be maintained via the aforementioned dynamic algorithms. 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 . with Yair Carmon, Arun Jambulapati, Qijia Jiang, Yin Tat Lee, Aaron Sidford and Kevin Tian [pdf] [poster] Jonathan A. Kelner, Yin Tat Lee, Lorenzo Orecchia, and Aaron Sidford; Computing maximum flows with augmenting electrical flows. Stability of the Lanczos Method for Matrix Function Approximation Cameron Musco, Christopher Musco, Aaron Sidford ACM-SIAM Symposium on Discrete Algorithms (SODA) 2018. Np%p `a!2D4! how . 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 . Aaron Sidford is an assistant professor in the department of Management Science and Engineering and the department of Computer Science at Stanford University. Roy Frostig, Sida Wang, Percy Liang, Chris Manning. 2022 - Learning and Games Program, Simons Institute, Sept. 2021 - Young Researcher Workshop, Cornell ORIE, Sept. 2021 - ACO Student Seminar, Georgia Tech, Dec. 2019 - NeurIPS Spotlight presentation. [last name]@stanford.edu where [last name]=sidford. Conference of Learning Theory (COLT), 2021, Towards Tight Bounds on the Sample Complexity of Average-reward MDPs Yair Carmon, Arun Jambulapati, Yujia Jin, Yin Tat Lee, Daogao Liu, Aaron Sidford, and Kevin Tian. Efficient Convex Optimization Requires Superlinear Memory. with Kevin Tian and Aaron Sidford Department of Electrical Engineering, Stanford University, 94305, Stanford, CA, USA "t a","H With Prateek Jain, Sham M. Kakade, Rahul Kidambi, and Praneeth Netrapalli. University, Research Institute for Interdisciplinary Sciences (RIIS) at Yin Tat Lee and Aaron Sidford; An almost-linear-time algorithm for approximate max flow in undirected graphs, and its multicommodity generalizations.
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