Academic Appointments


Current Research and Scholarly Interests


My research interests lie broadly in the optimization, the theory of computation, and the design and analysis of algorithms. I am particularly interested in work at the intersection of continuous optimization, graph theory, numerical linear algebra, and data structures.

2019-20 Courses


Stanford Advisees


  • Doctoral Dissertation Reader (AC)
    Yair Carmon
  • Orals Chair
    Junzi Zhang
  • Doctoral Dissertation Advisor (AC)
    Kiran Shiragur
  • Doctoral Dissertation Co-Advisor (AC)
    AmirMahdi Ahmadinejad
  • Master's Program Advisor
    Arturo Garrido Contreras, Liyang Sun, Xingzi Xu, Xueying Yan
  • Doctoral (Program)
    Yujia Jin, Kevin Tian

All Publications


  • Parallel Reachability in Almost Linear Work and Square Root Depth Jambulapati, A., Liu, Y. P., Sidford, A., IEEE IEEE COMPUTER SOC. 2019: 1664–86
  • Faster Matroid Intersection Chakrabarty, D., Lee, Y., Sidford, A., Singla, S., Wong, S., IEEE IEEE COMPUTER SOC. 2019: 1146–68
  • Exploiting Numerical Sparsity for Efficient Learning : Faster Eigenvector Computation and Regression Gupta, N., Sidford, A., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Approximating Cycles in Directed Graphs: Fast Algorithms for Girth and Roundtrip Spanners Pachocki, J., Roditty, L., Sidford, A., Tov, R., Williams, V., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 1374–92
  • Near-Optimal Time and Sample Complexities for Solving Markov Decision Processes with a Generative Model Sidford, A., Wang, M., Wu, X., Yang, L. F., Ye, Y., Bengio, S., Wallach, H., Larochelle, H., Grauman, K., CesaBianchi, N., Garnett, R. NEURAL INFORMATION PROCESSING SYSTEMS (NIPS). 2018
  • Coordinate Methods for Accelerating l(infinity) Regression and Faster Approximate Maximum Flow Sidford, A., Tian, K., Thorup, M. IEEE COMPUTER SOC. 2018: 922–33
  • Stability of the Lanczos Method for Matrix Function Approximation Musco, C., Musco, C., Sidford, A., Assoc Comp Machinery ASSOC COMPUTING MACHINERY. 2018: 1605–24
  • Parallelizing Stochastic Gradient Descent for Least Squares Regression: Mini-batching, Averaging, and Model Misspecification JOURNAL OF MACHINE LEARNING RESEARCH Jain, P., Netrapalli, P., Kakade, S. M., Kidambi, R., Sidford, A. 2018; 18
  • ACCELERATED METHODS FOR NONCONVEX OPTIMIZATION SIAM JOURNAL ON OPTIMIZATION Carmon, Y., Duchi, J. C., Hinder, O., Sidford, A. 2018; 28 (2): 1751–72

    View details for DOI 10.1137/17M1114296

    View details for Web of Science ID 000436991600031

  • SINGLE PASS SPECTRAL SPARSIFICATION IN DYNAMIC STREAMS SIAM JOURNAL ON COMPUTING Kapralov, M., Lee, Y. T., Musco, C. N., Musco, C. P., Sidford, A. 2017; 46 (1): 456-477

    View details for DOI 10.1137/141002281

    View details for Web of Science ID 000396677400017

  • Derandomization Beyond Connectivity: Undirected Laplacian Systems in Nearly Logarithmic Space Murtagh, J., Reingold, O., Sidford, A., Vadhan, S., IEEE IEEE. 2017: 801–12
  • Subquadratic Submodular Function Minimization Chakrabarty, D., Lee, Y., Sidford, A., Wong, S., Hatami, H., McKenzie, P., King ASSOC COMPUTING MACHINERY. 2017: 1220–31