Appointments |
| 2022-Now | Principal Researcher in Microsoft Research |
| 2022-Now | Associate Professor in University of Washington |
| 2017-2022 | Assistant Professor in University of Washington |
| 2018-2022 | Visiting Researcher in Microsoft Research |
| 2016-2017 | Postdoc in Microsoft Research |
Education |
| 2012-2016 | Ph.D. in Mathematics, Massachusetts Institute of Technology |
| 2008-2012 | B.S. in Mathematics, Chinese University of Hong Kong |
Awards |
| 2020 | Packard Fellowship |
| Sloan Research Fellowship |
| Best Student Paper by my PhD student Haotian Jiang, Symposium on Discrete Algorithms |
| 2019 | Microsoft Research Faculty Fellowship |
| 2018 | Best Paper Award, Neural Information Processing Systems |
| A. W. Tucker Prize |
| NSF CAREER Award |
| 2016 | Sprowls Award, MIT |
| 2015 | Best Student Paper, Symposium on Foundations of Computer Science |
| 2014 | Notable article in computing in 2014 by Computing Reviews |
| Best Student Paper, Symposium on Foundations of Computer Science |
| Best Paper Award, Symposium on Foundations of Computer Science |
| Best Paper Award, Symposium on Discrete Algorithms |
| 2013 | Charles W. and Jennifer C. Johnson Prize, MIT |
| 2012 | MIT Presidential Fellowship |
Selected Publications |
| 2021 | A Nearly-Linear Time Algorithm for Linear Programs with Small Treewidth: A Multiscale Representation of Robust Central Path (STOC 2021) |
| 2019 | Solving Linear Programs in the Current Matrix Multiplication Time (STOC 2019) |
| 2018 | Optimal Algorithms for Non-Smooth Distributed Optimization in Networks (NeurIPS 2018) |
| k-server via multiscale entropic regularization (STOC 2018) |
| Convergence rate of Riemannian Hamiltonian Monte Carlo and faster polytope volume computation (STOC 2018) |
| 2017 | Eldan's Stochastic Localization and the {KLS} Hyperplane Conjecture: An Improved Lower Bound for Expansion (FOCS 2017) |
| Kernel-based methods for bandit convex optimization (STOC 2017) |
| 2015 | A Faster Cutting Plane Method and its Implications for Combinatorial and Convex Optimization (FOCS 2015) |
| 2014 | Path Finding Methods for Linear Programming: Solving Linear Programs in $\tilde{O}(\sqrt{\mathrm{rank}})$ Iterations and Faster Algorithms for Maximum Flow (FOCS 2014) |
| An Almost-Linear-Time Algorithm for Approximate Max Flow in Undirected Graphs, and its Multicommodity Generalizations (SODA 2014) |
Other Publications |
| 2022 | Sampling with Riemannian Hamiltonian Monte Carlo in a Constrained Space (NeurIPS 2022) |
| Decomposable Non-Smooth Convex Optimization with Nearly-Linear Gradient Oracle Complexity (NeurIPS 2022) |
| A gradient sampling method with complexity guarantees for Lipschitz functions in high and low dimensions (NeurIPS 2022) |
| Differentially Private Fine-tuning of Language Models (NeurIPS 2022) |
| Private Convex Optimization via Exponential Mechanism (COLT 2022) |
| Faster maxflow via improved dynamic spectral vertex sparsifiers (STOC 2022) |
| Nested Dissection Meets IPMs: Planar Min-Cost Flow in Nearly-Linear Time (SODA 2022) |
| Computing Lewis Weights to High Precision (SODA 2022) |
| Differentially Private Fine-tuning of Language Models (ICLR 2022) |
| 2021 | Numerical Composition of Differential Privacy (NeurIPS 2021) |
| Private Non-smooth ERM and SCO in Subquadratic Steps (NeurIPS 2021) |
| Lower Bounds on Metropolized Sampling Methods for Well-Conditioned Distributions (NeurIPS 2021) |
| Fast and Memory Efficient Differentially Private-SGD via JL Projections (NeurIPS 2021) |
| Reducing Isotropy and Volume to KLS: An $O(n^3 \psi^2)$ Volume Algorithm (STOC 2021) |
| Minimum Cost Flows, MDPs, and l1-Regression in Nearly Linear Time for Dense Instances (STOC 2021) |
| 2020 | Leverage Score Sampling for Faster Accelerated Regression and ERM (ALT 2020) |
| Logsmooth Gradient Concentration and Tighter Runtimes for Metropolized Hamiltonian Monte Carlo (COLT 2020) |
| An $O(m/\epsilon^{3.5})$-Cost Algorithm for Semidefinite Programs with Diagonal Constraints (COLT 2020) |
| A Faster Interior Point Method for Semidefinite Programming (FOCS 2020) |
| Bipartite Matching in Nearly-linear Time on Moderately Dense Graphs (FOCS 2020) |
| Network size and size of the weights in memorization with two-layers neural networks (NeurIPS 2020) |
| Acceleration with a Ball Optimization Oracle (NeurIPS 2020) |
| Solving Tall Dense Linear Programs in Nearly Linear Time (STOC 2020) |
| An Improved Cutting Plane Method for Convex Optimization, Convex-Concave Games and its Applications (STOC 2020) |
| Strong Self-Concordance and Sampling (STOC 2020) |
| Computing Circle Packing Representations of Planar Graphs (SODA 2020) |
| Differentially Private Release of Synthetic Graphs (SODA 2020) |
| Chasing Nested Convex Bodies Nearly Optimally (SODA 2020) |
| A Generalized Central Limit Conjecture for Convex Bodies (GAFA Seminar Notes) |
| 2019 | Complexity of Highly Parallel Non-Smooth Convex Optimization (NeurIPS 19) |
| The Randomized Midpoint Method for Log-Concave Sampling (NeurIPS 2019) |
| Faster Matroid Intersection (FOCS 2019) |
| Competitively chasing convex bodies (STOC 2019) |
| A near-optimal algorithm for approximating the John Ellipsoid (COLT 2019) |
| Near Optimal Methods for Minimizing Convex Functions with Lipschitz p-th Derivatives (COLT 2019) |
| Solving Empirical Risk Minimization in the Current Matrix Multiplication Time (COLT 2019) |
| Adversarial examples from computational constraints (ICML 2019) |
| Metrical task systems on trees via mirror descent and unfair gluing (SODA 2019) |
| A Nearly-Linear Bound for Chasing Nested Convex Bodies (SODA 2019) |
| 2018 | Efficient Convex Optimization with Membership Oracles (COLT 2018) |
| Universal Barrier is n-Self-Concordant (arXiv) |
| The Kannan-Lovász-Simonovits Conjecture (arXiv) |
| The Paulsen problem, continuous operator scaling, and smoothed analysis (STOC 2018) |
| A matrix expander Chernoff bound (STOC 2018) |
| Stochastic localization + Stieltjes barrier = tight bound for log-Sobolev (STOC 2018) |
| An homotopy method for lp regression provably beyond self-concordance and in input-sparsity time (STOC 2018) |
| 2017 | Optimal Algorithms for Smooth and Strongly Convex Distributed Optimization in Networks (ICML 2017) |
| An SDP-based algorithm for linear-sized spectral sparsification (STOC 2017) |
| Geodesic walks in polytopes (STOC 2017) |
| Subquadratic submodular function minimization (STOC 2017) |
| 2016 | Faster Algorithms for Convex and Combinatorial Optimization () |
| Black-box Optimization with a Politician (ICML 2016) |
| Using Optimization to Obtain a Width-Independent, Parallel, Simpler, and Faster Positive SDP Solver (SODA 2016) |
| Improved Cheeger's Inequality and Analysis of Local Graph Partitioning using Vertex Expansion and Expansion Profile (SODA 2016) |
| Geometric median in nearly linear time (STOC 2016) |
| Sparsified Cholesky and multigrid solvers for connection laplacians (STOC 2016) |
| Landmark-Matching Transformation with Large Deformation Via n-dimensional Quasi-conformal Maps (J. Sci. Comput.) |
| 2015 | A geometric alternative to Nesterov's accelerated gradient descent (arXiv) |
| Efficient Inverse Maintenance and Faster Algorithms for Linear Programming (FOCS 2015) |
| Constructing Linear-Sized Spectral Sparsification in Almost-Linear Time (FOCS 2015, SIAM J. Comput.) |
| Uniform Sampling for Matrix Approximation (ITCS 2015) |
| 2014 | Single Pass Spectral Sparsification in Dynamic Streams (FOCS 2014, SIAM J. Comput.) |
| 2013 | Efficient Accelerated Coordinate Descent Methods and Faster Algorithms for Solving Linear Systems (FOCS 2013) |
| Improved Cheeger's inequality: analysis of spectral partitioning algorithms (STOC 2013, SIAM J. Comput.) |
| A new approach to computing maximum flows using electrical flows (STOC 2013) |
Teaching |
| 2020-2021 | CSE 421 Introduction to Algorithms |
| 2020-2021 | CSE 535 Theory of Optimization and Continuous Algorithms |
| CSE 599 Sketching Algorithms |
| 2019-2020 | CSE 535 Theory of Optimization and Continuous Algorithms |
| 2018-2019 | CSE 535 Theory of Optimization and Continuous Algorithms |
| CSE 421 Introduction to Algorithms |
| 2017-2018 | CSE 421 Introduction to Algorithms |
| CSE 599 Interplay between Convex Optimization and Geometry |
Service |
| 2021 | Co-organizer of a workshop on “Continuous approaches to discrete optimization” at the Hausdorff Center for Mathematics |
| Local Organization Chair of APPROX & RANDOM 2021 |
| Program Committee of Foundations of Computer Science (FOCS 2021) |
| 2020 | Local Organization Chair of APPROX & RANDOM 2020 |
| Program Committee of International Workshop on Randomization and Computation (RANDOM 2020) |
| 2018 | Program Committee of Foundations of Computer Science (FOCS 2018) |
| 2017 | Program Committee of Symposium on Discrete Algorithms (SODA 2017) |
| Co-organizer of a workshop in The 49th Annual ACM Symposium on the Theory of Computing (STOC 2017) |
| Program Committee of International Workshop on Randomization and Computation (RANDOM 2017) |
| 2016 | Co-organizer of three sessions in The fifth International Conference on Continuous Optimization (ICCOPT 2016) |
