University of Toronto

Associate Professor, University of Toronto Mississauga

Faculty Affiliate, Vector Institute

Fall 2019 | Visitor, Institute for Advanced Study |

2016-2017 | Research Scientist, Google |

2014-2016 |
Department of Computer Science,
Yale University Postdoctoral Associate, Hosted by Daniel Spielman |

Fall 2013 | Simons Fellow, Simons Institute, UC Berkeley |

2008-2013 |
Ph.D., Computer Science,
Princeton University Advised by Sanjeev Arora |

2004-2008 | B.Tech., Computer Science and Engg.,
IIT Bombay |

### Research Interests

Algorithms, and its connections to optimization and statistics.

**Focus areas: Design of fast algorithms, particularly for
graph problems** combining techniques from convex
optimization, and numerical linear algebra

Learning on Graphs, Optimization for machine learning

### Recent News

Feb 2023: Awarded the Sloan Research Fellowship 2023

Feb 2023: Awarded the Young Alumni Achiever Award by IIT Bombay

Jan 2023: Invited to give a Plenary Talk at SODA 2023

Oct 2022: Our paper received the Best Paper Award at FOCS 2022

Aug 2022: Deeksha Adil successfully defended her Ph.D.

### Current and Past Group Members

Lawrence Li (PhD, 2020--)

Yibin Zhao (PhD, 2020--)

Anthony Rinaldi (MScAC, 2023--)

Hantang Li (MScAC, 2023--)

Deepkamal Kaur Gill (MScAC, 2022--23)

Gramoz Goranci (Postdoc, 2020--21)

Deeksha Adil (PhD, 2017--22)

Hao Zhang (MScAC, 2021--22)

### Positions

I am seeking students with strong cs/math backgrounds interested in algorithms broadly. Please apply directly to the department, and indicate in your application if you're interested in working with me.

I am also looking for MScAC students working on projects related to model training, efficiency improvement, learning on graphs etc.

### Service

Program Committee member for FOCS 2023, ESA 2022, STOC 2022, SODA 2021, STOC 2019

Guest Editor for TALG (SODA 2019 Special Issue), SICOMP (STOC 2019 Special Issue)

Richard Peng (GaTech) and I organized a workshop at FOCS 2018: Laplacian Paradigm 2.0

### Selected Publications

Best Paper Award

We give an algorithm that computes exact maximum flows and
minimum-cost flows on directed graphs with m edges and
polynomially bounded integral demands, costs, and capacities
in \(m^{1+o(1)}\) time. Our algorithm builds the flow
through a sequence of \(m^{1+o(1)}\) approximate undirected
minimum-ratio cycles, each of which is computed and
processed in amortized \(m^{o(1)}\) time using a new dynamic graph
data structure.

Our framework extends to algorithms running in \(m^{1+o(1)}\) time
for computing flows that minimize general edge-separable
convex functions to high accuracy. This gives almost-linear
time algorithms for several problems including
entropy-regularized optimal transport, matrix scaling,
p-norm flows, and p-norm isotonic regression on arbitrary
directed acyclic graphs.

via Short Cycle Decompositions

We develop a framework for graph sparsification and sketching, based on a new tool, short cycle decomposition -- a decomposition of an unweighted graph into an edge-disjoint collection of short cycles, plus few extra edges. A simple observation gives that every graph \(G\) on \(n\) vertices with m edges can be decomposed in \(O(mn)\) time into cycles of length at most \(2 \log n\), and at most \(2n\) extra edges. We give an \(m^{1+o(1)}\) time algorithm for constructing a short cycle decomposition, with cycles of length \(n^{o(1)}\), and \(n^{1+o(1)}\) extra edges. These decompositions enable us to make progress on several open questions:

* We give an algorithm to find \((1\pm\epsilon)\)-approximations to effective resistances of all edges in time \(m^{1+o(1)}\epsilon^{-1.5}\), improving over the previous best of \(\widetilde{O}(\min\{m\epsilon^{-2},n^2 \epsilon^{-1}\})\). This gives an algorithm to approximate the determinant of a Laplacian up to \((1\pm\epsilon)\) in \(m^{1 + o(1)} + n^{15/8+o(1)}\epsilon^{-7/4}\) time.

* We show existence and efficient algorithms for constructing graphical spectral sketches -- a distribution over sparse graphs H such that for a fixed vector \(x\), we have w.h.p. \(x'L_Hx=(1\pm\epsilon)x'L_Gx\) and \(x'L_H^+x=(1\pm\epsilon)x'L_G^+x\). This implies the existence of resistance-sparsifiers with about \(n\epsilon^{-1}\) edges that preserve the effective resistances between every pair of vertices up to \((1\pm\epsilon).\)

* By combining short cycle decompositions with known tools in graph sparsification, we show the existence of nearly-linear sized degree-preserving spectral sparsifiers, as well as significantly sparser approximations of directed graphs. The latter is critical to recent breakthroughs on faster algorithms for solving linear systems in directed Laplacians.

Improved algorithms for constructing short cycle decompositions will lead to improvements for each of the above results.

Fast, Sparse, and Simple

We show how to perform sparse approximate Gaussian elimination for Laplacian matrices. We present a simple, nearly linear time algorithm that approximates a Laplacian by a matrix with a sparse Cholesky factorization, the version of Gaussian elimination for symmetric matrices. This is the first nearly linear time solver for Laplacian systems that is based purely on random sampling, and does not use any graph theoretic constructions such as low-stretch trees, sparsifiers, or expanders. The crux of our analysis is a novel concentration bound for matrix martingales where the differences are sums of conditionally independent variables.

We develop fast algorithms for solving regression problems on graphs where one is given the value of a function at some vertices, and must find its smoothest possible extension to all vertices. The extension we compute is the absolutely minimal Lipschitz extension, and is the limit for large p of p-Laplacian regularization. We present an algorithm that computes a minimal Lipschitz extension in expected linear time, and an algorithm that computes an absolutely minimal Lipschitz extension in expected time \(\widetilde{O}(mn)\). The latter algorithm has variants that seem to run much faster in practice. These extensions are particularly amenable to regularization: we can perform \(\ell_{0}\)-regularization on the given values in polynomial time and \(\ell_{1}\)-regularization on the initial function values and on graph edge weights in time \(\widetilde{O}(m^{\frac{3}{2}})\).

Faster Algorithms via Approximation Theory illustrates how
classical and modern techniques from approximation theory play a
crucial role in obtaining results that are relevant to the
emerging theory of fast algorithms. The key lies in the fact
that such results imply faster ways to approximate primitives
such as \(A^s v, A^{-1}v, \exp(-A)v\), eigenvalues and
eigenvectors, which are fundamental to many spectral
algorithms.

The first half of the book is devoted to the ideas and results from
approximation theory that are central, elegant, and may
have wider applicability in theoretical computer
science. These include not only techniques relating to
polynomial approximations but also those relating to
approximations by rational functions and beyond. The
remaining half illustrates a variety of ways that these
results can be used to design fast algorithms.

Faster Algorithms via Approximation Theory is
self-contained and should be of interest to researchers
and students in theoretical computer science, numerical
linear algebra, and related areas.

and Implications for Gaussian Mixtures and Autoencoders

We present a new algorithm for Independent Component Analysis (ICA) which has provable performance guarantees. In particular, suppose we are given samples of the form y = Ax + \(\eta\) where A is an unknown n X n matrix and x is chosen uniformly at random from \(\{+1, -1\}^n\), \(\eta\) is an n-dimensional Gaussian random variable with unknown covariance \(\Sigma\): We give an algorithm that provable recovers A and \(\Sigma\) up to an additive \(\epsilon\) whose running time and sample complexity are polynomial in n and \(1 / \epsilon\). To accomplish this, we introduce a novel "quasi-whitening" step that may be useful in other contexts in which the covariance of Gaussian noise is not known in advance. We also give a general framework for finding all local optima of a function (given an oracle for approximately finding just one) and this is a crucial step in our algorithm, one that has been overlooked in previous attempts, and allows us to control the accumulation of error when we find the columns of A one by one via local search.

an \(\widetilde{O}(m)\)-Time Spectral Algorithm for Balanced Separator

We give a novel spectral approximation algorithm for the balanced separator problem that, given a graph G, a constant balance b \(\in (0,\frac{1}{2}],\) and a parameter \(\gamma,\) either finds an \(\Omega(b)\)-balanced cut of conductance \(O(\sqrt{\gamma})\) in G, or outputs a certificate that all b-balanced cuts in G have conductance at least \(\gamma,\) and runs in time \(\widetilde{O}(m).\) This settles the question of designing asymptotically optimal spectral algorithms for balanced separator. Our algorithm relies on a variant of the heat kernel random walk and requires, as a subroutine, an algorithm to compute exp(-L)v where L is the Laplacian of a graph related to G and v is a vector. Algorithms for computing the matrix-exponential-vector product efficiently comprise our next set of results. Our main result here is a new algorithm which computes a good approximation to exp(-A)v for a class of symmetric positive semidefinite (PSD) matrices A and a given vector v, in time roughly \(\widetilde{O}(m_A),\) where \(m_A\) is the number of non-zero entries of A. This uses, in a non-trivial way, the breakthrough result of Spielman and Teng on inverting symmetric and diagonally-dominant matrices in \(\widetilde{O}(m_A)\) time. Finally, we prove that \(e^{-x}\) can be uniformly approximated up to a small additive error, in a non-negative interval [a,b] with a polynomial of degree roughly \(\sqrt{b-a}.\) While this result is of independent interest in approximation theory, we show that, via the Lanczos method from numerical analysis, it yields a simple algorithm to compute exp(-A)v for symmetric PSD matrices that runs in time roughly \(O(t_A\cdot \sqrt{\|A\|}),\) where \(t_A\) is time required for the computation of the vector Aw for given vector w. As an application, we obtain a simple and practical algorithm, with output conductance \(O(\sqrt{\gamma}),\) for balanced separator that runs in time \(\widetilde{O}(\frac{m}{\sqrt{\gamma}}).\) This latter algorithm matches the running time, but improves on the approximation guarantee of the Evolving-Sets-based algorithm by Andersen and Peres for balanced separator.