Peng Zhang's homepage
I am an assistant professor in Computer Science at Rutgers University, and a graduate faculty in Statistics at Rutgers.
I was a postdoc at Yale University, under the supervision of Prof. Daniel Spielman, during 2018 - 2021. I obtained my Ph.D. from Georgia Tech in 2018, advised by Prof. Richard Peng. Before that, I got my MS from Purdue University in 2015 and my BS from Zhejiang University in 2013.
Email: pz149@rutgers.edu
Hill Center, Room 444
I am broadly interested in the design of algorithms and data science. Specifically, I am interested in solving structured systems of linear equations, discrepancy theory and its applications in causal inference.
My research is generously supported by the National Science Foundation (NSF) (Faculty Early Career Development (CAREER) Award: CCF-2238682), Adobe Data Science Research Award, and Rutgers Research Council Individual Fulcrum Award.
Teaching
At Rutgers:
CS 323 Numerical Analysis and Computing: Spring 2024
CS 522 Network and Combinatorial Optimization: Fall 2023
CS 514 Advanced Algorithms II (Spectral Graph Algorithms): Spring 2023
CS 323 Numerical Analysis and Computing: Fall 2022
CS 673 Discrepancy Theory and Its Applications: Fall 2021
At Yale:
CPSC 662 Combinatorial Discrepancy and Its Applications: Fall 2020
Papers
Balancing Covariates in Randomized Controlled Trials with Non-Uniform Treatment Probabilities
with Anup Rao, 2023
Efficient Algorithms for Partitioning Circulant Graphs with Optimal Spectral Approximation
with Surya Teja Gavva, 2023
Balancing covariates in randomized experiments using the Gram-Schmidt walk
with Christopher Harshaw, Fredrik Savje, and Daniel Spielman
Journal of the American Statistical Association (JASA), 2023
Arxiv https://arxiv.org/abs/1911.03071
Efficient $1$-Laplacian Solvers for Well-Shaped Simplicial Complexes
with Ming Ding
ESA 2023
Hardness Results for Minimizing the Covariance of Randomly Signed Sum of Vectors
Arxiv abs/2211.14658, 2022
Hardness Results for Weaver's Discrepancy Problem
with Daniel Spielman
APPROX 2022
Hardness Results for Laplacians of Simplicial Complexes via Sparse-Linear Equation Complete Gadgets
with Ming Ding, Rasmus Kyng, and Maximilian Probst Gutenberg
ICALP 2022
Two-Commodity Flow is as Hard as Linear Programming under Nearly Linear-Time Reductions
with Ming Ding and Rasmus Kyng
ICALP 2022
Positive LPs are Hard to Solve Accurately, Assuming Linear Equations are Hard
with Rasmus Kyng and Di Wang
SODA 2020
with Rasmus Kyng, Richard Peng, and Robert Schwieterman
STOC 2018
Hardness Results for Structured Linear Systems
with Rasmus Kyng
FOCS 2017, won the best student paper award
SIAM Journal on Computing (2020)
On Approximate Pattern Matching with Thresholds
with Mikhail J. Atallah
Information Processing Letters 123 (2017): 21-26
Approximating the Solution to Mixed Packing and Covering LPs in Parallel O~(ε^{−3}) Time
with Michael W. Mahoney, Satish Rao, and Di Wang
ICALP 2016
Faster and Simpler Width-Independent Parallel Algorithms for Positive Semidefinite Programming
with Richard Peng, Kanat Tangwongsan
Arxiv abs/1201.5135, 2016
Optimal Query Complexity for Estimating the Trace of a Matrix
with Karl Wimmer and Yi Wu
ICALP 2014
Minimizing Seed Set Selection with Probabilistic Coverage Guarantee in a Social Network
with Wei Chen, Xiaoming Sun, Yajun Wang, and Jialin Zhang
SIGKDD 2014
My Ph.D. dissertation:
Hardness and Tractability For Structured Numerical Problems. 2018. Georgia Tech College of Computing Dissertation Award