We are an active group of researchers working both in theoretical and applied statistics, data science, and related problems in probability theory. This work concerns the mathematical foundations of mathematical statistics and machine learning, experimentation and data analysis. In addition we work on applications of this theory in the fields such as engineering, finance, computer science, and biology. In this regard, we benefit from close collaborations with other departments at USC including Electrical Engineering, Computer Science, the Information Science Institute, and Computational Biology. Ph.D. students are trained in Statistics through the Department’s Applied Mathematics Ph.D. program, moreover the Department offers professional master’s degrees in Statistics, Mathematical Data Science and Mathematical Finance. The department also runs a weekly Probability and Statistics seminar.

Faculty

  • Xiaohui Chen: high-dimensional statistics, machine learning and optimal transport theory
  • Larry Goldstein: Stein’s method in statistics and distributional approximation, concentration inequalities, sampling schemes, mathematical molecular biology, and optimal stopping
  • Sergey Lototsky: Statistical inference for stochastic differential equations
  • Jinchi Lv (joint w/ Marshall School of Business): statistics, machine learning, data science, business applications, and artificial intelligence and blockchain
  • Stanislav Minsker: Statistical learning theory, high-dimensional statistics, empirical processes theory and concentration-of-measure inequalities
  • Yinan Shen: high-dimensional and robust statistics, optimization of non-smooth functions
  • Yizhe Zhu: probability, combinatorics, and their applications in data science, including random matrices/tensors, random graphs/hypergraphs, community detection, neural networks, differential privacy

Faculty in Related Areas

  • Kenneth Alexander: Probability theory, statistical mechanics
  • Richard Arratia: Probability theory, combinatorics, number theory
  • Jason Fulman: Algebraic combinatorics and probability theory
  • Steven Heilman: Probability theory, analysis, geometry, theoretical computer science
  • Edmond Jonckheere: Applied topology/geometry, internet traffic signals analysis
  • Jin Ma: Stochastic analysis, stochastic differential equations, stochastic control theory, mathematical finance and actuarial sciences
  • Gary Rosen: Modelling, estimation, control, and optimal design of systems governed by infinite dimensional systems, and in particular by partial differential equations
  • Shanghua Teng: network analysis, spectral graph theory, computational economics and game theory, mathematical programming, combinatorial optimization, computational geometry and computer graphics
  • Jianfeng Zhang: Mathematical finance, stochastic differential equations, stochastic control theory

Undergraduate Probability and Statistics Courses

  • MATH 114 Foundations of Statistics
  • MATH 208x Elementary Probability and Statistic
  • MATH 218 Probability for Business
  • MATH 307 Statistical Inference and Data Analysis I
  • MATH 308 Statistical Inference and Data Analysis II
  • MATH 407 Probability Theory
  • MATH 408 Mathematical Statistics
  • MATH 446 Machine Learning through Python
  • MATH 447 Mathematics of Machine Learning

Graduate Probability Courses

  • MATH 503 Stochastic Calculus for Finance
  • MATH 505ab Applied Probability
  • MATH 506 Stochastic Processes
  • MATH 507ab Theory of Probability
  • MATH 509 Stochastic Differential Equations
  • MATH 530ab Stochastic Calculus and Mathematical Finance

Graduate Statistics Courses

  • MATH 511abL Data Analysis
  • MATH 541ab Introduction to Mathematical Statistics
  • MATH 542 Analysis of Variance and Regression
  • MATH 546 Mathematical Statistics for Data Science
  • MATH 547 Mathematical Foundations of Statistical Learning Theory
  • MATH 549 Foundations of Mathematical Data Science
  • MATH 550 Statistical Consulting and Data Analysis
  • MATH 545 Introduction to Time Series
  • MATH 548 Machine Learning in Quantitive Finance
  • PHYS 515 Python for Data Science and Scientific Computing

Graduate Computational Biology Courses

  • MATH 577ab Computational Molecular Biology Laboratory
  • MATH 578ab Computational Molecular Biology

Related Graduate Courses

  • MATH 585 Mathematical Theory of Optimal Control

Current Graduate Students

  • Inga Girshfeld (Chen)
  • Pengtao Li (Chen/Minsker)
  • Benjamin Gillen (Minsker)
  • Nicholas Karamyan (Minsker)
  • Oleksandra Lymar (Goldstein/Minsker)
  • Dylan Park (Goldstein)
  • Farhad de Sousa (Minsker/Bien)
  • Yiqiu Shen (Minsker)
  • Zixiang Zhou (Chen/Teng)

Recently Graduated Ph.D. Students

  • 2023
    • Shunan Yao (Minsker/Tong)
    • Ziyi Liang (Goldstein/Sessia)
    • Andy Lowy (Minsker/Razaviyayn)
    • Daniel Lundstrom (Minsker/Razaviyayn)
  •  2022
    • Jinting Liu (Bartroff/Sun)
    • Apoorva Shah (Lototsky)
    • Hao Wu (Lv/Zhang)
    • Jiajun Luo (Sun/Minsker)
  • 2021
    • Wang, Lang (Minsker)
    • Luo, Jiajun (Minsker)
  • 2020
    • Aronowitz, Julian (Bartroff). First position: Data Scientist, Palo Alto Networks
    • He, Xinrui (Bartroff). First position: Data Scientist, Google
  • 2019
    • Demirkaya, Emre (Yingying Fan/Goldstein/Lv), Reproducible Large-Scale Inference in High-Dimensional Nonlinear Models. First position: Assistant Professor, University of Tennessee Haslam College of Business.
    • He, Xinrui (Bartroff), Asymptotically optimal sequential multiple testing with (or without) prior information on the number of signals. First position: Statistician at Acumen LLC/The SPHERE Institute.
    • Wei, Xiaohan (Minsker/Neely), High-dimensional estimation under weak moment assumptions. First position: Research Scientist at Facebook.
    • Wiroonsri, Nathakhun (Goldstein), Stein’s Method via Approximate Zero Biasing and Positive Association with Applications to the Combinatorial Central Limit Theorem and Statistical Physics. First position: Lecturer at King Mongkut’s University of Technology, Thailand, Department of Mathematics.
  • 2018
    • Bhattacharjee, Chinmoy (Goldstein), Stein’s Method and its Applications in Strong Embeddings and Dickman Approximations. First position: Postdoctoral Fellow at University of Bern, Switzerland.
    • Kim, Hyun-Jung (Lototsky), Time-Homogeneous Parabolic Anderson Model.
    • Wang, Jian (Lototsky), Statistical Inference For Second-Order Ordinary Differential Equation Driven by Additive Gaussian White Noise.
  • 2017
    • Hankin, Michael (Bartroff), Sequential Testing of Multiple Hypotheses With FDR Control under Dependence. First position: Statistician at Google.
  • 2016
    • Tsilifis, Panagiotis (Ghanem/Mikulevicius), Design, adaptation and variational methods in Uncertainty Quantification. First position: Postdoc in the Viterbi School of Engineering at USC.
  • 2015
    • Zheng, Zemin (Goldstein/Lv), High-Dimensional Latent Variable Thresholded Regression. First position: Assistant Professor at the Chinese University of Science and Technology.
  • 2014
    • Bessam, Diogo (Lototsky), Large Deviations Rates in a Gaussian Setting and Related Topics. First position: Postdoc at PUC-RJ/IMPA (Brasil).
    • Sokolov, Grigory (Tartakovsky/Lototsky), Multi-Population Optimal Change-Point Detection. First position: Postdoc at SUNY Binghamton.
  • 2013
    • Chubatiuk, Alona (Schumitzky), Nonparametric Estimation of an Unknown Probability Distribution Using Maximum Likelihood and Bayesian Approaches. First position: Postdoc at Children’s Hospital LA.
    • Song, Jinlin (Bartroff), Time-Sequential Testing for Multiple Hypotheses. First position: Analyst at The Analysis Group, Boston.
    • Xu, Li (Lototsky), Parameter Estimate for Hyperbolic SPDE’s with Stochastic Coefficients. First position: Google.
    • Zhong, Jie (Lototsky), Second Order in Time Stochastic Evolution Equation and Wiener Chaos Approach. First position: Postdoc at Ritsumeikan University (Japan).
  • 2012
    • Ghosh, Subhankar (Goldstein), Couplings for Berry-Esseen Bounds and Concentration Inequalities. First position: SAS Statistical Software.
    • Kaligotla, Sivaditya (Lototsky), Asymptotic Problems in Stochastic Partial Differential Equations: A Wiener Chaos Approach. First position: Bloomberg LP.
    • Lin, Ning (Lototsky), Estimation of Coefficients in Stochsatic Differential Equations. First position: Citigroup.
    • Moers, Michael (Lototsky), Statistical Inference of Stochastic Differential Equations Driven by Gaussian Noise. First position: Deutsche Bank.
    • Xu, Shanshan (Lototsky/Wilcox), Initiative Non-Parametric Multivariate Regression Hypothesis Testing.
  • 2011
    • Lin, Wei (Goldstein), Survival Analysis With Missing Data and High Dimensionality. First position: Postdoc at University of Pennsylvania.