The research of the faculty covers a broad area of probability theory, mathematical statistics, and their applications.

FACULTY

  1. Alexander, Kenneth: Probability models in statistical mechanics: lattice models (Ising model, Potts model, percolation, etc.), phase transitions, disordered models.
  2. Arratia, Richard: Probability, especially as related to combinatorics and number theory, coupling, and approximation.
  3. Chen, Xiaohui: High-dimensional statistics, machine learning, and optimal transport.
  4. Dimitrov, Evgeni: Integrable probability.
  5. Fulman, Jason: Markov chains, probability on algebraic structures, random matrix theory, Stein’s method.
  6. Goldstein, Larry: Distributional approximation and Stein’s method, high dimensional statistics, concentration inequalities, sampling schemes in epidemiology, statistical efficiency, optimal stopping.
  7. Lototsky, Sergey: Stochastic partial differential equations, optimal nonlinear filtering of diffusion processes, statistical inference for continuous-time processes.
  8. Lv, Jinchi (Marshall School of Business): statistics, machine learning, data science, business applications, and artificial intelligence and blockchain.
  9. Ma, Jin: Stochastic analysis, stochastic differential equations, stochastic control theory, mathematical finance and insurance.
  10. Minsker, Stanislav (Stas): Statistical learning theory, non-parametric statistics, concentration inequalities, mathematical finance.
  11. Sun, Fengzhu (Department of Quantitative and Computational Biology): Quantitative and computational biology, probability, statistics.
  12. Zhang, Jianfeng: Stochastic analysis, backward stochastic differential equations, stochastic numerics, and mathematical finance.
  13. Zhu, Yizhe: high-dimensional probability and mathematical data science, with emphasis on random matrix theory, network analysis, community detection, and differential privacy.

EMERITUS FACULTY

  1. Baxendale, Peter: Stochastic dynamical systems; equilibrium, stability, and bifurcation for solutions of stochastic differential equations; applications to stochastic neuronal models.
  2. Mikulevicius, Remigijus (Remi): Stochastic differential equations, stochastic analysis.
  3. Waterman, Michael (Department of Quantitative and Computational Biology): Molecular and computational biology, probability, statistics.

POST DOCS

  • Du, Wenqin: Statistical modeling and inferences on directed networks
  • Heilman, Steven: High-Dimensional Probability, Concentration Inequalities, Theoretical Computer Science, Random Graphs.
  • Mei, Tianxing: High-Dimensional Probability, Asymptotic Statistics, Stochastic Processes.
  • Shen, Yinan: High-dimensional statistics, optimization of non-smooth functions, robustness of Lipschitz functions.
  • Xia, Weixuan: investment decisions under incomplete preferences, exotic derivatives, cryptocurrency markets, optimal control of set-valued stochastic processes, Lévy functionals and illiquidity measures, and neural network architectures applicable in these areas.
  • Zhou, Zhengye: integrable probability.

CURRENT Ph.D. STUDENTS

  1. Dominic Arcona (Fulman)
  2. Atiqah Almuzaini (Ma)
  3. Rundong Ding (Lv)
  4. Xinze Du (Lv)
  5. Levon Hakobyan (Lototsky)
  6. Oleksandra Lymar (Goldstein/Minsker)
  7. Dylan Park (Lv)
  8. Bixing Qiao (Zhang)
  9. Wes Wise (Fulman)
  10. Gaozhan Wang (Ma/Zhang)

ACTIVITIES

RECENT Ph.D. GRADUATES, THEIR ADVISORS, AND DISSERTATIONS

For the list of the next, and some current, jobs, of our graduates, see  below. Some other success stories are here.

2024

  • Gamage, Thejani (Ma), Reinforcement Learning for the Optimal Dividend Problem
  • Liang, Cora (Goldstein), Conformalized Post-Selection Inference and Structured Prediction
  • Wu, Bo (Alexander), Some Results Concerning the Critical Points of Directed Polymers

2023

  • Arslan, Aykut (Lv/Yingying  Fan), High-Dimensional Rescaled Square-Root Lasso under Distributional Shift and its Robustness
  • Iseri, Melih (Zhang), Set Values for Mean Field Games and Set Valued PDEs
  • Karakus, Abdullah (Alexander), First Passage Percolation in a Correlated Environment
  • Paguyo, J. E. (Fulman), Limit Theorems for Three Random Discrete Structures via Stein’s Method
  • Tan, Ying  (Ma), Stochastic Two-point Boundary Value Problem and Application in Kyle-Back Equilibrium Model
  • Wang, Lijia (Goldstein), Statistical Citation Network Analysis and Asymmetric Error Controls
  • Yao, Shunan (Minsker), New Methods For Asymmetric Classification and Robust Bayesian Inference

2022

  • Liu, Jinting (Goldstein/Sun), Selective Inference With Confident Directions
  • Pollok, Austin (Zhang), High-Frequency Kelly Criterion and Fat-Tails: Gambling with an Edge
  • Shah, Apoorva (Lototsky), Gaussian Free Fields and Stochastic Parabolic Equations
  • Tarter, Alex (Goldstein/Heilman), Dimension Reduction Techniques for Noise Stability Theorems
  • Vossler, Patrick (Yingying Fan/Lv), Nonparametric Ensemble Learning and Inference
  • Walker, Wayne (Goldstein), High Order Correlations in Sampling and Concentration Bounds via Size Biasing
  • Wu, Yusheng (Lototsky/Tong), Asymptotic Properties of Two Network Problems with Large Random Graphs

2021

  • Feng, Pengbin (Ma/Zhang), Dynamic Network Model for Systemic Risk
  • Gangopadhyay, Ujan (Alexander), Fluctuations of the Transverse Increments in the Two-dimensional First-passage Percolation Model
  • Luo, Man (Ma), Topics on Dynamic Limit Order Book and its Related Computation
  • Rahmani, John (Fulman), Mixing Times for the Commuting Chain
  • Wang, Lang (Minsker), Robust Estimation of High Dimensional Parameters
  • Wu, Wenqian (Ma), Topics on Set-Valued Backward Stochastic Differential Equations
  • Zhu, Zimu (Zhang), Some Topics on Continuous Time Principal-Agent Problem

2020

  • Aronowitz, Julian (Bartroff), Finite Sample Bounds in Group Sequential Analysis via Stein’s Method
  • Phonsom, Chukiat (Mikulevicius), On Stochastic Integro-Differential Equations
  • Ruan, Jie (Zhang), Numerical Methods for High-Dimensional Path-Dependent PDEs Driven by Stochastic Volterra Equations
  • Wei, Xiaohan (Minsker/Neely), Part I: Asynchronous optimization over weakly coupled renewal systems. Part II: High dimensional estimation under weak moment assumptions
  • Wu, Hao (Zhang/Lv), Statistical Insights into Deep Learning and Flexible Causal Inference

2019

  • Demirkaya, Emre (Yingying Fan/Goldstein/Lv), Reproducible Large-Scale Inference in High-Dimensional Nonlinear Models
  • He, Xinrui (Bartroff), Asymptotically optimal sequential multiple testing with (or without) prior information on the number of signals
  • Ozdemir, Alperen (Fulman), On Limiting Distribution and Convergence Rates of Random Processes Defined Over Discrete Structures
  • Wiroonsri, Nathakhun (Goldstein), Steins’ Method via Approximate Zero Biasing and Positive Association with Applications to the Combinatorial Central Limit Theorem and Statistical Physics
  • Xu, Fanhui (Mikulevicius), On the parabolic Kolmogorov integro-differential equation and its applications

2018

  • Bhattacharjee, Chinmoy (Goldstein), Stein’s Method and its Applications in Strong Embeddings and Dickman Approximations
  • Kim, Gene B (Fulman), Distribution of Descents in Matchings
  • Kim, Hyun-Jung (Lototsky), Time-Homogeneous Parabolic Anderson Model
  • Noh, Eunjung (Ma), Equilibrium Model of Limit Order Book and Optimal Execution Problem
  • Ozel, Enes (Fulman), Cycle Structures of Permutations with Restricted Positions
  • Sun, Rentao (Ma), Conditional Mean-Field Stochastic Differential Equations and Their Applications
  • 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
  • Nguyen, Dinh (Fulman), Random Walks on Finite Groups and Their Irreducible Representations
  • Wu, Cong (Zhang), Controlled McKean-Vlasov Equations and Related Topics
  • Xing, Xiaojing (Ma), Optimal Investment and Dividend under Sparre Andersen Model

2016

  • Kang, Yongjian (Lv/Zhang), Large-Scale Inference in Multiple Gaussian Graphical Models
  • Karnam, Chandrasekhar (Ma/Zhang), Dynamic Approaches for some Time Inconsistent Problems
  • Tsilifis, Panagiotis (Ghanem/Mikulevicius), Design, Adaptation and Variational Methods in Uncertainty Quantification
  • Xie, Weisheng (Ma), Stochastic Differential Equations Driven by Fractional Brownian Motion and Poisson Point Processes

2015

  • Keller, Christian (Zhang), Pathwise Stochastic Analysis and Related Topics
  • Ren, Haining (Fulman), Convergence Rates of i-Cycles After an m-Shelf Shuffle
  • Zhang, Tian (Ma), Optimal Investment and Reinsurance Problems and Related Non-Markovian FBSDEs With Constraints
  • Zheng, Zemin (Goldstein/Lv), High-Dimensional Latent Variable Thresholded Regression

2014

  • Daley, Timothy (Smith/Waterman), Non-Parametric Models For Large Capture-Recapture Experiments With Applications to DNA Sequencing
  • Bessam, Diogo (Lototsky), Large Deviations Rates in a Gaussian Setting and Related Topics
  • Ekren, Ibrahim (Zhang), Path-Dependent Partial Differential Equations and Related Topics
  • Islak, Umit (Fulman), Concentration Inequalities with Couplings from Stein’s Method
  • Sokolov, Grigory (Tartakovsky/Lototsky), Multi-Population Optimal Change-Point Detection
  • Zhuo, Jia (Zhang), Probabilistic Numerical Methods for Fully Nonlinear PDEs and Related Topics

2013

  • Chubatiuk, Alona (Schumitzky), Nonparametric Estimation of an Unknown Probability Distribution Using Maximum Likelihood and Bayesian Approaches
  • Marinov, Radoslav (Fulman), Applications of Stein’s Method on Statistics of Random Graphs
  • Pham, Triet (Zhang), Zero-Sum Stochastic Differential Games in Weak Formulation and Related Norms for Semi-Martingales
  • Pike, John (Fulman), Eigenfunctions for Random Walks on Hyperplane Arrangements
  • Song, Jinlin (Bartroff), Time-Sequential Testing for Multiple Hypotheses
  • Wang, Huanhuan (Ma), Asset Management with Incomplete Information
  • Wang, Xin (Ma), Nonlinear Expectations for Continuous Time Model with Jumps and Applications
  • Xu, Li (Lototsky), Parameter Estimate for Hyperbolic SPDE’s with Stochastic Coefficients
  • Yildirim, Gokhan (Alexander), On the Depinning Transition of the Directed Polymer in a Random Environment With a Defect Line
  • Zhong, Jie (Lototsky), Second Order in Time Stochastic Evolution Equation and Wiener Chaos Approach

2012

  • DeSalvo, Stephen (Arratia), Probabilistic Divide-and-Conquer: A New Method for Exact Simulation  and Lower Bound Expansions for Random Bernoulli Matrices via Novel Integer Partitions
  • Du, Jie (Zhang), Stochastic Games on Stopping Times
  • Ghosh, Subhankar (Goldstein), Couplings for Berry-Esseen Bounds and Concentration Inequalities
  • Kaligotla, Sivaditya (Lototsky), Asymptotic Problems in Stochastic Partial Differential Equations: A Wiener Chaos Approach
  • Lin, Ning (Lototsky), Estimation of Coefficients in Stochastic Differential Equations
  • Moers, Michael (Lototsky), Statistical Inference of Stochastic Differential Equations Driven by Gaussian Noise
  • Nibert, Joel (Baxendale), Invariant Measures of a Stochastic Predator-Prey Model
  • Xu, Shanshan (Lototsky/Wilcox), Initiative Non-Parametric Multivariate Regression Hypothesis Testing

2011

  • Chen, Jianfu (Ma), Regime Switch Term Structure Model With Forward-Backward Stochastic Differential Equations
  • Lin, Wei (Goldstein), Survival Analysis With Missing Data and High Dimensionality
  • Wang, Xinyang (Ma/Zhang), Dynamic Model for Limit Order Books and Optimal Liquidation Problems
  • Yun, Youngyun (Ma), Analysis of Correlated Defaults and Joint Default Probability in a Contagion Model

2010

  • Liu, Wei (Lototsky), Statistical Inference for Stochastic Hyperbolic Equations
  • Zhang, Changyong (Mikulevicius), Numerical Weak Approximation of Stochastic Differential Equations Driven by Levy Processes

2009

  • Knape, Mathias (Mikulevicius/Zapatero), A General Equilibrium Model for Exchange Rates and Asset Prices in an Economy Subject to Jump-Diffusion Uncertainty
  • Pehlivan, Lerna (Fulman), On Top to Random Shuffles, no Feedback Card Guessing and Fixed Points of Permutations
  • Polunchenko, Aleksey (Mikulevicius/Tartakovsky), Quickest Change Detection with Applications to Distributed Multi-Sensor Systems
  • Ross, Nathan (Fulman), Exchangeable Pairs in Stein’s Method of Distributional Approximation

THE (FIRST) JOBS OF SOME OF OUR RECENT Ph.D. GRADUATES

2024

  • Bo Wu: Senior Consultant in Transfer Pricing, Deloitte

2023

  • Aykut Arslan: Lecturer, USC
  • Melih Iseri: Postdoc at the University of Michigan
  • J.E. Paguyo: Postdoc at McMaster University (Hamilton, Ontario, Canada)
  • Ying Tan: Postdoc at UCSB

2022

  • Austin Pollok: Assistant Professor of Clinical Data Science and Operations, USC
  • Apoorva Shah: Data Scientist at Neal Analytics
  • Alex Tarter: Statistician at the NASS (USDA)
  • Patrick Vossler: Postdoctoral Scholar, Stanford University

2021

  • Pengbin Feng: Research Scientist at Amazon.
  • Ujan Gangopadhyay: Postdoc at Natl. Univ. of Singapore.
  • Man Luo: Quant Researcher at Guotai Junan Securities Asset Management (Shanghai, China).
  • John Rahmani: Data Scientist at Accenture (San Diego, CA).
  • Wenqian Wu: Quant Trader at Guotai Junan Securities (Shanghai, China).
  • Zimu Zhu: Postdoc at UCSB.

2020

  • Julian Aronowitz: Data Scientist at Palo Alto Networks
  • Chukiat Phonsom: Alexandria Technology, Research and Investment
  • Jie Ruan: Facebook
  • Xiaohan Wei: Facebook

2019

  • Emre Demirkaya: Assistant Professor at the Haslam College of Business, University of Tennessee
  • Xinrui He: Statistician at Acumen/The SPHERE Institute
  • Alperen Ozdemir: Postdoc, Department of Mathematics, Georgia Tech
  • Fanhui Xu: Postdoctoral Associate, Carnegie Mellon University
  • Nathakhun Wiroonsri: Instructor, King Mongkut University of Technology Thonburi (Bangkok, Thailand)

2018

  • Chinmoy Bhattacharjee: Postdoc, IMSV, University of Bern
  • Gene Kim: Lecturer, USC; currently Lecturer at Stanford
  • Hyun-Jung Kim: Postdoc, Illinois Institute of Technology
  • Eunjung Noh: Hills Assistant Professor, Rutgers Univ
  • Enes Ozel: Adjunct Assistant Professor, UCLA
  • Rentao Sun: Data scientist, The Data Incubator

2017

  • Michael Hankin: Google
  • Dinh Nguyen: Polytechnic School (Pasadena)
  • Cong Wu: Quantitative Associate at Wells Fargo
  • Xiaojing Xing: Wells Fargo (Charlotte, NC)

2016

  • Yongjian Kang: Google
  • Chandrasekhar Karnam: Morgan Stanley
  • Panagiotis Tsilifis: Postdoc in the Viterbi School of Engineering at USC
  • Weisheng Xie: Wells Fargo (Charlotte, NC)

2015

  • Christian Keller: Postdoc at the University of Michigan
  • Haining Ren: Pixalate (Santa Monica, CA)
  • Tian Zhang: Education Management Systems
  • Zemin Zheng: Assistant Professor at the Chinese University of Science and Technology

2014

  • Diogo Bessam: Postdoc at PUC-RJ/IMPA (Brasil)
  • Ibrahim Ekren: Postdoc at ETH Zurich
  • Umit Islak: Postdoc at the University of Minnesota – Twin Cities
  • Grigory Sokolov: Postdoc at SUNY Binghamton
  • Jia Zhuo: Morgan Stanley

2013

  • Alona Chubatiuk: Postdoc at Children’s Hospital LA
  • Triet Pham: Postdoc at Rutgers
  • John Pike: Postdoc at Cornell
  • Jinlin Song: Researcher at the Analysis Group
  • Huanhuan Wang: Capital One
  • Xin Wang: Morgan Stanley
  • Li Xu: Google
  • Gokhan Yildirim: Lecturer at USC
  • Jie Zhong: Postdoc at Ritsumeikan University (Japan); currently Assistant Professor at Cal State LA

2012

  • Stephen DeSalvo: Postdoc at UCLA; currently at Google
  • Jie Du: Guggenheim Partners
  • Subhankar Ghosh: SAS Statistical Software
  • Sivaditya Kaligotla: Bloomberg LP
  • Ning Lin: Citigroup
  • Michael Moers: Deutsche Bank
  • Joel Nibert: Lecturer at USC

2011

  • Jianfu Chen: Union Bank of California
  • Wei Lin: Postdoc at UPenn
  • Xinyang Wang: Morgan Stanley
  • Youngyun Yun: Union Bank of California

2010

  • Wei Liu: American Express
  • Changyong Zhang: Postdoc at Salzburg University

2009

  • Mathias Knape: Goldman Sachs
  • Lerna Pehlivan: Lecturer at York University
  • Aleksey Polunchenko: Postdoc at USC
  • Nathan Ross: Postdoc at UC Berkeley

The next step for some of our undergraduate and masters students:

  • Chuhuan Huang: Masters student, worked with Steven Heilman; in Fall 2023
    started Ph.D. program in math at Johns Hopkins University,
  • Benny Cohen: Undergraduate student, worked with Steven Heilman; in Fall 2023 started Ph.D. program in Mechanical Engineering at USC.
  • Jake Freeman: Undergraduate student, worked with Steven Heilman; in Fall 2023 started Ph.D. program in ORFE at Princeton. See here for even more.