Students

PhD

  • Ningwei Jiang, 2023. Characterization of Quasistationary Distributions for Markov Chains.

  • SangJoon Lee, 2019: Asymptotic Analysis of Quasi-limiting Behavior for Drifted Brownian Motion Conditioned to Stay Positive. Thesis.

  • Hugo Panzo, 2018: Scaling limits for Brownian Motion Penalized by its running maximum. Thesis.

UConn Undergrads (SURF Grant / Honors  Thesis / Senior Thesis / Other) 

  • Renee Haddad, 2024: Exchangeability and a Model of Biological Evolution

  • Mason DiCicco, 2020: On Efficiency of Markovian Couplings (part of Markov Chains REU 2018). Poster presented in 22nd Frontiers UG Research Exhibition 2019.

  • Trajan Murphy, 2019:  School Policy Evaluated with Time-Reversible Markov Chains. Poster presented in 22nd Frontiers UG Research Exhibition 2019.

  • Dennis Scheglov (co-advisor), 2018: University Scholars project.

  • Joseph Sweeney, 2018: Nonexistence of Efficient Markovian Coupling for Finite State Markov Chains. Thesis.

  • Sailesh Simhadri  2018: Information Theory for Conditioned Markov Chains (research supported by UCONN SURF grant). Poster presented in Frontiers UG Research Exhibition, 2018.

  • Rachel Lonchar,  2018: Nonexistence of Efficient Markovian Coupling for Finite State Markov Chains (research supported by UCONN SURF grant)

  • Thomas Bassine, 2016: Stein’s Method.

  • Elizabeth Tripp, 2015: Efficient Coupling for Random Walk with Redistribution (research supported by UCONN SURF grant), Honors thesis.

Markov Chains REU Students

Between 2018 and 2022 I ran an NSA-funded REU focusing on Markov chains and their applications.

2022

  • Alan Boles (University of Tennessee, Knoxville), Leila Dahlia (University of Illinois, Chicago), Scott McIntyre (University of California, Berkley), Bronson Zhou (University of Texas, Austin): An Implementation of  a Maximal Coupling Algorithm for Markov Chains.

  • Gabe Cantanelli (U Texas, Arlington): Sampling Minimal Quasi-Stationary Distributions through a Renewal Formula

  • Bram Silbert (Wesleyan U), Lillian (Ian) Makhoul (Lehigh University): Coalescing Random Walks

2021

  • Clay Allard (University of Utah), Shrikant Chand (New York University) and Julia Shapiro (University at Buffalo): Quasistationary Distributions for the Invasion and Voter model

  • Chris Vairogs (University of Florida) and Daniel Zou (Reed College): Coalescing and Annihilating Random Walks

  • Connor Bass (Macalester College),  Na’ama Nevo (Colorado College),  Caitlyn Powell (U of Alabama): Ballistic Deposition Model

2020

  • Aenea Ferguson (Whitworth College),  Jack Hanke (UConn): Maximal Couplings for Finite State Markov Chains

  • Van Hovenga (U of Colorado Colorado Springs), Edith Lee (U of Rochester):

  • Ruoyu Lin (Boston U), Josh Speckman (USC): Self-Similar Structure in an Exchangeable Model for Population Dynamics

2019

  • Jonah Green (Lehman College, CUNY), Taylor Meredith (NYU), Xioran (Rachel) Tan (UConn): Elephant Random Walks

  • R. Oliver VanderBerg (Kenyon College), Phil Speegle (U Alabama): Quasistationary Distribution for the Voter Model on the Complete Bipartite Graph.

  • Jonah Botvinick-Greenhouse (Amherst College), Mark Kong (Harvard), Connor Fitch (Bowdoin College): Nonlinear Random walk.

Mini projects: Inequalities in Probablity, Animated Logo (Connor Finch and Taylor Meredith), First Species Counterpoint Using Glouber Dynamics (Jonah Botvinick Greenhouse, Rachel Tan and Oliver Vanderberg), Optimization using the Metropolis-Hastings Algorithm (Philip Speegle), 15 Puzzle Solver (Mark Kong)

2018

  • Carter Bedsole (George Fox U), Grace O’Neil (UPenn):  Biological Evolution Models Exhibiting Power Law Tails.

  • Mason DiCicco (UCONN), Michael Dotzel (U Missouri),  Ewan Harlow (U Wisconsin) Efficient Markovian Couplings.

  • Emily Gentles (U Arkansas), Natalie Meacham (Bryn Mawr), Erica West (Colorado School of Mines): Memory in Random Sequences.