Research Opportunities for Students
I actively mentor undergraduate, master's, and Ph.D. students interested in probability theory, stochastic processes, and their applications in statistics, optimization, and machine learning. If you are curious about research and enjoy working with mathematical models, time series data, or applied machine learning, I encourage you to reach out.
Depending on your background and goals, there are opportunities for:
- Independent research (e.g., Honors projects, summer research, or directed readings)
- Potential collaboration on papers or software development
- Working on open problems in:
- Long-memory and semi-long-memory stochastic processes
- Tempered fractional calculus and stochastic modeling
- Stochastic optimization (e.g., perturbed SGD, Langevin-type dynamics)
- Machine learning models with structured noise (e.g., Transformers, S4)
- Time series applications in finance, climate, and biophysics
If you're interested in working with me, please email me with a short note about your background and research interests. I welcome motivated students from a range of mathematical and computational backgrounds.
Students Mentored (Past and Present)
Ph.D. Students
Rebell Yee — Ph.D., Fall 2024 – present
Noah Leavitt — Ph.D., Fall 2024 – present
Eva Biswas — Ph.D., 2020 – 2024
Master’s Students
Propser Tchoumo — M.S., Fall 2024 – present
Nathan Friedrichsen — M.S., 2022 – Fall 2024
Jinu Kabala — M.S., 2018 – 2020
Dinesh Poddaturi — M.S., 2017 – 2018
Undergraduate Students
Theodor Smith — Undergraduate, Fall 2023 – Spring 2024
Thenuki De Mel — Undergraduate, Spring 2025 – present