I’m a computer science Ph.D. student at the University of Pennsylvania advised by Brielin C. Brown. I design scalable probabilistic models for causal discovery to infer gene regulatory networks from perturbation data and computationally guide experimental design for biological discovery and precision medicine.

I earned Statistics AM from Wharton, where I was advised by Nandita Mitra and worked on Bayesian difference in difference estimation. Previously, I was a computational biologist at Memorial Sloan Kettering Institute, working with Quaid Morris. I completed my undergraduate studies in Mathematics at NYU, where I worked with Charlie Peskin at the Courant Institute of Mathematical Sciences.

I interned at Flatiron Institute, Center for Computational Mathematics in summer 2023, working on Bayesian modeling with Bob Carpenter.

Email : seonghan [at] seas [dot] upenn [dot] edu

Papers : Google Scholar

Research

Publications

Large-Scale Bayesian Causal Discovery with Interventional Data
Seong Woo Han, Daniel Duy Vo, Brielin C. Brown
Submitted, 2025
[paper]

PolyA_DB v4: systematic polyA site identification and isoform annotation in human mouse genomes using 3’ end and long-read sequencing data
Shan Yu, Wei Chun Chen, Luyang Wang, San Jewell, Ayna Mammedova, Seong Woo Han, Priyankara Wickramasinghe, Yoseph Barash, Bin Tian
Nucleic Acids Research, 2025

Bayesian Sensitivity Analyses for Policy Evaluation with Difference-in-Differences under Violations of Parallel Trends
Seong Woo Han, Nandita Mitra, Gary Hettinger, Arman Oganisian
Submitted, 2025
[paper]

Crowdsourcing with Difficulty: A Bayesian Rating Model for Heterogeneous Items
Seong Woo Han, Ozan Adiguzel, Bob Carpenter
Submitted, 2025
[paper]

Contrasting and Combining Transcriptome Complexity Captured by Short and Long RNA Sequencing Reads
Seong Woo Han, San Jewell, Andrei Thomas-Tikhonenko, Yoseph Barash
Genome Research, 2024
[paper]

Computer simulation of surgical interventions for the treatment of refractory pulmonary hypertension
Seong Woo Han, Charles Puelz, Craig Rusin, Dan Penny, Ryan Coleman, Charles S. Peskin
Mathematical Medicine and Biology, 2022
[paper]

Teaching Experience

ESE 546: Principles of Deep Learning, Fall 2022, Fall 2025 with Pratik Chaudhari