About Me

I am an assistant professor at Fox Chase Cancer Center, Temple Health. My lab is focusing on computational epigenetics, including but not limited to analyzing multi-omics data, and developing machine learning prediction models and visualization in cancer and aging.

I was a computational postdoctoral scholar at Snyder Lab, Genetics Department, Stanford University. My research interest is to apply machine learning algorithms to epigenetic data, e.g. genome-wide methylation or accessibility data, and further visualize them for biological interpretation and clinical applications. Before I moved to Stanford, I served as a Simons postdoctoral fellow at the Joint Genome Institute (JGI). I have earned my Ph.D. in Computer Science Department at Stony Brook University.  My dissertation is about “Algorithms and applications in genome assembly using long-read sequencing technology,” advised by Prof. Michael Schatz, Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory (CSHL). I received my Master’s degree from Carnegie Mellon University. The master thesis was published at 70th IEEE Vehicular Technology Conference. My undergraduate degree with Cum Laude was received at Seoul National University in Korea and I worked for 4 years at AhnLab, Inc., where I programmed Windows kernel-level file system filter driver for the V3 anti-virus program.

I am interested in developing computational methods by exploiting cutting-edge machine learning algorithms and applying them to clinical data to understand the mechanism behind and discover biomarkers for diagnostic, prognostic, and therapeutic purposes. Currently, I am designing a 3-way methylation biomarker discovery algorithm for early-stage colon cancer diagnosis. I am developing a somatic structural variant inference algorithm using long reads sequencing technology, integrating methylation and other omics data, looking for driver epimutation in cancer and rare diseases, and visualization is also my research priority. As an extension of my programming skill, I like to develop software packages (tools/software) using statistical machine learning and long reads produced by 3rd generation sequencing technology. My research also includes de novo genome assembly, statistical modeling, machine learning, cancer data analysis, and temporal and spatial data analysis to predict the future disease trajectory.

Keywords: Computational medicine, Computational Epigenetics, Machine Learning (ML), Deep Neural Network, Big data, Single-cell analysis, Cancer, Aging, Time series data, Spacial data

Scientific Mentor
  • GRIPS (Genomics Research Internship Program at Stanford) – 2022
  • SSRP (Stanford Summer Research Program)-Amgen scholar – 2021
  • Gilbert Feng (High school summer computational research intern) – 2017-2018. Now admitted and attending @ EECS, UC Berkeley
Program Committee
Paper Review
  • Bioinformatics, Oxford Journals, 2017
  • F1000Research, 2017
  • ISMB (Intelligent Systems for Molecular Biology) 2016
  • MCBIOS (The MidSouth Computational Biology and Bioinformatics Society), 2016
  • MCBIOS (The MidSouth Computational Biology and Bioinformatics Society), 2015
  • hayan dot lee at stanford dot edu

If you are interested in my research, want to collaborate or have a question, please email me.