About Me

I am a Computer Science PhD candidate at Columbia University advised by David A. Knowles at the New York Genome Center. My work is supported by the NSF Graduate Research Fellowship Program (GRFP).

My research focuses on applying deep learning and generative modeling to learn single-cell dynamics and alternative splicing mechanisms in neurodegenerative disease. Utilizing AI methods to derive biological insights from multiomics is an exciting research avenue that will shape the future of health and medicine, and I look forward to continuing my exploration of this unique intersection of machine learning and biology.

Interests
  • Deep Learning
  • Generative Modeling
  • Single Cell Dynamics
  • Alternative Splicing
Education
  • PhD in Computer Science, 2027

    Columbia University

  • MS in Computer Science, 2024

    Columbia University

  • BS in Computer Science, 2022

    University of California, Los Angeles

Publications

(2024). CellFlows: Inferring Splicing Kinetics from Latent and Mechanistic Cellular Dynamics. In ICML'24 Workshop ML for Life and Material Science.

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(2022). A comprehensive benchmarking of WGS-based deletion structural variant callers. Briefings in Bioinformatics.

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(2020). Benchmarking of computational error-correction methods for next-generation sequencing data. Genome Biology.

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(2017). A mobile application for fine dust monitoring system. 2017 18th IEEE International Conference on Mobile Data Management (MDM).

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Experience

 
 
 
 
 
New York Genome Center
Machine Learning Researcher
September 2022 – Present New York, NY

Current projects include:

  • Deep generative modeling of splicing dynamics in single cells. CellFlows presented in ICML'24 Workshop ML for Life and Material Sciences.
  • Causal structure learning of gene regulatory networks from large-scale perturbations of single cells
  • Deep learning-based prediction of alternative splicing from pre-mRNA sequence
  • Probabilistic differential analysis of alternative splicing in neurodegenerative disease
 
 
 
 
 
UCLA Health
Computational Medicine Researcher
September 2018 – June 2022 Los Angeles, CA
Performed large-scale benchmarking of computational error-correction methods and structural variant callers for whole genome sequencing data across different coverages and samples. Papers published in Genome Biology and Briefings in Bioinformatics.
 
 
 
 
 
Illumina
Bioinformatics Intern
June 2021 – September 2021 Los Angeles, CA
Designed algorithms that map instrumented sequencing reads containing quality metrics from Illumina’s Real Time Analysis (RTA) to annotated genome regions. Performed secondary analysis on read-to-region mappings using dimensionality reduction to identify sources of potential sequencer bias from Illumina flow cell clusters.