Hi there! I’m a fourth year PhD student at NYU’s Center for Data Science, co-advised by Kyunghyun Cho and Richard Bonneau. My research focuses on developing algorithms to infer gene regulatory networks from genome-wide sequencing data. My current work uses a variational inference approach to GRN inference by leveraging probabilistic matrix factorization. My research is supported by the National Science Foundation Graduate Research Fellowship Program (NSF-GRFP).

I have worked as a PhD intern at Genentech in the Sequence Modeling and Regulatory Element Design Group. Prior to starting my PhD, I worked as a research analyst in the Center for Computational Biology at the Simons Foundation’s Flatiron Institute from 2018 to 2021. I hold a Bachelors of Science in Mathematics from the University of Miami.

Outside of research, I enjoy training for marathons, cooking, photography, travelling and reading.

Publications

Kim, JH; Skok Gibbs, C; Yun, S; Song, HO; Cho, K. Targeted Cause Discovery with Data-Driven Learning. arXiv (2024) [Paper | Code]

Skok Gibbs, C; Mahmood, O; Bonneau, R; Cho, K. Probabilistic Matrix Factorization for Gene Regulatory Network Inference. Genome Biology (2024), [Paper | Code]

Özel, MN; Skok Gibbs, C, et al. Coordinated control of neuronal differentiation and wiring by a sustained code of transcription factors. Science (2022), DOI: 10.1126/science.add1884 [Paper | Code]

Skok Gibbs, C. et al. High performance single-cell gene regulatory network inference at scale: The Inferelator 3.0. Bioinformatics (2022). https://doi.org/10.1093/bioinformatics/btac117 [Paper | Code]

Awards

NSF Graduate Research Fellowship Program Recipient (2023-2026).

ICML Computational Biology Workshop Best Poster Award (2023).

Winter Q-Bio Best Poster Award (2020).

Interviews

Nature Technology Feature: Smart software untangles gene regulation in cells (2022) [Link]

Teaching

NYU Center for Data Science Teaching Assistant: Practical Training (2022, 2023, 2024)

Volunteering

Co-organizer for NYU AI School 2023 [Link]