CV

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Education

• B.S. in Chemical engineering, Biology (minor) KAIST, 2012
• M.S. in Chemical engineering practice, MIT, 2016
• Ph.D. in Chemical engineering and Statistics (minor) MIT, 2019. Thesis: “Optimizing vaccine dosing kinetics for stronger antibody response”

Work experience

• Computational Chemist 2, Relay Therapeutics (2023.6 - present)
• Pro Bono Contributor, BigBio, BigScience (Open source) (2022.1- 2022.12)
• Principal Machine Learning Scientist, Immuneering (2021.2 - 2023.5)
• Reviewer, Neurips Dataset and Benchmarks Track (2021 - present)
• Pro Bono Contributor, PathCheck Foundation (Open source) (2020.4-2020.10)
• Senior data scientist, Wayfair (2019.2 - 2021.2)
• Fellow, Insight Data Science (2018.9 - 2018.11)
• Graduate researcher, MIT (2012.9 - 2019.2)

Projects

Projects in computational chemistry

Projects in AI method development for hit-identification algorithm

High quality dataset curation for biomedical NLP and training open-source multi-lingual Large Language Model BLOOM

1D Convolutional neural nets for inferring distance from time series records of Bluetooth signals

Simulating Antigen dosage and key immune cell kinetics using modified explicit tau-leap Gillespie simulation

ChillPill

Publications

Affinity inequality among serum antibodies that originate in lymphoid germinal centers

Kang, M., T.J. Eisen, E.A. Eisen, A.K. Chakraborty, and H.N. Eisen. 2015. PLoS One. 10:e0139222

Proximity Sensing for Contact Tracing

Sheshank Shankar, Ayush Chopra, Rishank Kanaparti, Myungsun Kang, Abhishek Singh, Ramesh Raskar. 2020. arXiv

Sustained antigen availability during germinal center initiation enhances antibody responses to vaccination

H.H. Tam, M.B. Melo, M. Kang, J.M. Pelet, V.M. Ruda, M.H. Foley, J.K. Hu, S. Kumari, J. Crampton, A.D. Baldeon, et al. Proc. Natl. Acad. Sci. USA, 113 (2016), pp. E6639-E664

Skills and Experience

• Scientific method development in Python (Ph.D. and work experiences)
• Discrete stochastic simulation, vaccine-induced immunology, PK-PD modeling for adaptive immune response, statistical inference and modeling (Ph.D.)
• Machine learning algorithm development and engineering (work experiences and open-source development)
• Causal inference and Bayesian modeling (work experiences at Wayfair)
• NLP algorithm development and engineering, LLM dataset curation and analysis (dev experience at Bigscience)
• Deep Learning algorithm development for small molecule hit discovery and property prediction (work experience at Immuneering)
• Computational chemistry method development and application for drug discovery: Cheminformatics, MD, QM, FEP, AI-based protein structure prediction, de novo protein design (work experience at Relay therapeutics)