Michelle Li
Research Fellow at Harvard Medical School
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolMichelle Li is a research-focused scientist and educator with eight years of experience bridging biomedical research and STEM outreach, currently a Research Fellow at Harvard Medical School and soon-to-be tenure-track Assistant Professor at Carnegie Mellon. She completed a PhD at Harvard after a BS from Stanford and has a strong track record of translational lab work, industry internships (Microsoft, Genentech), and long-term research roles across multiple Stanford labs. As founder and director of Reroot STEM and a lead teacher, she combines rigorous research with proven leadership in education and community engagement. Her background spans wet-lab expertise, computational research during a Microsoft internship, and curriculum development—an uncommon mix that positions her to translate complex science into teachable, impactful programs. Based in Boston, she is building an academic career that emphasizes mentorship, public-facing STEM initiatives, and interdisciplinary collaboration.
8 years of coding experience
7 years of employment as a software developer
High School Diploma, International Baccalaureate (IB) Diploma, High School Diploma, International Baccalaureate (IB) Diploma at Brooklyn Friends SchoolDoctor of Philosophy - PhD, Doctor of Philosophy - PhD at Harvard UniversityB.S., B.S. at Stanford University
English, Chinese, Spanish
Github Skills (30)
graph-representation-learning10
medicine9
knowledge-graph8
biology8
bioinformatics8
datasets7
cheminformatics7
chemistry7
deep-learning7
drug-discovery6
python6
science5
subgraph5
linux5
conda5
Programming languages (3)
Github contributions (5)
mims-harvard/PINNACLE
Jul 2023 - Feb 2025
Contributions:1 PR, 79 pushes, 1 branch in 1 year 7 months
mims-harvard/SHEPHERD
Nov 2022 - Mar 2023
SHEPHERD: Few shot learning for phenotype-driven diagnosis of patients with rare genetic diseases
Contributions:44 commits, 47 pushes, 2 comments in 4 months
deep-learningembeddingsgraph-neural-networksgraph-representation-learningknowledge-graph
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