Boyang Fu
Postdoctoral Research Fellow at Harvard University
Boston, Massachusetts, United States
Summary
👤
Senior🎓
Top SchoolBoyang Fu is a postdoctoral researcher and machine learning scientist focused on scalable, interpretable algorithms for human genetics and causal inference. With a PhD from UCLA and eight years of research experience spanning UCLA and Harvard, he develops epistasis-aware models and novel Mendelian Randomization methods that bridge statistical genetics and modern ML. His background in bioinformatics and summa cum laude training in math and CS underpins a knack for translating biological questions into computationally efficient algorithms. Having been an AI Resident at X and taught core ML courses, he combines practical engineering, pedagogy, and high-impact research. Colleagues value his ability to make complex genetic architectures tractable while prioritizing interpretability for biological insight.
9 years of coding experience
5 years of employment as a software developer
Bachelor's degree Bioinformatics, Bachelor's degree Bioinformatics at Huazhong University of Science and TechnologyUniversity of California, Los AngelesBachelor's degree (summa cum laude) Mathematics and Computer Science (Honor Degree), Bachelor's degree (summa cum laude) Mathematics and Computer Science (Honor Degree) at Rutgers University
Chinese, English
Github Skills (19)
bayesian3
regression3
variational-autoencoder3
gaussian-processes3
file-format3
autoencoder2
machine-learning2
plink2
reader2
pytorch2
bayesian-inference2
generalized-linear-models2
dirichlet2
linear-models2
llama1
Programming languages (3)
Github contributions (5)
nlapier2/MRTwin-replication
Sep 2022 - Jan 2023
Scripts needed to replicate the MR-Twin analysis
Contributions:8 commits, 21 pushes in 3 months
twinreplicate
sriramlab/FAME
Oct 2023 - Jul 2025
This is the software for FAst Marginal Epistasis test (FAME)
Contributions:2 reviews, 5 PRs, 52 pushes in 1 year 8 months
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