Yining (Sam) Huang 🧬
Yining (Sam) Huang
Harvard Medical School

👋 Hey,

I am a master’s student in biomedical informatics at Harvard University. I am working on artificial intelligence for biology with Prof. Debora Marks. I received my bachelor’s degree in computer science and statistics from Northwestern University.

Interests
  • AI for Science
  • Computational Biology
  • Protein Engineering
Education

  • MS in Biomedical Informatics

    Harvard University

  • BS in Computer Science & Statistics

    Northwestern University

Featured Publications
Multi-Scale Representation Learning for Protein Fitness Prediction
Navigating Chemical Space with Latent Flows
Recent Publications
Zaixi Zhang *, Jiaxian Yan *, Yining Huang *, Qi Liu, Enhong Chen, Mengdi Wang, Marinka Zitnik (2024). Geometric Deep Learning for Structure-Based Drug Design: A Survey.
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Zuobai Zhang *, Pascal Notin *, Yining Huang, Aurélie Lozano, Vijil Chenthamarakshan, Debora Marks, Payel Das, Jian Tang (2024). Multi-Scale Representation Learning for Protein Fitness Prediction. In NeurIPS 2024: Conference on Neural Information Processing Systems.
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Guanghao Wei *, Yining Huang *, Chenru Duan, Yue Song, Yuanqi Du (2024). Navigating Chemical Space with Latent Flows. In NeurIPS 2024: Conference on Neural Information Processing Systems.
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Yining Huang *, Zuobai Zhang *, Jian Tang, Debora Susan Marks, Pascal Notin (2024). Augmenting Evolutionary Models with Structure-based Retrieval. In ICML 2024: ML4LMS Workshop.
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Dina A. Sharon *, Yining Huang *, Motolani Oyewole, Sammy Mustafa (2024). How to Go With the Flow: an Analysis of Flow Matching Molecular Docking Performance With Priors of Varying Information Content. In ICLR 2024: GEM Workshop.
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Yining Huang *, Steffanie Paul *, Debora Marks (2023). An Energy Based Model for Incorporating Sequence Priors for Target-Specific Antibody Design. In NeurIPS 2023: GenBio Workshop.
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Yuanqi Du *, Yingheng Wang *, Yining Huang, Jianan Canal Li, Yanqiao Zhu, Tian Xie, Chenru Duan, John Gregoire, Carla P Gomes (2023). M2Hub: Unlocking the Potential of Machine Learning for Materials Discovery. In NeurIPS 2023: Conference on Neural Information Processing Systems (Datasets and Benchmarks Track).
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