Haoyu Yu
Research Leader at ByteDance
Shanghai, United States
Summary
👤
Senior🎓
Top SchoolHaoyu Yu is a research leader and computational chemist with a PhD and over 11 years of experience building physics-driven methods and scientific software for drug discovery. He led development of free energy perturbation techniques, metalloenzyme force fields, crystal polymorph prediction, DFT-based pKa and ADMET prediction, and is a co-author of the MN15 and MN15-L density functionals used in many quantum chemistry packages. At Schrödinger he progressed from senior scientist to principal roles driving method and software delivery, and now leads research at ByteDance, bridging industrial R&D and scalable engineering. He also retrained as a full-stack developer at Fullstack Academy and contributes front-end and configuration improvements on GitHub projects, demonstrating rare fluency across computational chemistry and web engineering. Colleagues value his ability to translate deep theoretical insights into production-ready tools that accelerate discovery. Based in Shanghai with international research roots, he pairs academic rigor from the University of Minnesota with practical product-focused development.
11 years of coding experience
13 years of employment as a software developer
Bachelor’s Degree Chemistry, Bachelor’s Degree Chemistry at Beijing Normal UniversityDoctor of Philosophy (Ph.D.) Physical Chemistry, Doctor of Philosophy (Ph.D.) Physical Chemistry at University of MinnesotaFrontend and Backend Web Development, Frontend and Backend Web Development at Fullstack Academy
English, Chinese
Github Skills (7)
uid10
front-end-development10
ui-design10
javascript8
back-end-development8
php7
css6
Programming languages (16)
Github contributions (5)
orvice/ss-panel
Feb 2016 - Feb 2016
rm
Role in this project:
Full-stack Developer
Full-stack DeveloperContributions:8 commits, 7 PRs, 4 comments in 4 days
Contributions summary:Haoyu primarily contributed to the user interface and application configuration aspects of the project. They made changes to the node list display, improving its clarity and usability by enabling multi-line JSON formatting and easy URL selection. Furthermore, the user integrated a Surge configuration feature and updated the application's configuration for module location to improve functionality and user experience. Additional changes included UI updates, improvements to the Admin index panel, and code formatting.
jinyu121/video2frame
Jan 2019 - Apr 2019
Yet another easy-to-use tool to extract frames from videos, for deep learning and computer vision.
Contributions:71 commits, 3 PRs, 53 pushes in 3 months
pytorchvisiondeep-learningvideostool-use
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