Haodong Qin
Graduate Student Researcher, CNL-T
San Diego, California, United States
Summary
👤
Senior🎓
Top SchoolHaodong Qin is a physics-trained computational researcher with eight years of experience developing physics-informed machine learning and geometric manifold methods to probe biological aging, neural memory, and unsteady fluid dynamics. Based at the Salk Institute and UC San Diego during his PhD, he blends differential geometry, information theory, and dynamical systems to build interpretable, theory-grounded models from million-sample multi-omics and time-series datasets. His work has produced practical rules (e.g., Fisher-optimized initialization) that substantially speed RNN convergence and uncovered conserved low-dimensional structures across species, guiding experimental design and validation. Equally comfortable with hands-on pipeline engineering—cell-tracking vision systems and scalable preprocessing—and abstract theory, he frequently presents at major meetings (AHA Allen, SfN, La Jolla Aging). Colleagues describe him as an "old fashioned" thinker who favors transparent, physically consistent models over black-box solutions.
8 years of coding experience
6 years of employment as a software developer
University of California, San DiegoBachelor's degree Physics, Bachelor's degree Physics at UC Santa Barbara
Github Skills (92)
panning10
mpandroidchart10
bubble10
graph10
java10
android10
kotlin10
charts9
sdk9
dart9
windows9
gradle-plugin9
ios9
scaling9
desktop9
Programming languages (6)
Github contributions (5)
haodong404/TermuxWithGradle
Feb 2020 - Feb 2020
Using gradle to build apk in Android
Contributions:9 commits, 9 pushes, 1 branch in 4 days
gradleapkandroidkotlingradle-plugin
haodong404/VaingloryGo
Jun 2019 - Mar 2020
Contributions:13 commits, 12 pushes, 1 branch in 9 months
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