Jackson Gorham
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJackson Gorham is an applied scientist and technical lead with 15 years of experience translating statistical theory into production ML systems, currently leading hearing augmentation efforts at Meta. He holds a Ph.D. in statistics from Stanford (Savage award winner) and has built low-latency, on-device audio models at Whisper.ai and Meta that run on embedded accelerators and wearable hardware. His background spans large-scale experimentation and infrastructure at Facebook, valuation modeling at Opendoor, and edge-aware signal-processing innovations, giving him a rare blend of rigorous theory (Stein’s method, convex optimization) and pragmatic engineering. Known for shipping perceptually motivated metrics and ambisonics evaluation pipelines, he focuses on robustness and deployability rather than allegiance to any single paradigm or language.
15 years of coding experience
10 years of employment as a software developer
Bachelor of Arts - BA, Mathematics, Bachelor of Arts - BA, Mathematics at Stanford University
Github Skills (73)
meta-tags9
julia9
python9
deep-learning9
machine-learning9
tensorflow9
cli8
science8
sampling8
neural-network8
r8
metadata8
web-audio-api8
targets8
finder8
Programming languages (5)
Github contributions (5)
jgorham/stein_discrepancy
Sep 2015 - Jul 2017
Contributions:44 commits, 33 pushes, 6 branches in 1 year 10 months
jgorham/SteinDiscrepancy.jl
May 2017 - Sep 2017
Practical tools for quantifying how well a sample approximates a target distribution
Contributions:29 commits, 3 PRs, 24 pushes in 3 months
pythonpracticalprobabilistic-modelsstochastic-processessampling
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