Jason Liang
Research Scientist at Cognizant
San Francisco, California, United States
Summary
👤
Senior🎓
Top SchoolJason Liang is a research scientist based in San Francisco with 11 years of experience specializing in evolutionary algorithms, neural architecture search, and AutoML for deep learning. He holds a PhD from UT Austin and has translated academic research into industry impact at Sentient Technologies and now Cognizant, where he builds evolutionary AutoML systems for production-grade deep learning. His work blends rigorous experimentation with systems engineering, including a novel evolutionary NAS algorithm developed at Sentient and research into applying evolution to deep neural networks. Comfortable across academia and industry, he has a background in robotics simulation, computer vision, and AR benchmarking that informs a practical, multidisciplinary approach to AI problems. Colleagues describe him as someone who bridges theoretical insight and scalable implementation, with a particular interest in how evolutionary methods can accelerate progress toward more capable LLMs and AGI.
11 years of coding experience
3 years of employment as a software developer
BS EECS, BS EECS at University of California, BerkeleyDoctor of Philosophy - PhD Computer Science, Doctor of Philosophy - PhD Computer Science at The University of Texas at Austin
Chinese, English
Github Skills (47)
pytorch10
simplify10
coffeescript10
python10
model-building10
realms10
data-science9
machine-learning9
abstraction9
use-case9
tensorflow9
deep-learning9
jax8
agent8
alsa8
Programming languages (4)
Github contributions (5)
jasonzliang/autogen
Feb 2024 - Dec 2024
Enable Next-Gen Large Language Model Applications. Join our Discord: https://discord.gg/pAbnFJrkgZ
Contributions:51 pushes, 1 branch in 10 months
osrf/robocup3ds
Jun 2015 - Aug 2016
Contributions:460 commits in 1 year 2 months
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