Beidi Chen
Visiting Scholar at Carnegie Mellon University
Pittsburgh, Pennsylvania, United States
Summary
🤩
Rockstar🎓
Top SchoolBeidi Chen is a research-focused software engineer and academic with 13 years of experience bridging cutting-edge machine learning research and industry practice. Currently an Assistant Professor at Carnegie Mellon and a Visiting Scholar at Amazon, she has held research roles at Meta and Stanford and internships across NVIDIA, Microsoft, and Apple, demonstrating deep familiarity with both foundational research and production ML systems. Her background includes a Ph.D. in Computer Science from Rice and an undergraduate EECS degree from UC Berkeley, reflecting strong theoretical and engineering training. She routinely moves between academia and industry, translating novel ideas into scalable implementations and collaborations with large tech labs. Not obvious from titles alone, Beidi’s trajectory shows sustained engagement with corporate research environments (FAIR and industry research internships) that likely accelerates real-world impact of her academic work. She combines publication-quality research with hands-on system-building experience, making her adept at supervising interdisciplinary projects that target deployable AI.
13 years of coding experience
7 years of employment as a software developer
University of California, BerkeleyDoctor of Philosophy (Ph.D.) Computer Science, Doctor of Philosophy (Ph.D.) Computer Science at Rice University
Chinese
Github Skills (45)
parser10
offloading10
opt10
spl10
splunk9
algorithms9
large-language-models9
hardware-acceleration9
machine-learning9
deep-learning9
python8
gpu8
attention-mechanism7
generalization6
robustness6
Programming languages (6)
Github contributions (5)
YangZhou08/submitwithcron3
Apr 2024 - Jun 2024
Contributions:11621 pushes in 1 month
keroro824/HashingDeepLearning
Mar 2019 - Jul 2020
Codebase for "SLIDE : In Defense of Smart Algorithms over Hardware Acceleration for Large-Scale Deep Learning Systems"
Contributions:18 commits, 6 PRs, 20 pushes in 1 year 4 months
deep-learninghardware-acceleration
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