Forrest Iandola
AI Research Scientist at Meta
San Francisco Bay Area United States
Summary
🤩
Rockstar🎓
Top SchoolForrest Iandola is an AI research scientist and entrepreneur with 13 years of experience building resource-efficient deep learning and perception systems for robotics, autonomous vehicles, and spatial computing. He developed the influential SqueezeNet architecture at UC Berkeley and later led DeepScale as CEO, which was acquired by Tesla, then drove Autopilot ML and perception at Tesla and Anduril before joining Meta to work on AI for VR/AR. Forrest combines hands-on model compression and embedded deployment expertise with product-driven leadership—evident in battlefield-scale drone perception systems and an AI-enabled long-range autofocus used in real-world counter-drone deployments. He’s passionate about energy-efficient ML and novel crypto ecosystems like Solana, and has a track record of turning academic innovations into deployed systems at scale. A detail not immediately obvious: his work spans from low-level CUDA enhancements to squeezing NLP models onto smartphones, showing rare fluency across hardware, systems, and algorithmic optimization.
13 years of coding experience
10 years of employment as a software developer
PhD, Electrical Engineering and Computer Science (EECS), PhD, Electrical Engineering and Computer Science (EECS) at University of California, BerkeleyUniversity of Illinois Urbana-ChampaignIllinois Math and Science Academy
Stackoverflow
8,689reputation
996kreached
44answers
81questions
image-processing
top-5%
computer-vision
top-5%
beautifulsoup
top-5%
opencv
top-5%
Github Skills (148)
transformers10
language-model10
python10
flax10
deep-learning10
gpu10
efficientnet10
caffe10
loss-functions10
transformer10
nlp10
jax10
translation10
seq2seq10
tensorflow10
Programming languages (7)
Github contributions (5)
forresti/caffe_benchmarking
Mar 2014 - Apr 2016
Contributions:48 commits, 27 pushes, 1 branch in 2 years
forresti/SqueezeNet
Feb 2016 - Jul 2018
SqueezeNet: AlexNet-level accuracy with 50x fewer parameters
Contributions:24 commits, 5 PRs, 25 pushes in 2 years 4 months
pytorchparametersloss-functionssqueezenetefficientnet
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