PeopleofSuperintelligence

Researcher

Forrest Iandola

Also known as Forrest N. Iandola

Forrest Iandola is an AI researcher whose work makes deep neural networks more efficient on small devices. He earned a B.S. in computer science at the University of Illinois at Urbana-Champaign and a PhD in EECS at UC Berkeley, where he worked with Kurt Keutzer and coauthored SqueezeNet. He co-founded DeepScale for vehicle perception, worked on Tesla Autopilot, led Anduril's perception team, and coauthored SqueezeBERT for mobile NLP. He is an AI Research Scientist at Meta, where he works on on-device AI; his coauthored MobileLLM models and SqueezeMe avatars target mobile devices.

Links
Google Scholar, X and LinkedIn

Timeline

  1. 2012–2016

    Research at Berkeley

    At the University of Illinois at Urbana-Champaign, he earns a B.S. in computer science in 2012. At UC Berkeley, he works with Kurt Keutzer on resource-efficient deep learning and completes a PhD in EECS in 2016.

  2. 2015–2019

    DeepScale and SqueezeNet

    He co-founds DeepScale in 2015 and leads a team building efficient computer vision and perception software for road vehicles. In 2016, he coauthors SqueezeNet, which achieves AlexNet-level accuracy on ImageNet with 50 times fewer parameters. Tesla acquires DeepScale in October 2019.

  3. 2019–2020

    Machine learning at Tesla

    At Tesla, he works as a Senior Staff Machine Learning Scientist from 2019 to 2020 on Autopilot.

  4. 2020

    SqueezeBERT for mobile NLP

    In 2020, he coauthors SqueezeBERT, an NLP model that uses grouped convolutions to replace parts of self-attention and runs 4.3 times faster than BERT-base on a Pixel 3 with competitive GLUE accuracy.

  5. 2020–2022

    Head of perception at Anduril

    From 2020 to 2022, he leads computer vision and AI engineering for Anduril's drones and counter-drone system as Head of Perception.

  6. 2022–2024

    On-device AI at Meta

    He joins Meta as an AI Research Scientist in 2022 and works on an on-device AI research team in Reality Labs, making AI run efficiently on AR/VR headsets. In 2024, he coauthors MobileLLM, a model family with fewer than one billion parameters for mobile devices.

  7. 2025

    Meta's SqueezeMe avatars

    In 2025, he continues as an AI Research Scientist at Meta and coauthors SqueezeMe, a lightweight avatar system that runs three Gaussian avatars at 72 frames per second on a Meta Quest 3.

  1. Google had the most people shaping AI every year from 2014 to 2025Our estimates of which organizations employed the most people shaping AI from 1961 to 2025, with a year-by-year chart.
  2. The United States employs an estimated 53% of people in our data with a known employment countryOur estimates of where AI careers are concentrated now and how countries’ shares of dated positions changed over time.
  3. 46% of living people in our data with known birth and employment countries work outside their birth countryOur estimate of cross-border careers in AI, from country of birth and the country of each current organization.
  4. More people shaping AI studied at Massachusetts Institute of Technology than at any other institutionOur estimates of where the people shaping AI studied and where their careers led, from education records in cited biographies.

Sources and credits

Photo: We have no published photo of Forrest Iandola yet. Send us a free-licensed photo.

  1. Forrest Iandola forrestiandola.com. Checked 19 October 2026.
  2. [1602.07360] SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <0.5MB model size arxiv.org. Checked 19 October 2026.
  3. Tesla is Buying Computer Vision Start-up DeepScale in a Quest to Create Truly Driverless Cars siebelschool.illinois.edu. Checked 19 October 2026.
  4. Meet the Team Deploying Neural Networks in One Day | Built In LA builtinla.com. Checked 19 October 2026.
  5. SqueezeBERT: What can computer vision teach NLP about efficient neural networks? aclanthology.org. Checked 19 October 2026.
  6. [2402.14905] MobileLLM: Optimizing Sub-billion Parameter Language Models for On-Device Use Cases arxiv.org. Checked 19 October 2026.
  7. COGS 2025 - Organizers sites.google.com. Checked 19 October 2026.
  8. SqueezeMe: Mobile-Ready Distillation of Gaussian Full-Body Avatars forresti.github.io. Checked 19 October 2026.