Ian Goodfellow
Born 1987 · Age 38
American computer scientist and engineer known for inventing generative adversarial networks (GANs); research scientist at Google DeepMind; former Google Brain researcher, OpenAI early employee, and Director of Machine Learning at Apple; co‑author and first author of the MIT Press textbook Deep Learning.
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Life & Career Timeline
Began deep learning research (circa)
Described as a deep learning researcher since around 2008; began work that would lead to later contributions in adversarial examples and generative models.
Course assistant and student leadership at Stanford (during studies)
Served as a course assistant in machine learning and AI and was president of the Stanford Writer's Guild while earning BSc and MSc at Stanford.
Summer Intern — Willow Garage
Worked as a summer intern at Willow Garage (listed on speaker bio).
Completed Stanford B.S. and M.S. in Computer Science (date unspecified)
Earned B.S. and M.S. in Computer Science from Stanford University; studied with Andrew Ng and Gary Bradski.
Led development of Pylearn2 and other PhD-era software (during PhD)
During his PhD work, led development of Pylearn2 and other algorithms (multi-prediction deep-Boltzmann machines, fast inference for spike-and-slab sparse coding).
Software Engineering Intern — Google
Software engineering internship at Google; created a deep neural network for transcribing addresses from Street View photos.
Street View house-number transcription project (work revealed 2014)
Developed the deep neural system enabling Google Maps/Street View to automatically transcribe street addresses (work covered in MIT Technology Review Jan 2014).
Invented Generative Adversarial Networks (landmark contribution)
The creation of GANs established a widely used technique for generative modeling; later led to high-impact research, tools, and concerns about deepfakes.
Joined Google Brain as Research Scientist (after graduation)
After completing doctoral research, joined Google as part of the Google Brain research team working on TensorFlow and applied deep learning projects.
Early research on adversarial examples and defenses
Developed the first defenses against adversarial examples and helped popularize security and privacy research for neural networks (early work circulated around 2014).
Work covered in MIT Technology Review — street number recognition
MIT Technology Review published a piece ('How Google Cracked House Number Identification in Street View') (Jan 6, 2014) on the Street View transcription work he contributed to.
PhD thesis posted: 'Deep learning of representations and its application to computer vision'
Submitted/posted doctoral thesis at Université de Montréal (thesis record dated April 2014).
Published 'Generative Adversarial Networks' (GAN) on arXiv
Released the seminal arXiv paper (arXiv:1406.2661) introducing Generative Adversarial Networks (GANs), inventing a new class of generative models.
PhD defense presentation
Doctoral defense presentation (YouTube entry dated Sept 3, 2014) for the Université de Montréal PhD thesis.
Senior Research Scientist — Google
Held a senior research scientist role at Google (speaker bio lists 2015 as Senior Research Scientist).
PhD degree awarded — Université de Montréal (formal date)
PhD in machine learning conferred in February 2015, supervised by Yoshua Bengio and Aaron Courville.
Subject of Wired, New York Times and broader press reporting
Work and career moves (e.g., move to OpenAI) were covered in Wired (2016) and later in NYT (2018) reporting on AI researchers' compensation and influence.
Left Google to join OpenAI (one of OpenAI's earliest employees)
In March 2016 left Google to join the newly founded OpenAI research laboratory as a research scientist and one of its first employees.
Co‑wrote 'Deep Learning' textbook (first author)
Co‑author and first author of the MIT Press textbook Deep Learning (published 2016), a widely used authoritative text in the field.
Public demonstrations & reporting on ML security (coverage)
Work on adversarial examples and ML security was covered in Popular Science and other outlets (2016 coverage highlighted vulnerabilities).
Named to MIT Technology Review's 35 Innovators Under 35
Cited as one of MIT Technology Review's 35 Innovators Under 35 for contributions to deep learning and GANs.
Returned to Google Research (staff research scientist)
Approximately 11 months after joining OpenAI, returned to Google Research (March 2017) as a staff research scientist.
Senior Staff Research Scientist — Google (title listed)
Listed as Senior Staff Research Scientist at Google in speaker biography timeline for 2018.
Included in Foreign Policy's 100 Global Thinkers
Named among Foreign Policy's 'A Decade of Global Thinkers' (2019 list).
Joined Apple as Director of Machine Learning, Special Projects Group
Hired by Apple (reported April 5, 2019) as Director of Machine Learning for the Special Projects Group.
Authored chapter 'Deep Learning' in Artificial Intelligence: A Modern Approach
Wrote Chapter 21, 'Deep Learning', for the fourth edition of the canonical AI textbook Artificial Intelligence: A Modern Approach (PDF dated April 28, 2020).
Public recognition and commentary about move to DeepMind
Move from Apple to DeepMind was widely reported and commented on in technology press (May–July 2022 coverage).
Resigned from Apple over return-to-office policy
Resigned from Apple's machine learning leadership role in April 2022 in protest of Apple's plan to require in-person work.
Joined Google DeepMind as Research Scientist
Announced via tweet (July 6, 2022) that he joined DeepMind as a research scientist on Oriol Vinyals' Deep Learning team.
Ongoing research at DeepMind (public-facing role)
Continued research scientist role focusing on deep learning at Google DeepMind (ongoing contribution to research and publications).
Authority and influence in ML research
Recognized as a leading figure in generative models, adversarial ML, textbook author, and frequent speaker; frequently engaged for industry and academic talks.
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