CV
(Last updated: August 2023)
You can also find my CV in pdf format here.
Professional Experience
2021 - : Adjunct Faculty, Stanford University, CA, USA. Adjunct Lecturer 2021-2022, Adjunct Professor since 2023. Co-lecturer (with Prof. Niebles) for CS131: Computer Vision: Foundations and Applications.
2020 - : Head of Machine Learning, Toyota Research Institute (TRI), CA, USA. Leader of the ML org at TRI (3 departments, ~20 people, Senior Manager 2020-2021, Director 2022-). Responsible for strategy, research, and tech transfer to build Toyota’s ML foundations across applications. Contributed to 46+ papers at top ML venues, 50+ patents, and 5 practical applications since Jan. 2020. Part of TRI’s executive leadership team since 2022.
2017 - 2019: Manager & Senior Research Scientist, Machine Learning, Toyota Research Institute (TRI), CA, USA. Founder and Manager of the ML team at TRI. Responsible for creating and leading TRI’s ML strategy for Automated Driving, from academic collaborations (esp. with Stanford), research (22 publications in 2017-2019), to ML cloud infrastructure, and deployment of safety-critical models on public roads.
2013 - 2016: Research Scientist (Computer Vision), Xerox Research Center Europe, Meylan, France. Tech lead on deep learning for video understanding. Research in and transfer of computer vision and ML algorithms. Pioneer in simulation for deep learning (CVPR’16 VKITTI paper cited 900 times).
2008 - 2012: Doctoral Researcher, Microsoft Research - Inria joint center, Paris, France. Microsoft Research - Inria joint center}{Paris}{France} {Invented, implemented, and experimentally validated state-of-the-art Computer Vision and Machine Learning algorithms for action recognition in challenging real-world videos (e.g., movies, YouTube).
2008: R&D Engineer, LEAR team, Inria, Grenoble, France. Participation to two international Computer Vision competitions: TRECVID and PASCAL VOC (co-winner of classification and detection challenges). Experimentation on tens of thousands of images and videos, using a cluster of computers, under strong time constraints.
06-08/2007: Research intern, Inria Rocquencourt, Paris, France. Research on learning the structure of dynamic Bayesian networks using statistical tests and genetic algorithms.
06-08/2006: Research intern, LIG research lab, IMAG institute, Grenoble, France. Implementation (OCaml) of formal methods in the automatic proof research domain.
Education
- 2008 - 2012: PhD in Computer Science, Microsoft Research - INRIA, Paris \& LEAR Team, INRIA Grenoble, under the supervision of Cordelia Schmid and Zaid Harchaoui, in the fields of Computer Vision and Machine Learning. Title: Structured Models for Action Recognition in Real-world Videos.
- 2007 - 2008: MSc in Artificial Intelligence, Institut Polytechnique (INP), Grenoble, France.
- 2005 - 2008: Engineer Diploma in Computer Science and Applied Mathematics, ENSIMAG (Ecole Nationale Superieure d’Informatique et de Mathematiques Appliquees de Grenoble), France.
- 2003 - 2005: “Classes Preparatoires MPSI et MP*”, preparation courses (Mathematics and Physics) for the French “Grandes Ecoles”, Clermont-Ferrand, France.
- 2003, European Scientific Baccalaureate with distinction, equivalent to “A” levels, Lycee Jeanne d’Arc, Clermont-Ferrand.
Awards
- Outstanding reviewer award at CVPR 2021
- Winner with Blake Wulfe of the NeurIPS 2020 ProcGen RL Competition (report).
- Top 10% reviewer award at NeurIPS 2020
- COCO-Mapillary Competition Runner-Up at ECCV 2018
- Outstanding reviewer award at CVPR 2018
- Outstanding reviewer award at CVPR 2015
- Xerox Innovation Group President’s Award for innovative research in Computer Vision, 2015
- Xerox XTIN grant for high risk - high reward project on “Video Analytics in a Virtual World”, 2014
- Microsoft Research - Inria PhD scholarship grant, 2008 - 2012 2008, Co-winner of the PASCAL VOC 2008 challenge on object classification and detection
Press Coverage and Interviews
- TWiMLAI Podcast on Principle-centric AI
- Gradient Dissent Podcast: Advancing ML research in autonomous vehicles, 2021
- TechXplore article on our IROS’20 paper on A framework to increase the safety of robots operating in crowded environments
- This Week in ML \& AI (TWiMLAI) podcast: “Advancing Autonomous Vehicle Development Using Distributed Deep Learning”, 2019
- SF DL Summit interview on ML at Toyota, 2019
- W\&B interview on ML at TRI, 2019
- AWS Blog: “TRI accelerates safe automated driving with deep learning at a global scale on AWS”, 2018
- Forbes: “Artificial Intelligence: The Clever Ways Video Games Are Used To Train AIs”, 2018
- Forbes: “How Deep Learning Can Use Virtual Worlds To Solve Real World Problems”, 2016
- MIT Tech Review: “To Get Truly Smart, AI Might Need to Play More Video Games”, 2016
- Wired: “Making AI Play Lots of Videogames Could Be Huge (No, Seriously)”, 2016
- El Espanol: “Máquinas más listas gracias a los videojuegos”, 2016
Communication Skills
With Humans:
- French (native)
- English (fluent)
- German (used to be fluent, lived in Germany 1999-2002)
With Computers:
- Python, C/C++, bash
- PyTorch (main DL framework), Docker, Linux, AWS, HPC
- Open Source: contributor to and creator of Open Source Python projects, especially on action recognition, camera motion compensation, and kernel methods (cf. https://github.com/daien and https://github.com/AdrienGaidon-TRI).
With Researchers:
- Computer Vision, object detection, tracking, semantic segmentation, action recognition, 3D vision (esp. depth estimation), synthetic data (esp. from simulators and game engines)
- Machine Learning, Deep Learning (esp. convnets), supervised learning, self-supervised learning, domain adaptation, multi-task learning, optimization, kernel methods, time series analysis
Scientific Activities
- 80+ publications ($6,000$ citations, cf. below and Google Scholar profile), 80+ patents filed (30 granted, cf. Google Patents).
- Co-organizer of the CVPR 2021 workshop on the Frontiers of Monocular 3D Perception (Mono3D)
- Co-organizer of the ECCV 2020 workshop on Perception for Autonomous Driving (PAD)
- Co-organizer of the ICML 2020 workshop on AI for Autonomous Driving (AIAD)
- Co-organizer of the ICML 2019 workshop on AI for Autonomous Driving (AIAD)
- Guest Editor for the International Journal of Computer Vision (IJCV) Special Issue on “Synthetic Visual Data”
- Co-organizer of the First International Workshop on Virtual/Augmented Reality for Visual Artificial Intelligence (VARVAI) at ECCV 2016 and ACM-MM 2016
- Service: reviewer for the major machine learning, computer vision, and robotics conferences and journals (CVPR, ICLR, ICRA, RSS, NeurIPS, ICML, ICCV, ECCV, BMVC, IJCV, PAMI, TCSVT, …).
Publications
See my Google Scholar Profile, arxiv, my CV as pdf, or the Publications page (updated irregularly).