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Curriculum Vitae JONATHON SHLENS http://shlens.github.io EDUCATION University of California, San Diego May 2007 Doctor of Philosophy, Department of Neurosciences, Computational Neuroscience Thesis: Synchrony and concerted activity in the neural code of the retina. Advanced study in image and signal processing, statistics, machine learning, information theory. Swarthmore College May 1999 Bachelor of Arts with High Honors. Double major in Computer Science and Physics RESEARCH EXPERIENCE Google Research Mountain View, CA Senior Staff Research Scientist Oct 2019 - present Staff Research Scientist Nov 2015 - Oct 2019 Senior Research Scientist Nov 2012 - Nov 2015 Research Scientist May 2010 - Nov 2012 · Tech Lead and Manager of basic and applied research team (∼ 15 people) focusing on machine learning, computer vision and basic science research. · Extensive research in deep learning, computer vision and computational neuroscience. Co-authored 50+ peer-reviewed publications in top-tier conferences and scientific journals (50 h-index, 70K+ citations). · Taught multiple courses on deep learning and vision both internally and externally at universities, professional societies and graduate summer schools. Mentored 20+ graduate-level interns and residents. · Built distributed scalable deep learning systems as an early member of Google Brain team. Co- inventor and developer of TensorFlow, an open-source machine learning and deep learning system (http://tensorflow.org). Howard Hughes Medical Institute & New York University May 2009 - May 2010 Associate Research Fellow New York City, NY · Developed optimization strategies for extracting sparse signals from noisy data employing ideas from machine learning and convex optimization. · Mentored and taught graduate students and post-doctoral researchers in the domains of information theory, statistics and neuroscience. University of California, Berkeley July 2007 - May 2009 Miller Fellow Berkeley, CA · Developed statistical tools for the analysis of neural data exploiting techniques from digital signal processing, dimensional reduction, Bayesian statistics, information theory and graphical models. · Oversaw collaboration with independent research groups that lead to 5 journal publications. Salk Institute for Biological Studies September 2001 - May 2007 Graduate Researcher La Jolla, CA · Developed and implemented techniques for in vitro retina experiments in team environment, including technology for electrical recordings and visual stimulation. Performed intricate micro-dissections of biological tissue and electrophysiological experiments to characterize the light-driven activity of the retina. · Wrote and managed collaborative code base in Java and C/C++ for the analysis of electrophysiological neural data. Employed machine learning and statistical signal processing for extracting information for electrical signal of retina. · Lectured and instructed classes in statistics, physics, machine learning, and neurobiology at UC San Diego. Authored 5 peer-reviewed journal publications, presented research at 20+ conferences and awarded Ph.D. for dissertation. Pixar Animation Studios August 1999 - August 2001 Research Engineer Emeryville, CA · Researched and developed a color coordination system that transfers digital images to motion picture film (PixarVision R ) used in several films: Toy Story 2, Monsters Inc, Finding Nemo. · Developed adaptive models of film exposure in color space employing dimensional reduction techniques and psychophysical measurements. AWARDS AND RECOGNITIONS • Best Paper Award (2013) IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Portland, OR. • Miller Research Fellow (2008-2009). University of California, Berkeley. Berkeley, CA. • NSF Fellow in \Vision and Learning in Humans and Machines" (2005-2007) University of Califor- nia, San Diego, La Jolla, CA. • Burroughs Wellcome LJIS Graduate Fellow (2003-2005) University of California, San Diego, La Jolla, CA. • Brass Shim Award for Excellence (2001). Pixar Animation Studios, Research and Development. • Phi Beta Kappa Honors Society (1999) Swarthmore College, Swarthmore, PA. • SURF undergraduate research fellowships (1997, 1998) California Institute of Technology, Pasadena, CA. PUBLICATIONS { IN SUBMISSION 7. Jiquan Ngiam∗, Benjamin Caine∗, Vijay Vasudevan∗, Zhengdong Zhang, Hao-Tien Lewis Chiang, Jeffrey Ling, Rebecca Roelofs, Alex Bewley, Chenxi Liu, Ashish Venugopal, David J Weiss, Ben Sapp, Zhifeng Chen, Jonathon Shlens (2021). Scene Transformer: A unified multi-task model for behavior prediction and planning. Under review 6. Archit Karandikar, Nicholas Cain, Dustin Tran, Balaji Lakshminarayanan, Jonathon Shlens, Michael Curtis Mozer, Rebecca Roelofs. (2021). Soft Calibration Objectives for Neural Networks. Under review 5. Nishal Shah, Nora Brackbill, Ryan Samarakoon, Colleen Rhoades, Alexandra Kling, Alexander Sher, Alan Litke, Yoram Singer, Jonathon Shlens, and E.J. Chichilnisky (2021) Individual Variability of Neural Computations in the Primate Retina. Under review 4. Philipp Jund, Chris Sweeney, Nichola Abdo, Zhifeng Chen, Jonathon Shlens (2021). Scalable Scene Flow from Point Clouds in the Real World. Under review 3. Benjamin Caine∗, Rebecca Roelofs∗, Vijay Vasudevan, Jiquan Ngiam, Yuning Chai, Zhifeng Chen, Jonathon Shlens (2021) Pseudo-labeling for Scalable 3D Object Detection. Under review 2. Rebecca Roelofs, Nicholas Cain, Jonathon Shlens, Michael C. Mozer (2021) Mitigating Bias in Calibration Error Estimation. Under review 1. Irwan Bello, William Fedus, Xianzhi Du, Ekin D. Cubuk, Aravind Srinivas, Tsung-Yi Lin, Jonathon Shlens, Barret Zoph (2021). Revisiting ResNets: Improved Training and Scaling Strategies. Un- der review PUBLICATIONS { PEER REVIEWED CONFERENCES 36. Scott Ettinger, Shuyang Cheng, Benjamin Caine, Chenxi Liu, Hang Zhao, Sabeek Pradhan, Yuning Chai, Ben Sapp, Charles Qi, Yin Zhou, Zoey Yang, Aurelien Chouard, Pei Sun, Jiquan Ngiam, Vijay Vasudevan, Alexander McCauley, Jonathon Shlens, Dragomir Anguelov (2021). Large Scale Interactive Motion Forecasting for Autonomous Driving: The Waymo Open Motion Dataset. Proceedings of the IEEE International Conference on Computer Vision (ICCV). Selected for oral presentation. 35. Ashish Vaswani, Prajit Ramachandran, Aravind Srinivas, Niki Parmar, Blake Hechtman, Jonathon Shlens (2020) Scaling Local Attention For Parameter Efficient Visual Backbones. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). Selected for oral presentation. 34. Aravind Srinivas, Tsung-Yi Lin, Niki Parmar, Jonathon Shlens, Pieter Abbeel, Ashish Vaswani (2020) Bottleneck Transformers for Visual Recognition. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 33. Ekin Dogus Cubuk*, Barret Zoph*, Jonathon Shlens, Quoc V. Le (2020) Practical Auto- mated Data Augmentation with a Reduced Search Space Neural Information Processing Systems (NeurIPS) 32. Liang-Chieh Chen, Raphael Gontijo Lopes, Bowen Cheng, Maxwell D. Collins, Ekin D. Cubuk, Barret Zoph, Hartwig Adam, Jonathon Shlens (2020) Leveraging Semi-Supervised Learning in Video Sequences for Urban Scene Segmentation European Conference on Computer Vision (ECCV) 31. Wei Han, Zhengdong Zhang, Benjamin Caine, Brandon Yang, Christoph Sprunk, Ouais Alsharif, Jiquan Ngiam, Vijay Vasudevan, Jonathon Shlens, Zhifeng Chen (2020) Streaming Object De- tection for 3-D Point Clouds European Conference on Computer Vision (ECCV) 30. Shuyang Cheng, Zhaoqi Leng, Ekin Dogus Cubuk, Barret Zoph, Chunyan Bai, Jiquan Ngiam, Yang Song, Benjamin Caine, Vijay Vasudevan, Congcong Li, Quoc V. Le, Jonathon Shlens, Dragomir Anguelov (2020) Improving 3D Object Detection through Progressive Population Based Augmentation European Conference on Computer Vision (ECCV) 29. Barret Zoph*, Ekin Dogus Cubuk*, Golnaz Ghiasi, Tsung-Yi Lin, Jonathon Shlens, Quoc V. Le (2020) On the Importance of Data Augmentation for Object Detection European Conference on Computer Vision (ECCV) 28. Gamaleldin F. Elsayed, Prajit Ramachandran, Jonathon Shlens, Simon Kornblith (2020) Revis- iting Spatial Invariance with Low-Rank Local Connectivity Proceedings of the 37th International Conference of Machine Learning (ICML) 27. Pei Sun, Henrik Kretzschmar, Xerxes Dotiwalla, Aurlien Chouard, Vijaysai Patnaik,Paul Tsui, James Guo, Yin Zhou, Yuning Chai, Ben Caine, Vijay Vasudevan, Wei Han, Jiquan Ngiam, Hang Zhao, Scott Ettinger, Aleksei Timofeev, Maxim Krivokon, Amy Gao, Aditya Joshi, Yu Zhang, Jonathon Shlens, Zhifeng Chen, Dragomir Anguelov (2020) Scalability in Perception for Autonomous Driving: Waymo Open Dataset Proceedings of IEEE Conference on Computer Vision and Pattern Recognition (CVPR). 26. Prajit Ramachandran*, Niki Parmar*, Ashish Vaswani*, Irwan Bello, Anselm Levskaya, Jonathon Shlens (2019) Stand-Alone Self-Attention in Vision Models. Neural Information Processing Sys- tems (NeurIPS). 25. Dong Yin, Raphael Gontijo Lopes, Jonathon Shlens, Ekin D. Cubuk, Justin Gilmer (2019) A Fourier Perspective on Model Robustness in Computer Vision. Neural Information Processing Systems (NeurIPS). 24. Raphael Gontijo Lopes, David Ha, Douglas Eck, Jonathon Shlens (2019) A Learned Representa- tion for Scalable Vector Graphics. Proceedings of the IEEE International Conference on Computer Vision (ICCV). 23. Irwan Bello, Barret Zoph, Ashish Vaswani, Jonathon Shlens, Quoc V. Le. (2019) Attention Aug- mented Convolutional Networks. Proceedings of the IEEE International Conference on Computer Vision (ICCV). 22. Simon Kornblith, Jonathon Shlens, Quoc
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