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I am a quantitative researcher working at Jane Street Capital with a focus on machine learning.
Previously, I was a staff researcher working at Google Brain/DeepMind. My published work focused on AutoML, NLP and LLMs. I was also a technical lead and core contributor for Google's largest language modeling efforts, including Bard, PaLM 2, LaMDA 2, and Meena.
Publications
Google (Core Contributor)
Brainformers: Trading Simplicity for Efficiency
Yanqi Zhou, Nan Du, Yanping Huang, Daiyi Peng, Chang Lan, Da Huang, Siamak Shakeri,
David R. So, Andrew M. Dai, Yifeng Lu, Zhifeng Chen, Quoc V. Le, Claire Cui, James Laudon, Jeff Dean
In International Conference on Machine Learning 2023
EvoPrompting: Language Models for Code-Level Neural Architecture Search
Angelica Chen, David M. Dohan, David R. So
Unified Functional Hashing in Automatic Machine Learning
Ryan Gillard, Stephen Jonany, Yingjie Miao, Michael Munn, Connal de Souza, Jonathan Dungay, Chen Liang, David R. So, Quoc V. Le, Esteban Real
Transcending Scaling Laws with 0.1% Extra Compute
Yi Tay, Jason Wei, Hyung Won Chung, Vinh Q Tran, David R. So, Siamak Shakeri, Xavier Garcia, Huaixiu Steven Zheng, Jinfeng Rao, Aakanksha Chowdhery, Denny Zhou, Donald Metzler, Slav Petrov, Neil Houlsby, Quoc V Le, Mostafa Dehghani
The Carbon Footprint of Machine Learning Training Will Plateau, Then Shrink
David Patterson, Joseph Gonzalez, Urs Hölzle, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, Jeff Dean
In Computer 2022
Primer: Searching for Efficient Transformers for Language Modeling
David R. So, Wojciech Mańke, Hanxiao Liu, Zihang Dai, Noam Shazeer, Quoc V. Le
In Conference on Neural Information Processing Systems 2021
Hanxiao Liu, Zihang Dai, David R. So, Quoc V. Le
In Conference on Neural Information Processing Systems 2021
Carbon Emissions and Large Neural Network Training
David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang,
Lluis-Miquel Munguia, Daniel Rothchild, David R. So, Maud Texier, Jeff Dean
MUFASA: Multimodal Fusion Architecture Search for Electronic Health Records
Zhen Xu*, David R. So*, Andrew M. Dai
In Association for the Advancement of Artificial Intelligence 2021
AutoML-Zero: Evolving Machine Learning Algorithms from Scratch
Esteban Real, Chen Liang, David R. So, Quoc Le
In International Conference on Machine Learning 2020
Towards a Human-Like Open-Domain Chatbot
Daniel Adiwardana, Minh-Thang Luong, David R. So, Jamie Hall, Noah Fiedel,
Romal Thoppilan, Zi Yang, Apoorv Kulshreshtha, Gaurav Nemade, Yifeng Lu,
David R. So, Quoc V. Le, Chen Liang
In International Conference on Machine Learning 2019
Evolving Modular Neural Sequence Architectures with Genetic Programming
David Dohan, David R. So, Quoc V. Le
In Proceedings of the Genetic and Evolutionary Computation Conference Companion 2018
Classification of Crystallization Outcomes Using Deep Convolutional Neural Networks
Andrew E Bruno, Patrick Charbonneau, Janet Newman, Edward H Snell, David R. So, Vincent Vanhoucke, Christopher J Watkins, Shawn Williams, Julie Wilson
In PLOS One 2018
Improving Image Generative Models with Human Interactions
Andrew K. Lampinen, David R. So, Douglas Eck, Fred Bertsch