Sotabase

Career

· Assistant Professor, University of Washington2024–
· Senior Research Scientist, Google DeepMind2023–2022
· Senior Research Scientist, Google Brain2020–2023
· Postdoctoral Visiting Scholar, UC Berkeley2020–2022
· Research Intern, DeepMind2018–
· Research Intern, Google Brain2016–
· Ph.D. Media Arts and Sciences, Massachusetts Institute of Technology2014–2019
· Program Manager Intern, Microsoft2014–
· PhD Student, MIT Media Lab2014–2019
· Masters Student, University of British Columbia2012–2014
· M.Sc. Computer Science, University of British Columbia2012–2014
· B.A. Psychology, University of Regina2007–2012
· B.Sc. Computer Science, University of Regina2007–2012
· Honours Undergraduate Student, University of Regina2007–2012

Publications (64)

Tackling Climate Change with Machine Learning
ACM Computing Surveys · 2019
997
cited
Social Influence as Intrinsic Motivation for Multi-Agent Deep Reinforcement Learning
International Conference on Machine Learning · 2018
509
cited
Way Off-Policy Batch Deep Reinforcement Learning of Implicit Human Preferences in Dialog
arXiv.org · 2019
376
cited
Emergent Complexity and Zero-shot Transfer via Unsupervised Environment Design
Neural Information Processing Systems · 2020
293
cited
Automatic identification of artifacts in electrodermal activity data
Annual International Conference of the IEEE Engineering in Medicine and Biology Society · 2015
231
cited
Recognizing academic performance, sleep quality, stress level, and mental health using personality traits, wearable sensors and mobile phones
International Conference on Wearable and Implantable Body Sensor Networks · 2015
207
cited
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
International Conference on Machine Learning · 2016
204
cited
Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health
IEEE Transactions on Affective Computing · 2020
194
cited
Predicting Affect from Gaze Data during Interaction with an Intelligent Tutoring System
International Conference on Intelligent Tutoring Systems · 2014
132
cited
Impossibility theorems for feature attribution
Proceedings of the National Academy of Sciences of the United States of America · 2022
121
cited
Multimodal autoencoder: A deep learning approach to filling in missing sensor data and enabling better mood prediction
Affective Computing and Intelligent Interaction · 2017
117
cited
Predicting students' happiness from physiology, phone, mobility, and behavioral data
Affective Computing and Intelligent Interaction · 2015
115
cited
Tuning Recurrent Neural Networks with Reinforcement Learning
International Conference on Learning Representations · 2016
111
cited
Human-centric Dialog Training via Offline Reinforcement Learning
Conference on Empirical Methods in Natural Language Processing · 2020
109
cited
Approximating Interactive Human Evaluation with Self-Play for Open-Domain Dialog Systems
Neural Information Processing Systems · 2019
94
cited
Predicting Tomorrow's Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation
AffComp@IJCAI · 2017
84
cited
Less is More: Generating Grounded Navigation Instructions from Landmarks
Computer Vision and Pattern Recognition · 2021
81
cited
Moral Foundations of Large Language Models
Conference on Empirical Methods in Natural Language Processing · 2023
75
cited
Understanding Attention to Adaptive Hints in Educational Games: An Eye-Tracking Study
International Journal of Artificial Intelligence in Education · 2013
69
cited
Hierarchical Reinforcement Learning for Open-Domain Dialog
AAAI Conference on Artificial Intelligence · 2019
60
cited
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Natasha Jaques | Researcher Profile | Sotabase | Sotabase