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Natasha Jaques
Research areas.
Human-Computer Interaction and Visualization
Machine Intelligence
Natural Language Processing
Publications
- Health & Bioscience 1
- Human-Computer Interaction and Visualization 2
- Machine Intelligence 10
- Machine Perception 1
- Natural Language Processing 2
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Natasha Jaques
Assistant Professor, Paul G. Allen School of Computer Science & Engineering
Research focus
Deep reinforcement learning, multi-agent reinforcement learning, reinforcement learning from human feedback, human-AI interaction, social learning
Ph.D. Media Arts and Sciences, Massachusetts Institute of Technology, 2019 M.Sc. Computer Science, University of British Columbia, 2014 B.Sc. Computer Science, University of Regina, 2012 B.A. Psychology, University of Regina, 2012
Natasha Jaques will join the Allen School this winter from Google Brain, where she is a senior research scientist exploring if AI agents benefit from social learning. She has also interned at DeepMind and Google Brain, and was an OpenAI Scholars mentor.
Jaques’s research focuses on social reinforcement learning in multi-agent and human-AI interactions. Her interest in AI learning and collaboration extends into developing multi-training algorithms that create automatic curriculum to help AI learn from each other, and improving mechanisms that allow AI to learn from human partners. Jaques has received numerous awards including Best Demo at NeurIPS, Best of Collection in the IEEE Transactions on Affective Computing, and Best Paper at the NeurIPS workshops on ML for Healthcare and Cooperate AI. Her work has been featured in Science Magazine, MIT Technology Review, Quartz, IEEE Spectrum, Boston Magazine and on CBC Radio.
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Natasha Jaques wins AAAC Outstanding PhD Dissertation Award 2021
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Natasha Jaques
by Sarah Beckmann
Oct. 4, 2021
- Natasha Jaques Former Research Assistant
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Natasha Jaques, an alum of the Affective Computing group, has won this year's Association for the Advancement of Affective Computing (AAAC) Outstanding PhD Dissertation Award for her thesis: " Social and Affective Machine Learning ."
The AAAC Outstanding PhD Dissertation Award recognizes the most outstanding research contributions from recently graduated PhD students within the Affective Computing community. It was presented to Jaques at this year's International Conference on Affective Computing and Intelligent Interaction (ACII) on October 1, 2021.
AI Songsmith Cranks Out Surprisingly Catchy Tunes
Google’s songwriting program learns by combining statistical learning and explicit rules—the same approach may make it easier for engineers…
Natasha Jaques Dissertation Defense
Towards Social and Affective Artificial IntelligenceSocial learning is a crucial component of human intelligence, allowing us to rapidly ad…
Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-control
Jaques, N., Gu, S., Bahdanau, D., Hernandez-Lobato, J., Turner, R., and Eck, D. "Sequence Tutor: Conservative Fine-Tuning of Sequence Generation Models with KL-Control," International Conference on Machine Learning (ICML), Sydney, Australia, August 2017
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Natasha Jaques – Social Reinforcement Learning
Grier A (34-401A)
Abstract: Social learning helps humans and animals rapidly adapt to new circumstances, coordinate with others, and drives the emergence of complex learned behaviors. What if it could do the same for AI? This talk describes how Social Reinforcement Learning in multi-agent and human-AI interactions can address fundamental issues in AI such as learning and generalization, while improving social abilities like coordination. I propose a unified method for improving coordination and communication based on causal social influence. I then demonstrate that multi-agent training can be a useful tool for improving learning and generalization. I present PAIRED, in which an adversary learns to construct training environments to maximize regret between a pair of learners, leading to the generation of a complex curriculum of environments. Agents trained with PAIRED generalize more than 20x better to unknown test environments. Finally, I demonstrate the value of learning socially from interacting with other agents, whether those agents are AI or humans. Together, this work argues that Social RL is a valuable approach for developing more general, sophisticated, and cooperative AI, which is ultimately better able to serve human needs.
Bio: Natasha Jaques holds a joint position as a Senior Research Scientist at Google Brain and Visiting Postdoctoral Scholar at UC Berkeley. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. Natasha completed her PhD at MIT, where her thesis received the Outstanding PhD Dissertation Award from the Association for the Advancement of Affective Computing. Her work has also received Best Demo at NeurIPS, an honourable mention for Best Paper at ICML, Best of Collection in the IEEE Transactions on Affective Computing, and Best Paper at the NeurIPS workshops on ML for Healthcare and Cooperative AI. She has interned at DeepMind, Google Brain, and was an OpenAI Scholars mentor. Her work has been featured in Science Magazine, Quartz, IEEE Spectrum, MIT Technology Review, Boston Magazine, and on CBC radio. Natasha earned her Masters degree from the University of British Columbia, and undergraduate degrees in Computer Science and Psychology from the University of Regina.
- Date: Thursday, March 31
- Time: 2:00 pm - 3:00 pm
- Category: Special Seminar
- Location: Grier A (34-401A)
- Email: [email protected]
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Natasha Jaques
Contact information:.
- [email protected]
- Affective Computing
PhD candidate working on improving deep learning and AI agents by building in forms of affective and social intelligence. My past work has investigated methods for improving generalization of machine learning models via intrinsic motivation, transfer learning, multi-task learning, and learning from human preferences. I've interned with DeepMind and Google Brain, and was an OpenAI Scholars mentor. Experienced in traditional machine learning, deep learning, kernel methods, Bayesian non-parametrics, causal inference, and reinforcement learning.
My favourite past projects have included:
- Developing a unified method for promoting cooperation and communication among in multi-agent reinforcement learning (RL) by creating an intrinsic reward based on assessing causal influence between agents.
- Improving deep generative models by using human facial expression responses to samples from the model as a training signal.
- Effectively combining supervised learning and RL to train generative sequence models.
- Using multi-task learning techniques to personalize machine learning models and improve accuracy in predicting next day stress, happiness and health.
Multi-task Learning for Predicting Health, Stress, and Happiness
Jaques, N., Taylor, S., Nosakhare, E., Sano, A., Picard, R. In Proc. NIPS Workshop on ML in Health, Barcelona, Spain, December 2016. **BEST PAPER AWARD**
Multimodal Autoencoder: A Deep Learning Approach to Filling In Missing Sensor Data and Enabling Better Mood Prediction
Jaques, N., Taylor, S., Sano, A., and Picard, R. International Conference on Affective Computing and Intelligent Interaction (ACII), San Antonio, Texas, October 2017
EDA Explorer
Electrodermal Activity (EDA) is a physiological indicator of stress and strong emotion. While an increasing number of wearable devices can …
Predicting Tomorrow's Mood, Health, and Stress Level using Personalized Multitask Learning and Domain Adaptation
Jaques, N., Rudovic, O., Taylor, S., Sano, A., and Picard, R. Proceedings of Machine Learning Research, 48, 17-33. August 2017.
Improving RNN Sequence Generation with RL
This project investigates a general method for improving the structure and quality of sequences generated by a recurrent neural network (RN…
Causal Influence Intrinsic Social Motivation for Multi-Agent Reinforcement Learning
Teaching multiple AI agents to coordinate their behavior represents a challenging task, that can be difficult to achieve without training a…
Personality, Attitudes, and Bonding in Conversations
Jaques, N., Kim, Y. L., and Picard, R. W. "Personality, Attitudes, and Bonding in Conversations," In Proceedings of Intelligent Virtual Agents, California, USA, September 2016.
Understanding and Predicting Bonding in Conversations Using Thin Slices of Facial Expressions and Body Language
Jaques, N., McDuff, D., Kim, Y. K., and Picard, R. W. "Understanding and Predicting Bonding in Conversations Using Thin Slices of Facial Expressions and Body Language," In Proceedings of Intelligent Virtual Agents, California, USA, September 2016.
IMAGES
VIDEO
COMMENTS
Natasha Jaques. My thesis defense at the MIT Media Lab. I cover work on Affective Computing, learning from affective signals in human-AI interaction, and multi-agent coordination. Includes an in-depth question period with my PhD committee.
Presentation of my thesis "Towards Social and Affective Machine Learning" https://natashajaques.ai/publication/social-and-affective-machine-learning/
Natasha Jaques. Senior Research Scientist. Social learning helps humans and animals rapidly adapt to new circumstances, and drives the emergence of complex learned behaviors. My research is focused on Social Reinforcement Learning —developing algorithms that combine insights from social learning and multi-agent training to improve AI agents ...
Watch the full podcast here: https://youtu.be/8XpCnmvq49sNatasha Jaques is currently a Research Scientist at @Google Brain and post-doc fellow at @UC Berk...
Jaques, Natasha. Social and Affective Machine Learning. 2019. Massachusetts Institute of Technology, PhD dissertation. Abstract. Social learning is a crucial component of human intelligence, allowing us to rapidly adapt to new scenarios, learn new tasks, and communicate knowledge that can be built on by others. ... Natasha Jaques Dissertation ...
Natasha Jaques Dissertation Defense. ... Natasha Jaques wins AAAC Outstanding PhD Dissertation Award 2021. Natasha Jaques, an alum of the Affective Computing group, has won this year's award for her thesis: "Social and Affective Machine Learning" Oct. 4, 2021.
Natasha Jaques Dissertation Defense Play Video People. Natasha Jaques. Former Research Assistant. Groups. Share this event. Wednesday July 31, 2019 12:00pm — 2:00pm ET. MIT Media Lab, E15 - 341 20 Ames Street, Cambridge, MA ...
Natasha Jaques. University of Washington, Google Research. Verified email at google.com ... N Jaques, S Gu, D Bahdanau, JMH Lobato, RE Turner, D Eck. International Conference on Machine Learning, 2017. 225 * 2017: Emergent complexity and zero-shot transfer via unsupervised environment design.
Natasha Jaques holds a joint position as a Research Scientist at Google Brain and post-doc at UC Berkeley. Her research focuses on social reinforcement learning---developing multi-agent RL algorithms that can improve single-agent learning, generalization, coordination, and human-AI collaboration. Natasha received her PhD from MIT, where she ...
My PhD Thesis spans both Social Reinforcement Learning and Affective Computing, ... Natasha Jaques. 2019 PDF Cite Thesis Defense CV News write-up Abstract. Social learning is a crucial component of human intelligence, allowing us to rapidly adapt to new scenarios, learn new tasks, and communicate knowledge that can be built on by others. ...
Natasha Jaques natashajaques.ai 2132 NW 99th Street Seattle, WA 98117 (425) 463-9162 [email protected] [email protected] ... 2021 Outstanding PhD Dissertation Award from the Association for the Advancement of A ective Computing (AAAC) 2019 Rising Stars in EECS Pitch Competition Winner, Computer Science ...
Natasha Jaques holds a joint position as a Senior Research Scientist at Google Brain and Postdoctoral Fellow at UC Berkeley. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. ... Natasha completed her PhD at MIT, where her thesis received the Outstanding PhD Dissertation Award from the Association ...
Massachusetts Institute of Technology, PhD dissertation. Download PDF. Publication. Automatic Triage and Analysis of Online Suicide Risk with Document Embeddings and Latent Dirichlet Allocation. ... Natasha Jaques, Weixuan 'Vincent' Chen, and Rosalind W. Picard. 2015. SmileTracker: Automatically and Unobtrusively Recording Smiles and their Context.
Natasha Jaques will join the Allen School this winter from Google Brain, where she is a senior research scientist exploring if AI agents benefit from social learning. She has also interned at DeepMind and Google Brain, and was an OpenAI Scholars mentor. Jaques's research focuses on social reinforcement learning in multi-agent and human-AI interactions.
Natasha Jaques received the BSc (Hons.) degree in computer science, the BA degree in psychology, the graduated degree from the University of Regina, and the MSc degree in computer science from the University of British Columbia. She is working toward the PhD degree in the Affective Computing group, the Massachusetts Institute of Technology ...
Oct. 4, 2021. Natasha Jaques. Research Assistant. Natasha Jaques, an alum of the Affective Computing group, has won this year's Association for the Advancement of Affective Computing (AAAC) Outstanding PhD Dissertation Award for her thesis: " Social and Affective Machine Learning ." The AAAC Outstanding PhD Dissertation Award recognizes the ...
PDF Cite Thesis Defense CV News write-up ... My Master's Thesis investigated the usefulness of different data sources for automatically predicting when students using an Intelligent Tutoring System were engaged and curious, or disengaged and bored. ... Natasha Jaques. 2014 In University of British Columbia.
Natasha Jaques, an alum of the Affective Computing group, has won this year's Association for the Advancement of Affective Computing (AAAC) Outstanding PhD Dissertation Award for her thesis: "Social and Affective Machine Learning.". The AAAC Outstanding PhD Dissertation Award recognizes the most outstanding research contributions from recently graduated PhD students within the Affective ...
Natasha Jaques holds a joint position as a Senior Research Scientist at Google Brain and Visiting Postdoctoral Scholar at UC Berkeley. Her research focuses on Social Reinforcement Learning in multi-agent and human-AI interactions. Natasha completed her PhD at MIT, where her thesis received the Outstanding PhD Dissertation Award from the ...
Natasha Jaques *, S. Taylor *, E. Nosakhare, A. Sano, R. Picard (2017). Personalized Multitask Learning for Predicting Tomorrow's Mood, Stress, and Health.In IEEE Transactions on Affective Computing (TAFFC) Best Paper; NeurIPS Machine Learning for Healthcare (ML4HC) Workshop Best Paper.. Cite Code Video ML4HC Best Paper TAFFC Journal Best Paper
I submitted my PhD thesis in December 2022 and I am now preparing for my defense and submission of chapters for publication. Alongside my family, work and studies, I like to DIY, hike and hang out with my two German Shepards. ... Mangateparu · 84 connections on LinkedIn. View Natasha Jaques, Ph.D.'s profile on LinkedIn, a professional ...
Natasha Jaques wins AAAC Outstanding PhD Dissertation Award 2021. Natasha Jaques, an alum of the Affective Computing group, has won this year's award for her thesis: "Social and Affective Machine Learning". Oct. 4, 2021. in Affective Computing. PostResearch.
Affective Computing. PhD candidate working on improving deep learning and AI agents by building in forms of affective and social intelligence. My past work has investigated methods for improving generalization of machine learning models via intrinsic motivation, transfer learning, multi-task learning, and learning from human preferences.