Sotabase

Career

· Member of Policy Staff, OpenAI2023–
· Assembly AI Policy Fellow, Berkman Klein Center, Harvard Law School2018–2019
· Graduate Student, Harvard University2018–
· PhD in Computer Science, Harvard University2018–2023
· Economics of Machine Learning Research Assistant, MIT Sloan School of Management2017–2017
· M.Eng. in Computer Science, MIT2016–2018
· Embedded Software Intern, Nest Labs, Alphabet Inc.2015–
· Sensors Engineering Intern, Nest Labs, Alphabet Inc.2014–
· B.S. in Computer Science & Electrical Engineering, MIT2012–2016

Publications (28)

Frontier AI Regulation: Managing Emerging Risks to Public Safety
arXiv.org · 2023
156
cited
Practices for Governing Agentic AI Systems
148
cited
Causal Strategic Linear Regression
International Conference on Machine Learning · 2020
87
cited
What does it take to catch a Chinchilla? Verifying Rules on Large-Scale Neural Network Training via Compute Monitoring
arXiv.org · 2023
29
cited
Learning Augmented Joint-Space Task-Oriented Dynamical Systems: A Linear Parameter Varying and Synergetic Control Approach
IEEE Robotics and Automation Letters · 2018
24
cited
Tools for Verifying Neural Models' Training Data
Neural Information Processing Systems · 2023
23
cited
Learning From Strategic Agents: Accuracy, Improvement, and Causality
International Conference on Machine Learning · 2020
11
cited
Extracting Incentives from Black-Box Decisions
arXiv.org · 2019
9
cited
Exploring the use of Lipschitz Neural Networks for Automating the Design of Differentially Private Mechanisms
2019
5
cited
Strengthening Subcommunities: Towards Sustainable Growth in AI Research
arXiv.org · 2022
3
cited
Robots on Demand: A Democratized Robotics Research Cloud
Conference on Robot Learning · 2021
2
cited
BEST PRACTICES AND GUIDELINES FOR FAKE NEWS MITIGATION
2017
Expand Neurons, Not Parameters
arXiv.org · 2025
Supplement to Causal Strategic Linear Regression
2020
Sotabase
Yonadav Shavit | Researcher Profile | Sotabase | Sotabase