Stephen Casper (Cas)
I (Cas) work on a range of projects from technical safeguards to technical governance. This stream follows an academic collaboration model and work will likely focus on technical topics in AI governance.
Stream overview
Technical work: Making safeguards 'run deep', including safeguards and risk management for open-weight models.
Governance work: Critical review of industry self-governance, critical review of national AI governance institutes, open-weight model governance, predicting and mitigating future AI incidents.
Mentors
Stephen "Cas" Casper is a computer scientist and an Assistant Professor of Public Policy at the Harvard Kennedy School and a Faculty Affiliate of the Harvard School of Engineering and Applied Sciences. Prior to joining Harvard, he completed his PhD at MIT and did a research residency with the UK AI Security Institute. He is a writer for the International AI Safety Report and a lead writer for the Singapore Consensus. His research has been recognized with a Hoopes Prize, an ML Safety Workshop best paper award, a BioSafeGenAI best paper runner-up, a GenLaw spotlight paper award, a TMLR outstanding paper finalist distinction, and a handful of mentions in news articles and newsletters. Find him on Google Scholar, Twitter (sorry), BlueSky, and LinkedIn.
Mentorship style
2-3 meetings per week plus regular messaging and collaborative writing.
Fellows we are looking for
Green flags include:
- Research experience and taste
- Demonstrated initiative in ideating and leading prior projects
- Demonstrated ability to 'succeed even when not set up to succeed' in past research/projects.
- Taking a critical mindset about projects and impact
- Interest in academia
This stream will follow an academic collaboration model. Scholars will be free to discuss and collaborate externally. However, scholars should also expect to work in collaboration with others in the stream.
Project selection
Mentor(s) will talk through project ideas with scholar.
The Winter 2027 cohort offers a wide range of research streams led by experts across AI alignment, interpretability, governance, and safety. Each stream provides its own research agenda, methodology, and mentorship focus.
