He He

I'm interested in mentoring projects related to reward hacking and monitoring (agentic) models that produce long and complex trajectories. Scholar will have freedom to propose projects within this scope. Expect 30-60min 1-1 time on zoom.

Stream overview

I'm interested in mentoring projects related to reward hacking and monitoring (agentic) models that produce long and complex trajectories.

Mentors

He He
New York University
,
Associate Professor
New York City, SF Bay Area
Misalignment Science
AI Control and Monitoring
Capability and Propensity Evaluations
Adversarial Robustness and Safeguards

He He is an associate professor at New York University. She is interested in how large language models work and potential risks of this technology.

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Mentorship style

30min to 1 hour weekly meetings (on zoom) by default for high-level guidance. I'm active on Slack and typically respond within a day for quick questions or conceptual (not code) debugging. Expect async back-and-forth on experiment design and results between meetings. Scholars can also schedule ad-hoc calls if they're stuck or want to brainstorm—just ping me on Slack.

Fellows we are looking for

  • Strong engineering skills and experience in training deep learning models
  • Familiarity with modern large scale RL pipelines (e.g., using frameworks such as verl)

Project selection

Week 1-2: Mentor will provide high level directions or problems to work on, and scholar will have the freedom to propose specific projects and discuss with mentor.

Week 3: Figure out detailed plan of the project.

Streams

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.

London
Empirical
Interpretability
London
Interpretability, Red-Teaming, Monitoring
London
Monitoring, Adversarial Robustness, Control, Model Organisms, Red-Teaming, Dangerous Capability Evals, Safeguards
New York City
Policy and Governance
Dangerous Capability Evals, Control, Strategy & Forecasting, Policy & Governance, Scalable Oversight, Agent Foundations
SF Bay Area
Empirical
Theory
Dangerous Capability Evals, Adversarial Robustness, Security, Red-Teaming, Scalable Oversight
London
Control, Scheming & Deception, Dangerous Capability Evals, Monitoring
Washington, D.C.
Policy and Governance
Policy & Governance, Strategy & Forecasting
Oxford
Theory
AI Welfare
SF Bay Area
Control, Model Organisms, Scheming & Deception, Strategy & Forecasting
SF Bay Area
Theory
Interpretability
Tübingen
Dangerous Capability Evals, Agent Foundations, Adversarial Robustness, Monitoring, Scalable Oversight, Scheming & Deception
SF Bay Area
Policy and Governance
Dangerous Capability Evals, Policy & Governance
New York City
Monitoring, Dangerous Capability Evals, Scalable Oversight, Safeguards
SF Bay Area
Policy and Governance
Strategy & Forecasting, Policy & Governance
Montreal
Agent Foundations, Dangerous Capability Evals, Monitoring, Control, Red-Teaming, Scalable Oversight
SF Bay Area
Control, Model Organisms, Red-Teaming, Scheming & Deception