Academic Partners
Advance the science of human-AI orchestration through research collaboration. HUMΛN partners with leading universities and research institutions to explore AI safety, governance, and coordination theory.
Research Areas of Interest
AI Safety & Alignment
How do we ensure AI agents operate within human-defined bounds? What safety mechanisms scale from single agents to complex multi-agent systems?
- •Formal verification of delegation constraints
- •Anomaly detection in agent behavior
- •Human-in-the-loop optimization
Governance & Policy
What governance structures enable decentralized coordination at scale? How do we balance innovation with safety and accountability?
- •Decentralized protocol governance models
- •Capability-based access control theory
- •Provenance and audit systems
Coordination Science
How do humans and AI agents best coordinate on complex tasks? What patterns emerge in successful human-AI workflows?
- •Multi-agent orchestration algorithms
- •Task decomposition and routing
- •Human oversight patterns at scale
What We Offer
Research Grants
Funding for PhD students, postdocs, and research projects aligned with HUMΛN's research agenda.
Dataset Access
Anonymized provenance data, orchestration patterns, and workflow traces for research purposes.
Compute Credits
Free API access and compute resources for academic research projects and coursework.
Co-Authorship
Collaborate with our research team on publications at top-tier conferences (NeurIPS, ICML, etc.).
Guest Lectures
Our team presents at your institution on HUMΛN architecture, deployment patterns, and research directions.
Internships
PhD and graduate student internship opportunities on the core HUMΛN team.
Current Academic Collaborations
Stanford HAI
Human-Centered AI Institute - Research on capability-based access control and provenance systems
Status: Active | Focus: Governance
MIT CSAIL
Computer Science & AI Lab - Multi-agent coordination algorithms and safety verification
Status: Active | Focus: Coordination Science
UC Berkeley BAIR
Berkeley AI Research - Human-in-the-loop reinforcement learning and agent alignment
Status: Proposed | Focus: AI Safety
How to Collaborate
- 1
Submit Research Proposal
Outline your research question, methodology, and how HUMΛN data/infrastructure would support your work.
- 2
Review & Discussion
Our research team reviews proposals and schedules discussions with promising projects.
- 3
Formal Agreement
Sign research collaboration agreement (RCA) covering IP, data access, and publication rights.
- 4
Conduct Research
Access resources, collaborate with our team, and publish findings (open access encouraged).
Propose a Research Collaboration
Working on AI safety, governance, or coordination? We'd love to support your research and explore how HUMΛN can contribute to advancing the field.