Research Intern: Affective Computing for Collective Intelligence

Key Responsibilities

• Develop computational models (inverse reinforcement learning (IRL) or preference learning models) to infer latent reward functions from team interaction data.
• Design and implement theory-informed reward components inspired by collective intelligence and behavioral psychology (such as information sharing, participation balance, consensus dynamics).
• Process and model multimodal interaction datasets, including conversational transcripts, temporal interaction signals, and computer vision features.
• Generate and evaluate conterfactual or alternative interaction trajectories for IRL training.
• Conduct experiments on team perfomance prediction, early outcome inference, and generalization across datasets.
• Analyze learned reward strctures to identify distinct team strategies and behavioral patterns.

Minimum Qualifications

• Current PhD candidate in Computer Science, Electrical Engineering, Behavioral Psychology, or a related technical field.
• Strong programming skills in Python and experiences with deep learning frameworks.
• Solid understanding of machine learning fundamentals, including supervised learning and model evaluation.
• Familiarity with sequence modeling (such as RNN, Transformers) or probabilistic modeling.

Bonus Qualifications