Key Responsibilities

• Develop AI models for collective state sensing and group-level behavior modeling using real-world multimodal data
• Create robust methods for learning from noisy, sparse, incomplete, and temporally dynamic human behavioral signals
• Model social roles, norms, relationships, influence, coordination, attention, trust, affect, and emergent group patterns in human-human and human-AI systems
• Apply and advance methods in multimodal representation learning, graph and relational learning, temporal modeling, foundation models, probabilistic reasoning, causal inference, and interpretable AI
• Design evaluation methods, benchmarks, annotations, and experimental protocols for validating collective behavior models with empirical data
• Develop uncertainty-aware and interpretable models that support both automated sensing and human understanding of group dynamics
• Translate research results into proof-of-concept prototypes, internal demonstrations, and applied research directions
• Collaborate with researchers across AI, human behavior, cognitive modeling, social science, and human-AI interaction
• Publish in leading research nenues, contribute to intellectual property, and support HRI's long-term research strategy

Minimum Qualifications

• ​Ph.D. in computer science, AI or a closely related field.
• Strong research record in highly reputable conferences and journals in AI/ML, multimodal learning, social signal processing, and human-AI interaction.
• Strong research experience in multimodal signal processing with human data, including vision, audio, and behavioral signals under noisy or imperfect data conditions.
• Demonstrated ability to formulate open-ended research problems, design rigorous experiments, and evaluate AI models on real-world data.
• Experience developing and applying deep learning models, including both LLM-based and non-LLM architectures.
• Strong technical background in at least three of the following areas: multimodal learning, computer vision, speech/audio processing, natural language processing, graph or relational learning, temporal modeling, probabilistic modeling, causal inference, foundation models, or multi-agent modeling.
• Strong programming skills in Python and experience with modern machine learning frameworks such as PyTorch, TensorFlow, or similar.
• Ability to formulate open-ended research problems, design rigorous machine learning experiments, and evaluate model performance on real-world data.
• Strong written and verbal communication skills, with the ability to present technical results to both expert and cross-functional audiences.

Bonus Qualifications