Tesserae: Multimodal Sensing To Model Individual Differences and Job Performance at Workplaces

Faculty: 
Munmun De Choudhury
Students: 
Koustuv Saha, Vedant Das Swain

The goal of the project is to create robust sensing system that fuses a comprehensive suite of multimodal sensing modalities for automated modeling of individual differences and job performance. Twin sub-goals include: 1) validating that our proposed sensing streams fused together via machine learning coupled with ground truth reliably predict both individual differences, and in turn, job performances; 2) successfully creting and demonstrating generalizable models that reliably predict individual differences and job performance through only our proposed sensor data streams. In particular, the sensor data streams include bluetooth beacon data, phone agent, garmin fitness wearable, and social media data.

Lab: 
Director: 
Munmun De Choudhury
Faculty: 
Munmun De Choudhury