CEOInterviews.AI
Start App
Try: AI strategy tariffs earnings guidance layoffs interest rates growth

Shri Narayanan

Cofounder, Behavioral Signals

Search every verified Shri Narayanan interview, podcast appearance, and on-the-record quote — each transcript cross-checked by AI and human review to confirm speaker identity. Shri Narayanan, a University of Southern California professor and cofounder of Behavioral Signals, has presented on his research in human-centered multimodal machine intelligence across several academic venues. In a 2022 lecture for the Meet a Data Scientist series, he discussed using machine learning to analyze human behavior through audio and visual data, including methods like multiple instance learning to identify salient data features that explain outcomes. He also described applying these approaches to study clinical phenomena and how data scientists can contribute to quantitative thinking about conditions that are often described qualitatively. In earlier talks, Narayanan detailed his lab's work on behavioral signal processing, which aims to quantify expressed and felt behavior to support human decision-making rather than replace it. He highlighted projects involving continuous multimodal sensing of hospital workers using wearable sensors, badges, and smartphones to understand stress, performance, and well-being in workplace settings. He also discussed using machine learning to study vocal entrainment and synchrony in parent-child interactions, and to predict clinician decisions about autism severity based on interaction monitoring. Across these presentations, he emphasized the importance of collaboration among computer scientists, cognitive scientists, psychologists, and clinicians, as well as addressing challenges related to data ethics, provenance, and security.

Recent appearances

The summary and quote tags on this profile are produced with AI assistance from verified, first-person interview transcripts, then checked by our team to confirm the speaker's identity and the accuracy of every quote. See how we verify →