Kimaya Lecamwasam

Kimaya (Kimy) Lecamwasam is a neuroscientist, singer/songwriter, and PhD student at the MIT Media Lab  investigating the intersections between music and emotion, with focus on applications of both audio-based affective computing interventions and live music experiences for treating mental health conditions and supporting well-being. Kimaya is a 2026 MIT Graduate Student of Excellence (GSE) and was a 2023 MIT Presidential Fellow and the 2021 Harriet A. Shaw Fellow for music research. She has collaborated with a wide range of industry and academic partners, including PixMob, Myndstream, and Carnegie Hall's Weill Music Institute, and has presented her work at venues and conferences across the world, including Carnegie Hall, Boston's Symphony Hall, the Wellbeing Summit in Bilbao, Spain (2022), the ACM Conference on Human Factors in Computing Systems in Hamburg, Germany (2023), and the 2026 New Interfaces for Musical Expression (NIME) conference. Her paper titled “Promoting the well-being of infants and caregivers through music: insights from the Lullaby Project's international convening” was the most read article from the Arts & Health journal in 2025.

Kimaya's research interests include investigating the impact of music on mental health from both clinical intervention-based and performance/composition-based approaches. Her current projects include (1) investigating the impact of live music/concert experiences on the mental health and well-being of audience members and performers, (2) developing methods to allow clinicians to use music listening, composition, and performance as prescribable and validated interventions, in combination with psychotherapy and pharmaceutical intervention, (3) assessing and developing applications centered on rock, pop, and folk music in these spaces, and (4) investigating the emotional parameters that underlie human perception of AI-generated vs. human-composed music. During her time in Dr. Emery Brown's Neuroscience Research Statistics Lab (NSRL), she investigated methods of classifying consciousness versus unconsciousness in patients undergoing anesthesia and the use of reinforcement learning to better train brain computer interface-enabled robotic prosthetics. She received her B.A. from Wellesley College (2021) and her S.M. from MIT (2023).