Daniel Low is a data scientist and mental health researcher. He is a Postdoctoral Fellow in the CAUSALab at the Department of Epidemiology at the Harvard T. H. Chan School of Public Health, mentored by Miguel Hernán, and in the Nock Lab, Department of Psychology at Harvard University, mentored by Matthew Nock, as part of the NIMH T32 grant on Comparative Effectiveness Research for Suicide Prevention.

He completed his PhD in Speech and Hearing Bioscience and Technology at Harvard University, carried out at the Senseable Intelligence Group (McGovern Institute for Brain Research, MIT) and was advised by Satrajit Ghosh (MIT, Harvard Medical School).

His work focuses on using natural language processing and speech processing to predict suicidal thoughts and behaviors and understand the effects of meditation and psychedelics on mental health and affect. He is integrating these methods with psychometrics and causal inference to better measure and intervene on affect and symptoms during clinical and peer conversations. He uses data from ecological momentary assessments of hospitalized individuals, social media, psychotherapy sessions, and clinical trials.

He has co-founded the Harvard-MIT Speech and Language Biomarker Interest Group which organizes talks and discussions in this field. He teaches workshops and courses on machine learning and natural language processing at Harvard and other universities.

His work has been funded by an NIH NIMH T32 training grant, a RallyPoint Fellowship, an NIH NIDCD T32 training grant, the NIH Common Fund Bridge2AI program, an Amelia Peabody Professional Development Award, and a Fundacion Williams grant.

Education

• 2024 - PhD in Speech and Hearing Bioscience and Technology, Division of Medical Sciences, Harvard University
• 2018 - MA in Language and Communication Technologies, University of Groningen, Netherlands
• 2018 - MSc in Cognitive Science, University of Trento, Italy
• 2015 - BA in Neurolinguistics & Psycholinguistics, University of Buenos Aires, Argentina

Interests