Daniel Low works on detecting mental health symptoms from speech and text data using natural language processing, speech processing, and machine learning at the Senseable Intelligence Group (McGovern Institute for Brain Research, MIT & Harvard Medical School) and at the Nock Lab (Department of Psychology, Harvard University). He focuses on speech and language patterns of suicidal thoughts and behaviors, meditation, and psychedelics. He uses data from ecological momentary assessments of hospitalized and nonhospitalized individuals, social media, 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 a RallyPoint Fellowship, an NIH NIDCD T32 training grant, the NIH Common Fund Bridge2AI program, and an Amelia Peabody Professional Development Award. 

 

Education:

  • Present - PhD in Speech and Hearing Bioscience and Technology, 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

Summer schools:

  • Neurohackademy, University of Washington, USA

  • Center for Brains, Minds, and Machines (CBMM), MIT-Harvard University, USA

 

Awards

  • Amelia Peabody Professional Development Award

  • RallyPoint Fellowship

  • Erasmus Mundus Joint Masters Full Scholarship (EACEA, European Commision, EU)

  • University of Buenos Aires Science and Technology (UBACyT) Research Scholarship

 

Interests: natural language processing | speech signal processing | machine learning | causal inference | mental health

 

Email: dlow at g dot harvard dot edu

Office: MIT McGovern Insitute, 46-6193, 43 Vassar St, Cambridge, MA 02139

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