Summary
Daniel Low is a tenure-track research scientist and former Harvard postdoctoral fellow with eight years of experience applying NLP, speech processing, causal inference, and machine learning to mental health challenges such as suicide prevention, psychotherapy, and peer support. He leads interdisciplinary projects at the Child Mind Institute that blend large language models, speech biomarkers, and psychometrics to build clinical AI tools and chatbots for real-world mental health interventions. Trained across Harvard, MIT, Groningen, and Trento, he pairs rigorous experimental design with translational focus—turning complex signal-processing and causal methods into deployable biomarkers and decision-support systems. His work is notable for integrating causal inference into LLM and speech pipelines, a less obvious strength that helps move research beyond correlation to actionable insights.
8 years of coding experience
1 year of employment as a software developer
Master of Science - MS, Cognitive Science, Master of Science - MS, Cognitive Science at Università di Trento
PhD, NLP and speech processing for mental health, PhD, NLP and speech processing for mental health at Harvard University
Master's degree, Language and Communication Technologies, Master's degree, Language and Communication Technologies at University of Groningen
Licentiate degree, Psycholinguistics, Licentiate degree, Psycholinguistics at Universidad de Buenos Aires
English, Italian, Spanish