Harvineet Singh is a Machine Learning Scientist with 11 years of experience combining rigorous academic research (Ph.D. in Data Science from NYU) and industry impact across Adobe, Microsoft Research, Amazon Science, and UCSF. He specializes in responsible ML, causal inference, and robust AI systems, and has led efforts to monitor deployed models, diagnose failures, and benchmark fairness at scale. His work bridges theory and production—building predictive systems for advertising and marketing early in his career and later developing evaluation frameworks for clinical ML. Recognized as a Future Leader in Responsible Data Science and a research fellow at Harvard, he also serves in community leadership roles such as General Chair for the Machine Learning for Health Symposium. Based in San Francisco, he brings a rare blend of hands-on system deployment experience, publications and patents, and practical methods for trustworthy model monitoring.
11 years of coding experience
8 years of employment as a software developer
Indian Institute of Technology Delhi (IIT Delhi)
Doctor of Philosophy - PhD Data Science, Doctor of Philosophy - PhD Data Science at New York University
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Harvineet Singh - Machine Learning Scientist at Qualified Health