Jeremy Tien is a Machine Learning PhD student at Carnegie Mellon University with eight years of experience building and researching applied AI systems across academia and industry. He combines rigorous research in human-robot interaction from his time at Berkeley AI Research with hands-on engineering experience from internships at Amazon and Tact.ai, where he developed a Pointer Network for text-to-SQL on the challenging Spider dataset. Based in the San Francisco Bay Area, Jeremy bridges theory and product by translating state-of-the-art ML techniques into usable features for sales and consumer tech. He has published research in interactive learning settings and contributed to projects that emphasize practical integration and error analysis beyond pure model performance. Colleagues describe him as someone who quickly pivots between research depth and product constraints, often surfacing non-obvious failure modes that improve real-world reliability.
8 years of coding experience
4 years of employment as a software developer
B.S. Electrical Engineering and Computer Science, B.S. Electrical Engineering and Computer Science at University of California, Berkeley
Full-Time Training in Anaheim
Monta Vista High School
Doctor of Philosophy Machine Learning, Doctor of Philosophy Machine Learning at Carnegie Mellon University
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