Fourteen undergraduate researchers completed the AI-MI Summer Undergraduate Research Program (AI-MI SURP) on Friday, August 7, closing ten weeks at AI-MI with final project presentations across the Materials Track and AI Track.
AI-MI SURP runs in two tracks. The Materials Track, run jointly with CHESS/CLASSE, brought six students to Ithaca from institutions across the country — Binghamton University, the University at Albany, the University of Illinois Urbana-Champaign, Seton Hall University, Yale University, and Penn State. The AI Track, run with Cornell Bowers CIS through its BURE program, placed eight Cornell undergraduates in AI-MI research groups.
Poster presentations were showcasing students’ work, where students from both tracks could attend and discuss their work (first photo: AI-MI SURP AI Track, second photo: AI-MI SURP Materials Track)
The projects tracked the Institute’s core question — how to shorten and improve the path from data to discovery with AI. Materials Track students worked on machine learning for automated order-parameter extraction from X-ray scattering, on separating thin-film from substrate scattering to make 3D-ΔPDF analysis practical for strain-engineered quantum materials, on automating the X-TEC algorithm for the QM2 beamline at CHESS, and on a neural-network framework for identifying short-lived reaction intermediates in time-resolved spectroscopy. AI Track students built and tested grey-box Bayesian optimization on a working self-driving lab, and developed agentic approaches and benchmarks for science-ready large language models in materials workflows.
Students were mentored by AI-MI and CHESS researchers, including Arthur Woll, Suchismita Sarker, Jacob Ruff, Eli Kinigstein, and Erik Andersen on the Materials side, and Peter Frazier, Kilian Weinberger, Jennifer Sun, and Eun-Ah Kim on the AI side.
Congratulations to the students and the mentors for making the inaugural AI-MI SURP cohort a great success!
Applications for SURP 2027 open this fall: aimi.cornell.edu/surp
More form Materials Track Final Presentations:
More from AI Track presentations:
