This talk will focus on the arc from using longitudinal clinical data to generate real-world evidence, through predictive and generative AI, to agentic systems capable of acting across electronic health records, payer portals, and other operational infrastructure.
Aliya Bhatia is a Senior Policy Analyst at the Center for Democracy and Technology (CDT), where she works with the Free Expression Project.
CALS Innovation Day 2026 highlights the people, partnerships, and investment pathways that turn research into impact. Join us for inspiring faculty stories, discussions with corporate and philanthropic leaders, and showcases of emerging discoveries and ventures that demonstrate how CALS faculty advances commercialization, strengthens industry engagement, and supports faculty and student entrepreneurship.
Hosted by the CALS Research and Innovation Office (RIO), this annual, college-wide event supports the CALS Roadmap to 2050 strategic objective to transcend conventional boundaries with college-wide, transdisciplinary research and innovation programming.
Happy Hour & Employer Demo with the AI-Ready Workforce Team. Join us for drinks, appetizers, and a live demo of our AI fluency and relational fluency simulation tools!
Cornell's AI & The Future of Work Summit will take place Thursday, September 17, 2026, from 9:00am – 11:45am in the Memorial Room of Willard Straight Hall. Hosted by the Cornell AI Initiative and University Career Development, the summit brings together employer partners, faculty, and campus AI leaders for a morning of dialogue on hiring and the world of work — and how Cornell is preparing students to thrive in an AI-ready workforce. With 170+ employers already on campus for Cornell Career Fair Days, it's a timely opportunity for faculty to hear directly from industry and to share how Cornell […]
Machine learning is transforming materials discovery, but its greatest impact may come not from applying general-purpose AI to materials data, but from constructing learning methods that embody chemical and materials-physics knowledge. I will illustrate this through interpretable representations; differentiable design using Effective Atom Theory (EAT), differentiable Equiformer fast EAT (DEFEAT), and hereditary EAT (HEAT); Bayesian interpretation of experiments; and closed theory–experiment loops. Demonstrated applications span superconductors, photovoltaics, clean-energy catalysts, ptychography, and superconducting qubits.
