Agenda
Embedded Files
Agenda
All sessions will take place in Keller 3-180.
The posters will be displayed in the hallway outside Keller 3-180.
Food will be served in Keller 3-176. Tables will be set up in the area outside Keller 3-176.
9:15 AM Generative AI for Science and Engineering: Foundations, Frontiers, and the Role of Scientific Knowledge
Anuj Karpatne, Virginia Tech
Aryan Deshwal, UMN CS&E
Part 1: Anuj Karpatne
Recording
Part 2: Aryan Deshwal
Recording
Part 3: Anuj Karpatne
Recording
This session introduces the core components and historical roots of generative AI, examines how these methods can advance scientific and engineering discovery, and highlights the unique challenges posed by scientific problems. It emphasizes how incorporating scientific knowledge, constraints, and structure into GenAI frameworks is essential for advancing both scientific discovery and the next frontier of generative models.
10:45 AM BREAK
11:00 AM Seed Grant Awardee Showcase
Chris Bartel
Accelerating materials discovery with adaptively guided Generative AI
Seongjin Choi
Physics-informed Generative Model for High-Resolution Traffic State Estimation and Optimal Sensor Placement
Dongyeop Kang
Scaling Unverifiable Rewards: A Case Study of Data Science Workflow
Zhen Liu
GenAI-Enabled Representation of Quantum ChromoDynamics
Sapna Sarupria
Role of generative AI in molecular design of soft matter -- where are we and where can we go?
Qianwen Wang
Graph GenAI for Interpretable Combustion Reaction Mechanism Discovery in Propulsion and Energy
11:45 AM Poster Sparkler
Andrius Adomavicius
AI-Driven Precipitation Downscaling
Theresa Chen
Multimodal Machine Learning
Aryan Deshwal
Accelerating materials discovery with adaptively guided Generative AI
Leeje Jang
AI for Solar Science: Transient Detection and Connection
Leeje Jang
OMNI-Dent: Towards an Accessible and Explainable AI Framework for Automated Dental Diagnosis
Marlin Lee
K Init-SAE: Resolving Dictionary Stability in Mechanistic Interpretability
Peiran Li
Decoding the QCD Structure of Quark and Gluon Jets with Machine Learning
Lindong Liu
PMA-Diffusion: A Physics-guided Mask-Aware Diffusion Framework for TSE from Sparse Observations
Devin Mahon
Robustness of GNN-Based Reconstruction Algorithms for the CMS High Granularity Calorimeter
Sitong Pan
Human-Agent Collaboration in Web Navigation
Noon LUNCH
1:15 PM Lightning Talks: UMN Research Highlights
Ellad Tadmor
AI for Materials Discovery
Mehmet Akcakaya
Generative models for fast MRI reconstruction
Caiwen Ding
Agentic AI for computer system design
Lucy Fortson
Report on 2025 CCC Visioning Workshop on the Convergence of Computational and Citizen Science
Mohammad Ali Maddah-Ali
ODED-SMOOTHING: Coding Theory Helps Generalization
Jiyoon Pyo
Reading Maps with LLMs: Where They Fail, and Why It Matters
Murti Salapaka
AI based methods for Single Molecule Physics
Ju Sun
Generative Models for Scientific Inverse Modeling with Constraints
Ajay Kumar Gurumadaiah
GenAI for Remote Driving
2:15 PM BREAK
2:30 PM Panel: Emerging Federal Directions in AI for Science
Moderator: Ananth Grama, Purdue University
This panel brings together federal program managers to discuss emerging national directions in AI for science, including efforts aligned with the Genesis Mission, in the context of rapidly advancing generative AI frontiers. Academic institutions play a central role in advancing scientific frontiers and in training the future workforce. A key focus of the panel is how the federal government can leverage these strengths to help steer generative AI advances toward mission-relevant directions.
4:00 PM Concluding Remarks and Networking Reception
Posters will still be on display for the reception.
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