Incubating collaborative research
A goal of the RISE-AI Collaboration HQ is to foster new research communities built around topical focus areas, and we plan to organize teams to incubate novel research ideas. We are particularly interested in multidisciplinary topics that bring many different perspectives to the table and that include new RISE faculty.
Selected incubator teams will undertake collaborative activities such as seminars, symposia, ideation sessions for early-stage research, and networking events. The RISE-AI Collaboration HQ will support activities for selected teams.
More information will be shared soon regarding how to join the focus areas and working groups!
Focus Areas and Working Groups
Madison AI for Proteins
Anthony Gitter & Hannah Wayment-Steele
Madison AI for Proteins (MAIP) brings together researchers at UW–Madison focusing on machine learning/AI for molecular biology, structural biology, and more. The group draws inspiration (including their name) from the Maip dinosaur, a large raptorid that lived in South America.
AI+Fusion
Misha Khodak & Rogerio Jorge
AI + Fusion explores capabilities in artificial intelligence to accelerate issues in fusion research relating to control, design and daily operations. This group brings together plasma physicists, nuclear engineers and materials scientists amongst other research perspectives to discuss various topics such as advanced optimization, neural network surrogates/emulators, differentiable simulators and the use of generative AI to explore design spaces.
Center for Humanistic Inquiry into AI & Uncertainty
Jeremy Morris
The Center of Humanistic Inquiry into Uncertainty and AI (AI&U) engages in conversations with researchers and technologists working on AI and related technologies, scholars grappling with technological shifts more broadly, and the general public. Bringing together humanities scholars researching AI, AI&U strives to produce impactful academic publications and public-facing resources encompassing the uncertainties emerging AI technologies raise.
Multimodal Animal Health Informatics
David Brundage & Doerte Doepfer
Multimodal Animal Health Informatics (MAHI) creates an interdisciplinary research community across industries: computer science, clinical veterinary medicine, and wildlife health. Specifically, MAHI synthesizes multimodal data into informatics networks; all with a goal of creating transparent, safety-first AI frameworks that improve health outcomes in companion, wildlife and production animals.
Trustworthy AI
The trustworthy AI focus area brings together different disciplinary approaches what it means to be trustworthy and grounds that discussion in the challenges encountered in a range of application areas. The disciplinary approaches include formal guarantees, ethics, human psychology, and policy. The application areas include clinical and health settings, law, scientific discovery, and national security.
Machine Learning for Medical Imaging
Pallavi Tiwari & Alan McMillan
Machine Learning for Medical Imaging strives to develop and apply state-of-the-art ML solutions to challenging problems in medical imaging. This initiative responds to rapidly growing interest in ML techniques within medical imaging research, due to the unprecedented potential to solve challenging problems in areas such as image reconstruction, image processing, and computer-aided diagnosis.
Wisconsin Impact Nexus – Artificial Intelligence for Materials
Dane Morgan & Hyunseok Oh
Wisconsin Impact Nexus – Artificial Intelligence for Materials (WIN-AIM) seeks to harness the power of artificial intelligence to transform how we discover, design, optimize and qualify advanced materials. Supported by the Grainger Institute for Engineering and the Wisconsin Impact Nexus program, WIN-AIM is building a network of researchers and industry partners to pursue common innovation goals.
Join the Google Group: Wisconsin Impact Nexus – Artificial Intelligence for Materials!
AI Reasoning for Science
Moritz Münchmeyer
AI Reasoning for Science focuses on developing and evaluating AI reasoning methods for theoretical sciences; specifically physics and mathematics. This working group brings together computer scientists, physicists, mathematicians and others aiming to generate novel, verifiable theoretical results with AI-assisted reasoning.
Public Interest Technologies, by the Public Tech Media Lab
Tomas Dodds
The Public Tech Media Lab operates at the intersection of journalism and public interest technologies. Their objective is to support newsrooms in developing and implementing open-source tools for digital investigations, while designing artificial intelligence systems oriented toward the public good. Aiming to reinforce journalists’ professional autonomy, institutional accountability, and commitment to transparency; the Lab serves as a collaborative sandbox where journalists, technologists, and cross-disciplinary academics can collectively explore, solve for, and anticipate the ethical implications of emerging technologies.
Join the Google Group: Public Interest Technologies, by the Public Tech Media Lab!
AI-Catalysis
Xuhui Huang & Shannon Stahl
Discovering novel functional molecules is often a complex process involving both significant time and resources. However, recent advances in generative AI modeling have presented transformative opportunities for generative molecular design. As a working group, AI-Catalysis brings scientists across campus together to discuss chemically-intelligent AI models, novel catalysts, specific molecules and integrating high-throughput experimental techniques with computational approaches.
AI for Sustainable Agriculture
Jingyi Huang, Yakun Zhang, Zhou Zhang & Paul Stoy
The focus group convenes an interdisciplinary UW–Madison community to advance data-driven solutions for resilient, productive, and sustainable agriculture. The group will connect expertise in AI, agronomy, plant science, animal science, soil and environmental sciences, engineering, economics, climate science, and Extension, while engaging external partners from industry, government, producer organizations, and nonprofit sectors. Priority areas include AI-enabled sensing, forecasting, decision support, automation, digital agriculture, public policy, and economics. Through seminars, networking, and proposal development, the incubator will build partnerships, strengthen funding competitiveness, and translate AI innovations into practical tools that improve productivity, resource efficiency, sustainability, farm resilience, and rural prosperity.
AI for Uniquely Human Experiences
Nancy Wong
AI for Uniquely Human Experiences explores how AI can be harnessed in human-centered ways to promote optimal development and well-being across the lifespan, financial health, human relationships, and communities. Through collaboration efforts, this group digs deeper as to how uniquely human skills – such as empathy, care, and other tangible experiences – can be implemented within automated and changing AI technologies.
Any questions?
Contact the RISE-AI Collaboration HQ: rise-ai-hq@datascience.wisc.edu