Overview
MIDAS organizes this event annually to enable the use of artificial intelligence (AI) to achieve research breakthroughs that were previously unfathomable. In this symposium, we bring together world-class researchers to share their vision and work, share “AI Journeys” stories – how University of Michigan researchers have been incorporating AI in their research and the lessons they impart to the audience, and offer sessions for research brainstorming and skill building. The symposium will feature four themes:
- Expanding the limit of the Human Scientist
- Trusting AI accelerated research
- Global Collaboration
- The nature of scientific creativity
The intended audience are researchers and trainees regardless of the specific fields of their research and the focus of their intellectual pursuit, as well as the general public who are curious about AI and its impact on science and humanity.
Schedule
(click each day below to see schedule and presentation details)
Session 1: Expanding the Limit of the Human Scientists
Towards an artificial muse for new ideas in Science
Artificial intelligence (AI) is a potentially disruptive tool for physics and science in general. One crucial question is how this technology can contribute at a conceptual level to help acquire new scientific understanding or inspire new surprising ideas. I will talk about how AI can be used as an artificial muse in physics, which suggests surprising and unconventional ideas and techniques that the human scientist can interpret, understand and generalize to its fullest potential.
[1] Krenn, Kottmann, Tischler, Aspuru-Guzik, Conceptual understanding through efficient automated design of quantum optical experiments. Physical Review X 11(3), 031044 (2021).
[2] Krenn, Pollice, Guo, Aldeghi, Cervera-Lierta, Friederich, Gomes, Häse, Jinich, Nigam, Yao, Aspuru-Guzik, On scientific understanding with artificial intelligence. Nature Reviews Physics 4, 761–769 (2022).
[3] Krenn et al., Forecasting the future of artificial intelligence with machine learning-based link prediction in an exponentially growing knowledge network, Nature Machine Intelligence 5, 1326 (2023)
[4] Gu, Krenn, Interesting Scientific Idea Generation Using Knowledge Graphs and LLMs: Evaluations with 100 Research Group Leaders. arXiv:2405.17044 (2024)
“AI Journeys” are research stories presented by U-M faculty members who have started research projects with AI implementation. These presentations will focus on the researchers’ experience on determining a research question, identifying an AI method, enlisting collaborators, and recommendations of dos and don’ts.
AI Summary: The researchers aimed to improve astrophysical inference by applying Convolutional Neural Networks (CNNs) to complex astronomical imaging, addressing the limitations of traditional models in capturing the intricate physical relationships between stars and their environments. Collaboration required expertise across astrophysics, instrumentation, and AI, overcoming challenges related to interpretability and scientific tradition, ultimately leading to a restructured, NSF-funded research program focused on trustworthy AI-driven discoveries.
AI Summary: The project team deploys LLMs in air traffic flow management by developing CHATATC, a question-answering system trained on a comprehensive dataset of Ground Delay Programs. The presentation will focus on addressing challenges in query accuracy and information and share experience of working with collaborators in a highly regulated industry.
AI Summary: The researchers aimed to improve astrophysical inference by applying Convolutional Neural Networks (CNNs) to complex astronomical imaging, addressing the limitations of traditional models in capturing the intricate physical relationships between stars and their environments. Collaboration required expertise across astrophysics, instrumentation, and AI, overcoming challenges related to interpretability and scientific tradition, ultimately leading to a restructured, NSF-funded research program focused on trustworthy AI-driven discoveries.
AI Summary: The project team used AI, including large language models, radiomics, and deep learning, for predicting five-year survival rates in bladder cancer patients by integrating clinical reports and imaging data. A multidisciplinary team collaborated closely with clinical experts to define key tasks, annotate data, and evaluate results, addressing challenges in data collection, model selection, and robust validation through pilot experiments, transfer learning, and iterative refinement of AI methods.
12 – 2 PM: Working Lunch (concurrent sessions)
At lunchtime on both days of the symposium, postdoctoral researchers at MIDAS will offer hands-on tutorials on AI methods and their applications in research. No registration needed. Open to all symposium attendees.
Dr. James Boyko, Assistant Professor/Postdoc Scholar, Ecology and Evolutionary Biology, College of Literature, Science, and the Arts
This tutorial introduces using commercially available Generative AI for research. Assuming no background knowledge, we will discuss which tools are currently available, how to best use them, and address common misconceptions. This tutorial will not dive into the technical details underlying the models themselves, instead focusing on how to use these tools to enhance your research workflow. Most of the tutorial will be focused on Large Language models (LLMs) such as ChatGPT and the skill of prompting to receive high quality replies
Location: Hussey Room
Focus: Conceptual Topics
Open to all U-M faculty members. Faculty members will discuss their ideas with the group on using AI to address research questions and developing AI for research applications. They will also connect with collaborators. Below are some example prompts, but the specific focus of the sessions will be determined by participant interest.
- Methods to address issues of research rigor and reproducibility, and the validation and explainability of research output.
- Domain research questions that can be facilitated through the use of AI.
- AI methods that could be particularly amenable to science and engineering research.
- Connecting data and AI.
Session 2: Trusting AI-accelerated Research
“AI journeys” are research stories presented by University of Michigan faculty members who have started research projects with AI implementation. These presentations will focus on the researchers’ experience on determining a research question, identifying an AI method, enlisting collaborators, and recommendations of dos and don’ts.
AI Summary: This work addressed the challenge of ensuring trust in machine learning (ML) models for geosciences by focusing on explainability and interpretability to align model decisions with physical processes. Through international collaboration on projects using generative AI, sparse autoencoders, and dimensionality reduction, the team navigated challenges of model complexity, leveraging interdisciplinary expertise, robust validation, and transparent communication to foster trust in AI-driven approaches for high-stakes environmental applications.
AI Summary: This presentation will demonstrate the importance of deep, interdisciplinary collaboration in calibrating complex infectious disease models using AI-driven Bayesian techniques, moving beyond a traditional “consultative” approach to foster meaningful integration across Epidemiology, Biostatistics, and Statistics. By blurring the boundaries between methodological and substantive expertise and prioritizing cross-disciplinary mentorship, this project highlights the need for mutual understanding and transparency to navigate the growing complexity of data and model calibration.
AI Summary: This presentation will look at examining antibiotic resistance through AI-driven optimization of drug combination therapies. Challenges like bridging biological insights with AI and validating predictions were overcome through interdisciplinary collaboration with the Center for Chemical Genomics.
Session 3: Global Collaboration
“AI Journeys” are research stories presented by U-M faculty members who have started research projects with AI implementation. These presentations will focus on the researchers’ experience on determining a research question, identifying an AI method, enlisting collaborators, and recommendations of dos and don’ts.
Abstract
Abstract
Abstract
12 – 2 PM: Working Lunch (concurrent sessions)
At lunchtime on both days of the symposium, postdoctoral researchers at MIDAS will offer hands-on tutorials on AI methods and their applications in research. No registration needed. Open to all symposium attendees.
Dr. Nathan Fox, Schmidt AI in Science Fellow
This tutorial introduces the Segment Anything Model (SAM), a cutting-edge AI tool for universal image segmentation. Image segmentation is the process of dividing an image into meaningful parts, such as separating objects, regions, or structures within the image. It’s a fundamental technique for tasks like identifying animals in photos, analyzing satellite imagery, or segmenting cells in microscope imagery. Participants will learn how SAM works, explore its key features, and apply it to diverse research scenarios, including ecology, microbiology, and geography. Through hands-on exercises, attendees will gain practical experience in leveraging SAM for their projects.
Location: Hussey Room
Open to all U-M faculty members. Please register for one or both sessions. Faculty members will discuss their ideas with the group on using AI to address research questions and developing AI for research applications. They will also connect with collaborators. Below are some example prompts, but the specific focus of the sessions will be determined by participant interest.
We will focus on conceptual topics, such as:
- Opportunities and challenges in AI implementation in research
- How to develop the necessary skills and collaboration
- What institutional support is needed at the infrastructure, technical and intellectual levels
Session 4: The Nature of Scientific Creativity
Title: AI and Scientific Creativity
Artificial Intelligence (AI) is increasingly contributing to scientific discovery, yet its potential for enhancing scientific creativity remains largely unexplored. Drawing insight from the philosophy of science and creativity research, we describe a novel classification of AI’s contributions to scientific creativity based on the concreteness and autonomy of generated ideas. These include: (1) AI Muse, where AI systems suggest surprising combinations of concepts that researchers can develop into concrete research plans; (2) AI Designer, where systems propose executable ideas in the form of specific experiments, equations, or reaction pathways; and (3) AI Scientist, where AI systems autonomously execute discovered ideas through automated experimentation, simulation, or mathematical proofs. For each class, we analyze how to increase both novelty and functionality—the twin components of creativity. We further discuss novelty and functionality in the context of our mathematical theory of creativity, which provides an information-geometric tradeoff between the two dimensions, as well as formal mathematical relationships between statistical and structural notions of creativity. The broad framework provides a foundation for developing AI systems that can contribute more meaningfully to scientific creativity and accelerate the pace of scientific discovery.
“AI Journeys” are research stories presented by U-M faculty members who have started research projects with AI implementation. These presentations will focus on the researchers’ experience on determining a research question, identifying an AI method, enlisting collaborators, and recommendations of dos and don’ts.
AI Summary: The study used a human-in-the-loop machine learning (HITLML) approach to identify vacant, abandoned, and deteriorated (VAD) properties in Savannah, Georgia, addressing the challenge of limited municipal data and the need for transparency in AI processes. Collaboration between academic researchers and city planners revealed that while the AI system broadened property identification beyond traditional field surveys, the project faced challenges with expert involvement costs, data maintenance, and scaling the system, highlighting the importance of sustained human-AI interaction and infrastructure investment over technical model details.
AI Summary: An advanced AI model is being developed to predict financial risks that climate-induced water scarcity poses to corporations, particularly in water-intensive industries. Collaboration was established through the U-M Center for Digital Asset Finance, partnering with LimnoTech for water resource modeling, and Equarius for financial risk modeling. Key challenges in the project include defining system boundaries, curating diverse data sources, developing causal relationships, and extracting meaningful insights through natural language processing, all of which are being addressed through interdisciplinary collaboration and leveraging computational infrastructure.
AI Summary: The team analyzed extensive oceanographic data from autonomous profiling floats by applying unsupervised learning techniques, specifically Gaussian Mixture Modeling (GMM), to objectively identify large-scale ocean structures like gyres without relying on geographic information. To foster collaboration, an “AI Lab” was established to facilitate interactions between machine learning experts and domain scientists, addressing challenges such as data complexity and interpretability.
AI Summary: This presentation defines the scientific question as bridging low-dimensional models with generative AI to enhance efficiency, interpretability, and control in scientific and engineering applications. It will discuss collaboration through the integration of traditional model-based approaches with modern data-driven techniques, addressing challenges such as limited data, interpretability, and safety concerns by leveraging low-dimensional structures for efficient training, understanding generalization, and controlling content generation.
Keynote speakers
Dr. Mario Krenn, Research Group Leader of the Artificial Scientists Lab, Max Planck Institute for the Science of Light
An innovator in developing “artificial scientists” for conceptual advancements in science
Dr. Krenn is a research group leader of the Artificial Scientist Lab at the Max Planck Institute for the Science of Light (Theory Division). He is excited about the potential of artificial intelligence-inspired and -augmented science, and how we can use algorithms in a more “creative” way. To make progress, he believes it will be important to learn what humans mean by crucial scientific concepts such as surprising, creativity, understanding or interest. He has created AIs for designing quantum experiments and hardware (several actually built in labs) and inspiring novel ideas for quantum technologies. (See these Scientific American and National Academy of Science articles for recent coverage on part of this research). He also builds autonomously semantic networks from scientific publications, and use machine learning to predict and suggest personalized future research questions and ideas. In that sense, Krenn uses the machine as a source of inspiration to accelerate scientific progress. Ultimately, he wants to create algorithms that help us to uncover the secrets of the Universe. More about the research philosophy and ideas can be read in this Nature Review Physics perspective. Krenn’s work was awarded an ERC Starting Grant 2024.
Dr. Pat Langley, Director of the Institute for the Study of Learning and Expertise
A pioneer in computational discovery and formalizing the scientific process
Pat Langley received his Ph.D. in psychology from Carnegie Mellon University. He currently serves as Director for the Institute for the Study of Learning and Expertise. His research interests revolve around computational learning and discovery, especially their role in computational biology, scientific data analysis, adaptive user interfaces, and cognitive architectures for intelligent agents.
Nyalleng Moorosi, Senior Researcher, Distributed AI Research Institute (DAIR)
A trailblazer in global collaboration for local models and local challenges
Nyalleng is a senior researcher at DAIR, and her research interests are in understanding how we can build models which center populations often regarded as peripheral. Before DAIR, she was a research software engineer at Google, where she was one of the first employees at the Google Africa research lab. She has also been a senior researcher at the South African Council for Scientific and Industrial research, where she worked closely with government and academic institutions to develop a diversity of products for private and public institutions. Outside of formal work she is involved in efforts to democratize AI; she is a founding member of the Deep Learning Indaba, the largest machine learning consortium of AI/ML practitioners in Africa, a member of A+ Alliance an international coalition that seeks to not only detect, but correct, gender bias in Artificial Intelligence.
Dr. Lav R Varshney, Associate Professor, University of Illinois Urbana-Champaign
A discoverer of various forms of computation to boost individual and collective intelligence
Lav Varshney is a professor of electrical and computer engineering at the University of Illinois Urbana-Champaign. He leads the AI and hybrid cloud thrust of the IBM-Illinois Discovery Accelerator Institute, a $200M investment. He is also co-founder and CEO of Kocree, Inc., a startup company using AI in social music co-creativity platforms to enhance human wellbeing across society, and chief scientist of Ensaras, Inc., a startup company focused on AI to optimize wastewater treatment. He further holds affiliations with RAND Corporation and with Brookhaven National Laboratory. He is a former White House staffer, having served on the National Security Council staff as a White House Fellow, where he contributed to national/international AI and wireless communications policy. Previously at IBM Research, he led the development and deployment of the Chef Watson system for culinary creativity, the first commercially successful generative AI technology, which also received worldwide acclaim. At Salesforce Research, he was part of the team that open-weight released the largest and most capable large language model at the time, jumpstarting the open-weight movement. His work and public scholarship has been featured in media ranging from Fox News and the Wall Street Journal to the New York Times, NPR, Slate, and The New Yorker. He appeared in the Robert Downey, Jr. documentary series, Age of AI. He holds a B.S. degree in electrical and computer engineering from Cornell University and S.M. and Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology. His current research interests include information theory; AI foundations, explainability, and governance; agent-based policymaking; creativity and innovation; and AI applications in sustainability, science, and wellbeing.
Registration
This symposium is free to all members of the U-M community (faculty/staff, students, affiliates, alums)
For members of the public, the $200 registration fee covers attendance for both days including AI Tutorials and lunch. Fee payable by credit card.
RegisterThis event is made possible with the generous support of Schmidt Sciences.
Organizers
Walter Dempsey
Assistant Professor of Biostatistics, Assistant Research Professor at the Institute of Social Research, Department of Biostatistics
Facilities & Accessibility
- 2nd Floor Ballroom, Michigan League, 911 N University Ave., Ann Arbor, MI 48109
- Parking
- Metered parking is available on several streets surrounding the Michigan League. There is also a public parking lot on Maynard Street, three blocks from the League.
- All programming will be on the 2nd floor, accessible via elevators on the north end of the building.
- For more information, please consult the Michigan League section of https://uunions.umich.edu/plan-your-trip/ for floors plans and driving directions.
Questions? Contact Us.
For questions or more details, please contact Ben Surgalski ([email protected])