Day 1 – Tuesday, March 18
Expanding the Limit of the Human Scientists
9:00 AM – 9:10 AM
Welcome remarks
9:10 AM – 10:20 AM
KEYNOTE SPEAKER
Towards an Artificial Muse for New ideas in Science
Mario Krenn, Research Group Leader of the Artificial Scientists Lab, Max Planck Institute for the Science of Light
Keynote Abstract
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)
10:20 AM – 12:25 PM
AI JOURNEYS PRESENTATIONS
10:25 AM
Christopher Miller (Astronomy, U-M College of Literature, Science, and the Arts)
Untangling Galaxy Evolution with AI
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.
10:55 AM
Qing Qu (EECS, U-M College of Engineering)
Learning Deep Low-dimensional Models from High-Dimensional Data: From Theory to Practice
This presentation addresses the integration of low-dimensional models and deep learning in science and engineering, emphasizing the importance of identifying inherent low-dimensional structures in data to enhance the formulation and understanding of deep learning models. They propose that principles from low-dimensional models can guide the design of more parameter-efficient, robust, and interpretable deep learning models, offering new perspectives on learned representations, generalizability, and transferability.
11:25 AM
Lubomir Hadjiyski (Radiology, U-M Medical School)
Large Language Models, Deep learning and Radiomics for Survival Prediction of Bladder Cancer Patients
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.
11:55 AM
Revolutionizing African Great Lakes Management: AI for Smart Monitoring, and Data-Driven
This presentation defines the scientific question as the challenge of efficiently monitoring the African Great Lakes (AGL) amidst climate change, overexploitation, and regulatory constraints, proposing AI-powered tools such as machine learning, satellite imagery, and remote sensing analytics to automate environmental assessments and enhance decision-making. By integrating AI-driven predictive modeling and standardized data-sharing frameworks, the project fosters cross-border collaboration, improves monitoring accuracy, reduces costs, and supports sustainable resource management, addressing key implementation challenges through technological innovation and coordinated governance strategies.
Working Lunch (Concurrent Sessions)
12:30 PM
Faculty Brainstorming Session
Conceptual Topics (Open to all U-M faculty members by registration in advance):
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.
Invited faculty only – Hussey Room
12:30 PM
AI TUTORIAL
An Introduction to Generative AI Tools for Research
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
James Boyko (Ecology and Evolutionary Biology, U-M College of Literature, Science, and the Arts)
Open to All Attendees – Michigan League Ballroom
Trusting AI-accelerated Research
2:30 PM – 3:40 PM
KEYNOTE SPEAKER
Integrated Systems for Computational Scientific Discovery: Progress, Challenges, and Implications
Pat Langley, Principal Research Scientist, Georgia Tech Research Institute
Keynote Abstract
There has been a steady stream of AI work on scientific discovery since the 1970s, much of it leading to published results in fields like astronomy, biology, chemistry, and physics. However, most efforts have focused on isolated tasks rather than addressing their interaction. In this talk, I challenge the research community to develop and adopt integrated discovery systems. I note distinguishing features of scientific discovery and examine five component abilities, in each case specifying the problem and reviewing results in the area. After this, I note some successes at partial integration and consider some remaining hurdles that we must leap to transform the vision for integrated discovery into reality. I also discuss promising domains, natural and synthetic, in which to test such computational artifacts. In closing, I consider ways that integrate discovery can accelerate scientific progress and factors that influence whether results are trustworthy.
3:45 PM – 5:45 PM
AI JOURNEYS PRESENTATIONS
3:45 PM
Fraser King (Climate and Space Science and Engineering, U-M College of Engineering)
Towards Interpretable Machine Learning Models Across the Geosciences
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.
To learn more about what Dr. King is working on, please visit: https://frasertheking.com
4:15 PM
Jon Zelner (Epidemiology, U-M School of Public Health)
Confronting the Challenges of AI-assisted Infectious Disease Transmission Modeling Outside the ML ‘Black Box’
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.
4:45 AM
Harkirat Singh Arora (Biomedical Engineering, Michigan Medicine)
A Mechanistic Neural Network Model Optimizes both the Potency and Toxicity of Combination Therapies
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.
5:15 AM
Nick Kotov (Chemical Engineering, U-M College of Engineering)
Graph Theory Toolbox for AI/ML Design of Complex Biomimetic Nanostructures
Summary TBA
Day 2 – Wednesday, March 19
Global Collaboration
9:00 AM – 9:10 AM
Welcome remarks
9:10 AM – 10:20 AM
KEYNOTE SPEAKER
Moving Away From Imaginary Worlds for People in the Global South
Nyalleng Moorosi, Senior Researcher, Distributed AI Research Institute (DAIR)
Keynote Abstract
While there is significant excitement surrounding generative AI and the development of increasingly large models, smaller, localized, and specialized AI tools continue to demonstrate impressive performance, even when compared to larger AI models. And whereas large tech companies continue to concentrate on developing AI-for-everything models, smaller community-based developers leverage their understanding of local communities, cultures, and languages to create tools optimized for specific problems within those communities. This hyperlocalization influences decisions ranging from dataset construction and model architecture selection to product evaluation metrics. It also impacts prioritization, which directly relates to discussions about bias, safety, and alignment.
Over the last year and a half, my colleagues and I have engaged with developers who build for their communities to understand how being part of the target audience influences priorities. Our conversations have included developers from developing nations in Africa, Latin America, and Asia, as well as those from marginalized Indigenous populations in developed nations. In each interaction, we have sought to understand how the design choices of a researcher deeply familiar with a community’s political, cultural, and historical context differ from those of a researcher with limited understanding of this context.
In this presentation, I will share our findings from the past year and discuss how these insights continue to influence our efforts as we develop solutions for our communities.
10:20 AM – 12:25 PM
AI JOURNEYS PRESENTATIONS
10:25 AM
Geoffrey Siwo (Learning Health Sciences, U-M Medical School)
Can LLMs and AI agents accelerate real-world adoption of scientific evidence?
This presentation defines the scientific question as how to accelerate the translation of scientific evidence into real-world solutions, particularly in addressing the interconnected impacts of climate change, biodiversity loss, and food systems on human health. Through the Ecosystems, Finance & Health (EFH) initiative’s innovation sprint in Nairobi, they explore leveraging generative AI to enhance access to high-quality scientific evidence, emphasizing collaboration among researchers, policymakers, and funders while addressing challenges in integrating AI-driven tools into decision-making processes.
10:55 AM
Nkem Khumbah (STI Policy Systems, Governance and Partnerships, African Academy of Sciences)
AI Journey Title TBA
Summary TBA
11:25 AM
Barbara Glover (Program Manager, African Union Development Agency-NEPAD)
Bridging Innovation and Equity: How the African Union is driving AI for Sustainable Development in Africa
AI Summary: This presentation will examine how AI can accelerate Africa’s development and the role of the African Union (AU) in shaping policies, fostering innovation, and ensuring ethical AI deployment. It will highlight the AU’s collaborative efforts through the Continental AI Strategy, governance frameworks, and partnerships to drive AI adoption while addressing challenges like data sovereignty, ethical concerns, and capacity building through policy leadership and infrastructure investment.
11:55 AM
Abiodun Modupe (Computer Science, University of Pretoria)
All You Need Is Community!
AI Summary: The Data Science for Social Impact research group at the University of Pretoria defines its scientific question around expanding AI and data science education beyond academia, leveraging collaborations with industry and grassroots initiatives like Masakhane and Deep Learning Indaba to strengthen African NLP research. By fostering inclusivity and innovation, the team navigates challenges in representation and sustainability through strategic partnerships, cutting-edge technology, and community-driven initiatives to build a thriving AI ecosystem in Africa.
Working Lunch (Concurrent Sessions)
12:30 PM
Faculty Brainstorming Session
Implementation Topics (Open to all U-M faculty members by registration in advance):
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.
– 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
Invited faculty only – Hussey Room
12:30 PM
AI TUTORIAL
Segment Anything Model: A Beginner’s Guide to AI-Powered Image Segmentation
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.
Nathan Fox (MIDAS, U-M Office of the Vice Provost of Research)
Open to All Attendees – Michigan League Ballroom
The Nature of Scientific Creativity
2:30 PM – 3:40 PM
KEYNOTE SPEAKER
Lav R Varshney (Associate Professor, University of Illinois Urbana-Champaign)
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.
3:45 PM – 5:45 PM
AI JOURNEYS PRESENTATIONS
3:45 PM
Xiaofan Liang (U-M Taubman College of Architecture and Urban Planning)
Lessons from a human-in-the-loop machine learning approach for identifying vacant, abandoned, and deteriorated properties in Savannah, Georgia
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.
4:15 PM
Peter Adriaens (Civil and Environmental Engineering, U-M College of Engineering)
Designing an AI Model to Predict Financial Operational and Supply Chain Risk from Water Scarcity in Global Corporate Facilities
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.
4:45 PM
Dani Jones (Cooperative Institute for Great Lakes Research)
Navigating Environmental Data: Unsupervised Classification in Oceanic and Atmospheric Research
AI Summary: The team analyzed extensive oceanographic data from autonomous profiling floats was analyzed 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.
5:15 PM
CHATAC: An Air Traffic Flow Management LLM-powered System
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.
Questions? Contact Us.
Message the MIDAS team: [email protected]