At the forefront of AI-in-science innovation, MIDAS’s Schmidt AI in Science Postdoctoral Fellows are redefining the way artificial intelligence is applied across diverse research fields. Their cutting-edge research includes developing AI-driven techniques to uncover hidden patterns in complex genomic data, offering insights into large-scale biological processes like mass extinction events, developing methodologies for discovery and optimization of engineering processes and materials, and more. By bridging traditional research domains with AI-centric study, they enhance our ability to predict natural disasters, optimize aerospace engineering, and improve ecological monitoring. Their interdisciplinary collaborations and innovative solutions emphasize the potential of AI to drive scientific discovery and problem-solving.
AI tool reveals climate shifts may have fueled bursts of bird evolution
University of Michigan researchers used AI to uncover evidence that major climate shifts coincided with rapid bursts of bird evolution over the past 45 million years. Lead author Jacob Berv developed the novel statistical framework that enabled the team to analyze whole-skeleton data from thousands of bird species, revealing how climate change shaped the evolution … Read more
Commun-AI-ty Workshop Brings Researchers and Educators Together to Reimagine AI Science Communication
What if AI literacy weren’t developed in research labs alone, but co-designed with the communities it is intended to serve? That question brought together 54 participants from five countries for Commun-AI-ty: Building Science Communication Skills for Explaining AI in Science, a 3.5-day workshop held at the University of Michigan from June 23–26, 2026. Made possible … Read more
Large-Scale Evolution Simulations on PSC’s Neocortex Tackle Questions about Hypermutator Evolution
Matthew Andres Moreno, a Schmidt AI in Science Fellow, led groundbreaking research using the Pittsburgh Supercomputing Center’s Neocortex supercomputer to investigate why rapidly mutating organisms (“hypermutators”) rarely dominate in nature. By scaling evolutionary simulations from thousands to 1.5 billion virtual organisms, Moreno’s work uncovered how population size and the availability of beneficial mutations shape evolutionary … Read more
Data Visualization in R Workshop Helps Researchers Transform Data Into Publication-Ready Graphics
MIDAS African Faculty Fellow Dr. Verrah Otiende recently led a hands-on Data Visualization in R workshop for graduate students and postdocs, introducing participants to the R programming language and the ggplot2 visualization tool. The workshop brought together researchers with varying levels of coding experience to explore how raw research data can be transformed into clean, … Read more
U-M Uncertainty Quantification Incubator Brings Researchers Together to Advance Trustworthy AI
The Michigan Institute for Data and AI in Society (MIDAS) hosted the University of Michigan Uncertainty Quantification (UQ) Incubator from May 31 to June 3, 2026, bringing together researchers from a range of disciplines, including uncertainty quantification (UQ), artificial intelligence, science and engineering, to explore how AI systems can become more reliable, interpretable and trustworthy. … Read more
AI and Open Data Redesign Urban Transit: A Blueprint for Equity and Efficiency
Author: Yonas Minalu Emagnu, Schmidt Science African Faculty Fellow How do you design a public transport system for a city growing faster than its infrastructure? In two interconnected studies using Addis Ababa, Ethiopia as a case study, Dr. Yonas Minalu Emagnu, in collaboration with Tayo Fabusuyi, has developed a scalable, data-driven framework that answers this … Read more
Announcing the 2026 cohort of postdoctoral fellows
Michigan Institute for Data & AI in Society announces 2026 fellows Written by: Justin Varney The Michigan Institute for Data & AI in Society (MIDAS) is welcoming a new cohort of postdoctoral researchers and international faculty for 2026, further strengthening its global community that advances artificial intelligence and data science across disciplines. Eleven new postdoctoral … Read more
Bridging Knowledge and Experiment Gap with Differentiable Electrochemistry
Author: Haotian Chen Engineering Research Schmidt AI in Science Fellow Haotian Chen, along with his Science mentor Prof. Venkat Viswanathan and AI mentor Prof. Alexander Rodríguez are building the first differentiable electrochemistry framework to bridge the theory-experimental gap in electrochemistry. Electrochemistry simulations are made end-to-end differentiable to obtain gradients of physical processes for learning and optimization. … Read more
Optimizing Scientific Computing with Program Synthesis
Author: Zheng Guo Engineering Research Schmidt AI in Science Fellow Zheng Guo leverages program synthesis to accelerate scientific computing, with a particular focus on optimizing high-dimensional tensor approximations and computational kernels used across computational science. By automating the search for optimal network structures and contraction orders under diverse optimization objectives, his research enhances the efficiency and accuracy … Read more
Improving Unmanned Aerial Vehicle (UAV) Flights with Smarter Models
Author: Elena Shrestha Engineering Research Schmidt AI in Science fellow Elena Shrestha, along with Derrick Yeo, lecturer in robotics, lead research in coordination with the Center for Autonomous Air Mobility and Sensing (CAAMS), a partnership between academia, industry, and government. Their work aims to improve guidance and control techniques for multi-mode unmanned aircraft systems by … Read more
Machine Learning for the Analysis of Star-Forming Regions
Author: Vital Fernandez Physical Science Research Schmidt AI in Science Fellow Vital Gutiérrez Fernández is applying machine learning to the chemical and dynamical analysis of the light from star-forming regions. When stars are born, especially massive and young ones, they emit intense light and energy. This UV light is so powerful that it strips electrons from atoms in … Read more
Optimizing Telescope Scheduling with Reinforcement Learning: The Roboqueue Project
Authors: Kevin Napier, Vital Fernandez Physical Science Research Kevin Napier and Vital Fernández, both Schmidt AI in Science Fellows, participated in a Carpentry on Reinforcement Learning and are part of a group addressing the challenge of optimizing the scheduling of telescope tasks. Astronomers worldwide depend on a limited number of extremely large telescopes for their research. Due … Read more
Leveraging AI and Natural Hazard Modeling for Improved Disaster Understanding and Mitigation
Author: Xin Wei Earth and Environmental Science Research Schmidt AI in Science Fellow Xin Wei‘s research centers on assessing natural hazards, with a particular emphasis on geohazards such as landslides. He investigates historical losses and community impacts and develops innovative risk assessment tools to mitigate future disasters. His work integrates geotechnical engineering expertise with advanced AI … Read more
AI-Driven Flood Communication Analysis by Schmidt Fellows Showcased at ISCRAM Conference
Authors: Christin Salley, Nathan Fox, Alyssa Schubert Earth and Environmental Science Research Three Schmidt AI in Science Fellows–Christin Salley, Nathan Fox, and Alyssa Schubert–took part in a MIDAS Carpentry focusing on Natural Language Processing. In this regularly recurring AI-based research and work group, the three Fellows researched and applied Natural Language Processing methodologies, leading to a conference … Read more