Cheng Li

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My research focuses on developing advanced numerical models and computational tools to enhance our understanding and prediction capabilities for both terrestrial and extraterrestrial climate systems. By leveraging the power of data science, I aim to unravel the complexities of atmospheric dynamics and climate processes on Earth, as well as on other planets such as Mars, Venus, and Jupiter.

My approach involves the integration of large-scale datasets, including satellite observations and ground-based measurements, with statistical methods and sophisticated machine learning algorithms including vision-based large models. This enables me to extract meaningful insights and improve the accuracy of climate models, which are crucial for weather forecasting, climate change projections, and planetary exploration.

Uduak Inyang-Udoh

Uduak Inyang-Udoh

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My research seeks to exploit graph-based modeling theory and the tools of machine learning for efficient control of physical dynamical systems and control co-design in these systems. I am particularly interested in the design of graph-based machine/deep learning model structures that are compatible with basic physics, and using those model structures for real-time actions. Application of interest include advanced manufacturing, thermal and energy storage systems.

Alexander Rodríguez

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Alex’s research interests include machine learning, time series, multi-agent systems, uncertainty quantification, and scientific modeling. His recent focus is on developing trustworthy AI systems that can offer insightful guidance for critical decisions, especially in applications involving complex spatiotemporal dynamics. His work is primarily motivated by real-world problems in public health, environmental health and community resilience.

Irina Gaynanova

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Dr. Gaynanova’s research focuses on the development of statistical methods for analysis of modern high-dimensional biomedical data. Her methodological interests are in data integration, machine learning and high-dimensional statistics, motivated by challenges arising in analyses of multi-omics data (e.g., RNASeq, metabolomics, micribiome) and data from wearable devices (continuous glucose monitors, ambulatory blood pressure monitors, activity trackers).Dr. Gaynanova’s research has been funded by the National Science Foundation, and recognized with a David P. Byar Young Investigator Award and an NSF CAREER Award. She currently serves as an Associate Editor for Journal of the American Statistical Association, Biometrika and Data Science in Science.

Tian An Wong

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Analysis of policing technology and police data, including impact assessment of surveillance technology, media sentiment analysis, and fatal police violence. Methods include topological data analysis, natural language processing, multivariate time series analysis, difference-in-differences, and complex networks.

Cristian Minoccheri

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Dr. Minoccheri’s research interests focus on using mathematical tools to enhance existing machine learning methods and develop novel ones. A central topic is the use of tensor methods, multilinear algebra, and invariant theory to leverage higher order structural properties in data mining, classification, and deep learning. Other research interests include interpretable machine learning and transparent models. The main applications are in the computational medicine domain, such as phenotyping, medical image segmentation, drug design, patients’ prognosis.

Venkat Viswanathan

Venkat Viswanathan

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Research on computational modeling of energy materials design and optimization

1) I led this large research project on developing machine-learning guided materials discovery demonstrating speed-up of over 80% over traditional methods.

2) My research group runs a popular Scientific Machine Learning webinar series: https://micde.umich.edu/news-events/sciml-webinar-series/

Qiong Yang

Qiong Yang

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My research program at the University of Michigan (UM) integrates the fields of biophysics, quantitative systems biology, and bottom-up synthetic biology to understand complex stochastic cellular and developmental processes in early embryos.
We have developed innovative computational and experimental techniques in microfluidics and imaging to allow high-throughput quantitative manipulation and single-cell lineage tracking of cellular spatiotemporal dynamical processes in various powerful in vitro and in vivo systems we established in my lab. These systems range from cell-free extracts, synthetic cells reconstituted in microemulsion droplets, presomitic mesoderm (PSM) and progenitor zone (PZ) cells dissociated from the zebrafish tail buds, their re-aggregated 2D and 3D cell-cell communications, ex vivo live tissue explants, and live embryos.
Our current research questions center around the understanding of the design-function relation of robust biological timing, growth, and patterning, how individual molecules and cells communicate to generate collective patterns, and how biochemical, biophysical, and biomechanical signals work together to shape morphogenesis during early embryo development.

Mohammed Ombadi

Mohammed Ombadi

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My research focuses on understanding and quantifying climate change impacts on hydroclimatic extremes. From heavy storms and floods to extreme heatwaves and droughts, I study the changing characteristics of these events and their impacts on our daily lives. I use a wide range of data-driven methods such as causal inference, information theory, nonlinear dynamics and machine learning in the analysis of environmental systems. I am primarily interested in using causal inference to obtain new mechanistic insights on the impact of global warming on extreme weather events. My recent work has explored global warming impact on extreme events such as rainfall extremes, using a combination of observations and model simulations. Additionally, recent work has focused on developing new techniques to measure resilience of environmental systems to extreme events, with the ultimate goal of informing mitigation and adaptation strategies to climate change.Map of the Northern Hemisphere showing the projected increase in extreme daily rainfall by 2100 relative to 1950–1979 (the risk ratio). Darker areas are predicted to be more prone to increased rainfall extremes with global warming.

Map of the Northern Hemisphere showing the projected increase in extreme daily rainfall by 2100 relative to 1950–1979 (the risk ratio). Darker areas are predicted to be more prone to increased rainfall extremes with global warming.

What are some of your most interesting projects?

Resilience of Watersheds to Extreme Weather and Climate Events:

Hydrologic watersheds are the fundamental units of the land surface used in the analysis and management of water resources systems. The response of watersheds to extreme events is highly complex and determined by a multitude of factors, including the presence of dams and reservoirs, snowpack, groundwater-surface water interaction, and vegetation cover, among others. One significant knowledge gap in this field of research is how to objectively and unambiguously quantify the resilience and resistance of watersheds to extreme events, such as droughts and floods. Developing metrics to quantify resilience is of utmost importance, particularly in light of the changing characteristics of extreme events due to global warming.

In this project, I employ a wide range of statistical methods to quantify resilience. I then apply methods of machine learning, causal inference, and graph-based techniques to explore patterns of resilience across watersheds worldwide.

How did you end up where you are today?

I originally hail from Sudan, nestled at the heart of Africa. Growing up along the banks of the Nile River, I developed an early fascination with water and its profound connection to humanity. On one hand, it provides people with their needs for drinking, agriculture, transportation, and recreation. On the other hand, an excess or shortage of water often results in devastating natural disasters, such as floods, droughts, and famines. Throughout antiquity, humans have endeavored to regulate rivers by building dams, canals, and various other structures. This deep-seated interest in water sciences and related engineering disciplines led me to pursue a degree in Civil Engineering.

Following my college graduation, I chose to embark on graduate studies to delve deeper into the intricate relationship between climate change and the water cycle. Embarking on an arduous 18-hour flight, I traversed the vast Atlantic Ocean and the continental expanse of the United States to arrive in California, the Golden State. There, I successfully completed my MSc and PhD degrees at the University of California, Irvine, in just under five years. Subsequently, I relocated northward to Berkeley, where I conducted my postdoctoral research, focusing on the impact of global warming on climate extreme events. I come to University of Michigan with a deep research interest in exploring the impact of global warming on extreme events and the resilience of ecosystems to such events. My research group harness recent advances in data science (machine learning, causal inference and information theory) to obtain new mechanistic and predictive insights on these questions with the ultimate goal of informing climate change adaptation strategies.

Kamran Diba

Kamran Diba

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My lab is primarily interested in how the brain represents, coordinates, and stores memories. Hippocampal neuronal networks generate an assortment of firing patterns that vary depending on the behavior and state of an animal, from active exploration to resting and different stages of sleep. In our lab’s extracellular recordings from large populations of spiking neurons in rodents, we observe state-dependent temporal relationships between activities at multiple timescales. Recent work in my lab is aimed at understanding what role these unique spike patterns play and what they tell us about the function and limitations of different brain states for memory in healthy and compromised animals. To answer these and related questions, we combine behavioral studies of freely moving, learning and exploring rats, multi-channel recordings of the simultaneous electrical (spiking) activity from hundreds of neurons during behavior, sleep and sleep-deprivation, statistical and machine learning tools to uncover deep structure within high-dimensional spike trains, and chemogenetics and optogenetics to manipulate protein signaling and action potentials in specific neural populations in precise time windows.

Spike times recorded from a population of hippocampal neurons during running on a maze.

Spike times recorded from a population of hippocampal neurons during running on a maze.

What are some of your most interesting projects?

Evaluating the impact of sleep loss on hippocampal replay.
Using unsupervised machine learning to evaluate the temporal structure of hippocampal firing patterns during sleep.

What is the most significant scientific contribution you would like to make?

Understand how the hippocampus serves memory and what role sleep plays in this process.

What makes you excited about your data science and AI research?

The potential for AI models to help explain how the brain works.