Grant Schoenebeck

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My current research combines machine learning tools and economic approaches (e.g game theory, mechanism design, and information design) to develop and analyze systems for eliciting and aggregating information from of diverse group of agents with varying information, interests, and abilities.
This work applies to scenarios where a collective decision-making process is required, such as peer grading, peer review, crowd-sourcing, content moderation, misinformation detection, surveys, and employment hiring/evaluation.
More broadly, I am interested in multi-agent systems, a subfield of AI; data economics; and algorithmic game theory.

Derek Van Berkel

Derek Van Berkel

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Dr. Van Berkel is an assistant professor at The University of Michigan, School for Environment and Sustainability. His research focuses on understanding land change at diverse scales; the physical and psychological benefit of exposure to natural environments; and how digital visualization of data can add new place-based knowledge in science and community decision-making. He has expertise in spatial statistics, data science, big data, and machine learning. Van Berkel is currently a Co-PI on an NSF grant examining how online webtools can enable the public to co-create landscape designs for novel solutions to climate-change adaptation and mitigation in urban areas. He is also part of the NOAA funded GLISA project developing land change models to support knowledge discovery in municipalities throughout the Great Lake States. His work in AI focuses on deciphering complex sentiment from multimodal content, such as understanding image content and analyzing captions and tags posted by users, at scale. This research aims to provide objective measures of behavior and attitude for modeling diverse values and benefits of nature globally.


Accomplishments and Awards

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.

Alauddin Ahmed

Alauddin Ahmed

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My core research expertise involves developing and employing a wide array of computational methods to discover, design, and characterize materials and systems that address critical challenges in energy and the environment. These methods span from stochastic techniques to molecular dynamics, density functional theory, quantum chemistry, and data science. Beyond contributing fundamental design principles for high-performing materials, my research has led to the discovery of record-breaking materials for hydrogen storage, natural gas storage, and thermal energy storage, alongside creating open-access databases, machine learning models, and Python APIs.

In data science, I have uniquely contributed to feature engineering, compressed sensing, classical machine learning algorithms, symbolic regression, and interpretable ML. My approach to feature engineering involves crafting or identifying a concise set of meaningful features for developing interpretable machine learning models, diverging from traditional data reduction techniques that often disregard the underlying physics. Moreover, I have enabled the use of compressed sensing-based algorithms for developing symbolic regressions for large datasets, utilizing statistical sampling and high-throughput computing. I’ve also integrated symbolic regression and constrained optimization methods for the inverse design of materials/systems to meet specific performance metrics, and I continue to merge machine learning with fundamental physical laws to demystify material stability and instability under industrial conditions.

Looking forward, my ongoing and future projects include employing machine learning for causal inference in healthcare to understand and predict outcomes and integrating AI to conduct comprehensive environmental and social impact analyses of materials/systems via life cycle analysis. Furthermore, I am exploring quantum computing and machine learning to drive innovation and transform vehicle energy systems and manufacturing processes.

 


Accomplishments and Awards

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.

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.

 


Research Highlights

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/

Rebecca Lindsey

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Research in the Lindsey Lab focuses on using simulation to enable on-demand design, discovery, and synthesis of bespoke materials.

These efforts are made possible by Dr. Lindsey’s ChIMES framework, which comprises a unique physics-informed machine-learned (ML) interatomic potential (IAP) and artificial intelligence-automated development tool that enables “quantum accurate” simulation of complex systems on scales overlapping with experiment, with atomistic resolution. Using this tool, her group elucidates fundamental materials behavior and properties that can be manipulated through advanced material synthesis and modification techniques. At the same time, her group develops new approaches to overcome grand challenges in machine learning for physical sciences and engineering, including: training set generation, model uncertainty quantification, reproducibility and automation, robustness, and accessibility to the broader scientific community. Her also group seeks to understand what the models themselves can teach us about fundamental physics and chemistry.

Artists interpretation of a new laser-driven shockwave approach for nanocarbon synthesis predicted by ChIMES simulations and later validated experimentally.

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.