Catherine Kaczorowski

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The Kaczorowski laboratory, led by Dr. Catherine Kaczorowski, pioneers techniques to identify and validate genetic and cellular mechanisms that promote resilience to cognitive aging, Alzheimer’s disease, and other age-related dementias. By combining mouse and human systems; genomic, anatomic, and behavioral approaches; and integrative analyses across multiple scales, data types, environmental factors, and species, we are accelerating the discovery of the precise genetic mechanisms of cognitive resilience that could yield the next generation of targets and therapeutic strategies for promoting brain health. We are now uniquely poised to propel the field of personalized medicine forward using our genetically diverse, yet reproducible models of human neurodegenerative dementias, having already contributed conceptual and technical advances that revolutionized our ability to study complex diseases, specifically human AD dementia.

Mohamed Abouelenien

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Mohamed Abouelenien’s areas of interest broadly cover data science topics, including applied machine learning, computer vision, and natural language processing. He established the Affective Computing and Multimodal Systems Lab (ACMS) which focuses on modeling human behavior and developing multimodal approaches for different applications. He has worked on a number of projects in these areas, including multimodal deception detection, multimodal sensing of drivers’ alertness levels and thermal discomfort, distraction detection, circadian rhythm modeling, emotion and stress analysis, automated scoring of students’ progression, sentiment analysis, ensemble learning, and image processing, among others. His research is funded by Ford Motor Company (Ford), Educational Testing Service (ETS), Toyota Research institute (TRI), and Procter & Gamble (P&G). Abouelenien has published in several top venues in IEEE, ACM, Springer, and SPIE. He also served as a reviewer for IEEE transactions and Elsevier journals and served as a program committee member for multiple international conferences.

Angela Violi

Angela Violi

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The Violi Lab carries out cross-disciplinary research at the intersection of nanoscience and data science. By integrating machine learning techniques with molecular simulations, the team strives to unravel fundamental scientific principles while tackling practical problems in material science, healthcare, and environmental sustainability. Their methodological toolkit encompasses various cutting-edge approaches: active learning and Bayesian experimental design to improve sample efficiency; advanced gradient boosting techniques for predictive modeling; specialized neural networks to decode protein-nanoparticle interactions; and Lasso-like algorithms for feature selection and regularization. Through this integrated approach, the lab aims to make significant contributions to both scientific understanding and technological innovation.

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.

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.

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.

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.