David Kwabi

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We study and develop electrochemical devices containing organic materials for applications in grid energy storage and chemical separations (e.g., CO2 capture and nitrogen recovery). A critical aspect of our work involves discerning the impact of chemical reactions as well as mass and charge transport processes on device-level performance metrics. To accomplish this goal, we often conduct spectroscopic measurements of electrochemical systems while they are in operation. We apply a variety of mathematical modeling techniques to the spectroscopic data, such as multivariate curve resolution and Bayesian inference/model selection, to glean useful information about molecular transformation mechanisms and kinetics. These insights are informing closed-loop discovery of new and better-performing materials.

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.

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.

Dani Jones

Dani Jones

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Dani Jones’ research program drives CIGLR’s portfolio of research in data science, machine learning, and artificial intelligence, as applied to physical limnology, weather forecasting, water cycle predictions, ecology, and observing system design. This research program aims is to advance societal adaptations to the effects of climate change, including flooding of coasts, rivers, and cities. Dani’s background is in physical oceanography, with specific expertise in adjoint modeling for comprehensive sensitivity analysis and unsupervised classification for data analysis, mostly applied to the North Atlantic and Southern Ocean. In Dani’s current role, they are establishing CIGLR’s new Artificial Intelligence Laboratory, leveraging the institute’s extensive observing assets, datasets, modeling capacity, interdisciplinary expertise, and numerous regional and international partnerships.

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

Edward Lin

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Dr. Lin conducts research on the physical and orbital characteristics of minor planets within the solar system, including asteroids and Kuiper belt objects. His methodologies involve astrometry, photometry, and spectroscopy. Additionally, he employs large astronomical sky survey data and designs custom surveys to sample specific populations of minor planets for the purpose of establishing population models. This information contributes to our comprehension of the solar system’s evolution.

Research image: a model of distant minor planet populations of our Solar system

Anne McNeil

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Our research is aimed at addressing some of the world’s biggest challenges through chemical recycling or upcycling of waste plastics, developing methods to capture microplastics, measuring microplastics in the environment, and designing redox active molecules for energy storage applications.

What is your most interesting project?

Synthetic polymers have an enormous impact on our lives, yet the manner in which they are produced, used, and disposed of is unsustainable. Globally, we produce over 300 million tons of plastics per year, and a stunning >90% of plastics are made from petroleum feedstocks and only a scant 9% of plastics are recycled. Products that are recycled through current mechanical processes are frequently downgraded into lower-quality materials. We are currently exploring methods for “chemical recycling”. This approach includes developing depolymerization procedures and synthetic methods for repurposing degraded polymers into equal-quality or value-added materials.

Microplastics are everywhere due to the world’s prolific use of plastics in everyday items. Microplastics have been found in indoor and outdoor environments, in urban and rural areas, and even in the most remote locations on the planet. While most research has focused on microplastics in water and land environments, far fewer studies have examined microplastics in the atmosphere. Yet inhaling airborne microplastics is likely more harmful to human health than ingesting them through food and water. How many microplastics are found in our air? Where are the biggest emission sources? How are microplastics transported through the air? How does your race, income, and/or geographic location correlate with your exposure levels? Shouldn’t we know? This project is a collaboration with Profs. Andy Ault and Paul Zimmerman (in Chemistry), Ambuj Tewari (in Statistics), and Allison Steiner (in CLASP).

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

I am a newbie to data science and AI, and I am excited to learn more alongside my collaborators (Ambuj Tewari and Paul Zimmerman). In particular, I am excited by the opportunity to leverage the power of existing data to identify and create new pathways for chemical recycling of waste plastics.


Accomplishments and Awards

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.