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.

Fan Bu

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I am broadly interested in Bayesian and computational statistics for analyzing large-scale and complex data. I am particularly interested in spatio-temporal statistics, network inference, infectious disease models, and distributed learning. My methodological research has been motivated by applications in public health, observational healthcare studies, computational social science, and sports sciences.

I came from a math background but studied statistics in order to become a sports analyst (yes, Moneyball!). Throughout my PhD and postdoc training, I grew a strong appreciation for social sciences (how people behave and interact) and health sciences (how to provide high-quality healthcare for everyone). I see data science as the field to help us make sense of complex data that arise from our daily life and scientific endeavors, by building reliable and reproducible frameworks that transform data to evidence and then to scientific findings and decisions.

Terra Sztain

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The Sztain research group is broadly focused on computer-aided molecular design, intersecting fields of chemistry, physics, biology, and computer science. Ongoing projects involve integrating experimental data and enhanced sampling molecular dynamics simulations to improve computational models for allosteric inhibitor design and protein engineering.

Joelle Abramowitz

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Dr. Joelle Abramowitz’s research examines the effects of different policies on individuals’ major life decisions and wellbeing including on health insurance and medical out-of-pocket expenditures as well as bigger picture effects on outcomes such as marriage, fertility, and work. She has worked intimately with a variety of datasets containing health insurance, demographic, employer, and administrative information, developing an expertise in the benefits, shortcomings, and intricacies of using and linking alternate datasets as well as a familiarity with the relevant literature, analytical approaches, and policy history in this line of research. In ongoing work, she applies this experience to enhancing Health and Retirement Study data through linkage with Census Bureau data on employers as part of the CenHRS project. This work includes considering how employer-sponsored health insurance offerings are changing in response to an aging workforce as well as changes in the employment arrangements of individuals nearing retirement. To this end, she considers how such changes affect a range of health- and economic-related outcomes, including physical and emotional wellbeing as well as economic security in retirement.

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.

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.

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

Xueding Wang

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My research focuses on novel biomedical imaging and treatment technologies, especially those involving light and ultrasound. I have extensive experience in medical system development, laser-tissue interactions, ultrasound tissue characterization, and adaptation of novel technologies to preclinical and clinical settings. A major part of my research is the development and clinical applications of photoacoustic imaging technology. By working on small-animal models and human patients, I have been seeking for clinical applications of this exciting technology to inflammatory arthritis, cancer, inflammatory bowel disease, eye diseases, osteoporosis, and brain disorders. Besides photoacoustic imaging, I am also interested in development of other medical imaging and treatment technologies, such as ionizing radiation induced acoustic imaging (iRAI) and photo-mediated ultrasound therapy (PUT). As the PI or co-Investigator of several NIH, NSF and DoD-funded research, I have successfully administered the projects, collaborated with other researchers, and produced high-quality publications. My contribution to biomedical optics and ultrasound up to now including over 160 peer-reviewed journal papers is a solid evidence of my creativity and ability to surmount the challenges in this field. I received the Sontag Foundation Fellow of the Arthritis National Research Foundation in 2005, the Distinguished Investigator Award of the Academy of Radiology Research in 2013, and was elected as the fellow of AIMBE in 2020 and the fellow of SPIE in 2022.

What is your most interesting project?

Automated photoacoustic imaging of inflammatory arthritis: Our research has demonstrated the unique capability of photoacoustic imaging (PAI) in diagnosis and treatment monitoring of inflammatory arthritis. The new physiological and molecular biomarkers of synovitis presented by PAI can help in characterizing disease onset, progression, and response to therapy. Based on the endogenous optical contrast, PAI is extremely sensitive to the changes in hemodynamic properties in inflammatory joint tissues (e.g. enhanced flow and hypoxia). We are now conducting a preclinical research on patients affected by rheumatoid arthritis. The initial findings from this patient study are promising and suggest that the new optical contrast and physiological information introduced by PAI could greatly enhance the sensitivity and accuracy of diagnostic imaging and treatment monitoring of arthritis. Aiming at clinical translation, we are currently developing a point-of-care PAI and ultrasound dual-modality imaging system which is fully automated when powered by a robot and AI technologies.

Ionizing radiation acoustic imaging (iRAI) for personalized radiation therapy: iRAI, as a brand-new imaging technology relying on the detection of radiation-induced acoustic waves, allows online monitoring of radiation’s interactions with tissues during radiation therapy, providing real-time, adaptive feedback for cancer treatments. We are developing an iRAI volumetric imaging system that enables mapping of the three-dimensional (3D) radiation dose distribution in a complex clinical radiotherapy treatment. The feasibility of imaging temporal 3D dose accumulation was first validated in studies on phantoms and animal models. Then, real-time visualization of the 3D radiation dose delivered to a patient with liver metastases was accomplished with a clinical linear accelerator. These studies demonstrate the great potential of iRAI to monitor and quantify the 3D radiation dose deposition during treatment, potentially improving radiotherapy treatment efficacy using real-time adaptive treatment.

Describe your research journey.

2005 – 2007 Research Investigator, Department of Radiology, University of Michigan
2007 – 2008 Research Assistant Professor, Department of Radiology, University of Michigan
2008 – 2012 Assistant Professor, Department of Radiology, University of Michigan Medical School
2012 – 2014 Associate Professor, Department of Radiology, University of Michigan
2015 – 2018 Associate Professor, Department of Biomedical Engineering, University of Michigan
2018 – 2022 Professor, Department of Biomedical Engineering, University of Michigan
2022 – Now Jonathan Rubin Collegiate Professor of Biomedical Engineering, University of Michigan

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

Develop and translate state-of-the-art medical imaging and treatment technologies.

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

Date science and AI is super important in developing state-of-the-art medical imaging and treatment technologies, especially for achieving personalized diagnosis and treatment ensuring largely improved patient outcome. As mentioned in the above, the automated imaging system for rheumatology/radiology clinic for arthritis imaging would be strongly powered by AI, which is crucial to achieve our goal of a “smart” ultrasound imaging platform.

Automated dual-modality ultrasound and photoacoustic imaging system

Automated dual-modality ultrasound and photoacoustic imaging system

Kelly Psilidis

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Kelly Psilidis (P-see-lee-these) is the Faculty Training Program Manager with MIDAS. She is honored to be supporting a multi-university team of instructors to develop a groundbreaking nationwide training program for biomedical science faculty and staff scientists. Kelly also coordinates the MIDAS Summer Academies for faculty and staff to build data science and Artificial Intelligence (AI) skills.

As an experienced training manager and higher education administrator, Kelly brings over 19+ years of experience to the MIDAS department. She revels in unraveling the complexities of a project and piecing it back together in a way that not only makes sense but also sparks excitement. Her passion lies in empowering partners, fostering positive collaborations, and strives for the best possible outcome that contribute to the overall success of MIDAS.

While she’s not busy revolutionizing the training world, you can find Kelly hiking beautiful Michigan with her golden retriever, Jager or in the kitchen concocting new recipes. Her husband and two children play the role of her brave taste-testers.