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.

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

Michael Sjoding

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Application of machine learning and artificial intelligence in healthcare, particularly in the field of pulmonary and critical care medicine. Deep learning applied to radiologic imaging studies. Physician and artificial intelligence interactions and collaborations. Identifying and addressing algorithmic bias.

Maggie Makar

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My research focuses on developing reliable and efficient machine learning methods for causal inference as well as predictive models that leverage causal reasoning. My work typically involves applications to healthcare.

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.

Mark Draelos

Mark Draelos

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My work focuses on image-guided medical robots with an emphasis on clinical translation. My interests include medical robotics, biomedical imaging, data visualization, medical device development, and real-time algorithms.

A major ongoing project is the development of robotic system for automated eye examination. This system relies on machine learning models for tracking and eventually for interpretation of collected data. Other projects concern the live creation of virtual reality scenes from volumetric imaging modalities like optical coherence tomography and efficient acquisition strategies for such purposes.

John Prensner

John Prensner

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My research group uses molecular techniques and computational methods to dissect the biology of pediatric cancers. We are invested in fundamental genomic discovery of non-canonical open reading frames that are dysregulated in cancer. We use functional genomics techniques to facilitate biological analysis. We employ data science methodologies to model and predict the molecular biology of cancers. Our particular focus is on RNA translation and its regulation. We also focus on therapeutic interventions that may represent novel treatment strategies for cancer.

What are some of your most interesting projects?

I am fascinated by the ways in which cancer cells pattern RNA translation. We are currently performing large-scale -omic analyses to identify patterns in RNA translational control across pediatric brain cancers.

How did you end up where you are today?

I came to science late in life. I studied English Literature as an undergraduate at Tufts University before deciding to study biochemistry as a senior in college. This led me to join a cancer biology lab after college, which motivated me to go to medical school. I enrolled in the University of Michigan Medical School in 2006 but quickly became interested in the biology of cancer. I then joined the Medical Scientist Training Program and graduated with an MD/PhD dual degree in 2014. I pursued Pediatrics clinical training at Boston Children’s Hospital and pediatric hematology/oncology at Dana-Farber Cancer Institute. I then completed post-doctoral research at the Broad Institute of MIT and Harvard prior to joining the faculty of the University of Michigan.

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

My goal is to cure childhood brain cancers. That is what motivates me every day!