Chuan Zhou

Chuan Zhou

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With a passion for developing decision support systems that integrate cutting edge techniques from artificial intelligence, quantitative image analysis, computer vision, and multimodal biomedical data fusion. Research interests have been focusing on characterizing diseases abnormalities and predicting their likelihood of being significant, with the goal to enable early diagnosis and risk stratification, as well as aiding treatment decision making and monitoring.

Deepak Nagrath

Deepak Nagrath

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Our lab is focused on answering the question-What is the role of tumor microenvironment in modulating cancer cell metabolism? We have developed several metabolic isotope tracing and 13C-based metabolic flux analysis developed in our lab. cells. Notably, we are focusing on personalized metabolic therapy and circulating tumor cell organoids and tumor tissue slices in pancreatic, lung, and breast cancers. We integrate high dimensional imaging, tissue engineering, metabolic engineering, bioinformatics, machine learning, and systems biology tools developed in our labs to understand metabolic interactions between cancer and stromal cells. Using our recently developed platform, collateral lethal identification of metabolic targets (CLIM)-a machine learning and genome-scale metabolic flux analysis-based framework, we elucidate the broad efficacy of targeting MTHFD2 despite distinct cancer genetic profiles co-occurring with UQCR11 deletion and irrespective of stromal compositions of tumors.” Our CLIM method can be used to identify metabolic vulnerabilities in other cancers and could serve as a precision treatment plans for a host of malignancies.

Nicholas Henderson

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My research primarily focuses on the following main themes: 1) development of methods for risk prediction and analyzing treatment effect heterogeneity, 2) Bayesian nonparametrics and Bayesian machine learning methods with a particular emphasis on the use of these methods in the context of survival analysis, 3) statistical methods for analyzing heterogeneity in risk-benefit profiles and for supporting individualized treatment decisions, and 4) development of empirical Bayes and shrinkage methods for high-dimensional statistical applications. I am also broadly interested in collaborative work in biomedical research with a focus on the application of statistics in cancer research.

Nicholas Douville

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Dr. Douville is a critical care anesthesiologist with an investigative background in bioinformatics and perioperative outcomes research. He studies techniques for utilizing health care data, including genotype, to deliver personalized medicine in the perioperative period and intensive care unit. His research background has focused on ways technology can assist health care delivery to improve patient outcomes. This began designing microfluidic chips capable of recreating fluid mechanics of atelectatic alveoli and monitoring the resulting barrier breakdown real-time. His interest in bioinformatics was sparked when he observed how methodology designed for tissue engineering could be modified to the nano-scale to enable genomic analysis. Additionally, his engineering training provided the framework to apply data-driven modeling techniques, such as finite element analysis, to complex biological systems.

Nicole Seiberlich

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My research involves developing novel data collection strategies and image reconstruction techniques for Magnetic Resonance Imaging. In order to accelerate data collection, we take advantage of features of MRI data, including sparsity, spatiotemporal correlations, and adherence to underlying physics; each of these properties can be leveraged to reduce the amount of data required to generate an image and thus speed up imaging time. We also seek to understand what image information is essential for radiologists in order to optimize MRI data collection and personalize the imaging protocol for each patient. We deploy machine learning algorithms and optimization techniques in each of these projects. In some of our work, we can generate the data that we need to train and test our algorithms using numerical simulations. In other portions, we seek to utilize clinical images, prospectively collected MRI data, or MRI protocol information in order to refine our techniques.

We seek to develop technologies like cardiac Magnetic Resonance Fingerprinting (cMRF), which can be used to efficiently collect multiple forms of information to distinguish healthy and diseased tissue using MRI. By using rapid methods like cMRF, quantitative data describing disease processes can be gathered quickly, enabling more and sicker patients can be assessed via MRI. These data, collected from many patients over time, can also be used to further refine MRI technologies for the assessment of specific diseases in a tailored, patient-specific manner.