Lubomir Hadjiyski

Lubomir Hadjiyski

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Dr. Hadjiyski research interests include computer-aided diagnosis, artificial intelligence (AI), machine learning, predictive models, image processing and analysis, medical imaging, and control systems. His current research involves design of decision support systems for detection and diagnosis of cancer in different organs and quantitative analysis of integrated multimodality radiomics, histopathology and molecular biomarkers for treatment response monitoring using AI and machine learning techniques. He also studies the effect of the decision support systems on the physicians’ clinical performance.

Yasser Aboelkassem

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In this project, we use multi-scale models coupled with machine learning algorithms to study cardiac electromechanic coupling. The approach spans out the molecular, Brownian, and Langevin dynamics of the contractile (sarcomeric proteins) mechanism of cardiac cells and up-to-the finite element analysis of the tissue and organ levels. In this work, a novel surrogate machine learning algorithm for the sarcomere contraction is developed. The model is trained and established using in-silico data-driven dynamic sampling procedures implemented using our previously derived myofilament mathematical models.

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling

Multi-scale Machine Learning Modeling of Cardiac Electromechanics Coupling

Yixin Wang

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Yixin Wang works in the fields of Bayesian statistics, machine learning, and causal inference, with applications to recommender systems, text data, and genetics. She also works on algorithmic fairness and reinforcement learning, often via connections to causality. Her research centers around developing practical and trustworthy machine learning algorithms for large datasets that can enhance scientific understandings and inform daily decision-making. Her research interests lie in the intersection of theory and applications.

Elle O'Brien

Elle O’Brien

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My research focuses on building infrastructure for public health and health science research organizations to take advantage of cloud computing, strong software engineering practices, and MLOps (machine learning operations). By equipping biomedical research groups with tools that facilitate automation, better documentation, and portable code, we can improve the reproducibility and rigor of science while scaling up the kind of data collection and analysis possible.

Research topics include:
1. Open source software and cloud infrastructure for research,
2. Software development practices and conventions that work for academic units, like labs or research centers, and
3. The organizational factors that encourage best practices in reproducibility, data management, and transparency

The practice of science is a tug of war between competing incentives: the drive to do a lot fast, and the need to generate reproducible work. As data grows in size, code increases in complexity and the number of collaborators and institutions involved goes up, it becomes harder to preserve all the “artifacts” needed to understand and recreate your own work. Technical AND cultural solutions will be needed to keep data-centric research rigorous, shareable, and transparent to the broader scientific community.

View MIDAS Faculty Research Pitch, Fall 2021

 

Jodyn Platt

Jodyn Platt

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Our team leads research on the Ethical, Legal, and Social Implications (ELSI) of learning health systems and related enterprises. Our research uses mixed methods to understand policies and practices that make data science methods (data collection and curation, AI, computable algorithms) trustworthy for patients, providers, and the public. Our work engages multiple stakeholders including providers and health systems, as well as the general public and minoritized communities on issues such as AI-enabled clinical decision support, data sharing and privacy, and consent for data use in precision oncology.

Sardar Ansari

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I build data science tools to address challenges in medicine and clinical care. Specifically, I apply signal processing, image processing and machine learning techniques, including deep convolutional and recurrent neural networks and natural language processing, to aid diagnosis, prognosis and treatment of patients with acute and chronic conditions. In addition, I conduct research on novel approaches to represent clinical data and combine supervised and unsupervised methods to improve model performance and reduce the labeling burden. Another active area of my research is design, implementation and utilization of novel wearable devices for non-invasive patient monitoring in hospital and at home. This includes integration of the information that is measured by wearables with the data available in the electronic health records, including medical codes, waveforms and images, among others. Another area of my research involves linear, non-linear and discrete optimization and queuing theory to build new solutions for healthcare logistic planning, including stochastic approximation methods to model complex systems such as dispatch policies for emergency systems with multi-server dispatches, variable server load, multiple priority levels, etc.

Sara Lafia

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I am a Research Fellow in the Inter-university Consortium for Political and Social Research (ICPSR) at the University of Michigan. My research is currently supported by a NSF project, Developing Evidence-based Data Sharing and Archiving Policies, where I am analyzing curation activities, automatically detecting data citations, and contributing to metrics for tracking the impact of data reuse. I hold a Ph.D. in Geography from UC Santa Barbara and I have expertise in GIScience, spatial information science, and urban planning. My interests also include the Semantic Web, innovative GIS education, and the science of science. I have experience deploying geospatial applications, designing linked data models, and developing visualizations to support data discovery.

Rajiv Saran

Rajiv Saran

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Dr. Saran is an internationally recognized expert in kidney disease research – specifically, in the area of kidney disease surveillance and epidemiology. From 2014 – 2019, he served as Director of the United States Renal Data System (USRDS; www.usrds.org), a ‘gold standard’ for kidney disease data systems, worldwide. Since 2006 he has been Co-Principal Investigator for the Centers for the Disease Control and Prevention’s (CDC’s) National CKD Surveillance System for the US, a one of a kind project that complements the USRDS, while focusing on upstream surveillance of CKD and its risk factors (www.cdc.org/ckd/surveillance). Both projects have influenced policy related to kidney disease in the US and were cited extensively in the July 2019 Advancing American Kidney Health Federal policy document. Dr. Saran led the development of the first National Kidney Disease Information System (VA-REINS), for the Department of Veterans Affairs (VA), funded by the VA’s Center for Innovation, and one that led to the VA recognizing the importance of kidney disease as a health priority for US veterans. Dr. Saran has recently (2018-2021) been funded on a spin off project from VA REINS for investigation of ‘hot-spot’ of kidney disease among US Veterans involving both risk-prediction and geospatial analyses – a modern approach to health system big data being used for prevention and population health improvement, using kidney disease as an example. This approach has broad application for prevention and optimizing management of major chronic diseases.

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.

Lana Garmire

Lana Garmire

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My research interest lies in applying data science for actionable transformation of human health from the bench to bedside. Current research focus areas include cutting edge single-cell sequencing informatics and genomics; precision medicine through integration of multi-omics data types; novel modeling and computational methods for biomarker research; public health genomics. I apply my biomedical informatics and analytical expertise to study diseases such as cancers, as well the impact of pregnancy/early life complications on later life diseases.