Michael Craig

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Michael is an Assistant Professor of Energy Systems at the University of Michigan’s School for Environment and Sustainability and PI of the ASSET Lab. He researches how to equitably reduce global and local environmental impacts of energy systems while making those systems robust to future climate change. His research advances energy system models to address new challenges driven by decarbonization, climate adaptation, and equity objectives. He then applies these models to real-world systems to generate decision-relevant insights that account for engineering, economic, climatic, and policy features. His energy system models leverage optimization and simulation methods, depending on the problem at hand. Applying these models to climate mitigation or adaptation in real-world systems often runs into computational limits, which he overcomes through clustering, sampling, and other data reduction algorithms. His current interdisciplinary collaborations include climate scientists, hydrologists, economists, urban planners, epidemiologists, and diverse engineers.

Stefanus Jasin

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My research focus the application and development of new algorithms for solving complex business analytics problems. Applications vary from revenue management, dynamic pricing, marketing analytics, to retail logistics. In terms of methodology, I use a combination of operations research and machine learning/online optimization techniques.

 

Qing Qu

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His research interest lies in the intersection of signal processing, data science, machine learning, and numerical optimization. He is particularly interested in computational methods for learning low-complexity models from high-dimensional data, leveraging tools from machine learning, numerical optimization, and high dimensional geometry, with applications in imaging sciences, scientific discovery, and healthcare. Recently, he is also interested in understanding deep networks through the lens of low-dimensional modeling.

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.

Gen Li

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Dr. Gen Li is an Assistant Professor in the Department of Biostatistics. He is devoted to developing new statistical methods for analyzing complex biomedical data, including multi-way tensor array data, multi-view data, and compositional data. His methodological research interests include dimension reduction, predictive modeling, association analysis, and functional data analysis. He also has research interests in scientific domains including microbiome and genomics.

Novel tree-guided regularization methods can identify important microbial features at different taxonomic ranks that are predictive of the clinical outcome.

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

Lu Wang

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Lu’s research is focused on natural language processing, computational social science, and machine learning. More specifically, Lu works on algorithms for text summarization, language generation, argument mining, information extraction, and discourse analysis, as well as novel applications that apply such techniques to understand media bias and polarization and other interdisciplinary subjects.

Edgar Franco-Vivanco

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Edgar Franco-Vivanco is an Assistant Professor of Political Science and a faculty associate at the Center for Political Studies. His research interests include Latin American politics, historical political economy, criminal violence, and indigenous politics.

Prof. Franco-Vivanco is interested in implementing machine learning tools to improve the analysis of historical data, in particular handwritten documents. He is also working in the application of text analysis to study indigenous languages. In a parallel research agenda, he explores how marginalized communities interact with criminal organizations and abusive policing in Latin America. As part of this research, he is using NLP tools to identify different types of criminal behavior.

Examples of the digitization process of handwritten documents from colonial Mexico.

Matthew VanEseltine

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Dr. VanEseltine is a sociologist and data scientist working with large-scale administrative data for causal and policy analysis. His interests include studying the effects of scientific infrastructure, training, and initiatives, as well as the development of open, sustainable, and replicable systems for data construction, curation, and dissemination. As part of the Institute for Research on Innovation and Science (IRIS), he contributes to record linkage and data improvements in the research community releases of UMETRICS, a data system built from integrated records on federal award funding and spending from dozens of American universities. Dr. VanEseltine’s recent work includes studying the impacts of COVID-19 on academic research activity.