Thuy Le

Thuy Le

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Dr. Le is an assistant research scientist at the University of Michigan Department of Health Management and Policy. Dr Le is also a member of the UM/Georgetown TCORS Center for the Assessment of Tobacco Regulations (CAsToR). Dr. Le is interested in mathematical modeling for cancer- and tobacco-related problems, and machine-learning applications in tobacco regulatory science. Dr. Le has developed mathematical models to evaluate the benefits and harms of breast cancer mammography and predict the number of white blood cells during acute lymphoblastic maintenance therapy in children. Dr. Le’s recent work focuses on employing mathematical models to quantify the burden of menthol cigarettes on public health and estimate the smoking cessation rate. Dr. Le is working on applying machine learning techniques to predict and understand smoking behaviors.

Majdi Radaideh

Majdi Radaideh

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Prof. Majdi Radaideh leads the Artificial Intelligence and Multiphysics Simulations lab (AIMS), which focuses on the intersection between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced reactor research and improve the sustainability of the current reactor fleet. AIMS extensively employs data science and machine learning methods for various goals including but not limited to:
1- Development of surrogate models for expensive nuclear reactor simulations in steady-state and time-dependent modes using convolutional and recurrent neural networks.
2- Large-scale combinatorial optimization to improve the performance of the nuclear fuel inside nuclear power plants using physics-informed reinforcement learning and neuroevolution algorithms.
3- Long-short term memory and ensemble methods for anomaly detection and fault prognosis to monitor the health of the nuclear power plant components.
4- Uncertainty quantification of data-driven models with Bayesian inference and Gaussian processes.
5- Natural language processing methods to process nuclear plant maintenance and burnup records.

AIMS lab aims on bridging the gap between nuclear reactor design, nuclear multiphysics modeling and simulation, advanced computational methods, and machine learning algorithms to drive advanced nuclear reactor research and improve the sustainability of the current reactor fleet to promote nuclear power as a carbon-free energy source in order to achieve net-zero carbon emission.

Wei Hu

Wei Hu

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Wei Hu is broadly interested in the theoretical and scientific foundations of modern machine learning, especially deep learning. His research aims to obtain a solid, rigorous, and practically relevant theoretical understanding of machine learning pipelines, as well as to develop principles to make them more reliable and efficient.

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.

 

Cong Shi

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Cong Shi is an associate professor in the Department of Industrial and Operations Engineering at the University of Michigan College of Engineering. His primary research interest lies in developing efficient and provably-good data-driven algorithms for operations management models, including supply chain management, revenue management, service operations, and human-robot interactions. He received his Ph.D. in Operations Research at MIT in 2012, and his B.S. in Mathematics from the National University of Singapore in 2007.

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

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.

J.J. Prescott

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Broadly, I study legal decision making, including decisions related to crime and employment. I typically use large social science data bases, but also collect my own data using technology or surveys.

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.