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.
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 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.
Dr. Costa’s goal is to maximize survival and minimize morbidity for mechanically ventilated adults. She accomplishes this through her research on the organization and management of critical care. Specifically, her work identifies key structural and functional characteristics of ICU interprofessional teams that can be leveraged to improve the delivery of high quality, complex care to mechanically ventilated patients. She is a trained health services researcher with clinical expertise in adult critical care nursing. Her work care has been published in leading journals such as JAMA, Chest, and Critical Care Medicine. Her current research examines ICU teamwork and patient outcomes, linking individual clinicians to individual patients using the Electronic Health Record, and using qualitative approaches to understand how to improve ICU teams. Her research has focused on ICU clinician staffing, well-being and psychological outcomes of ICU clinicians as a way to improve care and outcomes of ICU patients.
Uncertainty quantification and decision making are increasingly demanded with the development of future technology in engineering and transportation systems. Among the uncertainty quantification problems, Dr. Wenbo Sun is particularly interested in statistical modelling of engineering system responses with considering the high dimensionality and complicated correlation structure, as well as quantifying the uncertainty from a variety of sources simultaneously, such as the inexactness of large-scale computer experiments, process variations, and measurement noises. He is also interested in data-driven decision making that is robust to the uncertainty. Specifically, he delivers methodologies for anomaly detection and system design optimization, which can be applied to manufacturing process monitoring, distracted driving detection, out-of-distribution object identification, vehicle safety design optimization, etc.
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.
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.
Tayo Fabusuyi is an assistant research scientist in the Human Factors Group at UMTRI. His research interests are in Urban Systems and Operations Research, specifically designing and implementing initiatives that support sustainable and resilient communities with a focus on efficiency and equity issues. Drawing on both quantitative and qualitative data, his research develops and applies hard and soft Operations Research methods to urban systems issues in a manner that emphasizes theory driven solutions with demonstrated value-added. A central theme of his research activities is the use of demand side interventions, via information and pricing strategies in influencing the public’s travel behavior with the objective of achieving more beneficial societal outcomes. Informed by the proliferation of big data and the influence of transportation in the urban sphere, these research activities are categorized broadly into three overlapping and interdependent areas – intelligent transportation systems (ITS), emerging mobility services and urban futures. Before joining the research faculty at UMTRI, Dr. Fabusuyi was a Planning Economist at the African Development Bank and an adjunct Economics faculty member at Carnegie Mellon University, where he received his Ph.D. in Engineering and Public Policy.
Today’s real-world problems are complex and large, often with overwhelmingly large number of unknown variables which render them doomed to the so-called “curse of dimensionality”. For instance, in energy systems, the system operators should solve optimal power flow, unit commitment, and transmission switching problems with tens of thousands of continuous and discrete variables in real time. In control systems, a long standing question is how to efficiently design structured and distributed controllers for large-scale and unknown dynamical systems. Finally, in machine learning, it is important to obtain simple, interpretable, and parsimonious models for high-dimensional and noisy datasets. Our research is motivated by two main goals: (1) to model these problems as tractable optimization problems; and (2) to develop structure-aware and scalable computational methods for these optimization problems that come equipped with certifiable optimality guarantees. We aim to show that exploiting hidden structures in these problems—such as graph-induced or spectral sparsity—is a key game-changer in the pursuit of massively scalable and guaranteed computational methods.
My research lies at the intersection of optimization, data analytics, and control.
Albert S. Berahas is an Assistant Professor in the department of Industrial & Operations Engineering. His research broadly focuses on designing, developing and analyzing algorithms for solving large scale nonlinear optimization problems. Such problems are ubiquitous, and arise in a plethora of areas such as engineering design, economics, transportation, robotics, machine learning and statistics. Specifically, he is interested in and has explored several sub-fields of nonlinear optimization such as: (i) general nonlinear optimization algorithms, (ii) optimization algorithms for machine learning, (iii) constrained optimization, (iv) stochastic optimization, (v) derivative-free optimization, and (vi) distributed optimization.