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.

Brendan Kochunas

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Dr. Kochunas’s research focus is on the next generation of numerical methods and parallel algorithms for high fidelity computational reactor physics and how to leverage these capabilities to develop digital twins. His group’s areas of expertise include neutron transport, nuclide transmutation, multi-physics, parallel programming, and HPC architectures. The Nuclear Reactor Analysis and Methods (NURAM) group is also developing techniques that integrate data-driven methods with conventional approaches in numerical analysis to produce “hybrid models” for accurate, real-time modeling applications. This is embodied by his recent efforts to combine high-fidelity simulation results simulation models in virtual reality through the Virtual Ford Nuclear Reactor.

Relationship of concepts for the Digital Model, Digital Shadow, Digital Twin, and the Physical Asset using images and models of the Ford Nuclear Reactor as an example. Large arrows represent automated information exchange and small arrows represent manual data exchange.

Ivy F. Tso

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My lab researches how the human brain processes social and affective information and how these processes are affected in psychiatric disorders, especially schizophrenia and bipolar disorder. We use behavioral, electrophysiological (EEG), neuroimaging (functional MRI), eye tracking, brain stimulation (TMS, tACS), and computational methods in our studies. One main focus of our work is building and validating computational models based on intensive, high-dimensional subject-level behavior and brain data to explain clinical phenomena, parse mechanisms, and predict patient outcome. The goal is to improve diagnostic and prognostic assessment, and to develop personalized treatments.

Brain activation (in parcellated map) during social and face processing.

Meha Jain

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​I am an Assistant Professor in the School for Environment and Sustainability at the University of Michigan and am part of the Sustainable Food Systems Initiative. My research examines the impacts of environmental change on agricultural production, and how farmers may adapt to reduce negative impacts. I also examine ways that we can sustainably enhance agricultural production. To do this work, I combine remote sensing and geospatial analyses with household-level and census datasets to examine farmer decision-making and agricultural production across large spatial and temporal scales.

Conducting wheat crop cuts to measure yield in India, which we use to train algorithms that map yield using satellite data

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.

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


Michael Rubyan

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My research focuses on the development and evaluation of novel interventions that leverage emerging technologies to train members of the healthcare workforce around adhering to guidelines. I study how to scale custom designed teaching and learning platforms and evaluate their use to motivate effective communication and dissemination of evidence based practice. Other emphases of my work include health policy literacy, translation and communication of health services research, and improving health system literacy in urban communities. I have developed and evaluated numerous web based educational interventions that employ the “flipped classroom” design with an emphasis on understanding the data and analytics that guide successful implementation and promote high fidelity for members of the healthcare workforce. As an implementation scientist, I rely on the integration of data and analytics to understand what motivates successful program implementation.

In addition to the development of these platforms, I have extensive experience developing and evaluating online, hybrid residential, residential courses, and MOOCs related to healthcare management, non-profit management, healthcare finance, and health economics that employ engaging lessons and modules, interactive graphics, and a blended learning format to aid health professions students, and both undergraduate and graduate public health students in understanding the healthcare system. My MOOC entitled “Understanding and Improving the U.S. Health Care System” has been taken by over 5,000 learners and is characterized by the use of “big data” to understand how future healthcare providers learn health policy.

Mithun Chakraborty

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My broad research interests are in multi-agent systems, computational economics and finance, and artificial intelligence. I apply techniques from algorithmic game theory, statistical machine learning, decision theory, etc. to a variety of problems at the intersection of the computational and social sciences. A major focus of my research has been the design and analysis of market-making algorithms for financial markets and, in particular, prediction markets — incentive-based mechanisms for aggregating data in the form of private beliefs about uncertain events (e.g. the outcome of an election) distributed among strategic agents. I use both analytical and simulation-based methods to investigate the impact of factors such as wealth, risk attitude, manipulative behavior, etc. on information aggregation in market ecosystems. Another line of work I am pursuing involves algorithms for allocating resources based on preference data collected from potential recipients, satisfying efficiency, fairness, and diversity criteria; my joint work on ethnicity quotas in Singapore public housing allocation deserves special mention in this vein. More recently, I have got involved in research on empirical game-theoretic analysis, a family of methods for building tractable models of complex, procedurally defined games from empirical/simulated payoff data and using them to reason about game outcomes.

Tayo Fabusuyi

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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.