Stefanus Jasin

By |

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

By |

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.

Brian Lin

By |

Dr. Brian Lin has 12 years of experience in automotive research at UMTRI after his Ph.D. His current research is focused on mining naturalistic driving data, evaluating driver assistance systems, modeling driver performance and behavior, and estimating driver distraction and workload, using statistical methods, classification, clustering, and survival analysis. His most recent work includes classifying human driver’s decision for a discretionary lane change and traversal at unsignalized intersections, driver’s response to lead vehicle’s movement, and subjective acceptance on automated lane change feature. Dr. Lin also has much experience applying data analytic methods to evaluate automotive system prototypes, including auto-braking, lane departure, driver-state monitoring, electronic head units, car-following and curve-assist systems on level-2 automation, and lane-change and intersection assist on L3 automation on public roads, test tracks, or driving simulators. He is also familiar with the human factors methods to investigate driver distraction, workload, and human-machine interaction with in-vehicle technologies and safety features. He serves as a peer reviewer for Applied Ergonomics, Behavior Research Methods, IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles and Transportation Research Part F.

Lubomir Hadjiyski

By |

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.

Ben Green

By |

Ben studies the social and political impacts of government algorithms. This work falls into several categories. First, evaluating how people make decisions in collaboration with algorithms. This work involves developing machine learning algorithms and studying how people use them in public sector prediction and decision settings. Second, studying the ethical and political implications of government algorithms. Much of this work draws on STS and legal theory to interrogate topics such as algorithmic fairness, smart cities, and criminal justice risk assessments. Third, developing algorithms for public sector applications. In addition to academic research, Ben spent a year developing data analytics tools as a data scientist for the City of Boston.

Mark Steven Cohen

By |

In his various roles, he has helped develop several educational programs in Innovation and Entrepreneurial Development (the only one of their kind in the world) for medical students, residents, and faculty as well as co-founding 4 start-up companies (including a consulting group, a pharmaceutical company, a device company, and a digital health startup) to improve the care of surgical patients and patients with cancer. He has given over 80 invited talks both nationally and internationally, written and published over 110 original scientific articles, 12 book chapters, as well as a textbook on “Success in Academic Surgery: Innovation and Entrepreneurship” published in 2019 by Springer-NATURE. His research is focused on drug development and nanoparticle drug delivery for cancer therapeutic development as well as evaluation of circulating tumor cells, tissue engineering for development of thyroid organoids, and evaluating the role of mixed reality technologies, AI and ML in surgical simulation, education and clinical care delivery as well as directing the Center for Surgical Innovation at Michigan. He has been externally funded for 13 consecutive years by donors and grants from Susan G. Komen Foundation, the American Cancer Society, and he currently has funding from three National Institute of Health R-01 grants through the National Cancer Institute. He has served on several grant study sections for the National Science Foundation, the National Institute of Health, the Department of Defense, and the Susan G. Komen Foundation. He also serves of several scientific journal editorial boards and has serves on committees and leadership roles in the Association for Academic Surgery, the Society of University Surgeons and the American Association of Endocrine Surgeons where he was the National Program Chair in 2013. For his innovation efforts, he was awarded a Distinguished Faculty Recognition Award by the University of Michigan in 2019. His clinical interests and national expertise are in the areas of Endocrine Surgery: specifically thyroid surgery for benign and malignant disease, minimally invasive thyroid and parathyroid surgery, and adrenal surgery, as well as advanced Melanoma Surgery including developing and running the hyperthermic isolated limb perfusion program for in transit metastatic melanoma (the only one in the state of Michigan) which is now one of the largest in the nation.

Xu Wang

By |

My research is to support more people learn in effective ways. I draw techniques and theories from Human-Computer Interaction, Learning Sciences, and Artificial Intelligence to develop computational methods and systems to support scalable teaching and learning. There are several directions in my research that draw on data science techniques and also contribute to interdisciplinary data science research, 1) data-driven authoring techniques of intelligent tutoring systems, with application domains in UX education and data science education 2) AI-augmented instructional design and the use Human-AI collaborative techniques in instructional design.

View MIDAS Faculty Research Pitch, Fall 2021

Michael Rubyan

By |

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.

Sindhu Kutty

By |

My research centers on studying the interaction between abstract, theoretically sound probabilistic algorithms and human beings. One aspect of my research explores connections of Machine Learning to Crowdsourcing and Economics; focused in both cases on better understanding the aggregation process. As Machine Learning algorithms are used in making decisions that affect human lives, I am interested in evaluating the fairness of Machine Learning algorithms as well as exploring various paradigms of fairness. I study how these notions interact with more traditional performance metrics. My research in Computer Science Education focuses on developing and using evidence-based techniques in educating undergraduates in Machine Learning. To this end, I have developed a pilot summer program to introduce students to current Machine Learning research and enable them to make a more informed decision about what role they would like research to play in their future. I have also mentored (and continue to mentor) undergraduate students and work with students to produce publishable, and award-winning, undergraduate research.