My research focuses on technology and innovation in health care with an emphasis on information technology (IT), pharmaceuticals, and empirical methods. Many of my studies explored the effect of electronic health record (EHR) systems on health care quality and productivity. While the short-run gains from health IT adoption may be modest, these technologies form the foundation for a health information infrastructure. We are just beginning to understand how to harness and apply medical information. This problem is complicated by the sheer complexity of medical care, the heterogeneity across patients, and the importance of treatment selection. My current work draws on methods from both machine learning and econometrics to address these issues. Current pharmaceutical studies examine the roles of consumer heterogeneity and learning about the value of products as well as the effect of direct-to-consumer advertising on health.
My current research focus is on modeling and simulating the value and benefits of various data sharing and policy trade offs. Typically these utilize system dynamics methodologies and tools.
I also have considerable experience across multiple industries with developing processes to enable industry and faculty to identify and solve data science problems using SAS tools.
Wearable health technology is drawing significant attention for good reasons. The pervasive nature of such systems providing ubiquitous access to the continuous personalized data will transform the way people interact with each other and their environment. The resulting information extracted from these systems will enable emerging applications in healthcare, wellness, emergency response, fitness monitoring, elderly care support, long-term preventive chronic care, assistive care, smart environments, sports, gaming, and entertainment which create many new research opportunities and transform researches from various disciplines into data science which is the methodological terminology for data collection, data management, data analysis, and data visualization. Despite the ground-breaking potentials, there are a number of interesting challenges in order to design and develop wearable medical embedded systems. Due to limited available resources in wearable processing architectures, power-efficiency is demanded to allow unobtrusive and long-term operation of the hardware. Also, the data-intensive nature of continuous health monitoring requires efficient signal processing and data analytic algorithms for real-time, scalable, reliable, accurate, and secure extraction of relevant information from an overwhelmingly large amount of data. Therefore, extensive research in their design, development, and assessment is necessary. Embedded Processing Platform Design The majority of my work concentrates on designing wearable embedded processing platforms in order to shift the conventional paradigms from hospital-centric healthcare with episodic and reactive focus on diseases to patient-centric and home-based healthcare as an alternative segment which demands outstanding specialized design in terms of hardware design, software development, signal processing and uncertainty reduction, data analysis, predictive modeling and information extraction. The objective is to reduce the costs and improve the effectiveness of healthcare by proactive early monitoring, diagnosis, and treatment of diseases (i.e. preventive) as shown in Figure 1.
My primary research interests lie at the intersection of machine learning, data mining, and healthcare. Within machine learning, I am particularly interested in time-series analysis, transfer/multitask learning, causal inference, and learning intelligible models. The overarching goal of my research is to develop the computational methods needed to help organize, process, and transform patient data into actionable knowledge. My work has applications in modeling disease progression and predicting adverse patient outcomes. For several years now, I have been focused on developing accurate patient risk stratification approaches that leverage spatiotemporal data, with the ultimate goal of reducing the rate of healthcare-associated infections among patients admitted to hospitals in the US. In addition to my research in the healthcare domain, I also spend a portion of my time developing new data mining techniques for analyzing player tracking data from the NBA.
I study quality of care and patient outcomes in healthcare delivery systems, largely focusing on cardiovascular diseases. I use large national and regional data sets to answer fundamental questions about the existing state of healthcare delivery systems and how new approaches to designing these systems could save lives and reduce disease burden in large patient populations. My research is beginning to explore the use of innovative data mining and machine learning tools to these healthcare applications.
Jun Li’s main research interests are empirical operations management and business analytics, with special emphases on revenue management, pricing, consumer behavior, economic and social networks. She has worked extensively with large-scale data, including transactions, pricing, inventory and capacity, consumer online search and click stream data, supply chain relationships and disruptions, clinical and healthcare claims. She is the Winner of INFORMS Revenue Management and Pricing Practice Award for her close collaboration with retailing practitioners in implementing best response pricing algorithms. Her paper on airline pricing and consumer behavior is the finalist for Best Management Science Papers in Operations Management 2012 to 2014. She is also the principal investigator of a National Science Foundation funded project: “Gaining Visibility Into Supply Network Risks Using Large-Scale Textual Analysis”. Her work has enjoyed coverage by The Economist, New York Times and Forbes.
The research of Shen’s group covers the following aspects that are closely related to data science and decision making.
- We develop modeling techniques and computational paradigms for complex service systems that incorporate a variety of information and decisions. We design risk optimization approaches that are capable of handling integrated system design and service operations with multiple resources, multiple stages of service, and multiple stakeholders with diverse decision preferences under data uncertainty.
- We develop new decomposition paradigms for stochastic integer programming models. We focus on two-stage stochastic integer programs, and advance decomposition paradigms based on special structures of specific risk-averse programs, and also based on special integer-programming structures. We have implemented the related approaches in a series of research projects that optimize stochastic problems of (i) project management, (ii) resource allocation, (iii) appointment scheduling, and (iv) network interdiction. Moreover, the new decomposition paradigms can be widely applied to large-scale complex system design and operations management, including optimizing critical interdependent infrastructures such as power grids, transportation systems, and cyber-clouds.
- We study network interdiction models and design algorithms for specially structured networks (e.g., trees, small-world networks) in defense-related problems. Dynamic programming and heuristic approaches are used for deriving solutions in polynomial time, and generating valid bounds to the corresponding stochastic optimization models.
- With increasing penetrations of fluctuating renewable energy sources, such as wind power plants and solar photovoltaics, and active participation of electric loads in power system operation, uncertainty will increase. Specifically, we develop data-driven and distribution-free optimization methods that are suited to dispatching power systems with both fluctuating renewable energy sources and flexible loads contributing to balancing reserves via load control. The flexibility of an aggregation of loads is difficult to compute and uncertain. Investigating, characterizing, and managing this uncertainty is the focus of this research. Moreover, we quantify the tradeoff between the uncertainty and profitability of load control, and the effect of uncertainty and methods for managing it on power system dispatch, which affects pollutant emissions.
Amy Cohn, PhD, is an Associate Professor and Thurnau Professor in the Department of Industrial and Operations Engineering at the University of Michigan College of Engineering and Director of the Center for Healthcare Engineering and Patient Safety. Her primary research interest is in robust and integrated planning for large-scale systems, predominantly in healthcare and aviation applications. She also collaborates on projects in satellite communications, vehicle routing problems for hybrid fleets, and robust network design for power systems and related applications. Her primary teaching interest is in optimization techniques, at both the graduate and undergraduate level.