Most of my research related to data science involves decision making around clinical trials. In particular, I am interested in how databases of past clinical trial results can inform future trial design and other decisions. Some of my work has involved using machine learning and mathematical optimization to design new combination therapies for cancer based on the results of past trials. Other work has used network meta-analysis to combine the results of randomized controlled trials (RCTs) to better summarize what is currently known about a disease, to design further trials that would be maximally informative, and to study the quality of the control arms used in Phase III trials (which are used for drug approvals). Other work combines toxicity data from clinical trials with toxicity data from other data sources (claims data and adverse event reporting databases) to accelerate detection of adverse drug reactions to newly approved drugs. Lastly, some of my work uses Bayesian inference to accelerate clinical trials with multiple endpoints, learning the link between different endpoints using past clinical trial results.
Greg’s research primarily investigates information flow in financial markets and the actions of agents in those markets – both consumers and producers of that information. His approach draws on theory from the social sciences (economics, psychology and sociology) combined with large data sets from diverse sources and a variety of data science approaches. Most projects combine data from across multiple sources, including commercial data bases, experimentally created data and extracting data from sources designed for other uses (commercial media, web scrapping, cellphone data etc.). In addition to a wide range of econometric and statistical methods, his work has included applying machine learning , textual analysis, mining social media, processes for missing data and combining mixed media.
My research explores the interplay between corporate decisions and employee actions. I currently use anonymized mobile device data to observe individual behaviors, and employ both unsupervised and supervised machine learning techniques.
He develops and applies operations research, data science, and systems approaches to public and private service industries. His research focuses on the management and policy analysis of emerging networked industries and innovative mobility services such as smart parking, connected vehicles, autonomous vehicles, ride-hailing, bike sharing, and car sharing. He has worked extensively with both public and private sector partners worldwide. He is a queueing theorist that uses statistics, stochastic modeling, simulation and dynamic optimization.
My research focuses on quantitative modeling approaches that help business or nonprofit institutions make efficient operational decisions. My research addresses decisions that are made: 1) on either a single independent operation or multiple integrated operations, and 2) by either a single party or multiple parties, most likely different supply chain members. I am specifically interested in the allocation of resources over time and/or among different parties, which often involve scheduling, i.e., the allocation of resources over time to optimize certain objectives, capacity allocation, i.e., the allocation of production capacity from supplier to retailers in a supply chain setting, and pricing, i.e., the determination of selling price of certain products. When multiple parties are involved, decisions can be made either cooperatively or non-cooperatively. The methodologies used in my work include game theory, real analysis, optimization, approximation, simulation, and statistics.
Efficient, low regret contextual multi-armed bandit approaches for real time learning including Thompson sampling, UCB, and knowledge gradient descent. Integration of optimization and predictive analytics for determining the time to next measurement, which modality to use, and the optimal control of risk factors to manage chronic disease. Integration of soft voting ensemble classifiers and multiple models Kalman filters for disease state prediction, Real-time (online) contextual multi-armed bandits integrated with optimization of hospital bed type dynamic control decisions for reducing 30-day readmission rates in hospitals. Robustness in system optimization when the system model is uncertain with emphasis on quantile regression forests, sample average approximation, robust optimization and distributionally robust optimization. Health care delivery systems models with prediction and control for inpatient and outpatient. Work has been done on Emergency Department redesign for improved patient flow; Capacity management and planning and scheduling for outpatient care, including integrated services networks; admission control with machine learning to ICUs, stepdown, and regular care units Surgical planning and scheduling for access delay control; Planning and scheduling for Clinical Research Units.
S. Sriram, PhD, is Professor of Marketing in the University of Michigan Ross School of Business, Ann Arbor.
Prof. Sriram’s research interests are in the areas of brand and product portfolio management, multi-sided platforms, healthcare policy, and online education. His research uses state of the art econometric methods to answer important managerial and policy-relevant questions. He has studied topics such as measuring and tracking brand equity and optimal allocation of resources to maintain long-term brand profitability, cannibalization, consumer adoption of technology products, and strategies for multi-sided platforms. Substantively, his research has spanned several industries including consumer packaged goods, technology products and services, retailing, news media, the interface of healthcare and marketing, and MOOCs.
My research focuses on the intended and unintended consequences of language in financial markets. I examine this relationship across a number of contexts, such as the Federal Reserve, initial public offerings, and mergers and acquisitions. More broadly, my work aims to develop new theoretical and methodological approaches to understand the role of language in society.
My research focus is on the development and application of machine learning tools to large scale financial and unstructured (textual) data to extract, quantify and predict risk profiles and investment grade rating of private and public companies. Example datasets include social media and financial aggregators such as Bloomberg, Pitchbook, and Privco.
My current research interest is focused on improving efficiency and utilization of outpatient clinics, using data mining techniques such as decision tree analysis, Bayesian networks, neural networks, and similar techniques. While our previous and continuing research have been focused on using some of these techniques to develop more sophisticated methods of patients scheduling within physical therapy clinics, we can see the applicability of the techniques to other types of health services providers. There is also applicability to university administration in developing predictive models using data mining techniques for assessing student success.