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.
My current data science research interest lies in the broad area of supply chain and its management. I am particularly interested in using longitudinal data set to identify early signals (or warning) and to draw causal inferences pertaining to supply chain security and product quality and safety. I am also interested in developing experiments to capture the behavioral side of decision makings to be complementary to secondary data analysis. Industry setting wise, I have based my research on the auto industry, and will expand my auto-industry centered research into a broader, transportation industry oriented context. I am also interested in food and agricultural products, pharmaceutical, and medical devices industries where product quality and safety have significant implications to human life and society as a whole.
Dr. Nalliah’s research expertise is process evaluation. He has studied various healthcare processes, educational processes and healthcare economics. Dr. Nalliah’s research studies were the first time nationwide data was used to highlight emergency room resource utilization for managing dental conditions in the United States. Dr. Nalliah is internationally recognized as a pioneer in the field of nationwide hospital dataset research for dental conditions and has numerous publications in peer reviewed journals. After completing a masters degree at Harvard School of Public Health, Dr. Nalliah’s interests have expanded and he has studied various public health issues including sports injuries, poisoning, child abuse, motor vehicle accidents and surgical processes (like stem cell transplants, cardiac valve surgery and fracture reduction). National recognition of his expertise in these broader topics of medicine have given rise to opportunities to lecture to medical residents, nurse practitioners, students in medical, pharmacy and nursing programs about oral health. This is his passion- that his research should inform an evolution of health education curriculum and practice.
Dr. Nalliah’s professional mission is to improve healthcare delivery systems and he is interested in improving processes, minimizing inefficiencies, reducing healthcare bottlenecks, increasing quality, and increase task sharing which will lead to a patient-centered, coherent healthcare system. Dr. Nalliah’s research has identified systems constraints and his goal is to influence policy and planning to break those constraints and improve healthcare delivery.
Prof. Lenk develops Bayesian models that disaggregate data to address individuals. He also studies Bayesian nonparametric methods and currently consider shape constraints. Prof. Lenk teaches and uses data mining methods such as recursive partition and neural networks.
Sy Banerjee studies the impact of mobile devices on consumer behavior and on the processing of signals emerging from location-based Social Media posts. He teaches a MBA class on digital marketing and Big Data and collaborates with researchers from Business, GIS and Computer Science. Some of his recent works include:
- Assessing Prime-Time for Geotargeting With Mobile Big Data, Sy Banerjee, Vijay Viswanathan, Kalyan Raman, Hao Ying, Journal of Marketing Analytics, 2013, Vol. 1(3), pp 174-183.
- “Visualizing active travel sentiment in an urban context” with Greg Rybarczyk, International Conference on Transport & Health MINETA Transportation Institute, San Jose, California, July 2016.
- “Assigning Geo-Relevance of Sentiments Mined from Location-Based Social Media Posts” with R. Sanborn and M. Farmer, in Advances in Intelligent Data Analysis XIV, LNCS
- “Understanding In-Store Consumer Experiences via User Generated Content from Social Media”, working paper with Karthik Sridhar and Ashwin Aravindakshan
- “Tweeted Customer Emotions as Currency for Competitive Performance: A Framework of Location-Based Social Media Listening”, working paper with Amit Poddar, Karthik Sridhar, Nanda Kumar
Muzammil M. Hussain is Assistant Professor of Communication and Media, Faculty Associate at the U-M International Institute and the U-M Institute for Social Research, and Faculty Affiliate at the U-M Ford School of Public Policy’s Science, Technology, and Public Policy Program (STPP) and the Michigan Institute for Data Science (MIDAS). Dr. Hussain’s interdisciplinary research is at the intersections of global communication, social analytics, and technology governance. At Michigan, Professor Hussain teaches courses on digital politics, research methods, and global innovation. He has authored numerous research articles, book chapters, and industry reports examining global ICT politics, innovation, and policy, including pieces in The Journal of Democracy, The Journal of International Affairs, The Brookings Institutions’ Issues in Technology and Innovation, The InterMedia Institute’s Development Research Series, International Studies Review, International Journal of Middle East Affairs, The Communication Review, Policy and Internet, and Journalism: Theory, Practice, and Criticism.
My interest is in using econometrics, especially Bayesian econometrics, and machine learning methods to infer causality. I tend to work with mostly parametric models of firm and consumer behavior to assess the effectiveness of firm actions. My work spans a variety of industries such as pharmaceuticals, e-commerce, gaming and hi-technology.
Jun Li, PhD, is Assistant Professor in the department of Technology and Operations in the Ross School of Business at the University of Michigan, Ann Arbor.
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.