S. Raghu Raghavan Directory Page
S. Raghu Raghavan
Dean's Professor of Management Science and Operations Management
Ph.D. in Operations Research, Massachusetts Institute of Technology
Dr. S. Raghu Raghavan is passionate about using quantitative methods for better decision-making. He enjoys teaching business analytics courses and has received many teaching awards, including the INFORMS Prize for the Teaching of OR/MS Practice, the Legg Mason Teaching Innovation Award at the Smith School, and several Smith School Distinguished Teaching Awards.
His research interests encompass a broad range, including auction design, data mining, economics, information systems, computational marketing, logistics, healthcare policy and management, networks, optimization, and telecommunications. He has published widely in academic outlets such as Computers & Operations Research, Decision Sciences, Discrete Applied Mathematics, INFORMS Journal on Computing, Management Science, Networks, Operations Research and Transportation Science.
He holds two patents and has won numerous awards for his work. These include the Dantzig Award for the best doctoral dissertation; the INFORMS Computing Society Prize (twice), once for contributions to data mining and again for public sector auction design; the Glover-Klingman Prize for the best paper in Networks; the Management Science Strategic Innovation Prize from the European Operations Research Society; the INFORMS Telecommunications Section Best Paper Award; second place in the INFORMS Junior Faculty Paper Competition; and finalist honors for both the European Operations Research Society Excellence in Practice Award and the Wagner Prize for Excellence in Operations Research Practice.
Before joining the Smith School, he led the Optimization Group at US West Advanced Technologies.
News
Research
Academic Publications
Improved LISA Analysis for Zero-Heavy Crack Cocaine Seizure Data
INFORMS Journal of Data Science
Local Indicators of Spatial Association (LISA) analysis is a useful tool for analyzing and extracting meaningful insights from geographic data. It provides informative statistical analysis that highlights areas of high and low activity. However, LISA analysis methods may not be appropriate for zero-heavy data, as without the correct mathematical context the meaning of the patterns identified by the analysis may be distorted. We demonstrate these issues through statistical analysis and provide the appropriate context for interpreting LISA results for zero-heavy data. We then propose an improved LISA analysis method for spatial data with a majority of zero values. This work constitutes a possible path to a more appropriate understanding of the underlying spatial relationships. Applying our proposed methodology to crack cocaine seizure data in the U.S., we show how our improved methods identify different spatial patterns, which in our context could lead to different real-world law enforcement strategies. As LISA analysis is a popular statistical approach that supports policy analysis and design, and as zero-heavy data is common in these scenarios, we provide a framework that is tailored to zero-heavy contexts, improving interpretations and providing finer categorization of observed data, ultimately leading to better decisions in multiple fields where spatial data is foundational.
Eunseong Jang, The Robert H. Smith School of Business, University of Maryland
Margret Bjarnadottir, The Robert H. Smith School of Business, University of Maryland
Marcus Boyd, National Consortium for the Study of Terrorism and Responses to Terrorism, University of Maryland
S. Raghavan, The Robert H. Smith School of Business & Institute for Systems Research, University of Maryland