Focus On Authors
Ram Akella (“Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces”) is currently professor and director of the Center for Large-scale Live Analytics and Smart Services (CLASS), and the Center for Knowledge, Information Systems and Technology Management (KISMT) at the University of California. He followed up a B.S., Indian Institute of Technology Madras, Ph.D., Indian Institute of Science, Bangalore with post-doctoral positions at Harvard and MIT (EECS/LIDS and LFM). He then joined Carnegie Mellon University as an associate professor in the Tepper Business School (GSIA) and the School of Computer Science (CS/RI), before working at other institutions including MIT, Berkeley, Stanford, and establishing ORU/SUNY, TIM(ISTM) at UCSC/SVC as Founding Director/Chair. His research interests include data/text analytics, mining, search, and recommender systems. He has worked with over 200 firms, including AOL (Faculty Award), Cisco, Google (Research Award), IBM (Faculty Award), and SAP. He has been on editorial boards and program committees of the ACM, IEEE, INFORMS, and IIE.
Joel Barajas (“Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces”) received his Ph.D. in electrical engineering with an emphasis in statistics from the University of California, Santa Cruz. He received his B.S. degree in electronic systems engineering from ITESM Queretaro with honors, and two M.S. degrees from UAB Barcelona and CICATA-IPN. He has worked with multidisciplinary research groups across industry and government agencies in different domains for more than 10 years. In online advertising attribution and causal inference he has published in top outlets such as ACM CIKM, ACM WWW, SIAM SDM, among others. His research interests span online advertising, causal inference, text and data mining, machine learning, and data science.
Michael Braun (“Scalable Rejection Sampling for Bayesian Hierarchical Models”) is an associate professor of marketing in the Cox School of Business at Southern Methodist University. He earned his Ph.D. from the Wharton School of the University of Pennsylvania, and he holds an AB with Honors in economics from Princeton University, and an MBA from the Fuqua School of Business at Duke University. As a noted expert on the statistical analysis of large and complex datasets, he has written on, spoken on, and taught about management topics such as sales forecasting, customer retention and valuation, marketing ROI, segmentation and targeting strategies, online advertising, and insurance.
Aron Culotta (“Mining Brand Perceptions from Twitter Social Networks”) is an assistant professor of computer science at the Illinois Institute of Technology, where he leads the text analysis in the public interest lab. He obtained his Ph.D. in computer science from the University of Massachusetts, Amherst, where he developed machine learning algorithms for natural language processing. He was a Microsoft Live Labs Fellow, and completed research internships at IBM, Google, and Microsoft Research. His work has received best paper awards at AAAI and CSCW.
Jennifer Cutler (“Mining Brand Perceptions from Twitter Social Networks”) is a visiting assistant professor of marketing at the Kellogg School of Management at Northwestern University, and an assistant professor of marketing at the Stuart School of Business at the Illinois Institute of Technology. She received her Ph.D. in business administration from Duke University, and her Sc.B. in cognitive and linguistic sciences from Brown University. Her industry experience includes working in speech recognition and the intersection of marketing and engineering at Microsoft.
Paul Damien (“Scalable Rejection Sampling for Bayesian Hierarchical Models”) earned his Ph.D. in mathematics from Imperial College, London, and is an elected Fellow of the Royal Statistical Society of England. At present, he is on the faculty at the McCombs School of Business at the University of Texas at Austin.
Min Ding (“A Video-Based Automated Recommender (VAR) System for Garments”) is the Bard Professor of Marketing at Smeal College of Business, Pennsylvania State University, and advisory professor of marketing and Director of Institute for Sustainable Innovation and Growth (iSIG) at School of Management, Fudan University. He received his Ph.D. in marketing (with a concentration in health care systems) from the Wharton School of Business, University of Pennsylvania, his Ph.D. in molecular, cellular, and developmental biology from the Ohio State University, and his B.S. in genetics and genetic engineering from Fudan University. He received the Maynard Award in 2007, the Davidson Award in 2012, and his work has also been voted as Paul Green Award finalists (2006 and 2008) and an O’Dell Award finalist (2010). He is a diehard trekkie.
Bas Donkers (“Model-Based Purchase Predictions for Large Assortments”) is a professor of marketing research at the Erasmus School of Economics. He has a strong background in econometrics and a genuine interest in understanding human decision making. As a result, his research focuses on understanding and modeling human decision processes, developing and improving preference elicitation instruments, and the translation of this information into improved decision support tools and ultimately better decisions. His research has been published in, among others, Marketing Science, Journal of Marketing Research, and Quantitative Marketing and Economics.
Aaron Flores (“Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces”) received a B.S. degree in electronic systems engineering from the Instituto Tecnologico de Estudio Superiores de Monterrey, and M.S. and Ph.D. degrees in electrical engineering from Stanford University. He currently works in the AOL, Inc. research and development department, as a research scientist.
Dennis Fok (“Model-Based Purchase Predictions for Large Assortments”) is a professor of applied econometrics at the Erasmus School of Economics. He specializes in developing models to describe, understand, and predict decisions made by consumers. Among his technical interests are modeling unobserved heterogeneity, marketing econometrics, and Bayesian statistics. His research has been published in journals such as Marketing Science, Journal of Marketing Research, Journal of Econometrics, and Journal of Applied Econometrics.
Marius Holtan (“Experimental Designs and Estimation for Online Display Advertising Attribution in Marketplaces”) is vice-president of research at AOL. He received his Ph.D. from Stanford University at the Department of Engineering Economic Systems, specializing in finance. He is currently focused on developing prediction and optimization systems leveraging large amounts of structured and unstructured data. He has written patents and is the author of papers centering around optimization and prediction in online advertising.
Dongling Huang (“Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning”) is an associate professor of marketing at the David Nazarian College of Business and Economics, California State University, Northridge. She holds a Ph.D. in management science with a concentration in marketing from University of Texas at Dallas. Her research interests include new product strategies and marketing analytics. Her research has been published in Journal of Industrial Economics, Marketing Letters, and Journal of Culture Economics, among other journals. Her papers have received awards such as the American Marketing Association (AMA) Marketing Analytics and Research Best Paper Award, the Academy of Management Conference Best Paper Award, and the AMA Advanced Research Techniques Forum Best Paper Award.
Bruno J.D. Jacobs (“Model-Based Purchase Predictions for Large Assortments”) is a Ph.D. candidate at the Erasmus School of Economics. He received a Master’s degree in econometrics from the Erasmus School of Economics. In his doctoral research he develops new methods to model, understand, and predict purchase decisions at the customer level, with the aim to improve the buying decision process. Scalability is one of the focal points in his research. His research interests include modeling unobserved heterogeneity in large-scale applications, probabilistic graphical modeling, and approximate inference in Bayesian statistics.
Zainab Jamal (“Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting”) is a data scientist currently working in teh customer and marketing analytics group at HP. She has a Ph.D. in marketing science from UCLA, an MBA from the Indian Institute of Management Ahmedabad, and a Masters in economics from Delhi School of Economics. Her research interests are in developing econometric and statistical models to predict customer response behavior on digital media and other customer touch points and to evaluate the performance of different marketing channels.
Xiao Liu (“A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing”) is an assistant professor at New York University’s Stern School of Business. Her research focuses on quantitative marketing and empirical industrial organization, with a particular interest in consumer financial service innovations and high-tech marketing. She is the recipient of the 2014 Marketing Science Institute (MSI) Alden G. Clayton Doctoral Dissertation Proposal Competition Award and the 2014 INFORMS Society for Marketing Science (ISMS) Doctoral Dissertation Proposal Competition Award.
Shasha Lu (“A Video-Based Automated Recommender (VAR) System for Garments”) is a lecturer in marketing at the Cambridge Judge Business School, University of Cambridge. She received her Ph.D. in marketing from the School of Management, Fudan University, B.S. in computer science and technology, and B.S. in marketing from Huazhong University of Science and Technology.
Lan Luo (“Consumer Preference Elicitation of Complex Products Using Fuzzy Support Vector Machine Active Learning”) is an associate professor of marketing at the University of Southern California. She holds a Ph.D. degree from the University of Maryland. Her research interests include new product development and the impact of new product launches on the marketplace. Her research has been published in Marketing Science and Journal of Marketing Research, among other journals. She was the recipient of the John D. C. Little Award, the Donald R. Lehmann Award, and a finalist for the Paul E. Green Award; she was also named as a Marketing Science Institute Young Scholar in 2011.
Liye Ma (“Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting”) is an assistant professor of marketing at the Robert H. Smith School of Business at the University of Maryland. He obtained his Ph.D. degree from the Tepper School of Business at Carnegie Mellon University. His research focuses on the dynamic interactions of firms and consumers in online social media and those mediated by mobile and Internet technologies. He uses quantitative models to investigate the drivers of consumer actions in these emerging areas, and uses the findings to help firms optimize marketing strategies.
Daniel M. Ringel (“Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data”) is a doctorate student at Goethe University Frankfurt. After pursuing a 10-year career as a management consultant and entrepreneur, he turned to academia to conduct research on substantive problems around competitive analysis and big data processing as well as electronic commerce and the digital market space. He won the ISMS Doctoral Dissertation Proposal Competition in 2014.
Param Vir Singh (“A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing”) is the Carnegie Bosch Junior Chair and associate professor of business technologies at Tepper School of Business, Carnegie Mellon University. He studies consumer behavior on social technologies. He serves as associate editor at Management Science and Information Systems Research. He is the recipient of the inaugural INFORMS Information Systems Society Sandy Slaughter Early Career Award.
Bernd Skiera (“Visualizing Asymmetric Competition Among More Than 1,000 Products Using Big Search Data”) received his Ph.D. and his habilitation (venia legendi) from the University of Kiel and took over the very first chair of electronic commerce at a German university in the spring of 1999, at the Faculty of Business and Economics at Goethe University Frankfurt. He is a director at the E-Finance Lab. His research projects focus on online marketing, electronic commerce, social media, pricing, and customer management. His publications appeared in journals such as, among others, Management Science, Marketing Science, Journal of Marketing Research, Journal of Marketing, International Journal of Research in Marketing, Journal of Retailing, and Journal of Product Innovation Management.
Kannan Srinivasan (“A Structured Analysis of Unstructured Big Data by Leveraging Cloud Computing”) is the H.J. Heinz II Professor of Management, Marketing and Business Technologies at the Tepper School of Business, Carnegie Mellon University. He has published over 70 articles including several in leading business and statistics journals. He is a Fellow of the INFORMS Society of Marketing Science.
Michael Trusov (“Crumbs of the Cookie: User Profiling in Customer-Base Analysis and Behavioral Targeting”) is an associate professor of marketing at the Robert H. Smith School of Business at the University of Maryland. He received his Ph.D. degree from the Anderson School of Management at UCLA. His research has won several awards, including the William F. O’Dell Award, the Paul E. Green Award, the Donald R. Lehmann Award, the Society for Marketing Advances Emerging Scholar Award, the Emerald Management Reviews Citation of Excellence Award, and the Marketing Science Institute’s Alden G. Clayton Award. His research interests include Internet marketing (social media marketing, search engine marketing, social networks, clickstream analysis, electronic word-of-mouth marketing, e-commerce, recommendation systems, consumer-generated content), text analysis, eye-tracking, and data mining.
Li Xiao (“A Video-Based Automated Recommender (VAR) System for Garments”) is an assistant professor of marketing at the School of Management, Fudan University. She received her Ph.D. in marketing from the Smeal College of Business, Pennsylvania State University, an M.S. in marketing from Wuhan University, and a B.S. in computer science from the Naval Academy of Engineering.
