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Data Sciences and Operations: Faculty and Research
Welcome to the DSO Faculty and Research webpage!
Our society faces wicked problems – global pandemics, climate change, extreme wealth inequity, and a ubiquity of opaque artificial intelligence algorithms that unintentionally increase ideological polarization and institutionalize discrimination. As we collectively search for a solution, emerging fields such as sustainable & smart supply chains, fair machine learning, and evidence-driven public policy offer a path forward.
The DSO department at USC Marshall is a unique place where one can find world experts in these, and other, areas of data science.
The department consists of three groups – information systems, operations management, and statistics. Faculty collaborate within and across these groups to:
i) Teach classes that prepare students to thrive in an increasingly data-driven world, and
ii) Conduct exciting, relevant research that pushes the boundaries of human knowledge. Importantly, faculty research spans both theoretical breakthroughs in fundamental science and practical strategies to help companies, governments, and non-profits better navigate those wicked problems.
You can learn more about our faculty, their research, and their outstanding scholarly achievements on this page and their linked personal websites below.
— Greys Sošić, Department Chair and Professor of Data Sciences and Operations
Full-Time Faculty
Mohammed's research examines the economic and societal impact of digital marketplaces, with a focus on examining and measuring the role of traditional barriers, related to market structure, race, and expertise, among others, on limiting the equality of benefits derived from these platforms.
Arif Ansari is an expert in the area of data mining, business intelligence, data warehousing, and intelligent systems and technologies, a field in which he has fifteen years of research experience. He has published in IEEE Transactions on Systems and Man and Cybernetics.
Murat Bayiz is a Professor of Clinical Data Sciences and Operations at Marshall School of Business. He has more than two decades of combined management consulting and academic experience and specializes in operations strategy.
Jacob Bien's research focuses on statistical machine learning and in particular the development of novel methods that balance flexibility and interpretability for analyzing complex data. He combines ideas from convex optimization and statistics to develop methods that are of direct use to scientists and others with large datasets. His work has been supported by an NSF CAREER award, a three-year NSF grant on high-dimensional covariance estimation, an NIH R01 grant on methods for multi-view data, and a grant from the Simons Foundation on developing new statistical methodology for oceanography. He serves as an associate editor of Biometrika and the Journal of Computational and Graphical Statistics, and he was previously an associate editor for Biostatistics. Before joining USC, he was an assistant professor at Cornell.
Courtesy + Joint Appointments
Dan focuses on artificial intelligence, emerging technologies & text mining. He is former editor of IEEE Intelligent Systems & Journal of Organizational Computing and Electronic Commerce. Awards: Fulbright Scholar (France), Paul Gray Award “Most Thought-Provoking Paper," 2017-top 100 IS researchers, JIS best paper for 2017-2019, AIS Distinguished Member, UiPath Visionary Educator in RPA, AIS Award for Innovation in Teaching, SET Outstanding Educator Award and listed among “Top 100000 Scientists” based on citations and publications. Grants-iORB–Robotic Process Analysis, KPMG KARP Award for Data and Analytics–Non-Traditional Measures, NSA Grant-MKIDS and DHS grant on security.
Part-Time + Adjunct Faculty
As a member of the Senior Technical Leadership for Multicloud Management at Red Hat, Joydeep’s focus is Observability, Far Edge & Scalability. Previously at The Walt Disney Studios, he partnered with the VP of Strategic Initiatives to drive the digital transformation of a key application, breaking it into micro services and migrating it to Cloud. In his many years at IBM, Joydeep has spear headed key projects and worked across many technologies in the areas of Distributed Computing, Streaming Analytics & J2EE. He is big believer in DevOps transformation, Agile Methodology & Design Thinking. He is interested in replicating the success of AI/ML in other fields to the area of Observability.
Sudi Bhattacharya is a Software and ML Engineering leader experienced in running business transformation programs that harvest(big) data as a core asset, extract machine learning model-based insights from data and nurture a culture of data-driven decision making for Fortune 500 companies. Sudi is currently working as a Senior Software Development Manager in Amazon Web Services where he is leading multiple teams of software, data and business intelligence engineers. Sudi has an MBA from University of Chicago Booth School of Business. In his spare time, Sudi plays Tennis, Table Tennis, fingerpicks the flamenco guitar and reads fantasy and science fiction novels.
Emeriti Faculty
Yehuda Bassok's research interests include supply chain management, design and analysis of purchasing contracts, management of centralized and decentralized inventory systems, and bargaining within supply chains. His research has been published in many top journals, including Management Science and Operations Research. Prior to joining USC, he was on the faculty of Northwestern University and a visiting professor at the University of Washington. Professor Bassok served as chief system analyst of SES, a company that specialized in developing real time data collection and scheduling systems. Professor Bassok has received several teaching awards, including Marshall's Golden Apple Award in 2003.
Richard B. Chase received his Ph.D. from the University of California, Los Angeles in the field of Operations Management. He is widely known for his work in service design Richard has published papers in journals that include Management Science, Operations Research, Journal of Service Research, Journal of Operations Management, and Manufacturing & Service Operations Management.He is on the editorial boards of Manufacturing & Service Operations Management; an Editorial Advisory boards of Production and Operations Management Journal, Journal of Operations Management, and the Journal of Service Research,and the Cornell Quarterly Dick has consulted with a variety of organizations including IBM, MGM Grand, Westin Hotels, and Aloha Airlines.
Delores Conway is a statistician who studies real estate markets. Her research has been published in the Journal of Real Estate Finance and the Journal of Econometrics. Professor Conway is director of the Casden Real Estate Economics Forecast at the USC Lusk Center for Real Estate, and in 2005-06 and 2007-08 was selected as one of 50 "Women of Influence in Real Estate" by Real Estate Southern California Magazine.
From 1987 to 2013, he was Professor of Operations Management in Department of Information and Operations Management and served as Department Chairman, Vice Dean for International and Graduate Programs. From 2009-2011, he served as Distinguished Professor and Dean of College of Business at Korea Advanced Institute of Science and Technology (KAIST).From 2013-2018, he was Shaw Chair Professor at Nanyang Business School (NBS) in NTU, Singapore, where he served as Dean and Associate Provost. During his tenure, NBS was globally ranked by the Financial Times as # 10 for its EMBA and # 22 for its MBA and he helped raise over SG$35 million.Professor Kumar has received awards for teaching excellence at both University of Illinois and USC.Professor Kumar is author or co-author of more than 70 articles in international peer-reviewed academic journals. Global companies have funded his research as well as the National Science Foundation.
RESEARCH + PUBLICATIONS
In a non-negative profit game that possesses a Population Monotonic Allocation Scheme (PMAS), being a member of a larger coalition implies that your profit cannot decrease. In this paper, we refer to such games as PMAS profit games. As population monotonicity is a nice and desirable property that encourages formation of larger coalitions and implies stability of the grand coalition, we explore if this special feature of PMAS games can help in identifying additional stable coalition structures under different stability concepts in cooperative game---namely, core partitions, the von Neumann--Morgenstern (vNM) stable set, the largest consistent set, and the equilibrium process of coalition formation (EPCF)---and in developing relationships between coalition structures that are stable under these different stability concepts.
We first define two special classes of players for PMAS profit games---extreme and strong players---and use them to develop an algorithm for construction of stable (core) partitions. We also use extreme players to identify absorbing states for equilibrium processes of coalition formation with high level of farsightedness.
We then explore the impact of population monotonicity on the relationship between stable coalition structures under abovementioned stability concepts. While we are able to obtain some results related to stability of the grand coalition and to establish relationships between stable coalition structures under different stability notions that are consistent with the existing body of knowledge, population monotonicity in general does not add enough for strengthening of the existing results. However, we are able to show a couple of more general result that hold for arbitrary cooperative TU profit games. That is, we show that the members of vNM farsighted stable sets are core partitions, and that core partitions are members of a vNM stable sets. Moreover, we show that the members of vNM farsighted stable sets are EPCF-stable partitions.
We study a special class of cooperative games with transferable utility (TU), called m-attribute games. Every player in an m-attribute game is endowed with a vector of attributes that can be combined in an additive fashion; that is, if players form a coalition, the attribute vector of this coalition is obtained by adding the attributes of its members. Another fundamental feature of m-attribute games is that their characteristic function is defined by a continuous attribute function—the value of a coalition depends only on evaluation of on the attribute vector possessed by the coalition, and not on the identity of coalition members. This class of games encompasses many well-known examples, such as queueing games and economic lot-sizing games. We believe that by studying attribute function and its properties, instead of specific examples of games, we are able to develop a common platform for studying different situations and obtain more general results with wider applicability. In this paper, we first show the relationship between nonemptiness of the core and identification of attribute prices that can be used to calculate core allocations. We then derive necessary and sufficient conditions under which every m-attribute game embedded in attribute function has a nonempty core, and a set of necessary and sufficient conditions that should satisfy for the embedded game to be convex. We also develop several sufficient conditions for nonemptiness of the core of m-attribute games, which are easier to check, and show how to find a core allocation when these conditions hold. Finally, we establish natural connections between TU games and m-attribute games.
FACULTY POSITIONS
Open faculty positions at the USC Marshall Data Science and Operations Department are posted HERE.