Business Analytics Series
In this Thought Leaders series organized by Tippie's Business Analytics department, leading-edge researchers from the field of Business Analytics spoke about some of the most critical issues in the field.
All seminars took place via Zoom and most were recorded. Video links are provided below.
Dr. Patrick Fan
11:00 a.m. CST
Topic: Idea Recommendation in Open Innovation Platforms: A Design Science Approach
Abstract: Collaborative crowdsourcing communities help firms obtain ideas generated by the public at a lower cost compared to those generated in-house. However, the growth of these communities has led to a large influx of ideas of mixed quality, which has made it difficult for firm experts to select and implement ideas. In this paper we propose a novel theoretical framework to (1) extract important features from a collaborative crowdsourcing community and (2) apply them to the practice of recommending ideas that are most likely to be implemented in the future. More specifically, we adopt the design science research paradigm, introduce the knowledge persuasion model as the kernel theory, operate users’ persuasive attempts and firm experts’ persuasive coping, and identify a rich set of features as predictors of the likelihood of idea implementation. We test our prediction framework on a large-scale collaborative crowdsourcing community. The results of our data analysis show that the proposed framework is effective and efficient in predicting the likelihood of idea implementation. To increase the interpretability of the prediction model, we also implement the SHapley Additive exPlanations (SHAP) analysis and discuss the relationships between important features and idea implementation. We conclude by discussing the theoretical and practical implications of these findings.
Bio: Dr. Weiguo (Patrick) Fan is Henry B. Tippie Chair in Business Analytics at the Tippie College of Business, University of Iowa. He received his Ph.D. in Business Administration from the Ross School of Business, University of Michigan, Ann Arbor, in 2002, a M. Sce in Computer Science from the National University of Singapore in 1997, and a B. E. in Information and Control Engineering from the Xi'an Jiaotong University, P.R. China, in 1995.
His research interests focus on the design and development of novel information technologies - information retrieval, data mining, text/web mining, business intelligence techniques, data science, business analytics - to support better business information management and decision making. He has also worked on causal inference on observational data using econometric models and field experiments in e-Commerce settings. His research findings have been widely cited and used in academia and industry.
His research has been cited more than 12000 times (H-index: 53) according to Google Scholar. His research has been funded by five USA NSF grants, three China NSF grants, one PWC grant, one KPMG grant, and several other industrial grants. He currently serves on the editorial boards for several well-known IS journals: MIS Quarterly, Journal of Association for Information Systems, Information Systems Journal, Information and Management, Information Technology and Management, Journal of Electronic Commerce Research and Journal of Database Management.
He has consulted with many Fortune 500 companies. He also has served as reviewers and advisors for NSF, National Homeland Security, and many startups.
Dr. Rong Jin
11:00 a.m. CST
Topic: Future AI: Bridge the Gap between Machine Intelligence and Human Intelligence
Abstract: Although we have witnessed amazing progress in deep learning, there are many fundamental questions related to deep learning that remain to be answered. In particular, even though deep learning framework is inspired by how human brains process information, a significant gap needs to fill out between machine intelligence empowered by deep learning and human intelligence. In this talk, we first examine the main limitations of deep learning in comparison with human intelligence, including a large gap in sample complexity, limited abilities in exploring knowledge and logic for effective inference, and large difference in information processing and network structure. We then present recent progress in these dimensions that may shed lights on how to bridge the gap between human intelligence and machine intelligence.
Bio: Rong Jin is currently an associate director of DAMO academy at Alibaba, leading the research and development of state-of-the-art AI technologies. Before joining Alibaba, he was a faculty member of the Computer and Science Engineering Dept. at Michigan State University from 2003 to 2015. His research is focused on statistical machine learning and its application to large-scale data analysis. He published over 300 technique papers, mostly on the top conferences and prestigious journals. He is an associate editor of IEEE Transaction at Pattern Analysis and Machine Intelligence (TPAMI) and ACM Transaction at Knowledge Discovery from Data. Dr. Jin holds Ph.D. in Computer Science from Carnegie Mellon University. He received the NSF career award in 2006.
Dr. Ruslan Salakhutdinov
Carnegie Mellon University
11:00 a.m. CST
Topic: From Differentiable Reasoning to Self-supervised Embodied Active Learning
Abstract: In this talk, I will first discuss deep learning models that can find semantically meaningful representations of words, learn to read documents, and answer questions about their content. I will introduce methods that can augment neural representation of text with structured data from Knowledge Bases (KBs) for question answering, and show how we can answer compositional questions over long structured documents using a text corpus as a virtual KB. In the second part of the talk, I will show how we can design modular hierarchical reinforcement learning agents for visual navigation that can perform tasks specified by natural language instructions, perform efficient exploration and long-term planning, build and utilize 3D semantic maps to learn both action and perception models in self-supervised manner, while generalizing across domains and tasks.
Bio: Russ Salakhutdinov is a UPMC Professor of Computer Science in the Department of Machine Learning at CMU. He received his PhD in computer science from the University of Toronto. After spending two post-doctoral years at MIT, he joined the University of Toronto and later moved to CMU. Russ's primary interests lie in deep learning, machine learning, and large-scale optimization. He is an action editor of the Journal of Machine Learning Research, served as a program co-chair for ICML2019, and served on the senior programme committee of several top-tier learning conferences including NeurIPS and ICML. He is an Alfred P. Sloan Research Fellow, Microsoft Research Faculty Fellow, Canada Research Chair in Statistical Machine Learning, and a recipient of the Early Researcher Award, Google Faculty Award, and Nvidia's Pioneers of AI award.
Dr. Zhongju (John) Zhang
Arizona State University
11:00 a.m. CST
Topic: What Types of Crowds Generate More Valuable Content? Evidence from Cross-Platform Posting
Abstract: This study examines the value of content generated by various groups of users on a financial social media platform. The value of such content is measured by the incremental accuracy of predicting stock volatility based on the cues from those content. We argue that the characteristics of a crowd such as crowd size, crowd diversity, and crowd independence have significant impact on the predictive value of the crowd-generated content. Leveraging a natural experiment setup where the financial platform no longer receives cross-postings from another major social media platform, we show empirical evidence that the composition of crowds (i.e., characteristics of the users who make up crowds) does matter. Furthermore, the impacts of the characteristic features on the value of crowd-generated content are likely heterogeneous and non-monotone. Finally, we discuss the implications of this study about digital platform envelopment and competition.
Bio: Zhongju Zhang is Professor of Information Systems and Data Analytics at the W. P. Carey School of Business, Arizona State University. Prior to that, Zhang was a tenured faculty member and the founding director of the MS degree program in business analytics and project management (MS-BAPM) at the School of Business, University of Connecticut. Zhang has formal training in the fields of information systems, data science, computer science, management science, operations research/management, and economics. He has extensive knowledge and strongly believes in experiential learning in higher education. Zhang has consulted extensively for various industries on data centric, operations, and strategic decision problems, and has experience developing strategic analytics initiatives for organizations. Zhang has also successfully sought external funding from both the industry and government agencies.
Zhang’s research primarily focuses on how information systems/technology and data analytics impact consumer behavior, create business value, and transform business models. He has specific interests in online collaborative platforms and social media, fraud and fake news detection as well as its business and social impacts, platform economy and business models, economic aspects of digital transformation, machine learning and data science. Zhang's work has appeared in academic journals including Information Systems Research, MIS Quarterly, Production and Operations Management, INFORMS Journal on Computing, Journal of Management Information Systems, Decision Support Systems, European Journal of Operational Research, and Decision Sciences. Zhang serves on the Senior Editorial Boards of a few journals including Production and Operations Management, Decision Support Systems, and Electronic Commerce Research and Applications. He has won numerous research and teaching awards, including the Best IS Publications of the Year Award, Research Excellence Award, MBA Teacher of the Year Award, and the Ackerman Scholar Award.
Dr. Amos Storkey
University of Edinburgh
11:00 a.m. CST
Topic: Towards a Distributed Transactional Artificial Intelligence
Abstract: The story of recent machine learning and artificial intelligence has been one of bigger and bigger models, and accumulation and use of more and more data. Improvements in current measures of performance though this approach are undeniable; some complex relationships cannot be discovered without more data to inform that discovery. However this emphasis on large data accumulation as the driver for artificial intelligence has led to natural monopolies and centralization of resource in the implementation of artificial intelligence. The story of machine learning in the 21st century has to a large extent been a story of accumulation and size. Yet in many fields waves of centralization are followed by waves of decentralization and distributed mechanisms -- whether it be market versus control economies or moves from mainframes to personal computing to cloud compute. I am persuaded the next piece in the story of artificial intelligence will be, and needs to be, a decentralised transactional artificial intelligence.
In this talk I discuss some of the technical pieces of the puzzle that will be key in the process of formulating a distributed artificial intelligence through autonomous agents, from meta-learning, multi-task and multi-agent learning through to economic and prediction-market models of machine learning, as well as discussing some of the unsolved problems in mechanism design for a distributed transactional artificial intelligence.
Bio: Amos Storkey is Professor of Machine Learning and AI in the School of Informatics, University of Edinburgh, with a background in mathematics (MA Mathematics, Trinity, Cambridge) and theoretical physics (Part III Mathematics) before focusing on Machine Learning (MSc, PhD Imperial London). He moved to Edinburgh after his PhD, where he now leads the Bayesian and Neural Systems Group focused on deep neural networks, transfer learning, probabilistic models, Bayesian methods, transactional machine learning and efficient learning and inference. He is known for the Storkey Learning Rule for Hopfield networks, for a first neural network capable of competitive Go play learned only from human play, for market models of machine learning and for his broader contributions to Bayesian methods, meta-learning, efficiency in neural networks and curiosity driven reinforcement learning. He is currently director of the UK EPSRC Centre for Doctoral Training in Data Science.
Dr. Asuman Ozdaglar
11:00 a.m. CST
Topic: Optimal and Differentially Private Data Acquisition from Strategic Users
Abstract: The data of billions of people around the world are used every day for improving search algorithms, recommendations on online platforms, personalized advertising, and the design of new drugs, services, and products. With rapid advances in machine learning (ML) algorithms and further growth in data collection, these practices will become only more widespread in the years to come. A common concern with many of these data-intensive applications centers on privacy — as a user’s data is harnessed, more and more information about her behavior and preferences are uncovered and potentially utilized by platforms and advertisers. A popular solution to the tension between privacy costs and benefits of data is to use methods such as differential privacy in order to limit the extent to which an individual’s data is uncovered and exploited. Although these methods are already used by many of the tech companies, a key practical question remains: How do we decide how much privacy heterogeneous, strategic users will obtain?
This talk is an attempt to answer this key question and study the impact of data market architecture on the design of mechanisms for purchasing data from privacy sensitive strategic users. We consider a platform interested in estimating an underlying parameter using data collected from users. We formulate this question as a Bayesian-optimal mechanism design problem, in which an individual can share her (verifiable) data in exchange for a monetary reward or services, but at the same time has a (private) heterogeneous privacy sensitivity that represents her cost per unit privacy loss. We consider two popular differential privacy settings for providing privacy guarantees for the users: central and local. In both cases, we establish minimax lower bounds for the estimation error and derive (near) optimal estimators for given heterogeneous privacy loss levels for users. Building on this characterization, we pose the mechanism design problem as the optimal selection of an estimator and payments that will elicit truthful reporting of privacy sensitivities of users under a regularity condition on the distribution of privacy sensitivities. Our mechanism in the central setting can be implemented in log-linear time in the number of users, and, in the local setting, it admits a Polynomial Time Approximation Scheme (PTAS).
Bio: Asu Ozdaglar received the B.S. degree in electrical engineering from the Middle East Technical University, Ankara, Turkey, in 1996, and the S.M. and Ph.D. degrees in electrical engineering and computer science from the Massachusetts Institute of Technology, Cambridge, in 1998 and 2003, respectively.
She is the Mathworks Professor of Electrical Engineering and Computer Science (EECS) at the Massachusetts Institute of Technology (MIT). She is also the department head of EECS and deputy dean of academics of the Schwarzman College of Computing at MIT. Her research expertise includes optimization theory and algorithms, game theory, machine learning and network analysis with applications in social, economic and financial networks. Her recent research focuses on designing incentives and algorithms for data-driven online systems with many diverse human-machine participants.
Professor Ozdaglar is the recipient of a Microsoft fellowship, the MIT Graduate Student Council Teaching award, the NSF Career award, the 2008 Donald P. Eckman award of the American Automatic Control Council, the 2014 Spira teaching award, and Keithley, Distinguished School of Engineering and Mathworks professorships. She is an IEEE fellow and was selected as an invited speaker at the International Congress of Mathematicians. She served on the Board of Governors of the Control System Society in 2010 and was an associate editor for IEEE Transactions on Automatic Control. She was the inaugural area co-editor for the area entitled "Games, Information and Networks” in the journal Operations Research. She is the co-author of the book entitled “Convex Analysis and Optimization” (Athena Scientific, 2003).
Dr. Gedas Adomavicius
University of Minnesota
11:00 a.m. CST
Topic: Recommender Systems and Preference Pollution
Abstract: Interactions between individuals and recommender systems can be viewed as a continuous feedback loop, consisting of pre-consumption and post-consumption phases. Pre-consumption, systems provide recommendations that are typically based on predictions of user preferences. They represent a valuable service for both providers and users as decision aids. After item consumption, the users often provide post-consumption feedback (e.g., preference ratings) to the system, which typically serves as the “ground truth” data that is used to further improve the system’s subsequent recommendations, completing the feedback loop. There is a growing understanding that this feedback loop can be a significant source of biases and unintended consequences. In particular, this talk focuses on “preference pollution”, a form of bias which reflects an unintended effect of system recommendations on the users’ post-consumption preference ratings. We provide a comprehensive exploration of the “preference pollution” phenomenon and discuss its importance and implications for the design and application of recommendation systems.
Bio: Gedas Adomavicius is a professor in the Department of Information and Decision Sciences at the Carlson School of Management, University of Minnesota, where he also holds the Larson Endowed Chair for Excellence in Business Education. He received his PhD degree in computer science from New York University. His general research interests revolve around computational techniques for aiding decision-making in information-intensive environments and include personalization technologies and recommender systems, machine learning and data analytics, and electronic market mechanisms. His research has been published in a number of leading academic journals in information systems and computer science, including Information Systems Research, MIS Quarterly, Management Science, Journal of Operations Management, IEEE Transactions on Knowledge and Data Engineering, ACM Transactions on Information Systems, and Data Mining and Knowledge Discovery, and has been cited more than 26,000 times to date (according to Google Scholar). He has received several research grants from major funding institutions, including the U.S. National Science Foundation CAREER award for his research on personalization technologies. He has served on the editorial boards of several leading academic journals, including as Senior Editor for Information Systems Research and MIS Quarterly. In 2017, Prof. Adomavicius received the INFORMS Information Systems Society’s Distinguished Fellow Award. At the Carlson School of Management, he has taught analytics-related courses in the undergraduate, MBA, MSBA, PhD, and Executive Education programs and is currently serving as the chair of the Information and Decision Sciences Department.