2024/2025 IDEAFEST
Idea Fest is series of events designed to spark creativity and innovation. This dynamic forum brings together students, faculty, and industry experts to explore cutting-edge ideas and advancements. Through engaging talks, interactive workshops, and collaborative discussions, participants delve into the latest trends and breakthroughs, fostering intellectual curiosity and interdisciplinary exchange. Idea Fest is not just a series of lectures, but a hands-on experience with thought leaders that encourages creative problem-solving and visionary thinking.
Each on-campus event will be held from 11:00 a.m. to 12:30 p.m. in EME 4116. The schedule includes a meet-and-greet session from 11:00 to 11:15 a.m., followed by the main presentation from 11:15 a.m. to 12:15 p.m., and concludes with a Q&A session from 12:15 to 12:30 p.m.
Registration to attend in-person is not required. Registration is required to join virtually.
Bias in Generative AI
This study analyzed images generated by three popular generative artificial intelligence (AI) tools – Midjourney, Stable Diffusion, and DALLE 2 – representing various occupations to investigate potential bias in AI generators. Our analysis revealed two overarching areas of concern in these AI generators, including (1) systematic gender and racial biases, and (2) subtle biases in facial expressions and appearances. Firstly, we found that all three AI generators exhibited bias against women and African Americans. Moreover, we found that the evident gender and racial biases uncovered in our analysis were even more pronounced than the status quo when compared to labor force statistics or Google images, intensifying the harmful biases we are actively striving to rectify in our society. Secondly, our study uncovered more nuanced prejudices in the portrayal of emotions and appearances. For example, women were depicted as younger with more smiles and happiness, while men were depicted as older with more neutral expressions and anger, posing a risk that generative AI models may unintentionally depict women as more submissive and less competent than men. Such nuanced biases, by their less overt nature, might be more problematic as they can permeate perceptions unconsciously and may be more difficult to rectify. Although the extent of bias varied depending on the model, the direction of bias remained consistent in both commercial and open-source AI generators. As these tools become commonplace, our study highlights the urgency to identify and mitigate various biases in generative AI, reinforcing the commitment to ensuring that AI technologies benefit all of humanity for a more inclusive future.
About Dr. Zhou
Mi Zhou is an Assistant Professor of Information Systems at UBC Sauder School of Business. Her research focuses on analyzing individual behaviour in technology-enabled markets using both structured and unstructured data (e.g., video, image, text, audio). Her work in this area has been published in top journals including Information Systems Research and the Journal of Marketing Research. Prior to joining UBC Sauder, she received a Ph.D. in Information Systems and Management from Carnegie Mellon University.
Queueing Analytics: Machine Learning, Casual Queueing, and SiMLQ for Data Driven Simulation
The objective of this talk is to expose researchers to the vast possibilities of using modern machinery and data for implementing effective management analytics for processes that can be modeled as queueing systems. Such process are ubiquitous in modern economies, e.g., customers waiting to service, inventory waiting for processing/transportation, payments and invoices waiting to be generated/cleared, computing tasks waiting for resources. I will thus discuss recent developments in queueing analysis based on several papers.
We will start by defining management analytics along descriptive, predictive, comparative, i.e., comparing performance indicators under different interventions, and prescriptive analytics dimensions. We then shortly discuss ML solution for a G/G/1 based upon [1] and its extension to G(t)/G/1 based on [2].
Our main focus would be on causal queueing models, based upon [3]. Not many organizations have queueing theorists (QTs) in their staff, but many organizations employ well trained Data Scientist (DS). Can DS use data to provide accurate comparative analytics without expertise in queueing? We suggest a data-driven representation of system building blocks to create a non-queueing simulator without prior knowledge of the system. We show that this approach is effective in comparative analytics, when analyzing expected waits for an GI/M/1 with speed-ups. We first demonstrate that DS can successfully refine the parent sets of queueing variables from data using an off-the-shelf algorithm (even under a moderate sample size). We then use machine learning to estimate the causal structure in this queue, e.g., the Lindley’s Recursion and use the G-computation to derive inference results of counterfactual interventions. For the GI/M/1 with speed-ups, we compare the performance of estimates obtained by a QT, who uses data driven estimates for the primitives of the queue, with those made by a DS that uses either parametric (where inter-arrival and service time distributions are known) or nonparametric (where both distributions are unknown) estimators. We find that the errors of the DS that requires no knowledge of the system’s dynamic and its features and these of the QT (which requires this knowledge) are comparable. Our results suggest that the DS approach would be effective for practical setting- where even experts QTs cannot provide closed-form results.
We will finish with a short demo of SiMLQ. SiMLQ software uses Machine Learning to automate the visualization, Simulation, and optimization of Queueing processes. SiMLQ automatically constructs data-driven simulation models from event-log data collected by common information systems and enables users to improve processes resource management, increase efficiency, reduce cost, and manage risks. SiMLQ- from data to action.
About Dr. BARON
Opher Baron is a Distinguished Professor of Operations Management at the Rotman School of Management, University of Toronto. He was a visiting associate Professor at the Industrial engineering and Management faculty of Technion (2009/10) and a visiting Professor at the School of Information Management and Engineering, Shanghai University of Finance and Economics (2016/17). He also served as the Academic Director, MMA Program (2021-2023), and the Operations management and statistics area coordinator (2015-2021). He has a PhD in Operations Management from the Sloan School of Management at the Massachusetts Institute of Technology, and an MBA and BSc in Industrial Engineering and Management from The Technion. On the teaching front, Opher is especially proud of the modeling and analytics courses he introduced and teaches at Rotman. On the application front he is proud of launching the Covidppehelp.ca platform with his colleagues. This platform has facilitated the flow of millions of PPE items to end-user customers during the global Covid19 pandemic. His research interests include queueing, business analytics, service operations (such as healthcare), autonomous vehicles, and revenue management. Opher’s work is published in leading journals such as Operations Research, and Manufacturing & Service Operations Management, and he has won several research and teaching awards and grants, including the 1000 Talents Plan Scholar from the Shanghai Municipal Government, 2017 and the Rotman 2023 Distinguished Scholarly Contribution Award. Opher is active in the operations research and operations management community. He has given numerous invited keynote lectures and seminars, chaired several conferences, clusters, and sessions, and is currently serving on the advisory board and editorial boards of several journals.
Consumer Collectives and Netnographic Research: Past, Present, and Future
More information to come.
Why Marketing Managers Need to Understand Fandom Studies
More information to come.
Evaluating Fairness and Mitigating Bias in the Medical AI
More information to come.
Past Speakers
AI from the Bottom Up: Can Work Crafting Save Jobs?
Generative AI burst on the scene in November of 2022, on the heels of calls to “return to work” following the COVID-19 office shutdown. We’ve all been whipsawed regarding how we live, work, and play. Machine learning changes the tools of our research. Generative AI changes how we teach and our students learn. The most successful at games are often neither expert humans nor AI, but rather human-AI ensembles. Our computer science and engineering colleagues work at a pace generally faster than ours in social science. Leveraging AI from the bottom up may help us keep up and adjust how we approach our creativity and innovation.
About Dr. Griffith
Terri L. Griffith holds the Keith Beedie Chair in Innovation and Entrepreneurship at Simon Fraser University’s Beedie School of Business and was the 2022 President of ISSIP, The International Society of Service Innovation Professionals. She is a member of the New Ventures BC Society Board of Directors and the advisory board of Geopogo. Terri has served as a Senior Editor for Organization Science, as Associate Editor for MIS Quarterly, and is Co-Editor for an upcoming artificial intelligence special issue in Small Group Research. She spent two decades in the Silicon Valley and was recognized by the Silicon Valley Business Journal as a Woman of Influence following the publication of her book, The Plugged-In Manager: Get in Tune with Your People, Technology, and Organization to Thrive. Her most recent research uses large-scale methods to take a “bottom-up” approach to automation and the future of work, including artificial intelligence. Terri’s credientials include: BA UC Berkeley; MS, PhD Carnegie Mellon University; ICD.D, ICD-Rotman Directors Education Program.
The Double-Edged Sword of AI: Market Gains and Inequality Concerns
We examine the effects of artificial intelligence (AI) adoption on stock market performance, volatility, and income inequality, with a focus on businesses of varying sizes (small, medium, and large). Our findings reveal that AI adoption significantly boosts stock market performance, particularly among large firms, while slightly reducing market volatility, indicating a stabilizing effect. However, it also exacerbates income inequality, evidenced by a 1.2% annual rise in the Gini index and a 5.2% decline in competitiveness. We observe a convex relationship between business size and AI adoption, likely driven by the high fixed costs associated with transitioning to AI, which disproportionately benefit larger firms. This relationship underscores the challenges faced by smaller businesses in adopting AI at the same pace. Our cross-country analysis indicates that more innovative nations, with strong university-industry R&D collaboration and top-ranked universities, exhibit higher levels of AI adoption. While AI improves market performance and stability, it also widens inequality and diminishes competitiveness, pointing to the need for targeted policy measures. Importantly, AI adoption does not lead to increased unemployment, providing some reassurance about its broader economic implications.
About Dr. Bonaparte
Professor Yosef Bonaparte, Ph.D., a finance scholar graduated from the University of Texas at Austin in 2008, is a leading researcher in Fintech, specializing in Crypto, Blockchain, AI, and ML. With numerous publications in esteemed journals such as the Journal of Financial Economics and Management Science, he’s a recognized authority in financial innovation. Dr. Bonaparte’s influential works have been featured in top-tier media like The New York Times and CNBC. As an accomplished educator, he imparts knowledge in various classes, including FinTech, Crypto & Blockchain Investing, and Entrepreneurship in Finance. His multidimensional expertise continues to shape the finance landscape.
Attribute versus Anchor based approaches to customer satisfaction- An integrated perspective
This talk will focus on nearly two decades of research by the presenter on understanding satisfaction formation within an Information Systems context. Both attribute and anchor-based approaches will be presented along with a first attempt on how to integrate these two perspectives. For the attribute-based approach, the role of IS service satisfaction is introduced as a potential factor in forming overall satisfaction. For anchor-based models, this talk suggests the need to consider other anchors beside prior-expectation. Furthermore, among the 7 approaches for assessing anchor-based judgements, the efficacy of a new modified affect percept (MEPD) approach will be presented.
About Dr. Chin
Wynne W. Chin is the C. T. Bauer Professor of Decision and Information Sciences in the C.T. Bauer College of Business at the University of Houston. He is known for Partial Least Squares Path Modeling with his PLS-Graph software being the first graphical based PLS software developed in 1986 and released widely in 1990. Over his 35 year span, he was the first to introduce the use of Monte Carlo simulation to the IS discipline for evaluating structural equation modeling algorithms. He also introduced bootstrapping for PLS analyses in 1988, two approaches for 2nd order models (1995), product indicator (1996, 2003) and Orthogonalizing (2010) for interaction effects, PLS nonlinear modeling (1996), multigroup comparison via bootstrap with t-test formula (2000) and permutation (2003, 2016), bootstrap cross-validation (2005), latent marker variable for common method bias (2013), and bootstrap differential path tests (2013). Wynne’s research has received over 98,000 citations, with a top-ten most cited paper in MIS Quarterly and top-five most cited papers in Information Systems Research, and ranked third overall in first authored articles published in MISQ and ISR for the period from 1990 through 2016, as well as a Google Scholar H-index of 69 placing him among the most impactful researchers in his field. Wynne is the 8th most cited researchers related to structural equation modeling. He is also ranked 9,994 in the world in the career database and 3,215 in the world in the 2019 single year database according to a 2019 research article list of the top 6.88 million scientists in the world for all disciplines in 22 fields and 176 subfields. For his main field of Information and Communication Technologies, Wynne ranks 295 and 147 out of 570,025 scientists for career and single year contribution respectively. He was awarded with Fellow of the Association of Information Systems in 2013, garnered an AIS Distinguished Member – Cum Laude designation in 2020, and a AIS Technology Legacy Award (ATLAS) in 2021 recognizing those who have served the community in a significant way through their lifetime. Dr. Chin recently received the Farfel award (the highest faculty honor at UH). He currently serves as the treasurer for both the Texas Council of Faculty Senates and the National Council of Faculty Senates of which he is also a founding member.
Co-creating Educational Consumer Journeys: A Sensemaking Perspective
About Dr. Wilner
Dr. Sarah Wilner is Associate Professor of Marketing, Chair, Brand Communications and Academic Director for PhD and Research-Based Masters Programs in Management at Wilfrid Laurier University’s Lazaridis School of Business and Economics. She studies marketing and brand management (especially how marketers interpret consumers and vice versa); product/service innovation and design and consumer culture. Sarah specializes in qualitative methods of inquiry, investigating phenomena by being immersed in them. Wilner is an award-winning researcher and educator: her paper (with co-authors), “Doing Design Thinking: Conceptual Review, Synthesis and Research Agenda” won the Journal of Product Innovation Management‘s Thomas P. Hustad Best Paper of 2019 award and she is the recipient of the Lazaridis School of Business and Economics Innovation Award. At Laurier, Wilner has taught at every level (sometimes concurrently!), including BBA, MSc, MBA and PhD courses. She has also taught Masters students in Canada, Denmark, France and Japan.
Entrepreneurial Impacts of Blockchain-backed Financing: Do Public Token Offerings Slowdown or Accelerate Innovation in Startups?
About Dr. Havakhor
Taha Havakhor is an Associate Professor of Information Systems and a Desautels Scholar at Desautels Faculty of Management at McGill University. In 2021, he won the Early Career Award from the Association for Information Systems (AIS). Prior to joining McGill, he was the Research Director of the Institute for Business and Information Technology (IBIT) and Assistant Professor of Management Information Systems at the Fox School of Business at Temple University. His research focuses on combining advanced computational and econometrics approaches to address problems at the intersections of science, technology, and economics. His scholarly work has been published/accepted in premier outlets such as Management Science, MIS Quarterly, Information Systems Research, Harvard Business Review, Journal of Marketing, Information Systems Journal, and the Journal of Management Information Systems.
Are Targeted Matching Schemes Effective in Stimulating Retirement Savings?
ABOUT DR. POLIDANO
Dr. Cain Polidano is a Principal Research Fellow at the Melbourne Institute, a research-only department with the Faculty of Business and Economics at the University of Melbourne. His work is often in partnership with government and involves the use of administrative data and econometric tools to conduct research to inform education, Indigenous and retirement policy. Currently he is working on Australian Research Council Grants related to Indigenous entrepreneurship, impacts and drivers of Australia’s frontier wars and equity and effectiveness of retirement policies. Prior to completing his PhD, Cain was a Senior Economist within the Australian Government.
Inverse Optimization to Learn Personalized Diets
About Dr. Ghobadi
Kimia Ghobadi is a John C. Malone Assistant Professor in the Department of Civil and Systems Engineering. She is an associate director of the Center for Systems Science and Engineering, a chair member of the Malone Center for Engineering in Healthcare, and a member of the Center for Data Science in Emergency Medicine. Her research focuses on using mathematical models, optimization techniques, and data analytics to solve problems in complex systems, particularly in healthcare systems and medical decision-making environments. She develops models and solution techniques in inverse optimization, mixed-integer programming, and online algorithms.
How Philosophy can contribute to Public Policy? The Case of Education
ABOUT DR. STOJANOV
Krassimir Stojanov is Professor and Chair of Philosophy of Education at the Catholic University of Eichstaett-Ingolstadt, Germany, and co-founder of the new Institute of Philosophical Research in Education in Munich. Main topics of his research are educational and transnational justice, human flourishing and its social prerequisites and conditions, critical social theory, philosophical foundations of democratic education, among others. He is author of many articles in English and German as well as of several books on these topics including “Education, Self-Consciousness, and Social Action. Bildung as a Neo-Hegelian Concept” (Routledge 2018) and “Education against Populism?! On Anti-Democratic Half-Education and Its Alternatives” (Springer 2022, in German). Further major recent publications of him are Inclusive Universalism as Normative Principle of Education. In: Educational Theory, Volume 73, Issue 2 (2023), pp. 245-257; Global Justice and Democratic Education. In: Culp J, Drerup J, Yacek D (Eds.): The Cambridge Handbook of Democratic Education. Cambridge University Press, Cambridge 2023, pp. 281-297; Democratic Education and Epistemic Justice. In: Webster/ Airaksinen/ Batra/ Koschevnikova (Eds.): Humanizing Education in the 3th Millennium. Springer. Singapore 2022, pp. 83-91
Do Remote Workers Deter Neighborhood Crime? Evidence from the Rise of Working from Home
About Dr. Matheson
Jesse Matheson is a Professor in the Department of Economics at the University of Sheffield. He has previously taught at the University of Leicester and the University of Calgary. Jesse studied economics in Canada at the University of Calgary (BA, PhD) and Queen’s University (MA), before moving to the UK in 2011. In 2015 he was a visiting scholar at Cornell University. Jesse’s work covers topics in public, labour and health economics. Previous work considers the effectiveness of policy interventions that target vulnerable populations. This includes a large-scale randomised field study in policing domestic violence. He has also published research on the effect of social and neighbourhood infrastructure on individual decisions in the context of smoking, marriage, and raising children. Jesse’s recent research explores the economic determinants, and consequences, of the spatial distribution of labour within urban settings. Of particular interest is how the post-pandemic rise in remote working is shifting economic activity and changing cities.
How Do Product Returns Affect Supply Chain Performance and Sustainability?
About Dr. Ülkü
Ali Ülkü, Ph.D., P.Eng., is a Full Professor in the Faculty of Management and the (founding) Director of the Centre for Research in Sustainable Supply Chain Analytics (CRSSCA) at Dalhousie University, Halifax, NS, Canada. He has a Ph.D. in Management Sciences (Waterloo), an M.Sc. in Operations Research (Çukurova), and a B.Sc. in Industrial Engineering (Bilkent). His research includes sustainable supply chain and logistics systems, manufacturing and service operations analysis, green marketing and optimal contract designs, analytical modeling of sustainable production and consumption, and interdisciplinary, community-based policy problems. His publications appear in such journals as the International Journal of Production Economics, Journal of Business Research, Journal of Retailing and Consumer Services, Journal of Cleaner Production, and Service Science. His co-edited book, Big Data Analytics in Supply Chain Management: Theory and Applications, was published in 2020 (CRC Press). He is an Associate Editor for INFOR: Information Systems and Operational Research. A recipient of teaching excellence awards, he has taught operations and supply chain management, business analytics, optimization, logistics, and transportation courses at various universities in Canada, Türkiye, and the USA. Dr. Ülkü was honored with the 2019 Distinguished Professor Award by the IEOM Society International and is a 2023 Global Triple-E Awardee (Education Champion).