Morrison Center for Marketing and Data Analytics

Velibor Misic is an associate professor of Decisions, Operations and Technology Management at the UCLA Anderson School of Management. Prior to joining UCLA, he received his BASc and MASc degrees in industrial engineering from the University of Toronto and his PhD degree in operations research from MIT. His research is on analytics, with a methodological focus on optimization, machine learning and stochastic control, and applications in revenue management and healthcare. His work has been recognized with several awards, including second place in the INFORMS Junior Faculty Interest Group (JFIG) Best Paper Award and as finalist in the INFORMS Data Mining Section Best Paper Competition. At UCLA Anderson, he has received the Master of Science in Business Analytics (MSBA) Faculty Excellence Award three times and the Eric and "E" Juline Faculty Excellence in Research Award. He currently serves as an associate editor for the INFORMS journals Operations Research, Manufacturing & Service Operations Management, Production & Operations Management and Service Science.

Hengchen Dai is an Associate Professor of Organization Behavior and Behavioral Decision Making at UCLA Anderson School of Management. She received her bachelor’s degree from Peking University and her Ph.D. from the University of Pennsylvania. She conducts field studies with organizations across industries to understand what drives motivation, how to steer people toward far-sighted decision-making, and when behavior change interventions fail to scale and even backfire. She has published at leading academic journals such as Nature, Proceedings of the National Academy of Sciences, Academy of Management Journal, Management Science, Organizational Behavior and Human Decision Processes, Psychological Science, Journal of Consumer Research, and Journal of Marketing Research. Her research has been covered in major media outlets such as The Financial Times, The Wall Street Journal, CNN, Harvard Business Review, The New York Times, The New Yorker, and Freakonomics. Hengchen serves as a Senior Editor at Organization Science and is on the editorial review board of Academy of Management Journal. She has won numerous awards, including the Association for Psychological Science’s Janet Taylor Spence Award for Transformative Early Career Contributions, the Association for Psychological Science Rising Star Award, and the Cialdini Prize from SPSP. She was named one of the “World’s 40 Best B-School Professors Under the Age of 40” by Poets & Quants. 

Fernanda Bravo joined the UCLA Anderson faculty in 2015. Her primary research goal is to inform health care policy and decision making using contracting theory and operations management modeling.

“I focus most on three dimensions,” she says: “Designing health care policy to align incentives; fostering health care innovation through market design; and supporting decision making within health systems via data-driven models.”

These areas are crucial to the long-term success of health care reform in the U.S. because they improve efficiency and patient health. Yet they involve subtle and counterintuitive interactions: Poorly designed policies aimed at reducing hospital inefficiencies can inadvertently incentivize physicians to provide lower levels of care, which hurts patients. Over-regulating drug development to ensure patient safety might unintentionally stifle technological innovation and deprive patients from treatments. Bravo examines these subtle effects by combining elements of game theory, queuing theory, optimization and machine learning to quantify and characterize the most critical trade-offs. The insights derived from her research can guide policymakers and private stakeholders in contracting for services, promoting new medical innovations and allocating scarce resources.

At Anderson, Bravo teaches operations and technology management to MBAs and FEMBAs, as well as an elective in health care analytics for MSBA students and a Ph.D. course in supply chain contracting. Having worked with professionals at every level of the organization at Boston-area Children’s Hospital, Beth Israel Deaconess Medical Center and Partners Healthcare and UCLA Health, she brings her experience interacting with providers and health care networks to the classroom. “Health care an interesting sector. It’s a highly regulated industry and patients’ well-being is the priority. It’s different from the standard manufacturing and service industries because you have to consider policymakers, industry incentives, the population and providers themselves, and put all those perspectives into in the mix to figure out how we can do things differently, how we can deliver care better.” The opportunity to teach a class using examples meaningful to all the students’ day-to-day lives increases when the topic is health care delivery and management. “We have all been a patient at some point in our life,” she says. “Everybody has experienced some kind of inefficiency. The management of health care delivery systems, health organizations and policy-focused institutions affects everybody, not just providers.”

Bravo’s other research streams include the design and management of shared mobility systems, such as bike or scooter shares, and the use of machine learning for model analysis. A challenging problem faced by shared urban mobility systems is how to manage resources to match short and long-term supply and demand. “My goal is to improve system performance while ensuring the long-term availability of resources and to improve welfare.” In the second stream, she and her collaborators propose to design machine learning-driven frameworks for discovering structural properties of mathematical models — for example, dynamic programs and queuing models. “With the abundance of data and computational power today we can solve larger and more complex mathematical models than ever before,” she says. “However, making sense of solutions remains a challenge.” This work adds a tool that researchers can use for deriving interpretable solutions to complex mathematical models.