May 15, 2019
Machine Learning Expertise on Display at Tepper School
The CMU Summer Workshop on Machine Learning prepares the next generation of researchers with an introduction to machine learning techniques.
Organized by Yan Huang, Assistant Professor of Business Technologies at the Tepper School of Business, and Tepper School doctoral graduate Xiao Liu (Ph.D. 2015), Assistant Professor of Marketing at the New York University Stern School of Business, the CMU Summer Workshop on Machine Learning is intended to introduce junior researchers to machine learning methods and their applications to the fields of marketing and information systems.
“Tepper is among the first business schools to embrace machine learning methods in business and social science research,” Huang said. “In the areas this workshop primarily targets, CMU faculty and students are recognized in research communities as pioneers in applying machine learning methods in business research. Thanks to the interdisciplinary culture at CMU, our faculty and Ph.D. students benefit greatly from collaborations and interactions with world-class machine learning experts.”
Faculty at the Tepper School are investigating and implementing novel methods and functions of machine learning in their research and in the classroom. The Tepper MBA program includes an option for a Business Analytics track, which incorporates machine learning into its curriculum. The track culminates in a capstone project solving a real-world business problem with data mining and machine learning, which Huang advised this fall. Huang’s own research focuses on understanding the implications involved in relying on algorithms in decision-making, such as how human biases influence training data behind machine learning algorithms or how users may or may not exploit the functions of algorithms when they are made transparent.
“I am continuously fascinated by emerging technologies, such as AI and machine learning, and their impact on individuals, businesses, and societies,” Huang said. “In the future, I plan to continue working on questions related to the efficiency, fairness, and transparency of machines and algorithms.”
The workshop includes lectures, tutorials, and panel presentations with faculty and scholars from the Tepper School — including Dokyun Lee, Assistant Professor of Business Analytics; Alan Montgomery, Professor of Marketing; Kannan Srinivasan, H.J. Heinz II Professor of Management, Marketing, and Business Technologies; and Param Vir Singh, Carnegie Bosch Associate Professor of Business Technologies, along with Ph.D. students Nikhil Malik and Shunyuan Zhang — as well as peers at Carnegie Mellon and other major research universities in the U.S.
Tepper Women in Tech
The event stands out not only because it targets junior researchers, but also because it is chaired by two women. Traditionally a male-dominated discipline, machine learning is a key element in much of the research at the Tepper School for women and men alike. “This workshop highlights both the significance of machine learning as a discipline and the importance of women in the discipline,” Vir Singh said. “These women are academic leaders in marketing and information systems, and I have appreciated having them as collaborators and colleagues.”
Huang is a 2013 Ph.D. graduate of CMU’s Heinz College of Information Systems and Public Policy who came to the Tepper School in 2018 after several years on the faculty of the University of Michigan Ross School of Business. Her recent research focuses on the economic and social impact of AI and algorithms. “I look at social issues related to machine learning, such as the fairness and transparency of algorithms, through an economic and social science lens.” Huang said. “For example, I study which types of human biases can be inherited, amplified, or removed by which type of algorithms, and how users may exploit the functions of algorithms when they are made transparent, and when such user “gaming” benefits or hurts firms.”
Liu’s research focuses on quantitative marketing. She applies parallel computing techniques to analyze unstructured data. As a Tepper School doctoral student she co-authored her dissertation, “Analyzing Overdraft Fees With Big Data,” with Montgomery and Srinivasan, and she remains engaged with Tepper School faculty in ongoing research projects.
With this workshop, Huang and Liu intend to build a common ground among researchers who are already using or hoping to incorporate machine learning methods in their work. Huang participated in events surrounding machine learning and business practice hosted by fellow Tepper School faculty members Srinivasan and Singh, and she saw an opportunity to provide a systematic introduction for researchers. “The value was confirmed by the large number of applications we received from interested Ph.D. students,” she said.
The event will be held in the Simmons Auditorium at the Tepper Quad. For a schedule and full list of speakers and lecturers, please visit here.