Prof. Iyengar Named an INFORMS Fellow

Nov 29 2018 | By Holly Evarts | PHOTO CREDIT: EILEEN BARROSO

Garud N. Iyengar, professor and chair of industrial engineering and operations research (IEOR), was recently elected a 2018 INFORMS Fellow. One of 12 new inductees, Iyengar was cited for his “exceptional research, ranging from theory to practice in areas from robust optimization to data science, and for outstanding contributions to education, management, and service."

INFORMS (the Institute for Operations Research and the Management Sciences) is the leading international association for professionals in operations research and analytics; its fellows are selected for their outstanding lifetime achievement in operations research and the management sciences.

“I am very pleased to be named an INFORMS Fellow,” said Iyengar, who is also the Data Science Institute’s associate director for research. “My research group is investigating many topics that are outside the traditional boundaries of operations research. I view being named a fellow as a sign that my peer community is taking a more expansive view of the profession.”

Iyengar’s work focuses on understanding uncertain systems and exploiting available information using data-driven control and optimization algorithms. With research interests in large scale optimization, information theory, financial engineering, and operations management, he and his students explore applications in a wide range of areas, including machine learning, systemic risk, asset management, operations management, sports analytics, and biology.

Iyengar received his PhD from the Information Systems Laboratory in the electrical engineering department at Stanford University. He noted that his advisor Tom Cover encouraged him “to be open to the application of information theory based algorithms to pretty much any context.” He joined Columbia’s Industrial Engineering and Operations Research Department right after completing his PhD in 1998, and became chair in 2013.

As the department chair, Iyengar has strengthened and expanded numerous IEOR programs, resulting in a steep rise in the quality and selectivity, graduation rates, and placement outcomes. He supported and encouraged the expansion of the Professional Development and Leadership course created for IEOR MS students to a program for all MS students in the School—this program is being piloted for BS and PhD students as well.

Last year, the IEOR Department launched a new full-time Master of Science degree in Business Analytics in collaboration with Columbia Business School. The curriculum of this program is focused on providing students the modeling and data science tools needed to help businesses use data to make better decisions. The first cohort began in September 2018. The admission to this program was very competitive—80 students were selected from more than 1,000 applications.

“We have always had a close collaboration with Decision, Risk and Operations Division within Columbia Business School. With this new program, we have expanded to the Marketing Division,” says Iyengar. “We expect that this will further broaden our programs, increase interdisciplinary research collaborations across the two schools, and attract a more diverse group of PhD and MS students.”

Iyengar has also helped lead the launch of a new BS concentration in Analytics for undergraduates. This highly popular major within the operations research program trains students to leverage advanced quantitative models, algorithms, and data to improve business operations. Students are exposed to a very broad range of business contexts, from staffing and scheduling at hospitals, to ride matching and pricing for on-demand car services, to personalized promotions in online retail, and efficient energy consumption.

Iyengar’s research group is currently exploring a wide variety of research topics—attribution schemes for allocating payment in multi-channel advertising; methods for automatically detecting events and defensive assignment in NBA games that can then be used to develop a new metrics for rating a player’s defensive abilities; deep neural-network-based framework for interpretable robust decision making; and natural-language-processing-based models for extracting knowledge from news reports to predict stock performance. In addition, the group has a long standing collaboration with the National Center for Biological Sciences in Bangalore, India, on the thermodynamics of sensing and memory in cells. A new doctoral student in his group is expanding this work to the immune system.

Iyengar joins three other INFORMS fellows in his department: Daniel Bienstock, Liu Family Professor of Industrial Engineering and Operations Research and professor of applied physics and applied mathematics; Ward Whitt, professor of industrial engineering and operations research; and David Yao, Piyasombatkul Family Professor of Industrial Engineering and Operations Research.

“These are exciting times for Operations Research. Data at a very granular level is becoming available for many applications. At-scale computing is now cheaply accessible via the various cloud computing platforms, and cutting-edge machine learning and natural-language algorithms are available as R or python packages on these platforms,” he observed. “All the tools are available—the only limit is creativity.”


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