Indian-origin scientist gets USD 500,000 research award
An Indian-origin researcher has received a USD 500,000 award for developing data-driven modelling and learning techniques to improve the accuracy of operational decision making.
New York: An Indian-origin researcher has received a USD 500,000 award for developing data-driven modelling and learning techniques to improve the accuracy of operational decision making.
Professor Srikanth Jagabathula, from the New York University Stern School of Business, was recently recognised by the National Science Foundation (NSF) with its Faculty Early Career Development Award (CAREER).
As part of this prestigious award, Jagabathula, an IIT Bombay alumnus, will receive a total of USD 500,000 over the next five years to further his research in developing data-driven modelling and learning techniques with the goal of improving the accuracy of operational decision making.
The Faculty Early Career Development (CAREER) Programme is a highly competitive, Foundation-wide activity that offers the NSF's most prestigious awards in support of junior faculty who exemplify the role of teacher-scholars through outstanding research, excellent education, and the integration of education and research within the context of the mission of their organisations.
"We are proud of Professor Jagabathula's research and the recognition it has received from the National Science Foundation," said Peter Henry, dean of NYU Stern.
Jagabathula's research is expected to lead to easy-to-use techniques for a wide range of managerial decisions: the right products to design, the right products and prices to offer to customers, and the right quantity of each product to carry.
Traditional approaches have focused either on selecting an appropriate model and fitting it to the data or on efficiently solving a decision problem when given the model, leaving the model selection itself to an expert.
Neither approach scales to current retail applications, which are characterised by diverse demand patterns, products, and types of data (purchase transactions, click-streams, browsing patterns, dwell times on products, etc.)
Jagabathula's research will instead blend techniques from machine learning, statistics, and operations to design an approach that starts with a type of data (purchase transactions, click-streams, marketing studies, choice of insurance policies) and ends with an operational decision.
The integrated approach will work "out-of-the-box" by automatically selecting a model customised to the data and the decision.