Vamsi Vallurupalli

Dr. Vallurupalli Vamsi earned his PhD in Management with a specialization in Management Information Systems (MIS) from the Indian Institute of Management Calcutta, India, in 2019. His doctoral research examined the factors influencing the impact of online reviews, employing natural language processing (NLP) techniques. Broadly, his research focuses on the application of machine learning methods in business contexts, with a particular emphasis on the intersection of data-driven technologies and business processes. His work has been published in leading academic journals, including Decision Support Systems, Communications of the Association for Information Systems, and Electronic Markets.

Following his PhD, Dr. Vamsi worked in the industry for a few years in reputed organizations, holding both technical and managerial roles. Most notably, he served as a Senior Manager (Analytics) at a fintech company that offered personal loans entirely through a mobile platform, where he led the data science team and developed credit risk models, fraud detection systems, and automation pipelines. He also contributed to anti-money laundering and customer analytics initiatives in other roles. After his industry experience, he spent four years as an Assistant Professor at Shiv Nadar Institution of Eminence, India, where he taught courses related to analytics, including Contemporary Business Analytics, Introduction to Artificial Intelligence, Quantitative Methods in Business, and Business Intelligence using Tableau. Currently, he is working as a MSCA Postdoctoral Fellow at J.E. Cairnes School of Business & Economics.

He possesses strong technical expertise in machine learning, deep learning, and NLP, and is proficient in Python, SQL, Tableau, Hadoop, and Spark. Additionally, he has a solid grounding in econometrics and working knowledge of the case study research method.

My research examines how organizations can leverage business analytics to create value while promoting the responsible and ethical use of technology.

My teaching interests focus on business analytics, machine learning, and data science, with an emphasis on bridging theory and application through hands-on, practice oriented learning.