Biography

Dr. Priya Verma is an Assistant Professor of Data Analytics at the Centre for Management Studies, Administrative Staff College of India (ASCI), Hyderabad. She holds a Ph.D. in Data Science and Applied Machine Learning from IIT Hyderabad, followed by a Post-Doctoral Fellowship at the same institution. Dr. Verma also possesses a Dual Degree (B. Tech and M. Tech) in Computer Science Engineering from Jayoti Vidyapeeth Women’s University, Jaipur, and has completed Executive Education in Applied Business Analytics from the Indian School of Business (ISB).

With over 9+ years of experience spanning research, teaching, and industry, Dr. Verma has taught at several prestigious business schools, including ICFAI University (Hyderabad), Welingkar Institute of Management Development and Research (WeSchool, Mumbai), and the Institute of Insurance and Risk Management (IIRM, Hyderabad). Her academic focus encompasses Data Analytics, Applied Machine Learning, and Fraud Analytics.

During her Ph.D. and postdoctoral research at IIT Hyderabad, Dr. Verma collaborated with the Government of Telangana, analysing extensive GST datasets using big data analytics and social network analysis techniques. Her contributions led to a significant enhancement in the state's revenue. She has published over 20+ research papers in Springer and IEEE, and has presented her research at renowned international conferences held in the US, UK, Spain, Italy, and Japan.

Dr. Verma has delivered specialized sessions for leading organizations, including Canara Bank, Central Bank, NMB Bank, SAIL, ONGC, NHPC, OPGC, the Armed Forces, and the Ministry of Ports, Shipping and Waterways. She brings extensive experience in conducting certification programs, courses, workshops, and webinars in business analytics for professionals, MBA students, and senior executives. Her areas of teaching and training expertise include Generative AI, Machine Learning, Artificial Intelligence, Insurance Analytics using Python and R, Fraud and Risk Analytics, FinTech Technologies, and Data Mining.

She is keen to pursue further research on machine learning and its applications in solving real-world problems.

Education