Prof. P. Balasubramanyam is Professor of Big Data Analytics at the Goa Institute of Management (GIM), with over two decades of combined experience across industry and academia. His professional journey brings together extensive industry experience in manufacturing and information technology with teaching, research, academic leadership, and industry engagement in the areas of Artificial Intelligence, Machine Learning, Business Analytics, and Data Science.

Prior to moving into academia, he spent nearly two decades in the manufacturing and IT sectors, where he worked extensively on predictive modelling, forecasting, supply chain analytics, and data-driven decision-making. He led and contributed to several analytics initiatives aimed at improving operational efficiency, productivity, forecasting accuracy, and managerial decision-making. This industry experience continues to strongly influence his teaching and research, with an emphasis on connecting analytical concepts with real-world business problems.
At GIM, Prof. Balasubramanyam has been closely associated with the PGDM–Big Data Analytics (BDA) programme and served as its Program Chair for four years. During this period, he contributed to the programme’s academic development, industry engagement, curriculum enhancement, and growth. His teaching interests include Data Preparation, Machine Learning, Managerial Statistics, Predictive Modelling, Business Analytics, and Artificial Intelligence.

He holds an M.Sc., M.Phil., and Ph.D. in Statistics from Sri Venkateswara University, Tirupati. His doctoral research focused on the application of neural networks to forecasting, providing an early foundation for his subsequent work in machine learning and predictive analytics.

His current academic interests increasingly focus on Artificial Intelligence and its practical applications in business and society, including Machine Learning, Generative AI, Agentic AI, AutoML, Human-in-the-Loop AI, Responsible AI, and AI-enabled decision-making. He is particularly interested in exploring how emerging AI technologies can complement human expertise and improve managerial and organisational decision-making.

Prof. Balasubramanyam actively engages with students, researchers, industry professionals, and executives through teaching, research, workshops, management development programmes, invited talks, and academic conferences. His broader academic philosophy is centred on bridging statistics, data science, artificial intelligence, and management practice, while encouraging students to use technology responsibly to address meaningful business and societal problems.
 

Publications:

• Tunuguntla, L. V. M., Balasubramanyam, P., & Alarnkar, A. A. (2026). Indian aggression detection through multiple ML models from Twitter data. Advances in Consumer Research, 3(2), 594–603.
• Naik, S., Mekoth, N., Abhinaya, U., Koloth, S., & Balasubramanyam, P. (2025). Evaluating tourism-led development and development-led tourism models: Socioeconomic and environmental perspectives in emerging economies. Journal of Policy Research in Tourism, Leisure and Events. https://doi.org/10.1080/19407963.2025.2481622
• Sreenivasa Murthy, K., Balasubramanyam, P., Talpaga, K. K., & Singh, S. (2023). Impact of soil minerals and other factors on the paddy production with a specific focus on the seven blocks of Tiruvarur District, Tamil Nadu. Journal of Data Acquisition and Processing, 38(1), 2399–2410. https://doi.org/10.5281/zenodo.7764395
• Sreenivasa Murthy, K., & Balasubramanyam, P. (2022). Few theoretical approaches to the problems of supply chain management based on analytics. Pacific Business Review (International), 15(6), 14–23.
• Balasubramanyam, P., & Sreenivasa Murthy, K. (2020). Trend based correction to the predicted results for time series forecasting. Advances and Applications in Statistics.
   

Books:

• Title: “On Some Aspects of Forecasting Methods: A Comparative Performance of Advanced Forecasting Models”
  Publisher: LAP LAMBERT Academic Publishing
  Language: English
  ISBN-10: 3659389692
  ISBN-13: 978-3659389696
   

Conferences:

• Dr Balasubramanyam.P, Shanu Agrawal, (2018) “Ensemble of Time series forecasting in complex approach,” 5th World Machine Learning and Deep Learning Congress and World Congress on Computer Science, Machine Learning and Big Data, . Dubai, UAE, August 30-31, 2018, 
• Dr Balasubramanyam.P, P Balasiddamuni et all(2011) “Comparative Performance of Advanced Forecasting Models”, International Conference on Stochastic Modeling and Simulation, Chennai., pp. 88-92. 15-17, December (2011)
• Dr Balasubramanyam.P, Johnavi.M, et all(2008) “On Some Aspects of Forecasting Methods”, International Conference on Operations Research for a growing Nation, Tirupati, 15-17, December 2008

Prof. P. Balasubramanyam brings over three decades of professional experience across industry and academia, spanning Artificial Intelligence, analytics, statistics, manufacturing, supply chain, strategy, research and management education. Prior to joining the Goa Institute of Management in 2020, he held industry roles with organizations including Robert Bosch Engineering Services, DuPont India, SAS R&D and Cranes Software International. At Bosch, he served as Senior Manager at the Bosch Center for Artificial Intelligence, contributing to AI strategy, business planning, team development and multi-location initiatives.

His industry career has involved the application of data analytics, predictive modelling, forecasting, statistical methods and AI to business and manufacturing problems. He has also worked extensively in analytics consulting, software analytics, customer analytics, supply chain and manufacturing applications.

He joined Goa Institute of Management in September 2020 in the Big Data Analytics area and served as Program Chair of the PGDM–Big Data Analytics programme for four years. He was promoted to Professor – Big Data Analytics in July 2026. His academic experience encompasses postgraduate teaching, research, curriculum development, industry-academia collaboration, executive training and institutional contributions.