Dr. Puja Sarkar received Ph.D. degree from Indian Institutes of Management Mumbai, M.Tech. degree in Computer Science and Data Processing from Indian Institute of Technology Kharagpur, and M.Sc. in Mathematics from West Bengal State University. Her expertise lies in Operation Research, Data Science, Machine Learning, Deep Learning, Generative AI, Large Language Models, and Agentic AI. As a researcher, Dr. Puja has Published articles in leading academic journals, including the EJOR, CAIE, IJPR and INS. Dr. Puja has professional experience as a data scientist in a cybersecurity company, where she applied her expertise in data analytics to strengthen network security frameworks and detect vulnerabilities. Also, she worked as a data scientist in Data Science Lab at IIM Mumbai, where she solved several industrial problems focusing on predicting the demand of 50 SKUs for a pharmaceutical firm, region and dealer-level demand forecasting for a major cement manufacturer, a dynamic capacitated vehicle routing problem with time windows for an Indian 3PL provider, and sentiment analysis of financial reports across sectors and banks to provide actionable insights for stock trend prediction.

Operation Research, Data Analytics, Business Statistics, Real-world application of supply chain analytics, Deep learning, Generative AI, Large Language Models, Agentic AI

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Integration of prediction and optimization for smart stock portfolio selection. European Journal of Operational Research, 321(1), 243-256. https://doi.org/10.1016/j.ejor.2024.08.027 

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Strategic decision-making for sustainable production and distribution in automotive industry: a machine learning enabled dynamic multi-objective optimisation. International Journal of Production Research, 63(7), 2339-2362. https://doi.org/10.1080/00207543.2024.2403111 

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Accelerating the stabilized column generation using machine learning. Computers & Industrial Engineering, 200, 110837.  https://doi.org/10.1016/j.cie.2024.110837

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Integrating machine learning with dynamic multi-objective optimization for real-time decision-making. Information Sciences, 690, 121524. https://doi.org/10.1016/j.ins.2024.121524

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Integration of prediction and optimization for smart stock portfolio selection. European Journal of Operational Research, 321(1), 243-256. https://doi.org/10.1016/j.ejor.2024.08.027 

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Strategic decision-making for sustainable production and distribution in automotive industry: a machine learning enabled dynamic multi-objective optimisation. International Journal of Production Research, 63(7), 2339-2362. https://doi.org/10.1080/00207543.2024.2403111 

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Accelerating the stabilized column generation using machine learning. Computers & Industrial Engineering, 200, 110837.  https://doi.org/10.1016/j.cie.2024.110837

Sarkar, P., Khanapuri, V. B., & Tiwari, M. K. (2025). Integrating machine learning with dynamic multi-objective optimization for real-time decision-making. Information Sciences, 690, 121524. https://doi.org/10.1016/j.ins.2024.121524

Sarkar, P., Khanapuri, V. B. & Tiwari, M. K. Forecasting of raw material Consumption using LightGBM - Case Study for Cable Accessories Manufacturing Plant. Presented at POMS 32nd Annual Conference 2022, Online. 

Sarkar, P., Khanapuri, V. B. & Tiwari, M. K. Efficient Supplier Selection in e-Procurement using Graph Based Model. Presented at International Conference on Data Analytics in Public Procurement and Supply Chain (ICDAPS2022). 

Sarkar, P., Mitra, R., Khanapuri, V. B., & Tiwari, M. K. (2022, June). Efficient Supplier Selection in e-Procurement Using Graph-Based Model. In International Conference on Data Analytics in Public Procurement and Supply Chain (pp. 17-26). Singapore: Springer Nature Singapore.