Shaukat Ali Shahee earned his Ph.D from SJM School of Management, Indian Institute of Technology Bombay. In his doctoral research, he explored various challenges posed by imbalanced data in presence of different data intrinsic characteristics. His work has been published in peer-reviewed journals like International Journal of Artificial Intelligence and Soft Computing, Applied Intelligence, Data Mining and Knowledge Discovery, and chapters in the reputed book series Advances in Data Mining: Applications and Theoretical Aspects. His teaching interests are Machine Learning, Artificial Intelligence and Deep Learning, Natural Language Processing and Business Statistics.
Shahee has industry experience of 5.5 years. Before joining academia, he worked as a Quantitative Research Analyst at AlphaCrest Capital Management. Earlier, he was a Deputy Manager at Bank of Maharashtra and Research Engineer at CSE department IIT Bombay.

Machine Learning under class Imbalance data, Application of Deep Learning and Natural Language Processing in Finance, Statistical learning, Optimization Techniques

• Shahee, S. A., & Ananthakumar, U. (2021). An overlap sensitive neural network for class imbalanced data. Data Mining and Knowledge Discovery, 1-34
• Shahee, S. A., & Ananthakumar, U. (2020). An effective distance based feature selection approach for imbalanced data. Applied Intelligence, 50(3), 717-745
• Shahee, S. A., & Ananthakumar, U. (2018). Synthetic sampling approach based on model-based clustering for imbalanced data. International Journal of Artificial Intelligence and Soft Computing, 6(4), 348-364.
• Shahee, S. A., & Ananthakumar, U. (2018, July). An adaptive oversampling technique for imbalanced datasets. In Industrial Conference on Data Mining (pp. 1-16). Springer, Cham

• Chairperson - Intellectual Property Rights Cell - IIM Kashipur
• Core-Committee member, FIED IIM Kashipur
• Committee member - Placement cell, IIM Kashipur
• Member, Anti-ragging cell, IIM Kashipur

• Business Analytics with Python
• Data Science & Machine Learning
• Natural Language Processing
• MIS