Rohit Babbar

Rohit Babbar serves as an Assistant Professor in the Department of Computer Science at Aalto University, Finland, leading a research group dedicated to advancing large-scale machine learning methodologies. His team specializes in tackling computational challenges inherent in extreme classification problems with massive output spaces while ensuring model robustness.

His primary research domains encompass large-scale learning systems, extreme multi-label classification architectures, deep learning integration, sequential data processing, and robustness engineering. This work directly addresses industry pain points like computational inefficiency in massive label spaces and model vulnerability to distribution shifts, with applications spanning natural language processing, information retrieval, and recommendation systems.

Publication trends reveal a strategic focus on algorithmic innovation for extreme classification, featuring breakthroughs in dynamic sparsity techniques, large language model integration for zero-shot scenarios, and calibration of extreme classifiers. Recent work demonstrates consistent emphasis on computational efficiency through optimized negative sampling, lightweight frameworks like InceptionXML, and specialized metrics for long-tail performance evaluation.

Scientific recognition includes:

• Outstanding Reviewer Award at ACL 2021 Conference (July 2021) for exceptional contributions to computer science peer review

As research group leader, Babbar directs collaborative efforts on next-generation classification systems while mentoring emerging scholars in machine learning. His team maintains active partnerships with industry leaders in search and recommendation technologies.

The research group operates at the intersection of theoretical machine learning and practical deployment, developing frameworks that balance computational feasibility with predictive accuracy in extreme-scale environments. Current initiatives focus on integrating foundation models with specialized classification architectures while addressing real-world challenges like data sparsity and concept drift.