Rishabh Iyer

Rishabh Iyer is an Assistant Professor at the University of Texas at Dallas (UT Dallas) and a Visiting Assistant Professor at the Indian Institute of Technology Bombay (IIT Bombay). He leads the CARAML Lab at UT Dallas, focusing on machine learning, computer vision, and natural language processing. His research emphasizes data-efficient learning, subset selection, and combinatorial optimization techniques such as submodular functions.

Education: Ph.D. and M.S. from the University of Washington (2011-2015), B.Tech from IIT Bombay (2011). He has held roles including Senior Research Scientist at Microsoft (2016–2019) and Postdoctoral Researcher at University of Washington (2015–2016).

Research Interests: Combinatorial loss functions, compute-efficient learning via subset selection, robust deep learning, weak supervision, and continuous learning. Key projects include GLISTER, GRADMATCH, and SMILE frameworks for subset selection and active learning.

Awards: NSF Medium Grant (2021), Adobe and Amazon Research Awards (2022), Best Paper Awards at ICML/NeurIPS (2013), Microsoft Research Fellowship (2014).

Grants: Supported by NSF, Adobe, Google, Amazon, and UT Dallas startup funds. Active in teaching courses like Machine Learning (CS 6375) and Optimization in Machine Learning (CS 7301).

Labs/Teams: CARAML Lab, collaborating on subset selection, active learning, and submodular optimization. Open-source tools include CORDS, DISTIL, and submodlib.