Rishabh Iyer is currently an Assistant Professor at University of Texas at Dallas where he heads the Machine Learning and Optimization Lab. Prior to this, he was a Research Scientist at Microsoft where he spent three years. He finished his PostDoc and Ph.D from the University of Washington, Seattle. He has worked on several problems including discrete and submodular optimization, large scale data selection, robust and efficient machine learning, visual data summarization, active and semi-supervised learning. His work has received best paper awards at ICML 2013 and NIPS (now NeurIPS), 2013. He also won the Microsoft Ph.D fellowship, a Facebook Ph.D Fellowship, and the Yang Outstanding Doctoral Student Award from University of Washington. He has organized tutorials on summarization and data subset selection in WACV 2019, ECAI 2020 and IJCAI 2020.