Ujwal Gadiraju is a tenured Assistant professor in the Software Technology Department of the faculty of Electrical Engineering, Mathematics, and Computer Science at Delft University of Technology in the Netherlands. He co-directs the TU Delft “Design@Scale” AI Lab and is a member of the program management team of the TU Delft AI Labs.

He is the Vice-Chair of CHI Netherlands, representing the human-computer interaction community of academics and industry practitioners in the Netherlands, and has served as an ACM Distinguished Speaker. Ujwal leads a research line on Human-Centered AI and Crowd Computing, actively collaborating with experts in healthcare, finance, and education and working with a variety of industry partners and NGOs to tackle important societal problems.

Before joining the WIS group, Ujwal worked at the L3S Research Center as a Postdoctoral researcher between 2017-2020. He received a PhD degree (Dr. rer. nat.) in Computer Science with a summa cum laude recognition from the Leibniz University of Hannover, Germany, in 2017, an MSc. Computer Science degree from TU Delft, the Netherlands, in 2012, and a B.Tech. Computer Science and Engineering degree from VIT University, India in 2010.

His research interests lie at the intersection of Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Information Retrieval (IR). Ujwal has published over 200 peer-reviewed articles in these fields. His work has been recognized with several honors, including 10 paper awards at top-tier HCI and AI conferences. His current research focuses on creating novel methods, interfaces, systems, and tools to overcome existing challenges on the path toward building more effective and inclusive AI systems and facilitating appropriate reliance of humans on such systems. For more information, see https://ujwalgadiraju.com.

Talk abstract:

The unprecedented rise in the adoption of artificial intelligence techniques and automation in many contexts is concomitant with the shortcomings of such technology concerning robustness, interpretability, usability, trustworthiness, and explainability. Crowd computing offers a viable means to leverage human intelligence at scale for data creation, enrichment, and interpretation, demonstrating a great potential to improve the performance of AI systems and improve the appropriate adoption of AI systems in general. How can we build AI systems that can augment human capabilities across different tasks and improve human experiences in various contexts? What can we do to facilitate appropriate trust and reliance of people on AI systems? By drawing from a series of recent empirical studies, this talk will highlight the intriguing and pertinent role of human input in propelling better AI technology in the quickly evolving age of generative models.