Human-AI Collaboration in Search and Recommendation
Behnam Rahdari

Abstract: Despite the popularity and recent advancements in Artificial intelligence, there is still a set of problems that can not be solved solely by more accurate and efficient algorithms. These problems share a common set of characteristics that together uncover the major limitations of current AI systems. In this talk, we will take a closer look at some of these problems and how we can address them by combining the forces of AI and human intelligence.

Bio: Behnam Rahdari is a Ph.D. student in the School of Computing and Information at the University of Pittsburgh. He is working with Dr. Peter Brusilovsky, head of the PAWS Lab, on building interactive and personalized search and recommender systems. Before joining Pitt, he earned a Master’s, and a Bachelor’s degree in Computer engineering and worked as a research fellow and software developer. His research interests include Human-Computer Interaction, Exploratory Search, and Human-in-the-Loop Machine Learning.

Skills Flow: Where Do They Come From and Where Do They Go?
Alireza Javadian Sabet

Abstract: Education can be considered as one of the crucial factors contributing to economic growth and social progress. This work presents a data-driven approach to investigate how college educations mechanism for workforce development. While labor economics has evolved to study workers' skills to explain labor trends, a similar lens has yet to be applied to workforce development at scale. To do so, this work employs various novel large datasets which offer the best opportunity for connecting labor dynamics and higher education. The problem area is of huge significance as higher education stakeholders are seeking new guidelines to adapt themselves to the future of work. There are very few studies using the data in this work which provides a unique opportunity to gain new insights into the problem.

Bio: Alireza Javadian Sabet is a Ph.D. student in the Department of Informatics and Networked Systems (DINS) at the University of Pittsburgh working under the supervision of Prof. Morgan Frank. He is a Computational Social Scientist conducting research on means to empirically study workforce development through education in order to inform policy decisions and improve upward mobility and access to opportunities.