Who Can Pivot? Measuring Career Adaptability Across Education, Work, and Migration

Online seminar Who Can Pivot? Measuring Career Adaptability Across Education, Work, and Migration, held by Alireza Javadian Sabet.

The event is part of the “Responsible Artificial Intelligence” (RAI) talk series.

When: Monday, June 1st 2026 at 4,30 pm
Where: Online at the link: http://tiny.cc/ac62101
Speaker: Alireza Javadian Sabet, University of Pittsburgh (USA)

Abstract: Careers can look straightforward on a CV—titles, employers, degrees—yet what really determines mobility is the evolving set of capabilities behind those labels: what people learn, what transfers, and what quietly stops transferring when opportunities shift. In this talk, I bring new measurements to that hidden layer. I translate millions of university syllabi into workforce-relevant skill profiles to compare what institutions teach to what jobs demand, and to see how the “same” credential can imply very different readiness depending on the skills it bundles. I then introduce Career Space, a representation that places education and jobs in a shared embedding to track occupation compatibility as something that grows, concentrates, or fades as experience accumulates—capturing the difference between pathways that keep many options open and those that build deep, targeted advantage. I close by looking at a higher-stakes pivot: cross-border mobility. Using large-scale career histories linked to origin-country conditions, I examine how women’s empowerment reshapes return versus stay decisions and how these choices redistribute knowledge production across countries.

Bio: Alireza Javadian Sabet is a computational social scientist and Ph.D. candidate in Information Science at the University of Pittsburgh’s Resilient Economy Lab (REL). His research develops scalable, data-driven methods to study careers as sequences of learned capabilities, measuring how skills form in education, transfer across jobs, and accumulate into career adaptability. He examines when trajectories benefit more from broad flexibility versus deep specialization. Prior to his Ph.D., he earned an M.Sc. in Computer Science and Engineering at Politecnico di Milano, where he built large-scale datasets and models to study behavior during major live events and developed context-aware personalization and recommender systems for multimodal travel.

Contacts
- Professor Daniele Quercia