Hello! — I’m Isabella, a Postdoctoral Researcher at the MIT Sloan School of Management.

As a Computational Social Scientist, I am committed to building an equitable and sustainable future of work. My research explores AI's impact on work, workers, and organizational talent practices, with a human-centered approach that emphasizes human-AI complementarities. My key research interests include automation and augmentation dynamics, skilling and reskilling, labor inequalities, and sustainable skills. Using computational methods, network science, and statistical techniques, I create insights to empower individual workers and entire workforces.

I've been interviewed as an expert source by Politico's Morning Tech Newsletter, and my work has been featured in ABC News, Forbes, TechTarget, NPR's Planet Money, Mashable, and the Penny Hoarder, among others.

Curriculum Vitae

The EPOCH OF AI: Human-Machine Complementarities at Work

Abstract (Job Market Paper)

We introduce the EPOCH framework (Empathy, Presence, Opinion, Creativity, and Hope) to capture human capabilities that complement, rather than substitute, artificial intelligence. Using network-based methods that map task interdependencies across all U.S. occupations, we develop three metrics: (i) an EPOCH score measuring human-intensive skills, (ii) a potential-for-augmentation score, and (iii) a risk-of-substitution score. This framework explicitly distinguishes AI’s roles in augmenting versus automating work, addressing a key gap in the literature. Our results show a clear shift toward more human-intensive work. New tasks emerging in 2024 carry significantly higher EPOCH scores than pre-existing tasks, and high-EPOCH tasks are performed more frequently. At the occupational level, EPOCH-intensive jobs experienced stronger employment growth from 2015 to 2023, higher hiring rates in 2024, and more favorable projections through 2034. In contrast, occupations with higher substitution risks show consistently negative outcomes across past employment, current hiring, and future projections. Finally, augmentation scores are negatively associated with recent employment and hiring trends, but show no significant link to long-run employment projections.

NPR Podcast