Indian-origin artificial intelligence expert Devi Parikh who was working with Meta, says PhD is no longer a compulsion for doing cutting edge work in the field. Devi shared these insights in an interview with Business Insider for young professionals, who are wanting to accelerate thier careers or make a shift in AI domain.
Who is Devi Parikh?
Devi Parikh is an AI expert having spent 15 plus years in her field. She is in research, academia and big tech has shared practical lessons for young professionals hoping to build a career in AI. She is currently the co-founder of an AI startup named Yutori.
https://t.co/MD8EZ4AkC2
— Devi Parikh (@deviparikh) December 6, 2025
What is Devi Parikh’s educationl background?
Devi Parikh has completed her B.Tech in Electrical Engineering from the prestigious Indian Institute of Technology (IIT) Delhi in 2004. Then she went to the US for an MS from the University of Maryland, College Park in 2007, and a PhD in Electrical and Computer Engineering from Carnegie Mellon University in 2009. Post-PhD, she took postdoctoral positions and jumped to faculty roles, including assistant professor at Virginia Tech and Georgia Tech, where she directed the Visual Question Answering (VQA) lab and mentored PhD students.
What is Devi Parikh’s work experience?
In her professional career, Devi Parikh joined Meta’s FAIR lab in 2016 as a research scientist, rising to research director and senior director of Generative AI by 2024. She was leading teams on projects like ParlAI, dance-generation models, Make-A-Video (text-to-video), and Phenaki.
She also co-founded Yutori in 2024 as co-CEO, focusing on AI for creativity and human-AI collaboration, while maintaining affiliations with Georgia Tech as an professor. Her work spans over 200 publications with 115,000+ citations, earning awards like the Sloan Fellowship, NSF CAREER, and ICCV David Marr Best Paper Prize.
What other things did Devi share for professionals?
Devi Parikh also emphasised the need to remain adaptable in a fast-changing field. Parikh recalled how some researchers resisted the shift to deep learning over a decade ago because they were too attached to older tools or professional identities. Her own work evolved from computer vision to multimodal systems and, eventually, generative models.
