Danielle C. Tarraf

Photo of Danielle Tarraf
Senior Information Scientist; Professor, Pardee RAND Graduate School
Boston Office

Education

Ph.D. in mechanical engineering (control theory), MIT; S.M. in mechanical engineering, MIT; B.E. in mechanical engineering, American University of Beirut

Overview

Danielle Tarraf is a seasoned AI leader with over 14 years of experience spanning strategy consulting, basic research, and venture capital. Since joining RAND in 2016, her work has focused on advising senior DoD decision-makers on technology strategy in the context of national security and global competition. Since 2018, Tarraf has also been building a portfolio of funded projects aiming to position RAND in the AI sphere. Most recently, she led a congressionally-mandated study to assess the DoD's posture in AI and provide recommendations. Beyond these lines of effort she’s leading, Tarraf has contributed to projects on cybersecurity, nuclear deterrence, and persistent logistics. She formerly served in a liaison role for RAND Arroyo, and currently serves on the Pardee faculty.

Tarraf started her career in academia, where she founded and directed a research lab focused on advancing state-of-the-art at the intersection of control theory, automata theory and reinforcement learning. As an Electrical and Computer Engineering faculty at JHU, Tarraf was honored with two prestigious national awards, the NSF CAREER award and the AFOSR YIP award. She was also competitively selected for faculty fellowships at the Air Force Research Lab and received the JHU Alumni Excellence in Teaching Award.

Tarraf serves as a judge for the IBM Watson AI Xprize, is a senior member of IEEE, and a member of the IEEE Control Systems Society Technical Committee on Hybrid Systems. She received her B.E. from AUB, her S.M. and Ph.D. from MIT, and was a postdoctoral scholar at MIT and Caltech.

Selected Publications

D. C. Tarraf (editor), Control of Cyber-Physical Systems, Springer, 2013

D. Fan and D. C. Tarraf, "Output observability of systems over finite alphabets with linear internal dynamics," IEEE Transactions on Automatic Control, 63(10), 2018

D. Fan and D. C. Tarraf, "Finite uniform bisimulations for linear systems with finite input alphabets," IEEE Transactions on Automatic Control, 62(8), 2017

M. C. Tsakiris and D. C. Tarraf, "Algebraic decompositions of DP problems with linear dynamics," Systems & Control Letters, 85, 2015

D. C. Tarraf, "An input-output construction of finite state rho/mu approximations for control design," IEEE Transactions on Automatic Control, 59(12), 2014

D. C. Tarraf and D. Bauso, "Finite alphabet control of logistic networks under discrete uncertainty," Systems & Control Letters, 64, 2014

D. C. Tarraf, "A control-oriented notion of finite state approximation," IEEE Transactions on Automatic Control, 57(12), 2012

D. C. Tarraf, A. Megretski and M. A. Dahleh, "A framework for robust stability of systems over finite alphabets," IEEE Transactions on Automatic Control, 53(5), 2008

Honors & Awards

  • Young Investigator Award, Air Force Office of Scientific Research
  • CAREER Award, National Science Foundation

Languages

Arabic, French

Commentary

  • Digital concept of a brain, photo by Vertigo3d/Getty Images

    Our Future Lies in Making AI Robust and Verifiable

    We are hurtling towards a future in which AI is omnipresent. This AI-enabled future is blinding in its possibilities for prosperity, security, and well-being. Yet, it is also crippling in its fragility. What might it take for it all to come to a screeching halt?

    Oct 22, 2019 War on the Rocks

Publications