Jelena Notaros
Jelena Notaros

Participating Instructor

Technology Leadership Program

Jelena Notaros is the Robert J. Shillman Career Development Assistant Professor of Electrical Engineering and Computer Science at the Massachusetts Institute of Technology. She received her Ph.D. and M.S. degrees from MIT in 2020 and 2017, respectively, and B.S. degree from the University of Colorado Boulder in 2015. Jelena was one of three Top DARPA Risers, a 2018 DARPA D60 Plenary Speaker, a 2023 NSF CAREER Award recipient, a 2021 Forbes 30 Under 30 Listee, a 2021 MIT Robert J. Shillman Career Development Chair recipient, a 2020 MIT RLE Early Career Development Award recipient, a 2015 MIT Herbert E. and Dorothy J. Grier Presidential Fellow, a 2015-2020 NSF Graduate Research Fellow, a 2019 OSA CLEO Chair's Pick Award recipient, a 2022 OSA APC Best Paper Award recipient, a 2022 OSA FiO Emil Wolf Best Paper Award Finalist, a 2014 IEEE Region 5 Paper Competition First Place recipient, a 2023 MIT Louis D. Smullin Award for Teaching Excellence recipient, a 2018 MIT EECS Rising Star, a 2014 Sigma Xi Undergraduate Research Award recipient, and a 2015 CU Boulder Chancellor's Recognition Award recipient, among other honors.

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Artificial intelligence (AI) is a powerful tool—but without the right system-wide architecture in place to support your initiatives, your organization is leaving value on the table. Featuring interactive exercises, industry speakers, and a hands-on group project, this dynamic five-day course is designed to equip you with the skills and strategies you need to deploy an AI systems engineering approach that maximizes the value of your digital products and services.
Gain a core understanding of the management approaches of the future. In this high-impact three-day course, featuring lectures, group exercises, and discussions, you’ll master specialized skills in people analytics, management, and ethics—and emerge ready to lead with impact across your enterprise.
Take a deep dive into the mathematical foundations of AI and machine learning. You’ll explore the math behind not only fundamental models and algorithms, but also recent innovations such as Transformers and Graph Neural Nets—and discover how these concepts relate to Python code and associated applications.
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches. Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic. Do you have the advanced knowledge you need to keep pace in the deep learning revolution? Over the past eight years, the amount of computing required to run these neural nets has increased over a hundred thousand times, which has become a significant challenge. Gain a deeper understanding of key design considerations for deep learning systems deployed in your hardware.
An active area of research, reinforcement learning has already achieved impressive results in solving complex games and a variety of real-world problems. However, organizations that attempt to leverage these strategies often encounter practical industry constraints. In this dynamic course, you will explore the cutting-edge of RL research, and enhance your ability to identify the correct approach for applying advanced frameworks to pressing industry challenges. 
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