معرفی
Amir Ali Ahmadi is a Professor at Princeton University's Department of Operations Research and Financial Engineering (ORFE), with affiliations across multiple disciplines including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical Engineering, and the Center for Statistics and Machine Learning. He serves as Director of Princeton's Optimization and Quantitative Decision Science Certificate Program and has taken temporary roles at Citadel GQS (2021-2022) and Google Brain (2020-2021).
His research bridges optimization theory, dynamical systems, and control theory, focusing on scalable algorithms for complex problems in robotics, autonomous systems, and machine learning. He has pioneered DSOS/SDSOS relaxations as alternatives to traditional sum-of-squares methods, enabling faster solutions through linear/second-order cone programming.
Recent publications explore:
- Higher-order Newton methods for socially responsible investment
- Data-efficient learning of dynamical systems
- Computational complexity of local minima
- Robust-to-dynamics optimization frameworks
Award highlights include:
- 2024 Egon Balas Prize in Optimization
- 2024 Princeton Engineering Council Teaching Award
- 2023 Distinguished Teaching Award (Princeton SEAS)
- 2019 NSF CAREER Award
- 2017 DARPA Young Faculty Award
- 2017 Sloan Fellowship in Computer Science
He advises prominent researchers like Georgina Hall (Tucker Prize finalist) and Bachir El Khadir (Goldstine Fellow), and leads the Princeton Optimization Seminar and MURI project on Control-Oriented Learning on the Fly.
