Amir Ali Ahmadi

About

Amir Ali Ahmadi is a Professor of Operations Research and Financial Engineering at Princeton University, with affiliations across multiple departments and research centers including PACM, Computer Science, Mechanical & Aerospace Engineering, Electrical & Computer Engineering, the Center for Statistics and Machine Learning, Robotics, and the AI Lab. He serves as the Director of the Optimization and Quantitative Decision Science Minor. His academic background includes a Ph.D. in Electrical Engineering and Computer Science from MIT, where he was affiliated with the Laboratory for Information and Decision Systems, and his advisor was Prof. Pablo Parrilo. Prior to his current position, he was an Assistant Professor at Princeton, a Goldstine Fellow at IBM Watson Research Center, and a Visiting Research Scientist at Google Brain. He has also held roles such as Visiting Senior Optimization Fellow at Citadel GQS and Volunteer Assistant Coach for Princeton's Tennis Teams. His research focuses on optimization, control theory, and their applications in machine learning and data science, with notable contributions recognized through awards such as the Egon Balas Prize in Optimization, the Princeton Engineering Council Teaching Award, and the INFORMS Optimization Society Young Researchers' Prize. Ahmadi is actively involved in organizing conferences, seminars, and workshops, and has been featured in popular science articles explaining complex research topics to broader audiences.

Research topics

• Mathematical analysis
• Mathematics
• Mathematical optimization
• Applied mathematics
• Geometry
• Combinatorics

Selected publications

• Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics

  Annual Review of Control Robotics and Autonomous Systems · 2019-12-24 · 90 citations

  articleOpen accessSenior author

  Historically, scalability has been a major challenge for the successful application of semidefinite programming in fields such as machine learning, control, and robotics. In this article, we survey recent approaches to this challenge, including those that exploit structure (e.g., sparsity and symmetry) in a problem, those that produce low-rank approximate solutions to semidefinite programs, those that use more scalable algorithms that rely on augmented Lagrangian techniques and the alternating-d…

  PublisherOA PDFDOI

• Improving efficiency and scalability of sum of squares optimization: Recent advances and limitations

  2017-12-01 · 34 citations

  preprint1st authorCorresponding

  It is well-known that any sum of squares (SOS) program can be cast as a semidefinite program (SDP) of a particular structure and that therein lies the computational bottleneck for SOS programs, as the SDPs generated by this procedure are large and costly to solve when the polynomials involved in the SOS programs have a large number of variables and degree. In this paper, we review SOS optimization techniques and present two new methods for improving their computational efficiency. The first meth…

  PublisherDOI

• On the complexity of finding a local minimizer of a quadratic function over a polytope

  Mathematical Programming · 2022 · 17 citations

  1st authorCorresponding
  DOI

• A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics

  arXiv (Cornell University) · 2019-08-14 · 12 citations

  preprintOpen accessSenior author

  Historically, scalability has been a major challenge to the successful application of semidefinite programming in fields such as machine learning, control, and robotics. In this paper, we survey recent approaches for addressing this challenge including (i) approaches for exploiting structure (e.g., sparsity and symmetry) in a problem, (ii) approaches that produce low-rank approximate solutions to semidefinite programs, (iii) more scalable algorithms that rely on augmented Lagrangian techniques a…

  PublisherOA PDFDOI

• Complexity aspects of local minima and related notions

  Advances in Mathematics · 2021-11-19 · 7 citations

  article1st authorCorresponding
  PublisherDOI

Recent grants

• CAREER: Polynomial Optimization and Dynamical Systems

  NSF · $500k · 2016–2022

Frequent coauthors

• Pablo A. Parrilo

  39 shared

• Raphaël M. Jungers

  24 shared

• Mardavij Roozbehani

  Massachusetts Institute of Technology

  16 shared

• Anirudha Majumdar

  Princeton University

  14 shared

• Georgina Hall

  INSEAD

  11 shared

• Russ Tedrake

  9 shared

• Jeffrey Zhang

  7 shared

• Bachir El Khadir

  IBM Research - Thomas J. Watson Research Center

  7 shared

Labs

• Research GroupPI

Education

• Ph.D., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2011

• S.M., Electrical Engineering and Computer Science

  Massachusetts Institute of Technology

  2008

• B.S., Electrical Engineering

  University of Maryland, Baltimore

  2006

• B.S., Mathematics

  Massachusetts Institute of Technology

  2006

Awards & honors

• Egon Balas Prize in Optimization (2024)
• Excellence in Teaching Award of the Princeton Engineering Co…
• Distinguished Teaching Award of the Princeton School of Engi…
• Young Researchers' Prize of the INFORMS Optimization Society…
• 5-year Multidisciplinary University Research Initiative (MUR…

Similar researchers at Princeton University

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