Amir Ali Ahmadi

About

Amir Ali Ahmadi is a leading figure in optimization, control theory, and robotics, renowned for developing scalable computational tools that bridge rigorous mathematical guarantees with real-world applications. His major contributions center on advancing sum-of-squares (SOS) programming and its alternatives—DSOS (diagonally dominant SOS) and SDSOS (scaled diagonally dominant SOS) programming—to tackle high-dimensional control and verification problems. Ahmadi’s work on “control design along trajectories” (2013, 142 citations) pioneered the use of invariant funnels for certifying stability and safety in challenging robotic tasks, directly impacting autonomous systems. His influential surveys on scalability improvements for semidefinite programming (2019, 90+ citations) have guided researchers in machine learning, control, and robotics toward exploiting structure like sparsity and symmetry. By introducing LP and SOCP-based frameworks (2014, 61 citations), he enabled control of high-dimensional systems previously intractable with traditional SOS methods. Ahmadi’s research has been recognized with prestigious awards, including the NSF CAREER Award and the SIAM Activity Group on Control and Systems Theory Prize. His work empowers engineers to design controllers with formal guarantees, making him a pivotal figure in modern optimization-driven control.

Research Focus

Key Achievements

Top Papers

1. 1
   Control design along trajectories with sums of squares programming

   142 citations · 2013
2. 2
   Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics

   90 citations · 2019
3. 3
   Control and verification of high-dimensional systems with DSOS and SDSOS programming

   61 citations · 2014
4. 4
   A Survey of Recent Scalability Improvements for Semidefinite Programming with Applications in Machine Learning, Control, and Robotics

   12 citations · 2019

Key Collaborators

Contact & Links