Ramtin Madani Assistant Professor Department of Electrical Engineering The University of Texas at Arlington
I develop control, optimization, and machine learning methods for interconnected dynamical systems such as power grids.
Email: ramtin.madani (at) uta.edu Office: Nedderman Hall, Room 539 Arlington, TX 76019
November 2018: We are selected to receive a grant from the Department of Energy to participate in the Grid Optimization (GO) competition with $4 million in prizes.
October 2018: New paper on polynomial optimization: Polynomial Optimization via Penalized Conic Relaxation.
October 2018: We recieved a grant to establish collaborations with NASA Jet Propulsion Laboratory (JPL).
October 2018: Our paper Convexification of Power Flow Equations for Power Systems in Presence of Noisy Measurements is accepted as a full paper for IEEE Transactions on Automatic Control.
August 2018: Our poster “Promises of Convex Optimization for Training Polynomial Neural Networks” will be presented in NASA Goddard Workshop on Artificial Intelligence.
August 2018: I am appointed as an Associate Editor of the Conference Editorial Board of IEEE Control Systems Society.
July 2018: Our collaborative project with UT Arlington's Pulsed Power and Energy Lab was funded by the Office of Naval Research (ONR).
July 2018: Our five papers will be presented in 57th IEEE Conference on Decision and Control:
Penalized Parabolic Relaxation for Optimal Power Flow Problem
Sequential Relaxation of Unit Commitment with AC Transmission Constraints
Convex Relaxation of Bilinear Matrix Inequalities – Part I: Theoretical Results
Convex Relaxation of Bilinear Matrix Inequalities – Part II: Applications to Optimal Control Synthesis
Conic Optimization for Robust Quadratic Regression: Deterministic Bounds and Statistical Analysis
June 2018: I recieved a grant from the National Science Foundation (NSF) as the principal investigator.
June 2018: Four talks at INFORMS Annual Meeting:
A Scalable Computational Method for Security-constrained Unit Commitment with Energy Storage
Convex Relaxation of Bilinear Matrix Inequalities with Applications to Optimal Control Synthesis
Sequential Convex Relaxation for Optimal Power Flow and Unit Commitment Problems
Conic Optimization for Robust State Estimation: Deterministic Bounds and Statistical Analysis
June 2018: I will organize the session “Applications of Conic Optimization for Energy Systems” at 2018 INFORMS annual meeting.
May 2018: I recieved a $725K grant from the Office of Naval Research (ONR) as the principal investigator.
October 2017: Our paper A Low-Complexity Parallelizable Numerical Algorithm for Sparse Semidefinite Programming is accepted for IEEE Transactions on Control of Network Systems.
July 2017: Fariba Zohrizadeh and Mohesn Kheirandishfard have joined my group.
July 2017: New paper on unit commitment with AC optimal power flow: A Scalable Semidefinite Relaxation Approach to Grid Scheduling
June 2017: Talk at the Federal Energy Regulatory Commission:
A Massively Scalable Approach to Power System Scheduling
June 2017: I will organize the session “Applications of Conic Optimization for Energy Systems” at 2017 INFORMS annual meeting.
May 2017: Edward Quarm Jnr. and Muhammad Adil have joined my group.
December 2016: Our paper Finding Low-rank Solutions of Sparse Linear Matrix Inequalities using Convex Optimization will appear in SIAM Journal on Optimization.
November 2016: Two talks at INFORMS Annual Meeting on power systems state estimation and bad data identification.
October 2016: New paper on a distributed numerical algorithm for large-scale semidefinite programming: A Low-Complexity Parallelizable Numerical Algorithm for Sparse Semidefinite Programming
September 2016: Our paper ‘‘Convex Relaxation for Optimal Power Flow Problem: Mesh Networks’’ joint work with Dr. Somayeh Sojoudi and Prof. Javad Lavaei has been selected to receive the INFORMS ENRE Energy 2016 Best Publication Award.
September 2016: Our paper Power System State Estimation and Bad Data Detection by Means of Conic Relaxation will be presented in Hawaii International Conference on System Sciences (HICSS-50).
September 2016: Our paper Constraint Screening for Security Analysis of Power Networks will appear in IEEE Transactions on Power Systems.
I recieved a $260K Science and Technology Acquisition and Retention (STARs) award from the University of Texas System.
August 2016: Talk at MOPTA conference
Convexification of Power Flow Equations for Power Systems in Presence of Noisy Measurements and Bad Data
July 2016: Our three papers will be presented in 55th IEEE Conference on Decision and Control:
Power System State Estimation with a Limited Number of Measurements
Power System State Estimation with Line Measurements
Characterization of Rank-Constrained Feasibility Problems via a Finite Number of Convex Programs
June 2016: New paper on power systems state estimation: Convexification of Power Flow Equations for Power Systems in Presence of Noisy Measurements
June 2016: Three talks at INFORMS International Conference:
On Minimal Characterization Of Feasible Sets Of Security-constrained Unit-commitment Problems
Low-rank Solutions Of Sparse Linear Matrix Inequalities
Penalized Semidefinite Programming Relaxation For Polynominal Optimization Problems
May 2016: I will join the Electrical Engineering Department at University of Texasâ€“Arlington as an assistant professor in September 2016.
May 2016: I will give a talk at Federal Energy Regulatory Commission.
May 2016: I will give two talks at INFORMS International Conference.
March 2016: Our paper Convex Relaxation for Optimal Distributed Control Problems to appear in IEEE Transactions on Automatic Control
March 2016: New paper on power system state estimation: Power System State Estimation with a Limited Number of Measurements
March 2016: New paper on power system state estimation: Power System State Estimation with Line Measurements
January 2016: I will give a tutorial on power system state estimation problem at INFORMS Optimization Society Conference 2016.
November 2015: New paper on large-scale security-constrained optimization problems: Constraint Screening for Security Analysis of Power Networks
November 2015: Three talks at INFORMS Annual Meeting.
A Fast Distributed Algorithm for Power Optimization Problems
Convexification of Power Flow Problem over Arbitrary Networks
Graph-theoretic Convexification of Power Optimization Problems
October 2015: I joined the department of Industrial Engineering and Operations Research at UC Berkeley as a Postdoctoral Scholar.
July 2015: Our three papers will be presented in IEEE Conference on Decision and Control 2015:
ADMM for Sparse Semidefinite Programming with Applications to Optimal Power Flow Problem,
Convexification of Power Flow Problem over Arbitrary Networks,
Inverse Function Theorem for Polynomial Equations using Semidefinite Programming.
July 2015: Two talks at International Symposium on Optimization.
Convexification of Optimal Power Flow Problem
Graph-Theoretic Convexification of Polynomial Optimization Problems: Theory and Applications
May 2015: I won a Research Award from the Electrical Engineering department of Columbia University.
March 2015: New paper on distributed computation for power systems: ADMM for Sparse Semidefinite Programming with Applications to Optimal Power Flow Problem
March 2015: New paper on convex formulation of power flows: Convexification of Power Flow Problem over Arbitrary Networks
March 2015: New paper on inverse of polynomial functions: Inverse Function Theorem for Polynomial Equations using Semidefinite Programming
March 2015: Our paper Promises of Conic Relaxation for Contingency-Constrained Optimal Power Flow Problem to appear in IEEE Transactions on Power Systems
November 2014: Our paper Low-Rank Solutions of Matrix Inequalities With Applications to Polynomial Optimization and Matrix Completion Problems has been selected as a finalists for the best student paper award at IEEE Conference on Decision and Control 2014.