Satyajith Amaran
I am a 3rd year doctoral student in the Department of Chemical Engineering at Carnegie Mellon University. I am advised by Nick Sahinidis and work on derivative-free optimization algorithms.
My current research involves the development of general-purpose black-box optimization algorithms. Black-box systems, such as simulations or physical experiments, are often expensive to perform. Optimization over these black boxes are hard to do as there is often no knowledge of derivatives for the system. Our approach involves the use of surrogate model-based methods derived from machine learning and statistical theory in combination with trust-region methods from the optimization literature. The aim of this work is to be able to find optimal parameters to experiments and simulations with the fewest number of runs and to apply this to various problems across engineering.
My interests include the application of nonlinear and integer optimization to problems in engineering and operations.
I was previously a Masters student with Nick Sahinidis and worked on the global optimization of parameter estimation problems.
Prior to 2008, Prof. Shreekumar's Process Modeling and Simulation course at NIT Surathkal and a summer research experience with Professor Arun Moharir at IIT Bombay in India were instrumental in getting me interested in the Process Systems Engineering (PSE) area. Subsequently, I became interested in optimization algorithms and applications during my Masters at Carnegie Mellon.
In my free time, I enjoy reading, playing intramural soccer and attending weekly meetings at the CMU Quiz Club. I was involved in organizing the 2012 ChEGSA Symposium in the Chemical Engineering department, and currently serve on the ChEGSA board.
My academic webpage is hosted here.