I am currently a doctoral student under the project on Embedded optimization for real-time machine learning at the cluster of large-scale optimization and control. This project is supported by the Wallenberg AI, Autonomous Systems and Software program, Sweden's largest individual research program. I am supervised by Professor Mikael Johansson at the Department of Automatic Control, Electrical Engineering School, KTH. My research interest is focused on optimization algorithms for large-scale machine learning applications.
I graduated from the Department of Electrical Engineering at Chulalongkorn University, Thailand, and studied a Master's degree in Systems, Control and Robotics at Electrical Engineering School, KTH.
- Best Student Paper Award, the 44th International Conference on Acoustics, Speech, and Signal Processing, ICASSP 2019.
- Khirirat, Sarit, Sindri Magnússon, and Mikael Johansson. "Convergence Bounds for Compressed Gradient Methods with Memory Based Error Compensation." ICASSP 2019-2019 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP). IEEE, 2019.
- Alistarh, Dan, Torsten Hoefler, Mikael Johansson, Nikola Konstantinov, Sarit Khirirat, and Cédric Renggli. "The Convergence of Sparsified Gradient Methods." In Advances in Neural Information Processing Systems, pp. 5973-5983. 2018.
- S. Khirirat, HR Feyzmahdavian and M. Johansson, " Mini-batch gradient descent: Faster convergence under data sparsity," 2017 IEEE 56th Annual Conference on Decision and Control (CDC), Melbourne, VIC, 2017, pp. 2880-2887.
- Khirirat, Sarit. "Randomized first-order methodologies for convex optimization: Improved convergence rate boundaries and experimental evaluations." (2016).