I am broadly interested in computational machine learning, designing and analysis of distributed algorithms with  focus on:

• Federated Optimization

• Large-scale Machine Learning

• Distributionally Robust Optimization (DRO)

• Falut-tolerant Distributed Computing

Ph.D. Thesis:  Communication-efficient and Fault-tolerant Algorithms for Distributed Machine Learning

P.s: I was extremely fortunate to work with Mehrdad Mahdavi and Viveck Cadambe as their PhD student. 

•  Research Intern, Baidu AI, Seattle, (summer 2020)

Mentor: Dr. Ping Li

• Research Intern, Chinese University of Hong Kong, Hong Kong (2013)

Mentor: Sidharth Jaggi 

Black-Box Generalization

 Konstantinos E. Nikolakakis, Farzin Haddadpour, Dionysios S. Kalogerias, and Amin Karbasi.

• NeurIPS, 2022

Learning distributionally robust models at scale via composite optimization

 Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi and Amin Karbasi

• ICLR, 2022

Federated learning with compression: Unified analysis and sharp guarantees

 Farzin Haddadpour, Mohammad Mahdi Kamani, Aryan Mokhtari and Mehrdad Mahdavi

• • AISTATS, 2021

Local SGD with periodic averaging: Tighter analysis and adaptive synchronization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi and Viveck R. Cadambe

• • NeurIPS, 2019

Trading redundancy for communication: Speeding up distributed SGD for non-convex optimization
Farzin Haddadpour, Mohammad Mahdi Kamani, Mehrdad Mahdavi and Viveck R. Cadambe

• • ICML, 2019

On the convergence of local descent methods in federated learning
Farzin Haddadpour and Mehrdad Mahdavi

• • arXiv, 2019