Optimization Algorithms for Distributed Machine Learning

Gauri Joshi, Ph.D., is an Associate Professor in the ECE department at Carnegie Mellon University. Dr. Joshi completed her Ph.D. from MIT EECS. Her current research is on designing algorithms for federated learning, distributed optimization, and parallel computing. Her awards and honors include being named as one of MIT Technology Review's 35 Innovators under 35 (2022), the NSF CAREER Award (2021), the ACM SIGMETRICS Best Paper Award (2020), Best Thesis Prize in Computer science at MIT (2012), and Institute Gold Medal of IIT Bombay (2010).

• ​Distributed Optimization in Machine Learning.- Calculus, Probability and Order Statistics Review.- Convergence of SGD and Variance-Reduced Variants.- Synchronous SGD and Straggler-Resilient Variants.- Asynchronous SGD and Staleness-Reduced Variants.- Local-update and Overlap SGD.- Quantized and Sparsified Distributed SGD.-Decentralized SGD and its Variants.