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Deepesh Data
Affiliation
Department of Electrical and Computer Engineering, University of California, Los Angeles, CA, USA
Topic
Communication Graph,Convergence Rate,Federated Learning,Gradient Descent,Heterogeneous Set,Local Function,Local Iterations,MNIST Dataset,Non-convex Objective,Stochastic Gradient,Synchronization Index,Test Accuracy,Adversarial Attacks,Arbitrary Vector,Average Estimates,Communication Network,Communication Of Ideas,Communication Rounds,Communication Time,Consensus Points,Convergence Results,Convex Combination,Convex Objective,Convex Optimization Problem,Convex Set,Corruption,Decoding Algorithm,Differential Privacy,Directed Graph,Efficient Communication,Error Probability,Estimation Problem,Filtering Algorithm,Functional Data,Global Optimization,Global Optimization Problem,Gradient Descent Step,Graph Topology,Heterogeneous Data,Heterogeneous Datasets,Homogeneous Data,Laplace Distribution,Learning Problem,Learning Rate,Local Dataset,Local Estimates,Local Gradient,Local Minimum Point,Local Vector,Loss Parameter,
Biography
Deepesh Data (Student Member, IEEE) received the B.Tech. degree in computer science and engineering from the International Institute of Information Technology, Hyderabad, India, in 2011, and the M.Sc. and Ph.D. degrees from the School of Technology and Computer Science, Tata Institute of Fundamental Research, Mumbai, India, in 2017.
After that, he spent about six months at the IIT Bombay as a Post-Doctoral Fellow, and since April 2018, he has been a Post-Doctoral Scholar with the University of California at Los Angeles. His research interests are in distributed optimization, machine learning, differential privacy, cryptography, algorithms, and information theory. He has received the Microsoft Research India Ph.D. Fellowship, the ACM India Doctoral Dissertation Award (Honorable Mention), and the TIFR-Sasken Best Ph.D. Thesis Award in computer and system sciences.
After that, he spent about six months at the IIT Bombay as a Post-Doctoral Fellow, and since April 2018, he has been a Post-Doctoral Scholar with the University of California at Los Angeles. His research interests are in distributed optimization, machine learning, differential privacy, cryptography, algorithms, and information theory. He has received the Microsoft Research India Ph.D. Fellowship, the ACM India Doctoral Dissertation Award (Honorable Mention), and the TIFR-Sasken Best Ph.D. Thesis Award in computer and system sciences.