Current
Neal Parikh
Ph.D. Candidate
Department of Computer Science
Stanford University
Advisors: Stephen Boyd, Daphne Koller
Support: NSF Graduate Research Fellowship
Contact
npparikh [at-sign] cs.stanford.edu
Packard 243
350 Serra Mall
Stanford, CA 94305
Places
Artificial Intelligence Laboratory
Information Systems Laboratory
Teaching
Instructor: EE 364a: Convex Optimization I (Summer 2011-2012)
TA: EE 364a: Convex Optimization I (Winter 2011-2012)
TA: CS 228T: Probabilistic Graphical Models: Theoretical Foundations (Spring 2010-2011)
Publications
N. Parikh and S. Boyd. Proximal algorithms. To appear in Foundations and Trends in Optimization, 2013.
E. Chu, N. Parikh, A. Domahidi, and S. Boyd. Code generation for embedded second-order cone programming. European Control Conference, 2013.
N. Parikh and S. Boyd. Block splitting for distributed optimization. Submitted for publication, 2012.
N. Parikh and S. Boyd. Block splitting for large-scale distributed learning. Neural Information Processing Systems (NIPS), Workshop on Big Learning, 2011.
S. Boyd, N. Parikh, E. Chu, B. Peleato, and J. Eckstein. Distributed optimization and statistical learning via the alternating direction method of multipliers. Foundations and Trends in Machine Learning 3(1):1-122, 2011.
Education
M.S. in Computer Science, Stanford University, 2012.
B.A.S. (summa cum laude) in Computer & Information Science and Mathematics, University of Pennsylvania, 2007.