**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**

TA: EE 364b: Convex Optimization II, Advanced Topics (Spring 2013-2014)

TA: CVX 101 (MOOC): Convex Optimization I (Winter 2013-2014)

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**

B. O'Donoghue, E. Chu, N. Parikh, and S. Boyd. Operator splitting for conic optimization via homogeneous self-dual embedding.
*Submitted*, 2014.

E. Chu, B. O'Donoghue, N. Parikh, and S. Boyd. A primal-dual operator splitting method for conic optimization.
*Working draft*, 2014.

N. Parikh and S. Boyd. Proximal algorithms.
*Foundations and Trends in Optimization*, volume 1, issue 3, pp. 127-239, 2014.

N. Parikh and S. Boyd. Block splitting
for distributed optimization. *Mathematical Programming Computation*, volume 6, issue 1, pp. 77-102, 2014.

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 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*, volume 3, issue 1, pp. 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.