Pramod Ganapathi

Pramod Ganapathi
Pramod Ganapathi
Research Assistant Professor

Department of Computer Science
Room 105
Stony Brook, NY 11794-2424

Phone: 
631-632-1728
Email: 
pramod.ganapathi [at] cs.stonybrook.edu

Interests

Parallel algorithms, Communication-efficient algorithms, and Mathematical puzzles

Biography

Pramod Ganapathi is a Research Assistant Professor in the Department of Computer Science at Stony Brook University. Prior to joining SBU, he was an Assistant Professor at the Indian Institute of Technology, Indore. Prior to joining IIT, he founded and ran an animation-based online higher education startup called "Learning is Beautiful", in India. Prior to founding his startup, he received his Ph.D. in computer science at SBU, specializing in parallel algorithms. His Ph.D. work is entitled "Automatic Discovery of Efficient Divide-and-Conquer Algorithms for Dynamic Programming Problems" and was supervised by Prof. Rezaul A. Chowdhury. Before pursuing Ph.D., he was a Software Engineer at IBM India Software Labs.

Research

Pramod Ganapathi was previously involved in designing algorithms/frameworks such as Autogen, Autogen-Wave, and Autogen-Fractile that can automatically/semi-automatically discover provably-correct, nontrivial, efficient algorithms for a wide class of dynamic programming problems. The algorithms take as input iterative algorithms and output nontrivial recursive divide-and-conquer algorithms. The auto-discovered recursive divide-and-conquer algorithms are efficient (highly parallel, communication-efficient, energy-efficient) and portable (cache-oblivious, processor-oblivious, cache-adaptive, processor-adaptive). The algorithmic frameworks can be easily adapted to discover algorithms for several architectures such as multicores, manycores, GPUs, and multidimensional grids/meshes of nodes.

Pramod Ganapathi is currently involved in designing highly parallel algorithms for matrix multiplication and other matrix-based problems. He is also involved in designing highly parallel and communication-efficient algorithms for machine learning.

Teaching Summary

CSE 102, CSE 215