About me
I am a fourth-year PhD student in the Department of Computer Science at Stanford University, advised by Prof. Matei Zaharia. I am affiliated with Stanford DAWN and supported by the National Science Foundation Graduate Research Fellowship.

My broad research interests include distributed systems and cloud computing -- in particular, I am interested in the Systems problems associated with learning and deploying machine learning models at scale. I currently work on Weld, a system that facilitates fast, parallel code generation. You can read more about Weld here.

I graduated from MIT in 2015 with a SB in Computer Science and Mathematics and a MEng in EECS.
Publications
Evaluating End-to-End Optimization for Data Analytics Applications in Weld
Shoumik Palkar, James Thomas, Deepak Narayanan, Pratiksha Thaker, Parimarjan Negi, Rahul Palamuttam, Anil Shanbhag, Holger Pirk, Malte Schwarzkopf, Saman Amarasinghe, Samuel Madden, Matei Zaharia.
VLDB 2018.

DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Christopher Re, Matei Zaharia.
NIPS MLSys 2017.

MacroBase: Prioritizing Attention in Fast Data
Peter Bailis, Edward Gan, Samuel Madden, Deepak Narayanan, Kexin Rong, Sahaana Suri.
SIGMOD 2017.

Weld: A Common Runtime for High Performance Data Analytics
Shoumik Palkar, James J. Thomas, Anil Shanbhag, Deepak Narayanan, Holger Pirk, Malte Schwarzkopf, Saman Amarasinghe, Matei Zaharia.
CIDR 2017.
Preprints
PipeDream: Fast and Efficient Pipeline Parallel DNN Training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, Phil Gibbons.
arXiv:1806.03377.

Analysis of DAWNBench, a Time-to-Accuracy Machine Learning Performance Benchmark.
Cody Coleman*, Daniel Kang*, Deepak Narayanan*, Luigi Nardi, Tian Zhao, Jian Zhang, Peter Bailis, Kunle Olukotun, Chris Re, Matei Zaharia.
arXiv:1806.01427.

Weld: Rethinking the Interface Between Data-Intensive Libraries
Shoumik Palkar, James Thomas, Deepak Narayanan, Anil Shanbhag, Holger Pirk, Malte Schwarzkopf, Saman Amarasinghe, Samuel Madden, Matei Zaharia.
arXiv:1709.06416.
Posters
DAWNBench: An End-to-End Deep Learning Benchmark and Competition
Cody Coleman, Deepak Narayanan, Daniel Kang, Tian Zhao, Jian Zhang, Luigi Nardi, Peter Bailis, Kunle Olukotun, Christopher Re, Matei Zaharia.
SysML 2018.

PipeDream: Fast and Efficient Pipeline Parallel DNN Training
Aaron Harlap, Deepak Narayanan, Amar Phanishayee, Vivek Seshadri, Nikhil Devanur, Greg Ganger, Phil Gibbons.
SysML 2018.

Accelerating Model Search with Model Batching
Deepak Narayanan, Keshav Santhanam, Matei Zaharia.
SysML 2018.
Teaching
I was a Teaching Assistant for Introduction to Algorithms (6.006) in Spring 2014, and for Design and Analysis of Algorithms (6.046) in Spring 2015.

Before that, I was a Lab Assistant for Elements of Software Construction (6.005) and Introduction to EECS I (6.01).
Contact me