Shoumik Palkar

I am a fourth year Ph.D. student in the Computer Science department at Stanford University, advised by Prof. Matei Zaharia. My research focuses on the performance of data analytics and machine learning systems and computer networks.

I am affiliated with Stanford DAWN where I work on Weld, a new interface for data analytics libraries that enables optimizations and fast code generation across libraries and frameworks such as NumPy and Spark. Weld is open source here.


Publications and Preprints

Exploring the Use of Learning Algorithms for Efficient Performance Profiling.
Shoumik Palkar, Sahaana Suri, Peter Bailis, and Matei Zaharia.
To appear at the NIPS 2018 Workshop on Machine Learning for Systems.

Splitability Annotations: Optimizing Black-Box Function Composition in Existing Libraries.
Shoumik Palkar and Matei Zaharia.
Arxiv Preprint 1810.12297.

Filter Before You Parse: Faster Analytics on Raw Data with Sparser.
Shoumik Palkar, Firas Abuzaid, Peter Bailis, and Matei Zaharia.
In VLDB 2018.

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, and Matei Zaharia.
In VLDB 2018.

DIY Hosting for Online Privacy.
Shoumik Palkar and Matei Zaharia.
In HotNets 2017.

Weld: Rethinking the Interface Between Data-Intensive Applications.
Shoumik Palkar, James Thomas, Deepak Narayanan, Anil Shanbhag, Holger Pirk, Malte Schwarzkopf, Saman Amarasinghe, Samuel Madden, and Matei Zaharia.
Arxiv Preprint 1709.06416.

Weld: A Common Runtime for Data Analytics.
Shoumik Palkar, James Thomas, Anil Shanbhag, Deepak Narayanan, Holger Pirk, Malte Schwarzkopf, Saman Amarasinghe, and Matei Zaharia.
In CIDR 2017.

E2: A Framework for NFV Applications.
Shoumik Palkar, Chang Lan, Sangjin Han, Keon Jang, Aurojit Panda, Sylvia Ratnasamy, Luigi Rizzo, and Scott Shenker.
In SOSP 2015.

SoftNIC: A Software NIC to Augment Hardware.
Sangjin Han, Keon Jang, Aurojit Panda, Shoumik Palkar, Dongsu Han, and Sylvia Ratnasamy
UC Berkeley Technical Report No. UCB/EECS-2015-155


Talks

Sparser: Fast Analytics over Raw Data by Avoiding Parsing
at Spark+AI Summit, June 2018, San Francisco, CA.

Weld: Accelerating Data Science by 100x
at DataEngConf, April 2018, San Francisco, CA.

DIY Hosting for Online Privacy
at the Stanford NetSeminar, January 2018, Stanford, CA.

DIY Hosting for Online Privacy
at HotNets 2017, November 2017, Palo Alto, CA.

Generating Fast Data Planes for Data-Intensive Systems
at the 17th International Workshop on High Performance Transaction Systems (HPTS), October 2017, Asilomar, CA.

Weld: Accelerating Data Science by 100x
at Strata Data Conference, September 2017, New York, NY.

Weld: An Optimizing Runtime for High-Performance Data Analytics
at Strata + HadoopWorld, March 2017, San Jose, CA.

Weld: A Common Runtime for Data Analytics
at the Stanford Platform Lab Seminar, January 2017, Stanford, CA.


In the Past

During the 2015-2016 academic year I was at MIT working in the PDOS Lab, and was supported by a Jacobs Presidential Fellowship. Before that, I received a B.S. in Electrical Engineering and Computer Science from UC Berkeley where I worked with Scott Shenker and Sylvia Ratnasamy in the NetSys Lab on E2, a scalable, high performance framework for NFV.


Contact