RASIGA GOWRISANKAR

4366 Computer Sciences
1210 W. Dayton Street, Madison, WI 53706
rasiga [at] cs.wisc.edu
gowrisankar [at] wisc.edu

Biography

I am a second year graduate student in the Department of Computer Sciences at the University of Wisconsin-Madison.
I am a Research Assistant at the Microsoft Gray Systems Lab and I am advised by Prof.Jeffrey Naughton
I graduated from College of Engineering, Guindy,Anna University, Chennai in 2014 with a Bachelors in Computer Science and Engineering

Courses

Fall 2014	CS 564: Database Management Systems: Design and Implementation (Prof. Jeffrey Naughton) CS 760: Machine Learning (Prof. Mark Craven)
Spring 2015	CS 764: Topics in Database Management Systems (Prof. Jeffrey Naughton) CS 537: Introduction to Operating Systems (Sankaralingam Panneerselvam)
Fall 2015	CS 784: Data Models and Languages - Data science theme (Prof. AnHai Doan) CS 838: Big Data Systems (Prof. Aditya Akella) CS 402: Introducing Computer Science to K-12 Students (Prof. Andrew Kuemmel)

Projects

Big Data Systems	Autotuning Spark Streaming: Autotuning of configuration parameters for Spark Streaming by building a Machine Learning model using data collected using HiBench benchmark. Mapreduce and Tez: Running benchmark workloads on Apache Hive atop MR and Tez to understand and tune the systems Spark: Running benchmark workloads on Apache Spark and Spark SQL, writing native spark queries using RDDs Storm, GraphX, MLlib Developing and running streaming, graph processing and machine learning applications in Apache Storm, GraphX and MLlib
Operating Systems	Virtualization: CPU - Modification in xv6 kernel scheduler implementing FIFO to a Ticket-based lottery scheduler Memory - alloc.h like library + modifications in memory structure of xv6 kernel Concurrency: multi-threading a web server + enabling multi-threading in xv6 kernel Persistence: Mirroring in xv6 file system
Machine Learning	ID3-like Decision tree learner for classification Bayesian network learning: Naives Bayes and TAN Bayes Distributed Representation of Sentences for Speculative Language Recognition in Biomedical Articles

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