Computer Sciences


4366 Computer Sciences
1210 W. Dayton Street, Madison, WI 53706
rasiga [at]
gowrisankar [at]
LinkedIn profile


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


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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