Aishwarya Sivaraman

Graduate Student Researcher ,UCLA

I’m a second year PhD student at University of California, Los Angeles. I completed my undergraduate in Computer Engineering from the National University of Singapore (NUS). Prior to joining UCLA, I was a research assistant at NUS advised by Prof. Khoo, Siau Cheng .

download cv


  • [Feb. 2019] The research artifact of active inductive logic programming for code search passed the ICSE artifact evaluation.
  • [Dec 2018] Our paper on code search using active learning and logic programming is accepted to ICSE 2019.
  • [Dec 2018] Passed Written Qualifying Examination (One step closer to getting my PhD :D ).
  • [Nov 2018] Invited Panelist, Grad School/Research Mentorship Event, Society of Women Engineers.
  • [Jul 2018] Conducted introductory classes on Computer Science and Python for high school students (LACC).
  • [Jan 2018] Student Volunteer at POPL 2018.

  • 2017-Present

    PhD in Computer Science, University of California - Los Angeles, USA

  • 2016-2017

    Research Assistant, National University of Singapore, Singapore

  • 2014-2015

    Technology Analyst, JPMorgan Chase, Singapore

  • 2010-2014

    Bachelor's in Computer Engineering, National University of Singapore, Singapore


Active Inductive Logic Programming for Code Search.

International Conference on Software Engineering (ICSE 19)

In this work, we designed a logic-based query language to model the structure and semantics of source code. We propose a new active learning-based technique that infers a logic query based on positive and negative code examples.

Read the abstract here

GEMS: An Extract Method Refactoring Recommender

International Symposium on Software Reliability Engineering (ISSRE), 2017

In this work, we present a novel system that learns these criteria for extract method refactorings from open source repositories. We extract structural and functional features, which encode the concepts of complexity, cohesion and coupling in our learning model, and train it to extract suitable code fragments from a given source of a method.

Read it here


dcssiva at cs dot ucla dot edu
  • Computer Science Department
  • Engineering VI,
  • Los Angeles, CA 90095