Konstantina Christakopoulou

PhD candidate in Machine Learning

Computer Science, University of Minnesota



My research interests are in Machine Learning and its application to large scale real-world problems, including content personalization and recommendation systems. My advisor is Prof. Arindam Banerjee.

Here is my research statement.


  • Q&R: A Two-Stage Approach Toward Interactive Recommendation [ pdf ][promo video]
    Konstantina Christakopoulou, Alex Beutel, Rui Li, Sagar Jain and Ed H. Chi
    KDD Applied Data Science Track, 2018.

  • Learning to Interact with Users: A Collaborative-Bandit Approach [ pdf ][ Suppl. ]
    Konstantina Christakopoulou and Arindam Banerjee
    18th SIAM International Conference on Data Mining (SDM), 2018. [acceptance rate: 23.2%]

  • Glass-box Program Synthesis: A Machine Learning Approach [ pdf ]
    Konstantina Christakopoulou and Adam Tauman Kalai
    32nd AAAI Conference on Artificial Intelligence (AAAI), 2018. [acceptance rate: 25%]

  • Recommendation with Capacity Constraints [ pdf ][CIKM'17 slides] [ CIKM Connect Poster]
    Konstantina Christakopoulou, Jaya Kawale and Arindam Banerjee
    26th ACM International Conference on Information and Knowledge Management (CIKM), 2017. [acceptance rate: 21%]

  • Towards Conversational Recommender Systems [ pdf ] [ KDD'16 Slides ] [ short video ] [ KDD video ]
    Konstantina Christakopoulou, Filip Radlinski and Katja Hofmann
    22nd ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016. [Full paper, acceptance rate: 8.93%]

  • Collaborative Ranking with a Push at the Top [ pdf ] [ WWW'15 Slides ]
    Konstantina Christakopoulou and Arindam Banerjee
    24th International World Wide Web Conference (WWW), 2015. [acceptance rate: 14.1%]

  • Accelerated Alternating Direction Method of Multipliers [ pdf ]
    M. Kadkhodaie, K. Christakopoulou, M. Sanjabi, and A. Banerjee
    21st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015. [acceptance rate: 19.5%]

Internship experience

  • Software Engineering Intern in Google Research , June 2017 - August 2017

    Mentors: Ed Chi, Alex Beutel, Sagar Jain
  • Research Intern in Microsoft Research New England , June 2016 - August 2016

    Mentor: Adam Kalai
    Project: Program Synthesis for automatically solving problems
  • Research Intern in the Machine Learning & Perception Group, at Microsoft Research Cambridge, UK, June 2015 - August 2015

    Mentors: Katja Hofmann, Filip Radlinski
    Project: Towards Conversational Recommender Systems: Online Restaurant Recommendation under Bandit Feedback
  • Enterprise Sales Engineer Intern at Google Ireland, July 2012 - December 2012

    Mentor: Matthieu Mayran
    Highlight project: Developed an automatic document categorization tool for Google Drive. Fully integrated with Google Drive SDK, running on Google App Engine and making extensive use of the Prediction API, it is used by Google partners as a cross-product solution between Cloud Platform and Google Apps for organizing documents in the cloud, based on their contents.

Recent Awards

  • CIKM'17 Student Travel Award

  • Finalist for MSR PhD Fellowship 2016-2017

  • Gerondelis Scholarship for 2016-2017

  • Student Travel Award for KDD 2016

  • Google Student Grant for WWW 2015

  • Two-year Graduate Fellowship from the College of Science & Engineering, University of Minnesota, 2013-2014, 2014-2015

  • Award of academic excellence by the LIMMAT foundation of Zurich, 2014.


  • PhD Computer Science, University of Minnesota, Fall 2013 - current

  • MSc Computer Science, University of Minnesota, Fall 2013 - Spring 2015

  • Diploma Electrical & Computer Engineering, University of Patras, Greece, Fall 2013 - current

Recent Activities

  • Program Committee for WWW 2018, Poster Session
  • Invited to give a talk in AI With The Best, April 2017: Recommendation under Constraints [slides]
  • Invited for contributed talk at Women In Machine Learning workshop 2016 (Link)
  • Invited to give a talk in AI With The Best, September 2016: Interactive Learning for Recommendatio [video]
  • Program Committee Member for WWW 2016, Content Analysis Track.
  • Reviewer for NIPS 2015.


  • My sister Evangelia is a PhD student in Data Mining at the University of Minnesota.