About Me

I am a PhD student in Computer Science at Stanford University, advised by Prof. Jure Leskovec. I also frequently collaborate with Prof. Cynthia Rudin, Prof. Sendhil Mullainathan, Prof. Jon Kleinberg and Prof. Jens Ludwig. I recently interned at the Adaptive Systems and Interaction (ASI) group of Microsoft Research, Redmond with Ece Kamar, Rich Caruana, and Eric Horvitz.

My research interests involve using machine learning for understanding and complementing human decision making. To this end, I have been working on a multitude of topics such as empirical analysis of datasets from domains such as health care, education, judiciary and crowdsourcing, probabilistic modeling of confusions in human decision making, and building interpretable machine learning models which can aid human decision making. In addition, I recently started studying how human feedback can be leveraged to build reliable machine learning models.

My research is supported by a Stanford Graduate Fellowship and a Google Anita Borg Scholarship.

Research

Manuscripts


  • Human Decisions and Machine Predictions.
    Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan.
    (under review at Quarterly Journal of Economics)

  • Psycho-Demographic Profiles of Online Social Activists: A Case Study of the Facebook Rainbow Campaign. [PDF]
    Yilun Wang, Himabindu Lakkaraju, Michal Kosinski, Jure Leskovec.

Publications


  • The Selective Labels Problem: Evaluating Algorithmic Predictions in the Presence of Unobservables.
    Himabindu Lakkaraju, Jon Kleinberg, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2017

  • Learning Cost-Effective Treatment Regimes using Markov Decision Processes. [PDF]
    Himabindu Lakkaraju, Cynthia Rudin.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2017

  • Identifying Unknown Unknowns in the Open World: Representations and Policies for Guided Exploration. [PDF]
    Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz.
    AAAI Conference on Artificial Intelligence (AAAI), 2017

  • Confusions over Time: An Interpretable Bayesian Model to Characterize Trends in Decision Making. [PDF]
    Himabindu Lakkaraju, Jure Leskovec.
    Advances in Neural Information Processing Systems (NIPS), 2016

  • Interpretable Decision Sets: A Joint Framework for Description and Prediction. [PDF]
    Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016

  • Mining Big Data to Extract Patterns and Predict Real-Life Outcomes. [PDF]
    Michal Kosinki, Yilun Wang, Himabindu Lakkaraju, Jure Leskovec.
    Psychological Methods (Impact Factor = 7.338), 2016

  • Learning Cost-Effective and Interpretable Regimes for Treatment Recommendation. [PDF]
    Himabindu Lakkaraju, Cynthia Rudin.
    NIPS Workshop on Interpretable Machine Learning in Complex Systems, 2016

  • Learning Cost-Effective and Interpretable Treatment Regimes for Judicial Bail Decisions. [PDF]
    Himabindu Lakkaraju, Cynthia Rudin.
    NIPS Symposium on Machine Learning and the Law, 2016

  • Discovering Unknown Unknowns of Predictive Models. [PDF]
    Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Eric Horvitz.
    NIPS Workshop on Reliable Machine Learning in the Wild, 2016

  • A Machine Learning Framework to Identify Students at Risk of Adverse Academic Outcomes. [PDF]
    Himabindu Lakkaraju, Everaldo Aguiar, Carl Shan, David Miller, Nasir Bhanpuri, Rayid Ghani.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2015

  • A Bayesian Framework for Modeling Human Evaluations. [PDF]
    Himabindu Lakkaraju, Jure Leskovec, Jon Kleinberg, Sendhil Mullainathan.
    SIAM International Conference on Data Mining (SDM), 2015

  • Who, When, and Why: A Machine Learning Approach to Prioritizing Students at Risk of not Graduating High School on Time. [PDF]
    Everaldo Aguiar, Himabindu Lakkaraju, Nasir Bhanpuri, David Miller, Ben Yuhas, Kecia Addison, Rayid Ghani.
    Learning Analytics and Knowledge Conference (LAK), 2015

  • Aspect Specific Sentiment Analysis using Hierarchical Deep Learning. [PDF]
    Himabindu Lakkaraju, Richard Socher, Chris Manning.
    NIPS Workshop on Deep Learning and Representation Learning, 2014

  • Using Big Data to Improve Social Policy.
    Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan.
    NBER Economics of Crime Working Group, 2014

  • What's in a name ? Understanding the Interplay Between Titles, Content, and Communities in Social Media. [PDF]
    Himabindu Lakkaraju, Julian McAuley, Jure Leskovec.
    International AAAI Conference on Weblogs and Social Media (ICWSM), 2013

  • Dynamic Multi-Relational Chinese Restaurant Process for Analyzing Influences on Users in Social Media. [PDF]
    Himabindu Lakkaraju, Indrajit Bhattacharya, Chiranjib Bhattacharyya.
    IEEE International Conference on Data Mining (ICDM), 2012

  • TEM: a novel perspective to modeling content on microblogs. [PDF]
    Himabindu Lakkaraju, Hyung-Il Ahn.
    International World Wide Web Conference (WWW), short paper, 2012

  • Exploiting Coherence for the Simultaneous Discovery of Latent Facets and associated Sentiments. [PDF]
    Himabindu Lakkaraju, Chiranjib Bhattacharyya, Indrajit Bhattacharya, Srujana Merugu.
    SIAM International Conference on Data Mining (SDM), 2011
    Best Paper Award

  • Attention prediction on social media brand pages. [PDF]
    Himabindu Lakkaraju, Jitendra Ajmera.
    ACM Conference on Information and Knowledge Management (CIKM), 2011

  • Smart news feeds for social networks using scalable joint latent factor models. [PDF]
    Himabindu Lakkaraju, Angshu Rai, Srujana Merugu.
    International World Wide Web Conference (WWW), short paper, 2011

  • A Non Parametric Theme Event Topic Model for Characterizing Microblogs. [PDF]
    Himabindu Lakkaraju, Hyung-Il Ahn.
    NIPS Workshop on Computational Social Science and the Wisdom of Crowds, 2011

  • Unified Modeling of User Activities on Social Networking Sites. [PDF]
    Himabindu Lakkaraju, Angshu Rai.
    NIPS Workshop on Computational Social Science and the Wisdom of Crowds, 2011

Patents


  • Extraction and Grouping of Feature Words.
    Himabindu Lakkaraju, Chiranjib Bhattacharyya, Sunil Aravindam, Kaushik Nath.
    US8484228

  • Enhancing knowledge bases using rich social media.
    Jitendra Ajmera, Shantanu Ravindra Godbole, Himabindu Lakkaraju, Bernard Andrew Roden, Ashish Verma.
    US20130224714

Teaching & Service

  • PC Member for NIPS 2017, KDD 2017, WWW 2017, CIKM 2017.

  • Reviewer for NIPS 2016, TKDD 2016, TKDE 2015, KDD 2015, SDM 2015, CIKM 2011, UAI 2011, AAAI 2011.

  • Steering Committee Member for ICML, NIPS Workshops on Interpretable Machine Learning, 2016.

  • Head Teaching Assistant, CS224W: Social and Information Network Analysis, Autumn 2014, Stanford University.

  • Teaching Assistant, CS246: Mining Massive Datasets, Winter 2015, Stanford University.

  • Student Volunteer for KDD (2014 - 2016).

Resume

Please find my CV here.