Hima Lakkaraju



Contact

hlakkaraju@hbs.edu
hlakkaraju@seas.harvard.edu

428 Morgan Hall
337 Maxwell Dworkin

@hima_lakkaraju
lvhimabindu


I am an Assistant Professor at Harvard University with appointments in Business School and Department of Computer Science.

My research interests lie within the broad area of trustworthy machine learning. More specifically, my research spans explainable ML, fairness, adversarial robustness, reinforcement learning, and causal inference.

I develop machine learning tools and techniques which enable human decision makers to make better decisions. More specifically, my research addresses the following fundamental questions pertaining to human and algorithmic decision-making:

  1. How do we build fair and interpretable models that can aid human decision-making?
  2. How do we ensure that models and their explanations are robust to adversarial attacks?
  3. How do we evaluate the effectiveness of algorithmic predictions in the presence of missing counterfactuals?
  4. How do we detect and correct underlying biases in human decisions and algorithmic predictions?

These questions have far-reaching implications in domains involving high-stakes decisions such as criminal justice, health care, public policy, business, and education.

My current research is being generously supported by Google. Prior to my stint at Harvard, I received my PhD in Computer Science from Stanford University.

For more details about me and my research, here is my CV and here is a one-pager about my research.

NOTE: I am looking for motivated undergraduate and graduate students as well as postdocs who are broadly interested in trustworthy machine learning and its applications to health care and criminal justice. If you are excited about this line of research and would like to work with me, please read this before contacting me.

Publications

  • Robust and Stable Black Box Explanations.
    Himabindu Lakkaraju, Nino Arsov, Osbert Bastani.
    International Conference on Machine Learning (ICML), 2020.

  • Fooling LIME and SHAP: Adversarial Attacks on Post hoc Explanation Methods.
    Dylan Slack, Sophie Hilgard, Emily Jia, Sameer Singh, Himabindu Lakkaraju.
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2020.
    pdf
    Press: deeplearning.ai | Harvard Business Review

  • "How do I fool you?": Manipulating User Trust via Misleading Black Box Explanations.
    Himabindu Lakkaraju, Osbert Bastani.
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2020.
    pdf

  • Faithful and Customizable Explanations of Black Box Models.
    Himabindu Lakkaraju, Ece Kamar, Rich Carauna, Jure Leskovec.
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2019.
    pdf

  • Human Decisions and Machine Predictions.
    Jon Kleinberg, Himabindu Lakkaraju, Jure Leskovec, Jens Ludwig, Sendhil Mullainathan.
    Quarterly Journal of Economics (QJE), 2018.
    Featured in MIT Technology Review, Harvard Business Review, The New York Times,
    and as Research Spotlight on National Bureau of Economics front page.
    pdf

  • 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.
    pdf

  • Learning Cost-Effective and Interpretable Treatment Regimes.
    Himabindu Lakkaraju, Cynthia Rudin.
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2017.
    INFORMS Data Mining Best Paper Award - Finalist.
    Invited Talk at INFORMS Annual Meeting.
    pdf

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

  • Interpretable and Explorable Approximations of Black Box Models.
    Himabindu Lakkaraju, Ece Kamar, Rich Caruana, Jure Leskovec.
    KDD Workshop on Fairness, Accountability, and Transparency in Machine Learning (FAT ML), 2017.
    Invited Talk at INFORMS Annual Meeting.
    pdf

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

  • Interpretable Decision Sets: A Joint Framework for Description and Prediction.
    Himabindu Lakkaraju, Stephen H. Bach, Jure Leskovec.
    ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD), 2016.
    Invited Talk at INFORMS Annual Meeting.
    pdf

  • Mining Big Data to Extract Patterns and Predict Real-Life Outcomes.
    Michal Kosinki, Yilun Wang, Himabindu Lakkaraju, Jure Leskovec.
    Psychological Methods, 2016.
    pdf

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

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

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

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

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

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

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

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

  • What's in a name ? Understanding the Interplay Between Titles, Content, and Communities in Social Media.
    Himabindu Lakkaraju, Julian McAuley, Jure Leskovec.
    International AAAI Conference on Weblogs and Social Media (ICWSM), 2013.
    Featured in Time, Forbes, Phys.Org, Business Insider.
    pdf

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

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

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

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

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

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

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

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

I am very fortunate to be collaborating with the following students:

  • Shahin Jabbari (Postdoc, Harvard CS)
  • Sophie Hilgard (PhD Student, Harvard CS)
  • Dylan Slack (PhD Student, UCI CS)
  • Julius Adebayo (PhD Student, MIT EECS)
  • Aida Rahmttalabi (PhD Student, USC CS)
  • Nino Arsov (Researcher, Macedonian Academy of Arts and Sciences)
  • Aditya Karan (Masters Student, Harvard CS)
  • Kaivalya Rawal (Masters Student, Harvard Data Science)
  • Jorma Gorns (Masters Student, Harvard CS)
  • Emily Jia (Undergraduate, Harvard CS)
  • Alexis Ross (Undergraduate, Harvard CS + Philosophy)
  • Wanqian Yang (Undergraduate, Harvard CS)
  • Topics in Machine Learning: Interpretability and Explainability
    Instructor
    Harvard University, 2019.

  • Introduction to Data Science
    Guest Lecture
    Stanford Law School, 2016.

  • Probability with Mathemagic
    Co-Instructor
    Stanford Splash Initiative for High School Students, 2016.

  • Mining Massive Datasets Course
    Teaching Assistant
    Stanford Computer Science, 2016.

  • Submodular Optimization
    Guest Lecture
    Mining Massive Datasets Course, Stanford, 2016.

  • Introduction to Python Programming
    Co-Instructor
    Stanford Girls Teaching Girls to Code Initiative for High School Students, 2015.

  • Mathematics and Science
    Tutor
    Dreamcatchers Non-Profit Organization, Palo Alto, 2015.

  • Social and Information Network Analysis Course
    Head Teaching Assistant
    Stanford Computer Science, 2014.

  • Machine Learning Course
    Teaching Assistant
    Indian Institute of Science, 2010.

  • English and Mathematics
    Tutor
    UNICEF's Teach India Initiative, 2008 - 2010.

  • Object Oriented Programming
    Co-Instructor
    Visvesvaraya Technological University, 2007.

  • Introduction to Databases
    Co-Instructor
    Visvesvaraya Technological University, 2007.