Hima Lakkaraju



Contact

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

442 Morgan Hall
337 Maxwell Dworkin

Wikipedia
@hima_lakkaraju
lvhimabindu


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

My research interests lie within the broad area of trustworthy machine learning. More specifically, my research spans explainable, fair, and robust ML. I am also very interested in 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 to build fair and interpretable models that can aid human decision-making?
  2. How to ensure that models and their explanations are robust to adversarial attacks?
  3. How to train and evaluate models in the presence of missing counterfactuals?
  4. How to 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 health care, policy, law, and business.

I lead the AI4LIFE research group at Harvard and I recently co-founded the Trustworthy ML Initiative (TrustML) to help lower entry barriers into trustworthy ML and bring together researchers and practitioners working in the field.

My research is being generously supported by NSF, Google, Amazon, JP Morgan, Bayer, Harvard Data Science Initiative, and D^3 Insitute at Harvard. Prior to my stint at Harvard, I received my PhD in Computer Science from Stanford University.

For more details about me and my research, please check out my CV.

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


  • Which Explanation Should I Choose? A Function Approximation Perspective to Characterizing Post hoc Explanations
    Tessa Han, Suraj Srinivas, Himabindu Lakkaraju
    Advances in Neural Information Processing Systems (NeurIPS), 2022.
    Best Paper Award, ICML Workshop on Interpretable Machine Learning in Healthcare, 2022.
    pdf

  • Flatten the Curve: Efficiently Training Low-Curvature Neural Networks
    Suraj Srinivas, Kyle Matoba, Himabindu Lakkaraju, Francois Fleuret
    Advances in Neural Information Processing Systems (NeurIPS), 2022.
    pdf

  • OpenXAI: Towards a Transparent Evaluation of Model Explanations
    Chirag Agarwal, Satyapriya Krishna, Eshika Saxena, Martin Pawelczyk, Nari Johnson, Isha Puri, Marinka Zitnik, Himabindu Lakkaraju
    Advances in Neural Information Processing Systems (NeurIPS), 2022.
    pdf

  • Data Poisoning Attacks on Off-Policy Evaluation Methods
    Elita Lobo, Harvineet Singh, Marek Petrik, Cynthia Rudin, Himabindu Lakkaraju
    Conference on Uncertainty in Artificial Intelligence (UAI), 2022.
    Oral Presentation [Top 5%]
    pdf

  • Exploring Counterfactual Explanations Through the Lens of Adversarial Examples: A Theoretical and Empirical Analysis.
    Martin Pawelczyk, Chirag Agarwal, Shalmali Joshi, Sohini Upadhyay, Himabindu Lakkaraju
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
    pdf

  • Probing GNN Explainers: A Rigorous Theoretical and Empirical Analysis of GNN Explanation Methods.
    Chirag Agarwal, Marinka Zitnik*, Himabindu Lakkaraju*
    International Conference on Artificial Intelligence and Statistics (AISTATS), 2022.
    pdf

  • Fairness via Explanation Quality: Evaluating Disparities in the Quality of Post hoc Explanations.
    Jessica Dai, Sohini Upadhyay, Ulrich Aivodji, Stephen Bach, Himabindu Lakkaraju
    AAAI/ACM Conference on AI, Society, and Ethics (AIES), 2022.
    pdf

  • Towards Robust Off-Policy Evaluation via Human Inputs.
    Harvineet Singh, Shalmali Joshi, Finale Doshi-Velez, Himabindu Lakkaraju
    AAAI/ACM Conference on AI, Society, and Ethics (AIES), 2022.
    pdf

  • A Human-Centric Take on Model Monitoring.
    Murtuza N Shergadwala, Himabindu Lakkaraju, Krishnaram Kenthapadi
    AAAI Conference on Human Computation and Crowdsourcing (HCOMP), 2022.
    pdf

  • Towards the Unification and Robustness of Post hoc Explanation Methods.
    Sushant Agarwal, Shahin Jabbari, Chirag Agarwal*, Sohini Upadhyay*, Steven Wu, Himabindu Lakkaraju
    Symposium on Foundations of Responsible Computing (FORC), 2022.
    pdf

  • Towards Robust and Reliable Algorithmic Recourse.
    Sohini Upadhyay*, Shalmali Joshi*, Himabindu Lakkaraju
    Advances in Neural Information Processing Systems (NeurIPS), 2021.
    Best Paper Runner Up, ICML Workshop on Algorithmic Recourse, 2021.
    pdf

  • Reliable Post hoc Explanations: Modeling Uncertainty in Explainability.
    Dylan Slack, Sophie Hilgard, Sameer Singh, Himabindu Lakkaraju
    Advances in Neural Information Processing Systems (NeurIPS), 2021.
    pdf

  • Counterfactual Explanations Can Be Manipulated
    Dylan Slack, Sophie Hilgard, Himabindu Lakkaraju, Sameer Singh
    Advances in Neural Information Processing Systems (NeurIPS), 2021.
    pdf

  • Learning Models for Algorithmic Recourse
    Alexis Ross, Himabindu Lakkaraju, Osbert Bastani
    Advances in Neural Information Processing Systems (NeurIPS), 2021.
    pdf

  • Towards the Unification and Robustness of Perturbation and Gradient Based Explanations.
    Sushant Agarwal, Shahin Jabbari, Chirag Agarwal*, Sohini Upadhyay*, Steven Wu, Himabindu Lakkaraju
    International Conference on Machine Learning (ICML), 2021.
    pdf

  • Towards a Unified Framework for Fair and Stable Graph Representation Learning.
    Chirag Agarwal, Himabindu Lakkaraju*, Marinka Zitnik*
    Conference on Uncertainty in Artificial Intelligence (UAI), 2021.
    pdf

  • Fair influence maximization: A welfare optimization approach.
    Aida Rahmattalabi, Shahin Jabbari, Himabindu Lakkaraju, Phebe Vayanos, Eric Rice, Milind Tambe
    AAAI International Conference on Artificial Intelligence (AAAI), 2021.
    pdf

  • Does Fair Ranking Improve Minority Outcomes? Understanding the Interplay of Human and Algorithmic Biases in Online Hiring.
    Tom Suhr, Sophie Hilgard, Himabindu Lakkaraju
    AAAI/ACM Conference on Artificial Intelligence, Ethics, and Society (AIES), 2021.
    pdf

  • Beyond Individualized Recourse: Interpretable and Interactive Summaries of Actionable Recourses
    Kaivalya Rawal and Himabindu Lakkaraju.
    Advances in Neural Information Processing Systems (NeurIPS), 2020.
    pdf

  • Incorporating Interpretable Output Constraints in Bayesian Neural Networks
    Wanqian Yang, Lars Lorch, Moritz Gaule, Himabindu Lakkaraju, Finale Doshi-Velez.
    Advances in Neural Information Processing Systems (NeurIPS), 2020.
    Spotlight Presentation [Top 3%]
    pdf

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

  • 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.
    Oral Presentation
    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.
    Oral Presentation
    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.
    Oral Presentation
    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.
    Oral Presentation
    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 .
    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.
    Oral Presentation
    pdf

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

  • 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.
    Oral Presentation
    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.
    Oral Presentation
    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

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 working with the following core group of students, interns, postdocs, and research affiliates

  • Suraj Srinivas (Postdoc, Harvard University)
  • Jiaqi Ma (Postdoc, Harvard University)
  • Abhi Dubey (Research Scientist, Facebook AI Research; Research Affiliate, Harvard University)
  • Chirag Agarwal (Research Scientist, Adobe Research; Research Affiliate, Harvard University)
  • Satyapriya Krishna (PhD Student, Harvard University)
  • Tessa Han (PhD Student, Harvard University)
  • Dan Ley (PhD Student, Harvard University)
  • Usha Bhalla (PhD Student, Harvard University); Co-advised with Hanspeter Pfister
  • Alex Oesterling (PhD Student, Harvard University); Co-advised with Flavio Calmon
  • Paul Hamilton (PhD Student, Harvard University)
  • Dylan Slack (PhD Student, UC Irvine); Co-advised with Sameer Singh
  • Isha Puri (Undergrad, Harvard University)
  • Eshika Saxena (Undergrad, Harvard University)

  • Martin Pawelczyk (PhD Student, University of Tubingen; Research Fellow, Harvard University)
  • Umang Bhatt (PhD Student, University of Cambridge; Research Intern, Harvard University)
  • Davor Ljubenkov (Fullbright Scholar; Research Fellow, Harvard University)

Alumni (Past Advisees, Close Collaborators, and Visitors):

  • Chirag Agarwal (Postdoc, Harvard University --> Research Scientist, Adobe Research)
  • Alexis Ross (Undergraduate Student, Harvard University --> MIT EECS PhD, Winner of Hoopes Prize for Best Undergrad Thesis)
  • Kaivalya Rawal (Masters Student, Harvard University --> Fiddler AI)
  • Aditya Karan (Masters Student, Harvard University --> PhD Student, UIUC)
  • Jessica Dai (Undergraduate Student, Brown University --> UC Berkeley EECS PhD)
  • Ethan Kim (Undergraduate Student, Harvard University --> Cyndx)

  • Shahin Jabbari (CRCS Postdoctoral Fellow, Harvard University --> Assistant Professor, Drexel University)
  • Sophie Hilgard (PhD Student, Harvard University --> Research Scientist, Twitter)
  • Sushant Agarwal (Masters Student, University of Waterloo --> PhD Student, Northeastern University)

  • Harvineet Singh (PhD Student, New York University; Research Intern, Harvard University)
  • Elita Lobo (PhD Student, UMass Amherst; Research Intern, Harvard University)
  • Anna Meyer (PhD Student, University of Wisconsin; Research Intern, Harvard University)
  • Ruijiang Gao (PhD Student, University of Texas at Austin; Research Intern, Harvard University)
  • Vishwali Mhasawade (PhD Student, New York University; Research Intern, Harvard University)
  • Tom Suhr (PhD Student, Max Planck Institute; Research Fellow, Harvard University)