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Flavio du Pin Calmon

Thomas D. Cabot Associate Professor of Electrical Engineering

I am the Thomas D. Cabot Associate Professor of Electrical Engineering at Harvard's John A. Paulson School of Engineering and Applied Sciences. Before joining Harvard I was a social good post-doctoral fellow at IBM Research in Yorktown Heights, New York. I received my Ph.D. in Electrical Engineering and Computer Science at MIT. My main research interests are information theory, signal processing, machine learning, and artificial intelligence.

I lead the Harvard Information Theory Laboratory. Check out my group's website for up-to-date information about research, announcements, members, and publications.

Research

Note for prospective students: I am recruiting Ph.D. students for Fall 2026. For more information about applying, please visit the Harvard SEAS admissions website.

My research group works on information theory, signal processing, machine learning, and artificial intelligence. We aim to develop foundational theory that guides the responsible design of ML and AI algorithms. To learn more about our work, check my group's website.

I consider myself a scientist who is an engineer at heart, and I enjoy doing fundamental research that serves as a design driver for practical applications. My research adopts the information-theoretic blueprint: for a given performance metric (e.g., reliability, safety, privacy), I ask not "How can a given system be made better?" but instead "What is the absolute best a system can be?" I use information theory to characterize fundamental performance limits of machine learning systems, and then design algorithms that try to achieve these limits.

As a Brazilian-American engineer, I am passionate about broadening the participation of students from all backgrounds, including from Latin-America, in information theory and machine learning. I am also excited about addressing data-driven challenges specific to the developing world. If this is something that interests you as well, please get in touch!

My research group is supported by the NSF and by generous gifts and awards from Google, IBM, Amazon, JPMorganChase, and Oracle Research.

Recent announcements

  • May, 2025 — Congratulations Lucas Monteiro Paes for defending his Ph.D.!

    Lucas will be joining Apple in June.

  • April, 2025 — Organized the AI in Brazil Panel Series.

    This series highlighted AI research and opportunities in Brazil. More information can be found here.

  • April, 2025 — Our paper "AI Alignment at Your Discretion" won the Best Paper Award at NENLP.

    The award announcement is here. Read the paper here.

  • January, 2025 — Named the Thomas D. Cabot Associate Professor of Electrical Engineering.

    Excited to start this new chapter as the Thomas D. Cabot Associate Professor of Electrical Engineering.

  • July, 2024 — James L. Massey Research & Teaching Award for Young Scholars.

    Honored to receive the 2024 James L. Massey Award given by the IEEE Information Theory Society. The award recognizes outstanding achievement in research and teaching by young scholars in the Information Theory community. The official announcement can be found here.

  • March, 2024 — Congratulations Lucas Monteiro Paes for becoming an Apple Scholar!

    The announcement can be found here.

  • September, 2023 — Two new NSF awards and a Harvard Brazil Lemann Research Award.

    We just started two exciting new projects sponsored by the NSF on approximate coded computing and information theory and differential privacy. We also kicked-off a project to study algorithmic discrimination in education sponsored by the Lemann Brazil Research Fund Award.

  • May, 2023 — Congratulations Dr. Hsiang Hsu and Dr. Wael Alghamdi on defending your Ph.Ds.!
  • September, 2022 — Symposium on Machine Learning and Information Theory at CNMAC 2022

    I organized a symposium at the 2022 Congresso Nacional de Matemática Aplicada e Computacional in Campinas, São Paulo, Brazil. The workshop was supported in part by a Harvard Brazil Lemann Research Award.

  • August, 2022 — Hsiang Hsu's research on predictive multiplicity and the Rashomon effect featured as the Meta Research PhD Fellowship Spotlight!
  • July, 2022 — Tutorial at ISIT 2022 on Information-Theoretic Tools for Responsible Machine Learning

    I organized a tutorial at ISIT 2022 on responsible ML together with Haewon Jeong, Shahab Asoodeh, and Mario Diaz. Slides can be found here!

  • May, 2022 — Congratulations Dr. Hao Wang on defending your Ph.D.!

    And congratulations for joining the MIT-IBM research lab in Cambridge! You can read Hao's Thesis here.

  • July, 2021 — Haewon's research featured at SEAS!

    Read more about Haewon Jeong's exciting work at the SEAS website.

  • June, 2021 — NSF-Amazon Fairness in AI Award.

    We just kicked-off the research sponsored by a recent NSF and Amazon FAI grant! Very grateful to the National Science Foundation and Amazon for supporting our work.

  • May, 2021 — Congratulations Hsiang Hsu for becoming a Meta Ph.D. Fellow!

    The announcement can be found here.

  • April, 2021 — Título de Honra ao Mérito da Universidade de Brasília.

    Extremely honored for receiving the inaugural Título de Honra ao Mérito (Honor to the Merit award) from the University of Brasilia (UnB), my alma mater. This new award recognizes alumni who have achieved national and/or international recognition in their field of study at UnB. I was the first alumni chosen for the award from the area of "technology and exact sciences" (i.e., engineering, math, statistics, and CS).

  • July, 2020 — Teaching commendation.

    Honored for receiving a commendation from the Harvard College's Dean of Undergraduate Education for "extraordinary teaching during extraordinary times." Signals & Communications (ES 156) is more important than ever!

  • June, 2020 — Amazon Research Award.

    Alexa, thank you for sponsoring our work with an Amazon Research Award!

  • September, 2019 — NSF Grants.

    I am once again grateful to National Science Foundation for supporting our work on privacy with an NSF Medium grant and in fair machine learning with an NSF Eager grant.

  • March, 2019 — Google Faculty Research Award.

    Ok Google, thank you so very much for your generous gift (see under "Machine Learning and Data Mining").

  • Dec, 2018 — NSF CAREER Award!

    I am very grateful for the support of the National Science Foundation for our research on information-theoretic foundations of fair machine learning (check out this GSAS feature). You can learn more details about the award at the Harvard SEAS website.

  • Nov, 2018 —IBM Open Collaborative Research Award.

    Thank you IBM!

  • April, 2018 —Lemann Brazil Research Fund Award.

    We are excited to organize a course on ML at FEEC/Unicamp in August 2019! You can find more details here.

    • Research Group

    For more information about my research group and alumni, check the Harvard Information Theory Lab website.

    Selected Publications

    An (almost) up-to-date list of publications can be found on Google Scholar.

    The list below includes a few selected recent publications and pre-prints.
  • AI Alignment at Your Discretion
    M. Buyl, H. Khalaf, C. M. Verdun, L. M. Paes, C. C. V. Machado, F. P. Calmon
    ACM FAccT 2025, Best Paper Award and NENLP
  • Correlated privacy mechanisms for differentially private distributed mean estimation
    S. Vithana, V. R. Cadambe, F. P. Calmon, H. Jeong
    2025 IEEE Conference on Secure and Trustworthy Machine Learning (SaTML)
  • Interpreting CLIP with Sparse Linear Concept Embeddings (SPLICE)
    U. Bhalla, A. Oesterling, S. Srinivas, F. P. Calmon, H. Lakkaraju
    Advances in Neural Information Processing Systems 37 (NeurIPS 2024)
  • Multi-Group Proportional Representation in Retrieval
    A. Oesterling, C. M. Verdun, A. Glynn, C. Long, L. Monteiro Paes, S. Vithana, M. Cardone, F. P. Calmon
    Advances in Neural Information Processing Systems 37 (NeurIPS 2024)
  • Aleatoric and epistemic discrimination: Fundamental limits of fairness interventions
    H. Wang, L. He, R. Gao, F. P. Calmon
    Advances in Neural Information Processing Systems 36 (NeurIPS 2023, Spotlight Presentation)
  • Individual arbitrariness and group fairness
    C. Long, H. Hsu, W. Alghamdi, F. P. Calmon
    Advances in Neural Information Processing Systems 36 (NeurIPS 2023, Spotlight Presentation)
  • Algorithmic Arbitrariness in Content Moderation
    J. F. Gomez, C. Machado, L. M. Paes, F. P. Calmon
    The 2024 ACM Conference on Fairness, Accountability, and Transparency
  • Generalization bounds for noisy iterative algorithms using properties of additive noise channels
    H. Wang, R. Gao, and F. P. Calmon
    Journal of Machine Learning Research.
  • Beyond Adult and COMPAS: Fair Multi-Class Prediction via Information Projection
    W. Alghamdi, H. Hsu, H. Jeong, H. Wang, P.W. Michalak, S. Asoodeh, and F. P. Calmon
    Advances in Neural Information Processing Systems 35 (NeurIPS 2022, Oral Presentation)
  • Rashomon Capacity: A Metric for Predictive Multiplicity in Probabilistic Classification
    H. Hsu and F. P. Calmon
    Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
  • On the Epistemic Limits of Personalized Prediction
    L.M. Paes, C.X. Long, B. Ustun, and F. P. Calmon
    Advances in Neural Information Processing Systems 35 (NeurIPS 2022)
  • Measuring Information from Moments
    W. Alghamdi and F.P. Calmon
    IEEE Trans. Inf. Theory, vol. 7, issue 2, pp. 763 - 802, 2024.
  • Fairness without imputation: A decision tree approach for fair prediction with missing values
    H. Jeong, H. Wang, and F.P. Calmon
    Proceedings of the AAAI Conference on Artificial Intelligence, vol. 36, issue 9, pp. 9558-9566, 2022 (Oral Presentation).
  • Cactus mechanisms: Optimal differential privacy mechanisms in the large-composition regime
    W. Alghamdi, S. Asoodeh, F. P. Calmon, O. Kosut, L. Sankar, and F. Wei
    2022 IEEE International Symposium on Information Theory (ISIT).
  • The Saddle-Point Method in Differential Privacy
    W. Alghamdi, S. Asoodeh, F. P. Calmon, J.F. Gomez, O. Kosut, L. Sankar
    2023 International Conference on Machine Learning (ICML).
  • Analyzing the generalization capability of SGLD using properties of gaussian channels
    H. Wang, Y. Huang, R. Gao, and F.P. Calmon
    Advances in Neural Information Processing Systems 34 (NeurIPS 2021), pp. 24222-24234, 2021
  • Generalizing Correspondence Analysis for Applications in Machine Learning
    H. Hsu, S. Salamatian, and F.P. Calmon
    IEEE Trans. on Pattern Matching and Machine Intelligence, vol. 44, issue 12, 2022.
  • Optimized Score Transformation for Consistent Fair Classification
    D. Wei, K. N. Ramamurthy, and F. P. Calmon
    Journal of Machine Learning Research, vol. 22(258), pp. 1 - 78, 2021.
  • To Split or Not to Split: The Impact of Disparate Treatment in Classification
    H. Wang, H. Hsu, M. Diaz, and F. P. Calmon
    IEEE Trans. Inf. Theory, vol. 67, no. 10, pp. 6733 - 6757, April 2021.
  • ε-Approximate Coded Matrix Multiplication Is Nearly Twice as Efficient as Exact Multiplication
    H. Jeong, A. Devulapalli, V.R. Cadambe, F.P. Calmon
    IEEE Journal on Selected Areas in Information Theory, vol. 2, no. 3, pp. 845-854, 2021
  • Predictive Multiplicity in Classification
    C. T. Marx, F. P. Calmon, and B. Ustun
    Int. Conf. on Machine Learning (ICML), 2020

    • Patents

    Teaching

    At Harvard, I have taught Information Theory (ES 250), Signals & Communications (ES 156), and the Seminar on Effective Research Practices & Academic Culture (CS 290).

    Contact

    Email: flavio (at) seas (dot) harvard (dot) edu

    Office:
    Science & Engineering Complex
    150 Western Ave
    Office 3.306
    Allston, MA 02134