Seth Neel

Seth Neel

Assistant Professor of Business Administration

Assistant Professor of Business Administration

Technology and Operations Management
+1 (617) 495-4981
 
Seth Neel
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(617) 495-4981
Featured Work Publications Awards & Honors

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Seth Neel is an Assistant Professor housed in the Department of Technology and Operations Management (TOM) at HBS, and a Faculty Affiliate in Computer Science at SEAS. He is Principal Investigator of the Trustworthy AI Lab in Harvard's new D^3 Institute.

Professor Neel's primary academic interest is in responsible A.I., with a focus on red-teaming models to uncover practical privacy risks, developing efficient algorithms to remove the influence of user data on trained models, and training models that are consistent with notions like fairness or interpretability. His best known work develops fair algorithms that can accomodate very flexible definitions of protected subgroups while maintaining accuracy, and have been adopted and incorporated into the open source efforts of companies like IBM AI Research. He has been featured in Forbes, WIRED, Axios, and CNBC. 

Outside of research, he is also a co-founder of the energy data company Welligence for which he was named to the Forbes 30 under 30 list in 2019.

For more information about Professor Neel's work, see his personal website.
Featured Work
Preventing Fairness Gerrymandering: Auditing and Learning for Subgroup Fairness
We introduce a new family of fairness definitions that interpolate between statistical and individual notions of fairness, obtaining some of the best properties of each. We show that checking whether these notions are satisfied is computationally hard in the worst case, but give practical oracle-efficient algorithms for learning subject to these constraints, and confirm our findings with experiments.
A New Analysis of Differential Privacy’s Generalization Guarantees
We give a new proof of the “transfer theorem” underlying adaptive data analysis: that any mechanism for answering adaptively chosen statistical queries that is differentially private and sample-accurate is also accurate out-of-sample. Our new proof is elementary and gives structural insights that we expect will be useful elsewhere. We show: 1) that differential privacy ensures that the expectation of any query on the conditional distribution on datasets induced by the transcript of the interaction is close to its expectation on the data distribution, and 2) sample accuracy on its own ensures that any query answer produced by the mechanism is close to the expectation of the query on the conditional distribution. This second claim follows from a thought experiment in which we imagine that the dataset is resampled from the conditional distribution after the mechanism has committed to its answers. The transfer theorem then follows by summing these two bounds, and in particular, avoids the “monitor argument” used to derive high probability bounds in prior work. An upshot of our new proof technique is that the concrete bounds we obtain are substantially better than the best previously known bounds, even though the improvements are in the constants, rather than the asymptotics (which are known to be tight). As we show, our new bounds outperform the naive “sample-splitting” baseline at dramatically smaller dataset sizes comp
Descent-to-Delete: Gradient-Based Methods for Machine Unlearning
We study the data deletion problem for convex models. By leveraging techniques from convex optimization and reservoir sampling, we give the first data deletion algorithms that are able to handle an arbitrarily long sequence of adversarial updates while promising both per-deletion run-time and steady-state error that do not grow with the length of the update sequence. We also introduce several new conceptual distinctions: for example, we can ask that after a deletion, the entire state maintained by the optimization algorithm is statistically indistinguishable from the state that would have resulted had we retrained, or we can ask for the weaker condition that only the observable output is statistically indistinguishable from the observable output that would have resulted from retraining. We are able to give more efficient deletion algorithms under this weaker deletion criterion.
Journal Articles
Working Papers
Cases and Teaching Materials
Awards & Honors
Named to the 2019 Forbes 30 Under 30 list in the Energy category.
Recipient of National Science Foundation Graduate Research Fellowships, 2017—2020.
Winner of the 2022 DrivenData Labs U.S. Privacy-Enhancing Technologies (PETs) Prize Challenge, Phase 1: Concept Development, for “MusCAT (a multi-scale federated system for privacy-preserving pandemic risk prediction)” with Hyunghoon Cho, David Froelicher, Denis Loginov, David Wu, and Yun William Yu.
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