Career Center

The Digital, Data, and Design (D^3) Institute at Harvard is accepting applications for multiple postdoctoral fellows to work on research activities at our newly formed research labs. D^3 will have a formal launch in July 2022 with 10 newly minted labs that will provide cutting edge research faced by academics and practitioners. For more information on D^3, please visit https://d3.harvard.edu.

The postdoctoral fellows will work under the direct supervision of faculty Principal Investigators and Lab Manager of each lab. D^3 is looking for candidates with diverse backgrounds and/or new perspectives. There are no teaching requirements for these open positions.

The Secure and Fair Machine Learning (SAFR ML) Lab, led by HBS Professor Seth Neel and Harvard SEAS Professor Salil Vadhan, is seeking a Postdoctoral Fellow. The lab focuses on developing algorithms that allow data science practitioners to trade-off ethical considerations like privacy, interpretability, and bias with accuracy, and to mitigate the risks of overfitting. Recent works on fairness have included new definitions of statistical fairness that account for a more complex protected group structure or a more flexible notion of similarity, new algorithms for efficiently deleting user data from neural networks, the SOTA bounds for adaptive data analysis, and new techniques for differentially private optimization. Ensuring privacy and fairness in large-scale genomic analyses is a new research interest.

The successful candidates will work on projects that could include (i) private release of aggregate genomic data, (ii) data deletion & machine unlearning (iii) privacy risks in explainable models (iv) fair or interpretable machine learning. The successful candidates will leverage their strong theoretical and computational background and communication skills to engage in all stages of the research, including
the design, theoretical analysis, implementation, evaluation, and demonstration on real-world datasets.

The ideal candidate will have:

· Demonstrably strong research skills, ideally with publications in top venues in machine learning, artificial intelligence, or sister conferences (e.g., ICML, NeurIPS, ICLR, KDD, AAAI, IJCAI, UAI, FAccT, AIES, AIStat, ACMEC, WINE), and/or top-tier interdisciplinary journals (e.g., Nature family of journals, PNAS, Science).

· Prior research experience related to privacy/security or algorithmic fairness

This is a term position through June 30, 2023, with the strong possibility of renewal based on funding and performance. Relocation funding not provided.

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