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Title Postdoctoral Fellow - Trustworthy AI Lab - Digital, Data, and Design Institute, Harvard University

School Harvard Business School

Department/Area

Position Description

The Digital, Data, and Design (D^3) Institute at Harvard is accepting applications for multiple postdoctoral fellows for academic year 2024-2025 to work on research activities at our research labs. D^3 conducts research at the intersection of academia and practice. For more information on D^3, please visit https://d3.harvard.edu/labs.

The Postdoctoral Fellows will work under the direct supervision of faculty Principal Investigators and the Senior Director of Labs. They will work closely with the lab manager and research associate(s) at 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 Trustworthy AI Lab, led by HBS Professors Hima Lakkaraju, Marco Iansiti, and 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.

Theory of Differential Privacy. The selected candidate will be expected to lead research in privacy-preserving data analysis/machine learning that is motivated by practice but has a strong theoretical underpinning. A background and strong interest in differential privacy required. Potential projects could concern private linear regression and related problems, the connection between differential privacy and properties like generalization and replicability, and various relaxations or alternative privacy notions. Successful applicants will be strong technically as well as have an inclination towards real-world problems. We are looking for applicants with demonstrably strong research skills, ideally, with publications in top venues in machine learning or theoretical CS — although this is not a hard requirement (e.g., ICML , NeurIPS, ICLR , STOC , FOCS , ALT , COLT ).

Basic Qualifications

• A Ph.D. or equivalent degree in computer science, statistics, or a closely related field.

• If you have obtained your Ph.D. in the past 12 months you must be able to provide a certificate of completion from the degree-granting institution OR a letter from the institute’s registrar stating all requirements for the degree have been successfully completed and should verify the date the degree has been conferred. No exceptions.

  Additional Qualifications

• Experience implementing DP algorithms and machine learning models in Python preferred.

  Special Instructions

  Application Details:

Applications will be accepted until the position is filled. Please apply here via the Harvard system.

Please do not contact lab faculty; if you have any questions, please contact d3@harvard.edu.

All applications must include the following:

• Curriculum vitae

• Please include a Link to Github account and/or any software developed

• Copy of academic records (official records are acceptable)

• 2-page research statement describing prior research experience and future research plans

• Two representative publications (preprints are acceptable)

• Two letters of recommendation.

Candidates may be asked to undergo an assessment as part of the interview process.

Additional Information:

These are term positions starting July 2024 (start date flexible) through June 30, 2025, with the possibility of renewal based on funding and performance. Relocation funding not provided.

This role is offered as a hybrid (some combination of onsite and remote) where you are required to be onsite at our Boston, MA based campus. Specific days and schedule will be determined between you and your manager.

Please note that we will be conducting interviews virtually (phone and/or Zoom) for selected candidates.

Culture of Inclusion: The work and well-being of HBS is profoundly strengthened by the diversity of our network and our differences in background, culture, national origin, religion, sexual orientation, and life experiences. Explore HBS work culture at https://www.hbs.edu/employment/.

Commitment to Equity, Diversity, Inclusion, and Belonging

Harvard University views equity, diversity, inclusion, and belonging as the pathway to achieving inclusive excellence and fostering a campus culture where everyone can thrive. We strive to create a community that draws upon the widest possible pool of talent to unify excellence and diversity while fully embracing individuals from varied backgrounds, cultures, races, identities, life experiences, perspectives, beliefs, and values.

EEO Statement

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions, or any other characteristic protected by law.

Contact Information

Becky Wickel, Recruiting Manager, Research Staff Services

Harvard Business School

Soldiers Field Road

Boston, MA 02163

Contact Email rwickel@hbs.edu

Equal Opportunity Employer

We are an equal opportunity employer and all qualified applicants will receive consideration for employment without regard to race, color, religion, sex, national origin, disability status, protected veteran status, gender identity, sexual orientation, pregnancy and pregnancy-related conditions or any other characteristic protected by law.

Minimum Number of References Required 2

Maximum Number of References Allowed 3

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