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Nidhi Hegde

Nidhi Hegde

Appointment

Canada CIFAR AI Chair

National Program Committee member

Pan-Canadian AI Strategy

Connect

University of Alberta

Google Scholar

About

Nidhi Hegde is a Canada CIFAR AI Chair and a fellow at Amii. She is an associate professor in the Department of Computing Science at the University of Alberta. 

Hegde’s research focuses on a fundamental understanding of privacy and fairness in machine learning algorithms and systems, and the design of privacy-preserving and fair algorithms. She is  interested in the generation of private synthetic data, privacy-preserving sequential sharing of data, the understanding of privacy in the context of sequential and non-independent data, and the understanding of potential tradeoffs between privacy and fairness.

Relevant Publications

  • Wang, B., & Hegde, N. (2019). Privacy-preserving q-learning with functional noise in continuous spaces. In Advances in Neural Information Processing Systems (pp. 11327-11337).

  • Mukhopadhyay, A., Hegde, N., & Lelarge, M. (2019). Asymptotics of Replication and Matching in Large Caching Systems. IEEE/ACM Transactions on Networking, 27(4), 1657-1668.

  • Cecchi, F., & Hegde, N. (2017). Adaptive active hypothesis testing under limited information. In Advances in Neural Information Processing Systems (pp. 4035-4043).

  • Banerjee, S., Hegde, N., & Massoulié, L. (2015). The price of privacy in untrusted recommender systems. IEEE Journal of Selected Topics in Signal Processing, 9(7), 1319-1331.

  • van de Ven, P. M., Hegde, N., Massoulié, L., & Salonidis, T. (2013). Optimal control of end-user energy storage. IEEE Transactions on Smart Grid, 4(2), 789-797.

Institution

Amii

University of Alberta

Department

Department of Computing Science

Education

  • PhD (Computer Science), University of Missouri
  • MSc (Computer Science), University of Missouri
  • BSc (Biochemistry), University of Alberta

Country

Canada

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