Wei-Ning Chen (陳偉寧)

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I am a final year Electrical Engineering PhD student at Stanford University advised by Ayfer Özgür and supported by a 3-year Stanford Graduate Fellowhip (SGF). My research focuses on information theory, statistics and theoretical machine learning, with applications to differential privacy and federated learning/ anlytics. Before joining Stanford, I obtained a master degree in Electrical Engineering from National Taiwan University. Prior to that, I received bachelor's degrees in Electric Engineering and Mathematics from the same university in 2016.

In 2023 summer and Fall, I was a student researcher at Google working on copyright protection for language models. In 2022 summer, I worked as a research intern at Meta, hosted by Graham Cormode and Akash Bharadwaj. Prior to that, I spent 2021 summer and fall at Google as a research intern, hosted by Peter Kairouz.

Outside lab, I play volleyball and badminton and enjoy all kinds of outdoor activities. Recently, I start photographing.

I will be on the job market starting in Fall 2024 and am looking for research positions in industry or academia. Feel free to reach out!

Research Interest

I am broadly interested in information-theoretic and algorithmic aspects of data science, and my current focus lies on differential privacy and federated learning/analytics. My research adopts tools from information theory, theoretical machine learning and high-dimensional statistics.


Email: wnchen [at] stanford [dot] edu

Packard Building, Room No. 201
350 Serra Mall
Stanford, CA 94305


  • [Mar. 2024] I will give a (invited) talk at IZS 2024 in Zurich!

  • [Jan. 2024] A paper accepted to AISTATS 2024!

  • [Dec. 2023] I will present three posters in NeurIPS at New Orleans.

  • [Oct. 2023] I will present a (invited) poster in Asilomar Conference at Monterey.

  • [Sept. 2023] I will present four posters TPDP 2023 at Boston.

  • [Sept. 2023] Three papers accepted to NeurIPS 2023!

Invited Talks

  • “Achieving Joint Privacy and Communication Efficiency in Federated Learning and Analytics,” Conference on Information Sciences and Systems (CISS), March 2023.

  • “On the Optimal Communication Cost in Private Federated Learning and Analytics,” Information Theory and Data Science Workshop (National University of Singapore), January 2023.

  • “On the Optimal Communication Cost in Private Federated Learning and Analytics,” National Taiwan University, December 2022.

  • “On the Optimal Communication Cost in Private Federated Learning,” at ITW invited talk (student research), October 2022.

  • “The Communication Cost of Security and Privacy in Federated Frequency Estimation,” at Google Research, September 2022.

  • “The Poisson Binomial Mechanism for Unbiased Federated Learning with Secure Aggregation,” at Meta RAI, July 2022.

  • “Fundamental Limits of Distributed Estimation under Information Constraints: beyond the Worst-case Analysis,” at Google Research, September 2021.

  • “Distributed Estimation under Information Constraints,” at National Taiwan University, December 2020.

Professional Activities

  • Reviewer for ICLR 2021, ITCS 2021, ICML 2021-2023, AISTATS 2022-2023, ALT 2021, NeurIPS 2021-2022, ISIT 2021-2023, WCNC 2022, ICC 2018

  • Area chair for ICML 2021 ITR3 workshop

  • Reviewer for IEEE Transactions on Knowledge and Data Engineering, IEEE Transactions on Information Theory