Bogdan Kulynych

/bogh-duhn koo-lin-ich/

I am a researcher studying privacy, security, reliability, and broader social implications of AI and algorithmic systems. I am currently a research scientist at the Lausanne University Hospital (Switzerland) in the Clinical Data Science group. I hold a Ph.D. in Computer Science from EPFL (Switzerland) where I was advised by Carmela Troncoso, and a B.Sc. in Applied Mathematics from Kyiv Mohyla Academy (Ukraine). Before that, I had the opportunity to work as a visiting fellow at Harvard University with Flavio du Pin Calmon, and intern at Google and CERN.

Bluesky: @bogdankulynych
Twitter: @hiddenmarkov (not that active anymore)
Email: [first name] at kulyny.ch

Latest and Upcoming Talks

• Dec 17, 2024 (coming up!) Attack-Aware Noise Calibration for Differential Privacy
  Harvard Privacy Tools Seminar (Harvard SEAS @ 11am).
  
• Dec 12, 2024 Attack-Aware Noise Calibration for Differential Privacy
  NeurIPS. Poster presentation (Evening West session).
  
• Nov 18, 2024 Attack-Aware Noise Calibration for Differential Privacy
  Imperial College London (Yves-Alexandre de Montjoye's group).
  
• Sep 20, 2024 Constraining Risks and the Constraints of Risk Limitation in Private Machine Learning
  University of Toronto (Nicolas Papernot's group).
  
• Jul 04, 2024 Attack-Aware Noise Calibration for Differential Privacy
  Microsoft Research.
  

Selected Publications

A Scoping Review of Privacy and Utility Metrics in Medical Synthetic Data
B. Kaabachi, J. Despraz, T. Meurers, K. Otte, M. Halilovic, B. Kulynych, F. Prasser, JL Raisaro. npj Digital Medicine. In press, 2024
[medRxiv]

Attack-Aware Noise Calibration for Differential Privacy
B. Kulynych*, J. Gomez*, G. Kaissis, F. Calmon, C. Troncoso. NeurIPS, 2024
[arxiv] [twitter thread]

The Fundamental Limits of Least-Privilege Learning
T. Stadler*, B. Kulynych*, N. Papernot, M. Gastpar, C. Troncoso. ICML, 2024
[arxiv]

Prediction without Preclusion: Recourse Verification with Reachable Sets
A. Kothari*, B. Kulynych*, T. Weng, B. Ustun. ICLR (spotlight), 2024
[arxiv]

Arbitrary Decisions are a Hidden Cost of Differentially Private Training
B. Kulynych, H. Hsu, C. Troncoso, F. Calmon. FAccT, 2023
[arxiv] [twitter thread]

Adversarial Robustness for Tabular Data through Cost and Utility Awareness
K. Kireev*, B. Kulynych*, C. Troncoso. NDSS, 2023
[arxiv] [twitter thread]

What You See is What You Get: Principled Deep Learning via Distributional Generalization
B. Kulynych*, Yao-Yuan Yang*, Y. Yu, J. Blasiok, P. Nakkiran. NeurIPS, 2022
[arxiv] [video] [twitter thread]

Disparate Vulnerability to Membership Inference Attacks
B. Kulynych, M. Yaghini, G. Cherubin, M. Veale, C. Troncoso. PETS, 2022
[arxiv] [twitter thread]

* denotes equal contribution.

Media Coverage and Comments

• Twitter algorithmic bias challenge: The Guardian, Wired, The Verge, CNET
• Premise app safety concerns in the beginning of 2022 Russia's invasion of Ukraine: The Wall Street Journal

Latest Academic Service

• ICML 2023 – 2024. Reviewer
• NeurIPS 2022 – 2024. Reviewer, ethics reviewer
• FAccT 2022 – 2023. Program committee member
• IEEE Journal on Selected Areas in Information Theory 2023. Reviewer
• The Web Conference 2023 FATE. Program committee member
• NeurIPS 2021 – 2022. Ethics reviewer
• LSE Justice, Equity, and Technology Table (JETT). Advisory board member
• GenBench 2024. Reviewer
• Participatory Approaches to Machine Learning Workshop at ICML 2020. Co-organizer
• Workshop reviewing: ICLR 2023 Trustworthy ML, FAccT CRAFT Track 2021, NeurIPS 2021 Privacy in Machine Learning Workshop, NeurIPS 2021 I Can’t Believe It’s Not Better! (ICBINB), ICLR 2020 Towards Trustworthy ML

Last update: December 2024