Bogdan Kulynych

/bogh-duhn koo-lin-ich/

I am a researcher studying privacy, security, reliability, and broader social implications of 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 be 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

Feel free to request the slides!

Selected Work

Algorithmic Accountability, Verification, and Reliability

Methods for obtaining practically relevant guarantees on (non)-stability, robustness, and reliability of predictive models, as well as auditing and verifying these properties.
ICLR (spotlight)'24 NDSS'23 NeurIPS'22 FAccT'20 Show details »

Statistical Inference for Responsiveness Verification
S. Cheon, M. Stewart, B. Kulynych, T. Weng, B. Ustun. Preprint, 2025
[arxiv]

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

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]

Adversarial for Good? How the Adversarial ML Community's Values Impede Socially Beneficial Uses of Attacks
K. Albert*, M. Delano*, B. Kulynych*, R. Shankar Siva Kumar*. ICML WS, 2021
[arxiv] [video] [twitter thread]

Protective Optimization Technologies
B. Overdorf*, B. Kulynych*, C. Troncoso, S. Gurses. FAccT, 2020
[arxiv] [video] [twitter thread]

Questioning the Assumptions Behind Fairness Solutions
B. Overdorf*, B. Kulynych*, E. Balsa, C. Troncoso, S. Gurses. NeurIPS WS, 2018
[arxiv]

Privacy-Preserving Learning and Statistics

I work on systems and methods that ensure practical privacy guarantees, as well as reliable and equitable evaluations of privacy in machine learning and statistics releases. I specifically focus on methods which provide guarantees or analyses in terms of operational privacy risks that are legally legible and interpretable.
NeurIPS'24 ICML'24 FAccT'23 PETS'22 Show details »

(ε,δ) Considered Harmful: Best Practices for Reporting Differential Privacy Guarantees
J. Gomez, B. Kulynych, G. Kaissis, J. Hayes, B. Balle, A. Honkela. Preprint, 2025
[arxiv]

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]

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

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

Algorithmic Systems in Healthcare

As a researcher at an academic medical center, I critically study the deployment of algorithmic systems in healthcare and clinical practice in collaboration with clinicians and medical informatics practitioners.
npj Digital Medicine'25 JMIR'25 MIE'24 Show details »

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, J. Raisaro. npj Digital Medicine, 2025
[medRxiv]

Finding consensus on trust in AI in health care: recommendations from a panel of international experts
Georg Starke, Felix Gille, Alberto Termine, Yves Saint James Aquino, Ricardo Chavarriaga, Andrea Ferrario, Janna Hastings, Karin Jongsma, Philipp Kellmeyer, Bogdan Kulynych, Emily Postan, Elise Racine, Derya Sahin, Paulina Tomaszewska, Karina Vold, Jamie Webb, Alessandro Facchini, Marcello Ienca. JMIR, 2025
[link]

Evaluating Synthetic Data Augmentation to Correct for Data Imbalance in Realistic Clinical Prediction Settings
N. Wahler, B. Kaabachi, B. Kulynych, J. Despraz, C. Simon, J. Raisaro. MIE, 2024
[link]

Participatory Assessment of Large Language Model Applications in an Academic Medical Center
G. Carra, B. Kulynych, F. Bastardot, D. Kaufmann, N. Boillat-Blanco, J. Raisaro. NeurIPS WS, 2024
[arxiv]

Media Coverage and Comments

Last update: July 2025