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 postdoctoral fellow at the Lausanne University Hospital (Switzerland) in the Clinical Data Science group. I hold a Ph.D. in Computer Science from EPFL (Switzerland), 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, and intern at Google and CERN.

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

🇺🇦 Since February 2022, my home country, Ukraine, has been suffering from a brutal full-scale Russian military invasion, which brought enormous devastation to everyone in Ukraine. Some of the ways you can help:

Advocacy: No peace without justice. Many people believe that pursuing "peace at all costs" is the right course of action in Ukraine. While appearing moral, halting military action at the current stage means that the mass killings, summary executions, rape and torture, deportations, arbitrary violence and detention, looting of people's belongings and cultural heritage in the occupied territories will continue with impunity. Not only "peace at all costs" is a call for subjugation in disguise, but it cannot stop even the military action in the long term. After a pause, the attack is bound to resume with fresh forces unless the mainstream imperialist chauvinist ideology loses its cultural and political domination in Russia. If you have political representation in one of the countries that are capable of providing assistance to Ukraine, please call for aid and sanctions that would help Ukraine to achieve not only peace but justice — at the very least, the liberation of the occupied territories — for as long as Ukrainians are willing to fight for it.

Host scholars and students. Consider hosting Ukrainian scholars and students at your institutions through, e.g., Science for Ukraine.

Platform Ukrainian voices. If you hold events or curate publications on Ukraine, make sure to invite Ukrainians and experts on Ukraine, not Poland or Russia. If you are looking for Ukrainian academics who could comment on the war taking into account the technological aspects, I have compiled a list for you.

Donate to vetted local organizations on the ground. The majority of humanitarian work in Ukraine is done by local organizations, yet they receive only 0.003% of all humanitarian contributions. Reluctance to donate to unfamiliar organizations is understandable, but I encourage you to try this curated list of local organizations: standforukraine.com See more resources here.

Latest and Upcoming Talks

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. Preprint, 2024
[medRxiv]

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

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]

Exploring Data Pipelines through the Process Lens: a Reference Model for Computer Vision
A. Balayn, B. Kulynych, S. Gürses. CVPR "Beyond Fair Computer Vision" Workshop, 2021
[arxiv]

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 "A Blessing in Disguise: The Prospects and Perils of Adversarial Machine Learning" Workshop, 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 Critiquing and Correcting Trends in ML, 2018
[arxiv]

Evading Classifiers in Discrete Domains with Provable Optimality Guarantees
B. Kulynych, J. Hayes, N. Samarin, C. Troncoso. NeurIPS Workshop on Security and Privacy in ML, 2018
[arxiv]

zksk: A Library for Composable Zero-Knowledge Proofs
W. Lueks, B. Kulynych, J. Fasquelle, S. Le Bail-Collet, C. Troncoso. WPES, 2019
[arxiv]

ClaimChain: Improving the Security and Privacy of In-band Key Distribution for Messaging
B. Kulynych*, M. Isaakidis*, Wouter Lueks, George Danezis, Carmela Troncoso. WPES, 2018
[arxiv]

* denotes equal contribution.

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Last update: September 2024