PhD candidate in Computer Science at EPFL, Fellow at Harvard SEAS. BSc from Kyiv Mohyla Academy in Ukraine. Formerly an intern at Google, CERN. I study privacy, security, reliability, and broader societal harms of algorithmic systems.
Twitter: @hiddenmarkov. Email: first.last at epfl.ch
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.
Adversarial Robustness for Tabular Data through Cost and Utility Awareness
K. Kireev*, B. Kulynych*, C. Troncoso. To appear in NDSS 2023, 2023
Causal Prediction Can Induce Performative Stability
B. Kulynych. Short note at ICML 2022 Workshop on Spurious Correlations, Invariance, and Stability, 2022
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
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]
Questioning the Assumptions Behind Fairness Solutions
B. Overdorf*, B. Kulynych*, E. Balsa, C. Troncoso, S. Gurses. NeurIPS Critiquing and Correcting Trends in ML, 2018
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
zksk: A Library for Composable Zero-Knowledge Proofs
W. Lueks, B. Kulynych, J. Fasquelle, S. Le Bail-Collet, C. Troncoso. WPES, 2019
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