Bogdan Kulynych

/bogh-duhn koo-lin-ich/

PhD candidate in Computer Science at EPFL, incoming Fellow at Harvard SEAS. I study privacy, security, and broader societal harms of algorithmic systems.

Twitter: @hiddenmarkov. Email: first.last at epfl.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, stopping military action at the current stage means that the mass killings, summary executions, 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 atack 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.
• 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 you can try this curated list if you trust me.
• Host: Consider hosting Ukrainian scholars 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.

🇺🇦 Some resources about the context of the war in Ukraine and Russian colonialism:

• On the decolonial approach to Russian culture, and why it is not appropriate to give equal platform to Russians and Ukrainians in the context of the war.
• An explainer on whether this war is about denazification, and whether it is a conflict between the NATO and Russia.
• An explainer on the Azov regiment and movement.

Academic publications

What You See Is What You Get: Distributional Generalization for Algorithm Design in Deep Learning
B. Kulynych*, Yao-Yuan Yang*, Y. Yu, J. Blasiok, P. Nakkiran. Preprint, 2022
[arxiv] [twitter thread]

Disparate Vulnerability: On the Unfairness of Privacy Attacks against Machine Learning
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*, E. Balsa, 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.

Media Coverage and Comments

• Twitter algorithmic bias challenge: The Guardian, Wired, The Verge, CNET
• Premise app safety concerns amidst Ukraine war: The Wall Street Journal

Latest Academic Service

• Reviewer, NeurIPS 2022
• Ethics reviewer, NeurIPS 2022
• Reviewer, FAccT 2022
• Ethics reviewer, NeurIPS 2021
• Reviewer, FAccT CRACT Track 2021
• Advisory board member, LSE Justice, Equity, and Technology Table (JETT)
• Co-organizer, Participatory Approaches to Machine Learning Workshop at ICML 2020

Last update: June 2022