/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
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]