Faculty
Adam Dziedzic is a tenure-track faculty member at CISPA, where he co-leads the SprintML group with a research focus on Secure, Private, Robust, Interpretable, and Trustworthy Machine Learning. Before joining CISPA, he was a Postdoctoral Fellow at the Vector Institute and the University of Toronto, a member of the CleverHans Lab, advised by Prof. Nicolas Papernot. He earned his PhD in computer science at the University of Chicago, where he was advised by Prof. Sanjay Krishnan and worked on input and model compression for adaptive and robust neural networks.
International Conference on Machine Learning (ICML) Unlocking Post-hoc Dataset Inference with Synthetic Data
International Conference on Machine Learning (ICML) Efficient and Privacy-Preserving Soft Prompt Transfer for LLMs
ACM Conference on Computer and Communications Security (CCS) Secure Noise Sampling for Differentially Private Collaborative Learning
International Conference on Machine Learning (ICML) Privacy Attacks on Image AutoRegressive Models
International Conference on Learning Representations (ICLR) Precise Parameter Localization for Textual Generation in Diffusion Models
National Conference of the American Association for Artificial Intelligence (AAAI) Differentially Private Prototypes for Imbalanced Transfer Learning
International Conference on Learning Representations (ICLR) Captured by Captions: On Memorization and its Mitigation in CLIP Models
International Conference on Learning Representations (ICLR) Differentially Private Federated Learning with Time-Adaptive Privacy Spending
European Conference on Artificial Intelligence (ECAI) Efficient Model-Stealing Attacks Against Inductive Graph Neural Networks
NeurIPS-Workshop (NeurIPS-W) Auditing Empirical Privacy Protection for Adaptations of Large Language Models