I work on making machine learning models more trustworthy — understanding what they memorize, whether that leaks private data, and how to evaluate them rigorously before deployment.
I study the connections between memorization, generalization, and privacy in deep neural networks, using input loss curvature as a lens for understanding how models behave differently on training versus unseen data. This work led to a black-box membership inference attack based on input loss curvature (NeurIPS 2024, Spotlight) and a follow-up study formally linking privacy, memorization, and curvature (ICML 2024). More recently, this line of work has extended into fast, formal memorization estimation (ICML 2025) and sample-gradient-based memorization proxies (ICLR 2026), alongside out-of-distribution detection methods (CVPR 2025) — framing memorization as a general-purpose diagnostic tool for both privacy auditing and dataset quality. I also work on differentially private generative modeling: DP-ImgSyn releases synthetic images with formal DP guarantees while remaining visually dissimilar to the private data they’re derived from, and DP-CARE (led by mentee Dimitris Karpontinis) applies differentially private fine-tuning to mental-health classification on social media text.
I build evaluation pipelines for large language models in low-resource domains, including Plutus, a benchmark for Greek-language financial NLP, and MultiFinBen, a multilingual and multimodal benchmark for financial LLMs. This extends to cross-modal generative augmentation for biological classification (led by mentee Hyunwoo Yoo, TMLR 2026), applying similar rigor to evaluating models on scientific and biomedical data.
Earlier work in my PhD focused on making deep learning more efficient and hardware-friendly: a mixed-precision quantization method that uses a multi-layer perceptron to predict layer-wise bit-widths, achieving up to 6x compression with no accuracy loss (IEEE Access 2021); a hybrid RRAM-SRAM system for energy-efficient, error-resilient in-memory computing (DATE 2022); and compressed-video action recognition pipelines using progressive knowledge distillation to significantly speed up inference while retaining accuracy.