Hey, I’m Avi! I’m currently a masters student at New York University, after recently graduating from UC Berkeley where I double majored in Computer Science and Applied Mathematics. I’m broadly interested in both machine learning applications for computer security, and security/robustness of machine learning models themselves. Right now, I’m particularly focused on adversarial attacks on language models, prompt injection defenses for vision-language models, and the effects of training data poisoning on model robustness.

Research

My latest paper, Stronger Universal and Transfer Attacks by Suppressing Refusals, has been accepted to the NeurIPS 2024 SafeGenAI Workshop, as well as the NAACL 2025 main conference, where I delivered a poster presentation!

Recent projects include SSH anomaly detection using keystroke inter-arrival times, creating universal transfer attacks on black-box language models, and adversarial curriculum learning for bus-bunching mitigation.

I’m extremely fortunate for the opportunity to conduct research with amazing mentors Julien Piet and Chawin Sitawarin at Berkeley as a part of David Wagner’s group. See my research page for more information.

Other

This summer, I’m interning at Adobe as a Machine Learning Engineer. Previously, I’ve worked at Millennium Management as a quantitative research intern for two summers, where I assisted with data pipeline development, created a ML-based anomaly detection tool for market data, and analyzed trading signals. I have also worked under Kalyan Veeramachaneni as a member of MIT’s Data to AI Lab, where I built an open source tool to fit ML pipelines for daily anomaly detection on publicly available time-series datasets.

In my free time, I enjoy reading (sociology and fantasy!), weightlifting, running, photography, and playing the piano. Feel free to reach out if you’d like to connect on anything!