About

Hello! My name is Zachary Novack, and I am currently a Computer Science PhD Candidate at UC San Diego, where I am advised by Prof. Julian McAuley and Prof. Taylor Berg-Kirkpatrick. Previously, I studied statistics and machine learning at Carnegie Mellon University, and was primarily advised by Prof. Zachary Lipton and Prof. Simon DeDeo.

I am actively on the job market and looking for industry, academic, and postdoc positions starting fall 2026!

As someone passionate about building Interactive Generative Audio/Music systems, my research primarily focuses on two main pillars of bringing the state of the art in audio generation to practical usability: Controllability and Efficiency. In particular, I have worked on extending generative audio systems with training-free (ICML 2024, Oral) and multimodal (ISMIR 2025) controls, how to accelerate both music (ICLR 2025, Spotlight) and general audio (WASPAA 2025) generation models, and how to make such interactive controls faster (ISMIR 2024).

Additionally, I’ve also collaborated extensively on researching mutli-modal text-audio reasoning, both on the modeling side, working on improving long-form audio retrieval (ICASSP 2025, Oral), as well as designing better evaluation benchmarks to accurately measure audio perception (ISMIR 2025, Best Paper Nominee).

In the past, I’ve worked on general multi-modal reasoning tasks (ICML 2023) and empirical deep optimization theory (ICLR 2023).

In my free time, I enjoy cooking, playing beach volleyball (a must in San Diego!), as well as teaching the front ensemble at 11-time world championship finalist POW Percussion Ensemble!

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