About

Hello! My name is Zachary Novack, and I am currently a 2nd Year Computer Science PhD Student 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.

My research interests primarily lie in Controllable Audio and Music Generation. I am interested in making generative music systems both controllable and interactive, particularly in investigating training-free control for generative music models (ICML 2024). Additionally, a high level goal of mine is to leverage AI systems to help real musicians understand, learn, and perform music more effectively.

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 making experimental computer music, writing about scientific issues in society, and cooking!


Updates

May 2024: Our work on training-free editing and control for text-to-music diffusion models is accepted at ICML 2024 in Vienna, and our work on unsupervised lead sheet generation is accepted at the AES Symposium for AI and the Musician in Boston!
January 2024: Our work on training-free editing and control for text-to-music diffusion models is out on arxiv!
October 2023: Our work on unsupervised lead sheet generation is out on arxiv!
June 2023: Started Research Scientist internship with Nicholas Bryan at the Adobe Research Audio Group!
April 2023: Our work on augmenting CLIP zero-shot inference with hierarchical label sets was accepted to ICML 2023 in Honolulu, Hawaii!
March 2023: Our work on augmenting CLIP zero-shot inference with hierarchical label sets was accepted to the ICLR 2023 1st Workshop on Multimodal Representation Learning!
January 2023: Our work on understanding implicit regularization mechanisms in SGD was accepted to ICLR 2023 in Kigali, Rwanda!
December 2022: Our work on understanding implicit regularization mechanisms in SGD got accepted to the NeurIPS 2022 Workshop on the Benefits of Higher Order Optimization in Machine Learning (HOO-ML), as a Spotlight and won Best Poster!
September 2022: Began CS PhD at UCSD!
May 2022: Submitted senior thesis on modeling social media addiction on Twitter to CMU Kilthub.
May 2022: Graduated from CMU with B.S. in Statistics & Machine Learning, and a minor in Sonic Arts!