Speakers
Rachel Manzelli, Staff Machine Learning Engineer, Modulate
Rachel Manzelli is a Staff Machine Learning Engineer at Modulate. Her work focuses on leading the technical direction & development of audio-native, cost-effective machine learning models that support Modulate's Platform, ToxMod, and other product lines. Rachel holds almost a decade of machine learning research experience, alongside several peer-reviewed publications at the intersection of machine learning and audio. Her work has previously been featured at top venues such as NeurIPS, ICML and ISMIR. Rachel is passionate about creating spaces for researchers to share ideas; she loves to help organize audio-related events at major machine learning conferences, including workshops at NeurIPS ("Machine Learning for Audio" in 2023) and ICML ("AI Heard That!" in 2025, "Machine Learning for Audio Synthesis" in 2022). She regularly brings Modulate's researchers to machine learning conferences & events to share and discover cutting edge research. Before Modulate, Rachel worked at Macro (acq.) as a machine learning engineer, where she developed audio source separation models used in remote coworking spaces. She earned her B.S. in Computer Engineering from Boston University in 2019.