Paris Smaragdis Joins MIT Faculty in 2025
The audio research expert brings his machine-learning expertise to a new group in Cambridge.
Updated on Oct. 28, 2026 in Language Learning
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In 2025, Professor Paris Smaragdis joined the faculty at MIT to lead a research group dedicated to machine-learning systems for audio analysis. His team explores innovative algorithms that process sound without relying on traditional fixed sampling rates.
Why it matters
Machine learning offers significant advantages for sound understanding that conventional signal processing methods have historically struggled to achieve.
Smaragdis previously spent 15 years as faculty at the University of Illinois at Urbana-Champaign before joining MIT in 2025. His background includes creating an audio research team at Adobe and working on AI systems for video soundtrack analysis.
The players
Paris Smaragdis
An expert in audio processing who leads an MIT research group focused on machine learning for sound analysis.
MIT Media Lab
An interdisciplinary research laboratory at the Massachusetts Institute of Technology where Smaragdis completed his graduate studies.
Barry Vercoe
A prominent figure in computer music who passed away in 2025.
The details
The research group utilizes advanced machine-learning techniques to teach computers how to better interpret and understand complex audio environments. Before his arrival in Cambridge, Smaragdis held notable positions at Mitsubishi Electric Research Labs and previously earned both his master's degree and PhD from the MIT Media Lab.
Timeline
Paris Smaragdis joined the MIT faculty in 2025.
Barry Vercoe passed away in 2025.
Smaragdis contributed to The Beatles: Get Back in 2021.
He won an Emmy Award for sound editing in 2022.
Culture Shift
The research conducted by Smaragdis extends the foundational work in audio analysis previously established at the MIT Media Lab.
While the research is highly technical, the development of smarter audio analysis tools could eventually improve consumer audio technology and accessibility features. Residents of Cambridge may also notice a revival of local interest in niche activities, as Smaragdis plans to lead the MIT underwater hockey club.
The takeaway
The move reflects a growing trend of industry-experienced experts transitioning back into academia to bridge the gap between AI theory and practical audio applications. The integration of advanced machine learning into sound processing marks a departure from rigid traditional methods.
Further reading
Learn more about advancements in audio and communication research on our Language Learning page.
Live Poll
Do you believe developing machines with human-like hearing is a positive step for artificial intelligence advancement?
