Michael Carbin

Michael Carbin is an Associate Professor at the Massachusetts Institute of Technology in the Department of Electrical Engineering and Computer Science, where he leads the Programming Systems Group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research spans programming languages, software engineering, systems, and security with a focus on language-driven systems that operate in uncertain environments including perception, neural networks, and unreliable hardware.

Carbin's research interests center on programming languages and systems that operate effectively in uncertain environments. His work spans probabilistic programming, quantum computing, neural network optimization, and approximate computing. He investigates how to design language-driven systems that can handle uncertainty in perception, implementation (neural networks or approximate transformations), and execution (unreliable hardware). His research bridges theoretical foundations with practical systems implementation, often developing novel programming models, compilers, and runtime systems.

His publication record shows consistent contributions across multiple domains including programming languages (PLDI, POPL), machine learning (ICLR, NeurIPS), and systems (ASPLOS, OSDI). Recent work focuses on neural network pruning, quantum programming, probabilistic inference, and compiling programs to neural networks. His research demonstrates strong interdisciplinary connections between programming languages, machine learning, and systems.

Carbin has received numerous prestigious awards including the Sloan Research Fellowship (2020), NSF CAREER Award (2018), and multiple best paper awards at top conferences including ICLR (2019), OOPSLA (2014, 2013), and ICFP (2019). He has also been recognized with the MIT Frank E. Perkins Award for Excellence in Graduate Advising (2020).

Carbin actively serves the academic community as a program committee member for major conferences including MLSys (as Program Co-Chair in 2023), POPL, PLDI, and NeurIPS. He has advised numerous graduate students who have gone on to positions at leading technology companies and research institutions. His Programming Systems Group at CSAIL focuses on creating robust, efficient systems that can handle uncertainty across multiple dimensions of computing.