Michael Carbin

Michael Carbin is an associate professor in MIT’s Department of Electrical Engineering and Computer Science and a principal investigator at the Computer Science and Artificial Intelligence Laboratory, where he leads the Programming Systems Group. His group investigates the semantics, design, and implementation of systems that operate in the presence of uncertainty in their environment (perception), implementation (neural networks or approximate transformations), or execution (unreliable hardware). Carbin has received a Sloan Research Fellowship, a Facebook Research Award, a Google Faculty Research Award and an NSF Career Award. He earned a BS in computer science at Stanford University and an MS and PhD in electrical engineering and computer science from MIT.

Publications

• Baudart, G., Mandel, L., Atkinson, E., Sherman, B., Pouzet, M., Carbin, M. (2020). Reactive Probabilistic Programming. Programming Language Design and Implementation (PLDI).
• Lew, A. K., Towner, M. F. C., Sherman, B., Carbin, M., Mansinghka, V. K. (2020). A Type System and Semantics for Sound Programmable Inference in Probabilistic Languages. Principles of Programming Languages (POPL).
• Renda, A., Frankle, J., Carbin, M. (2020). Comparing Fine-tuning and Rewinding in Neural Network Pruning. International Conference on Learning Representations (ICLR).
• Mendis, C., Renda, A., Amarasinghe, S., Carbin, M. (2019). Ithemal: Accurate, Portable, and Fast Basic Block Throughput Estimation using Deep Neural Networks. International Conference on Machine Learning (ICML).
• Frankle, J., Carbin, M. (2019). The Lottery Ticket Hypothesis: Finding Small, Trainable Neural Networks. International Conference on Learning Representations (ICLR).

Media

• January 6, 2020: MIT News, Tool predicts how fast code will run on a chip.
• November 27, 2019: MIT News, School of Engineering third quarter 2019 awards.
• September 16, 2019: MIT News, What a little more computing power can do.
• May 6, 2019: MIT News, Smarter training of neural networks.