Gregory Wornell

Gregory Wornell serves as the Sumitomo Electric Industries Professor in Engineering within MIT's Department of Electrical Engineering and Computer Science (EECS), part of the School of Engineering. He maintains key affiliations with the Computer Science and Artificial Intelligence Laboratory (CSAIL) and the Institute for Data, Systems, and Society (IDSS), while leading the Signals, Information, and Algorithms Laboratory in the Research Laboratory of Electronics (RLE).

Education:

• BASc from the University of British Columbia
• SM and PhD from MIT

His research program integrates theoretical foundations with practical systems across signal processing, information theory, and statistical inference. Current work explores architectures for sensing, learning, and communication systems alongside computational imaging, vision, and perception frameworks. Neuroscience applications form an emerging thread in his interdisciplinary approach, particularly regarding information processing in biological systems.

Analysis of recent publications (2021-2025) reveals three dominant trends: 1) Uncertainty quantification and calibration methods for machine learning systems, 2) Fairness frameworks for AI with uncertain sensitive attributes, and 3) Novel signal processing techniques for RF communications and acoustic imaging. His work consistently bridges information-theoretic principles with deep learning implementations.

Scientific awards: None specified in source materials.

Advising and grants: Source materials indicate active PhD supervision through publications with students like Shah, Shen, and Sattigeri, though formal advisee lists aren't provided. Research appears supported by MIT-IBM Watson AI Lab collaborations and institutional resources from RLE/CSAIL.

He directs the Signals, Information, and Algorithms Laboratory (SIAL), which focuses on developing mathematical frameworks for information extraction from complex systems. The lab maintains strong connections with MIT's wireless communications and computational imaging communities through RLE and CSAIL collaborations.