Darrell Coles is a senior research geophysicist at Schlumberger. His research efforts have been in optimal experimental design for industrial-scale geoscientific applications, inverse and optimization theory, uncertainty characterization and control, all in the context of seismic data acquisition and analysis. He has obtained several additional patents and written several peer-reviewed publications since joining Schlumberger and is currently branching into commercial software development and data science.
F. Dale Morgan is a Professor of Geophysics at the Massachusetts Institute of Technology in the Department of Earth, Atmospheric, and Planetary Sciences and associated with the Earth Resources Laboratory. His current interests are in rock physics, geoelectromagnetism, applied seismology, inverse theory, environmental and engineering geophysics, electrochemistry, and electronic instrumentation.
Wojciech Matusik
Lead Instructor
Professor Wojciech Matusik is an Associate Professor of Electrical Engineering and Computer Science at the Computer Science and Artificial Intelligence Laboratory at MIT, where he leads the Computational Fabrication Group. His research interests are in direct digital manufacturing and computer graphics.
John R. Williams
Lead Instructor
John R. Williams is a Professor of Information Engineering and Civil and Environmental Engineering at MIT. His area of specialty is large scale computer analysis applied to both physical systems and to information.
Stefanie Jegelka
Lead Instructor
Stefanie Jegelka is an X-Consortium Career Development Associate Professor in the Department of Electrical Engineering and Computer Science at MIT, where she is a member of CSAIL, and affiliated with IDSS.
Tommi S. Jaakkola
Tommi Jaakkola is a Thomas Siebel Professor of Electrical Engineering and Computer Science and the Institute for Data, Systems, and Society. He is also a member of the Computer Science and Artificial Intelligence Laboratory. His work pertains to inferential, algorithmic and estimation questions in machine learning, including large scale probabilistic distributed inference, deep learning, and causal inference.
Regina Barzilay is a School of Engineering Distinguished Professor for AI and Health in the Department of Electrical Engineering and Computer Science and a member of the Computer Science and Artificial Intelligence Laboratory at the Massachusetts Institute of Technology.
Examine how the latest tools, techniques, and algorithms driving modern and predictive analysis can be applied to produce powerful results, even when using unstructured data. In this highly interactive course, you’ll gain insights into what kinds of problems these methods can and cannot solve, how they can be applied effectively, and what issues are likely to arise in practical applications, particularly in the healthcare field.
Autonomous robots. Self-driving cars. Smart refrigerators. Now embedded in countless applications, deep learning provides unparalleled accuracy relative to previous AI approaches. Yet, cutting through computational complexity and developing custom hardware to support deep learning can prove challenging for many enterprises—and the cost of getting it wrong can be catastrophic. Do you have the advanced knowledge you need to keep pace in the deep learning revolution?
Over the past eight years, the amount of computing required to run these neural nets has increased over a hundred thousand times, which has become a significant challenge. Gain a deeper understanding of key design considerations for deep learning systems deployed in your hardware.
Vivienne Sze
Participating Instructor
Vivienne Sze is an associate professor in MIT’s Department of Electrical Engineering and Computer Science and leads the Research Lab of Electronics’ Energy-Efficient Multimedia Systems research group. Her group works on computing systems that enable energy-efficient machine learning, computer vision, and video compression/processing for a wide range of applications, including autonomous navigation, digital health, and the internet of things.