About
With advances in machine learning (ML), the field of computer vision and its applications are growing by leaps and bounds, triggering transformations across industries and in daily life. Computer Vision is an online program offered by the Executive Education division of Carnegie Mellon University’s School of Computer Science. It enables software developers, ML engineers, and technology professionals to expand their knowledge with computer vision and image processing skills to become truly future-ready.

Register for this informative webinar to learn more about the upcoming Computer Vision program from Carnegie Mellon University’s School of Computer Science Executive Education.
When
Mon, May 2, 2022 · 11:00 AM Eastern Time (US & Canada) (GMT -4:00)
Agenda
  • Get introduced to the pioneering faculty who created the course
  • Gain a deeper understanding of the topics covered in the program
  • Become more familiar with the learning experience
  • ...and so much more!
Presenters
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Kris Kitani
Associate Research Professor, Robotics Institute, School of Computer Science Courtesy Professor, Electrical and Computer Engineering Department, Carnegie Mellon University
Kris Kitani works in the areas of computer vision, machine learning and human-computer interaction. His research interests lie at the intersection of first-person vision, human activity modeling, and inverse reinforcement learning. His work has applications spanning personal and assistive robotics, surveillance and security, infrastructure, field robotics, and manufacturing. Kitani earned his Ph.D. and master’s degree in science from the University of Tokyo. He also has a bachelor’s degree in science from the University of Southern California.
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Ioannis Gkioulekas
Assistant Professor, Robotics Institute, Carnegie Mellon University
Ioannis Gkioulekas works on computational imaging — the process of forming images from measurements using algorithms that rely on a significant amount of computing. While imaging involves optics, sensors, and illumination, computation includes physics-based modeling and rendering, inverse algorithms, and learning. Gkioulekas’ research interests include imaging around walls or through skin, lightweight depth sensing, material acquisition, and adaptive imaging. He is also broadly interested in computer vision and computer graphics.

Gkioulekas earned his Ph.D. from the School of Engineering and Applied Sciences at Harvard University. He also has a Diploma in Electrical and Computer Engineering (five-year degree) from the National Technical University of Athens.
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Ram Konduru
Ram Konduru Director of Executive Education, School of Computer Science; Director of UDL Project, Institute for Software Research