Contact
Assistant
Sustainability is a broad and popular topic. Renewable energy; energy transition; recycling and the circular economy; climate and environment; water and food – these topics are quickly maturing into fields of their own. But what is next for sustainability? What lies beyond what we now consider sustainable technologies and business practices, and how will they affect your industry? What does emerging government policy suggest will be the hot sustainability topics of the future? Join MIT faculty, researchers, and startups as we review core topics like energy and climate, but also explore new ones, like digital sustainability, sustainability for the built environment, and how we teach sustainability – both to the workforce of the present and the workforce of the future.
Computational Imaging systems consist of two parts: the physical part where light propagates through free space or optical elements such as lenses, prisms, etc. finally forming a raw intensity image on the digital camera; and the computational part, where algorithms try to restore the image quality or extract other type of information from the raw intensity image data. Computational Imaging promises to solve the challenge of imaging objects that are too small, i.e. of size at about the wavelength of illumination or smaller; too far, i.e. with extremely low numerical aperture; too dark, i.e. at very low photon counts; or too foggy, i.e. when the light has to propagate through a strongly scattering medium before reaching the detector. In this talk I will discuss the emerging trend in computational imaging to train deep neural networks (DNNs) to attack the quad of challenging objects. In several imaging experiments carried out by our group, objects rendered “invisible” due to various adverse conditions such as extreme defocus, scatter, or very low photon counts were “revealed” after processing of the raw images by DNNs. The DNNs were trained from examples consisting of pairs of known objects and their corresponding raw images. The objects were drawn from databases of faces and natural images, with the brightness converted to phase through a liquid-crystal spatial phase modulator. After training, the DNNs were capable of recovering unknown, i.e. hitherto not presented during training, objects from the raw images and recovery was robust to disturbances in the optical system, such as additional defocus or various misalignments. This suggests that DNNs may form robust internal models of the physics of light propagation and detection and generalize priors from the training set.
Yossi Sheffi Elisha Gray II Professor, Engineering Systems Director, Center for Transportation and Logistics (MIT CTL) Professor, Civil and Environmental Engineering Professor, Institute of Data Science and Society Jason Jay Senior Lecturer, Sustainability Director, Sustainability Initiative at Sloan School of Management C. Adam Schlosser Senior Research Scientist, Center for Global Change Science Deputy Director, MIT Joint Program on Science and Policy of Global Change Leonardo Bonanni Founder and CEO, Sourcemap Christopher Raymond Chief Sustainability Officer, The Boeing Company