Multispectral Imaging for Digital Humanity

Fawwaz Habbal is the Harvard SEAS Executive Dean for Education and Research.  He is an educator, writer, inventor, entrepreneur, and visual artist. Dr. Habbal thinks that most of the humanity’s challenges are systems problems that require innovation. He champions the transdisciplinary ethos, experiential learning in sciences, engineering and the arts, and systems thinking and design. 

As a scientist and engineer, Dr. Habbal has significant research contributions to several topics in physics and materials, nanophotonics, and imaging. More recently, Dr. Habbal’s research has been in the domain of metamaterials, imaging and machine learning. As an educator, he supervised the creation of innovative undergraduate engineering curriculum and created active learning and computational labs. He is also the Director of Graduate Engineering Studies at Harvard. Dr. Habbal is an active participant in creating interdisciplinary education across different Harvard schools, as well as at Universidad de Ingenieria y Technologia (UTEC) and Elms College. He was the founding director for new interdisciplinary joint master’s degrees in engineering designs with SEAS and GSD, engineering entrepreneurship with SEAS and HBS, and data science and machine learning with SEAS and FAS. He also created several new courses with colleagues in humanities and social sciences to address human challenges and the future of humanity. He is a co-director for Harvard Initiative on Social Technologies for the Elderly, and co-director of the Learning Incubator at SEAS. 

Dr. Habbal has a long career in industry too, where he worked at Polaroid Corporation as Senior Research and Engineering Fellow, and Corporate VP. At Polaroid, he was responsible for research and product design, and was also tasked to develop the electronic imaging business. After leaving Polaroid, he established three start-ups in electronic imaging for the mobile market.

Dr. Habbal obtained his BS degree in Mathematics from the University of Damascus- Syria, MS degree in Physics from the American University of Beirut- Lebanon, and PhD in Physics in superconductivity from the University of Cincinnati- Ohio. He also attended three executive business programs at Harvard, Penn State and Wharton. Later he did postdoctoral work in laser physics at the University of Colorado, Boulder and became a research fellow in materials at Harvard and visiting scientist at MIT National Magnet Lab.

In recognition for his contributions to academic development and diverse cultural approaches, he was awarded an honorary doctorate in engineering from the Ulsan National Institute of Science and Technology of the Republic of Korea, and an honorary professor in bioengineering at the University of Sydney, Australia. Dr. Habbal enjoys readings in archeology and philosophy, and he has been engaged in visual arts as a photographer, oil painter, and ceramicist. 

Multispectral imaging is a well-known, non-destructive technique used to discern the properties of various physical, chemical and biological materials.  This technique is very general and used for many applications including remote sensing.  It relies on analyzing the optical spectrum of reflected light from the object under investigation.  Current techniques have several shortcomings including having large-size, high-power requirements, complex operation, and very expensive.  With these limitations, deployment in mobile environments is challenging, and many opportunities for timely delivery of critical information is missed.

We present here, for the first time, an accurate multispectral sensor that is integrated in a smartphone camera.  With such a miniaturized and easy-to-use device, we created a paradigm shift.  The doors are now open for new applications and many end-users.  The key element for the sensor is the invention of nanostructured silicon materials that absorb light at precise wavelengths.  By integrating these nano-filters with CMOS cameras, we created easy-to-use devices that are useful for a large number of applications.  We will discuss the physics of these materials and present some applications where we used machine learning to create information devices.