(she/her/hers)
Rice University
computational imaging, circuits, computer vision
Mel White is currently a postdoctoral researcher and member of the Academy of Fellows at Rice University. She earned her PhD at Cornell University in Electrical and Computer Engineering in 2022, where she was an NSF Graduate Research Fellowship recipient. Mel's career began in economic development and public policy, earning a BA in International Relations from the College of William & Mary, and serving as a Peace Corps volunteer in Morocco. She later transitioned to a STEM-focused career, completing a BS in Electrical and Computer Engineering from Rensselaer Polytechnic Institute as a non-traditional student.
Mel's academic research interests lie at the intersection of integrated circuit design, computational imaging, and machine learning, leveraging interdisciplinary techniques to reinvent conventional imaging pipelines. Her graduate work centered on the design of two novel imaging sensors built in standard CMOS, and her work has been published in various conferences focused both on circuits and systems and computer vision.
Beyond the lab, Mel is committed to education and outreach within and beyond academia. At Cornell, she designed, taught, and piloted several programs for K-12 students and teachers at Cornell and local schools, and taught a math course at a prison. This work is in conjunction with -- and not extraneous to -- her research, following a conviction that as academics and researchers, our work is not created in a vacuum of impartiality, but within local and global contexts; it is our responsibility not just to move science forward and train future generations, but to do so in ways that are socially responsible and inclusive.
Lights, Camera, Computation: Chips for the Future of Imaging
There is an as-yet underexplored space in computational imaging where the focus is on redesigning an imaging system at the point of data capture (sensor), as opposed to in the optics before the sensor, or the computation that occurs after. My doctoral work focused on this space via the design of two novel imaging sensors in 180 nm CMOS. One of these sensors exploits the ability to create precise nanoscale structures in conventional processes, which can also function as diffraction gratings. This optical effect – created within the microchip itself – can be used to perform phase filtering, with applications in holography and optical computational tomography. The second sensor incorporates concepts from compressed sensing and digital signal processing directly into hardware for single-photon sensors. Typically, such sensor arrays have low dynamic range, low resolution, and high data throughput, but we address these problems simultaneously by redesigning the digital architecture within the sensing chip.
At Rice, I am collaborating with experts in lensless imaging, compressed sensing, computer vision, and machine learning with the shared goal of developing low-cost, lightweight, and flexible imaging systems for biomedical devices and histopathology. Our broader aim with this research is democratizing access to health technology by making it inexpensive and portable. By starting with the sensing chip, we are working in a space that few other research groups are currently exploring, and enabling technologies that can be manufactured cheaply at scale.