Emma Alexander


Google Scholar


I'm interested in low-level, physics-based, bio-inspired artificial vision.


I develop bio-inspired visual sensing techniques. Combining the forward engineering approach of computer vision with the reverse engineering perspective of vision science, I extract new principles of visual sensing and apply them to produce novel devices and a deeper understanding of vision questions across a wide span of imaging subfields. This approach draws on and advances principles in optics, algorithms, and neuroscience, while developing applications in imaging modalities from photography to microscopy.

By explaining vision systems through their underlying mechanisms rather than relying on statistical regularities in datasets that are never made explicit, this approach allows analytical reasoning about when and why systems will succeed or fail. I am particularly inspired by animals with small nervous systems, which are remarkably resource-aware in contrast to mainstream, data-driven computer vision systems. These systems often far exceed the state of the art in computer vision, not only in terms of raw performance, but also in weight, size, energy consumption, computational efficiency, and training data. One way small animals manage this is through task-specific sensing: in place of general-purpose hardware, evolution often develops specialized optics and brain structures that cooperate to prioritize vital tasks. This computational sensing approach leads to an enormous diversity in sensor design across natural vision systems, each driven by the challenges its specific ecological niche, providing ample inspiration for technologists. 

For more details see Research, Publications, Talks.

Teaching & Outreach

I am passionate about sharing the beauty of computational imaging and the math and science that make it work. I currently teach a graduate seminar, Computational Optics, focusing on optical models suitable for computational imaging applications, and an advanced elective, Natural and Artificial Vision, suitable for computer science, cognitive science, and neuroscience students.

During my PhD I received the Harvard University Certificate of Distinction in Teaching. I served as head TA for Harvard's undergraduate-level Introduction to the Theory of Computation (CS121) and Mathematical Methods in the Sciences (AM21b, a combined introduction to differential equations and linear algebra), and as a course TA for the graduate-level Computer Vision course (CS283). Previously, I spent several years as a course tutor for Yale's two-semester Fundamentals of Physics series, covering mechanics and electromagnetism (PHYS200, PHYS201), which taught me the importance of teaching and leadership opportunities for undergraduates.

In the wider community, I have taught introductory computer skills classes through Tech Goes Home and developed and taught content for elementary schoolers through Bay Area Scientists Inspiring Students. I am currently supporting the YW Tech Lab at YWCA with a great team of Northwestern student tutors.

I have mentored undergraduate students through their personal experiences of diversity and access through Harvard's Women in STEM and Women in Computer Science organizations, as well as through the Berkeley Artificial Intelligence Research undergraduate mentoring program. I have spoken at the Women Engineers Code conference and taught for ProjectCSGirls and Youth Inventa.

As part of the Waller lab, I led undergraduate and rotation student projects, publishing an undergraduate-coauthored ICCP paper.

For more details see Teaching, DEI.