Dr. Hannah Grant on Opportunities and Challenges in Silicon Photonics

This week we spoke with Dr. Hannah Grant, who completed her PhD at UCSD with a focus on photonics on silicon. We last spoke with Hannah about her prior research on optical switches. Today, Dr. Grant walks us through her dissertation, titled “Opportunities and Challenges in Silicon Photonics Systems”. In addition, Dr. Grant shares her outlook on the graduate process, advice for the job search, and where the future of optical communication may be.

As always, we thank our guest Dr. Hannah Grant look forward to our listeners comments!

References

Optical Switches

Photonics

Crosstalk

Lisa Li on Vision, Computer Science, and Culture

This week we spoke with Lisa Li, who is a Ph.D. student at the College of Optics as University of Arizona. She completed her MSc at Newcastle University, where her thesis, title ‘Colour constancy modelling with a biologically-inspired neural network structure’ was jointly done between the Computer Science and Neuroscience departments, under advisers Prof. Marcus Kaiser and Prof. Anya Hurlbert. Lisa briefly discussed her past work at Newcastle. Additionally, she comments about the cultural differences she witnessed between graduate schools, and some of the unique experiences she has encountered as a woman in the sciences. Lisa Li shed light on a fascinating field of science which we all have intuitively experienced and provides valuable insight on how to navigate a career in the sciences!

 

As always, we thank our guest Lisa Li and we eagerly look forward to our listeners comments!

 

References:

Neural Network Links:

Basic Explanations:

For a more rigorous explanation:

Color Constancy Links:

Modrian Tile Links:

Aftab on using Chebyshev gradient polynomials for modal integration

In this weeks episode we sit down with Maham Aftab, who has an extensive background in the sciences as well as activism for a variety of causes. We discuss her most recent publication, in which she used Chebyshev gradient polynomials as a basis set for modal integration. She discusses the recursive nature of the polynomial set which allowed for her method to generate a high number of fitting polynomials. The integration’s ortho-normality is discussed, as well as its unique benefits and how it fits into the general universe of integration methods for slope data. Additionally, Maham speaks about her academic experience and her work in activism.

 

Resources:

Aftab’s Paper:

Maham Aftab, James H. Burge, Greg A. Smith, Logan Graves, Chang-jin Oh, and Dae Wook Kim, “Modal Data Processing for High Resolution Deflectometry,” Int. J. of Precis. Eng. and Manuf.-Green Tech. (2018). (in press

Southwell Integration Paper: https://www.osapublishing.org/josa/abstract.cfm?uri=josa-70-8-998  

 

 
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