Computer Science Speaker Series Master Calendar

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Seminar: "Applications of Stochastic Optimization and Machine Learning in Photonic Nanostructures and Quantum Optical Systems"

Event Type
wifi event
Sep 25, 2020   9:00 am  
Safura Sharifi, LSU
Becky McDuffee
Originating Calendar
IQUIST Seminar Series

Abstract: Recent advances in stochastic optimization and machine learning methods, along with
successful innovative applications across a wide variety of fields, promise game-changing impacts,
potentially resulting in new, intelligent development and design tools for nanophotonic devices
and quantum optical systems with more diverse and better functionalities. This presentation
addresses four such innovative approaches and novel designs.

     There has been an explosion of interest in graphene for photonic applications, as it provides a
degree of freedom to manipulate electromagnetic waves. I developed a micro-genetic global
optimization algorithm and designed graphene-based nanophotonic structures that enable
electrically selective, switchable, and tunable thermal emitters. This study may contribute towards
the realization of wavelength-selective detectors with switchable intensity for sensing applications.

     The Laser Interferometer Gravitational-Wave Observatory (LIGO) has opened a new window to
the universe by detecting the first gravitational waves in 2015. The discovery impels the need for
better detection schemes by decreasing the limiting noise sources in gravitational-wave
interferometers. The second part of the research in this presentation employs a new design for
mechanical microresonators in Michelson interferometry to minimize thermal noise below the
standard quantum limit (SQL). The proposed microresonator allows it to serve as a testbed for
quantum non-demolition measurements, and to open new regimes of precision measurement that
are relevant for many practical sensing applications, including advanced gravitational wave

     One of the techniques to reduce shot noise in an interferometric gravitational wave measurement
is to use squeezed light, more specific ponderomotively squeezed light. Ponderomotive squeezing
has been measured in the presence of a strong optical spring, but one limitation to the amount of
squeezing measured was excess noise injected by the feedback needed to stabilize the optical
spring. The third part of this presentation employs a new method to remove the feedback noise
from the measurement by using the coherent.

     Laser beam profiling is necessary for most laser applications and enabling automated detection of
orbital angular momentum (OAM) can tremendously contribute to quantum optical experiments.
The fourth part of this presentation develops the convolutional neural network (CNN) models to
automatically identify and classify the noisy images of LG modes collected from two different
experimental setups. The classification performance measures of the CNN models are studied for
generalizing and adapting to experimental conditions. This study may contribute towards enabling
OAM light with increased degrees of freedom and thereby its various applications in
telecommunications, sensing, and high-resolution imaging systems. 

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