Physics 838 Graduate Student Seminar

In 1990, a seminar was initiated for QMC (formerly CNAM/CSR) graduate students in order to present their research to the other students, postdocs, and faculty in the Center. In addition to fostering a rich, collaborative environment in which students learn about the breadth and scope of research being done in QMC, the idea of this series is to teach several crucial skills to our students:

1) How to present their research in a clear and time-efficient way to an audience that was not expert in their area of research;

2) How to best answer questions during their presentations;

3) How to ask good questions when in an audience (or interview), in particular about research beyond their own narrow PhD topic.

In this seminar, students submit formalized feedback to each weekly presenter, providing informative information about presentation style, research content and tips for improvement.

Best Speaker Awards

At the end of each term, a cash prize award is given for the best student and postdoc presentations based on class feedback scores. Previous winners are listed here:

2018 (fall) Chris Eckberg (student), Jen-Hao Yeh (postdoc)

2015 Paul Syers, Jasper Drisko

2014 Sean Fackler, Paul Syers,

2013 Kevin Kirshenbaum, Kirsten Burson

2012 Baladitya Suri, Kristen Burson

2011 (fall) Sergii Pershoguba, Ted Thorbeck

2011 (spring) Anirban Gangopadhyay, Baladitya Suri

2010 (fall) Christian J. Long, Tomasz M. Kott

2010 (spring) Tomasz M. Kott, Kevin Kirshenbaum

2009 (fall) Arun Luykx, Jen-Hao Yeh

PHYS 838C: No Class

Physics 838 Seminar
04.13.2020 4:00 pm - 5:30 pm


Speaker 1: Shukai Ma

Title:  Experimental Realization of Reservoir Computing with Wave Chaotic Systems


The execution of machine learning (ML) software largely depends on the computing `substrate', which is often not optimized for running ML tasks. The invention of ML-tailored hardware may greatly improve the computing speed and power efficiency. Photonic devices are well-suited for ML due to the parallelism of light. Reservoir computing (RC) is essentially a one-layer neural network (NN) with nonlinear connections, but radically simpler than NN since only the coupling between the reservoir nodes and outputs is trained. Thus RC is well-suited for physical realizations. 

Here we utilize the complicated wave dynamics inside a chaotic-shaped electromagnetic cavity containing nonlinear elements to emulate the complex dynamics of an RC. Due to the short-wavelength property of the waves, their equivalent ray-propagation is chaotic. We propose unique techniques to create virtual RC nodes by both frequency stirring and spatial perturbation. The computational power of the wave chaotic RC is experimentally demonstrated with the so-called observer task. Different tasks are executed with a single RC physical device by simply switching output couplers. Since such systems are widely encountered in multiple settings (electromagnetics, quantum mechanics, acoustics, etc.), the work is of interest to many researchers in the physics and engineering communities.

This work is supported by ONR Grants No. N000141512134, No. N000141912481, and AFOSR COE Grant FA9550-15-10171.

Speaker 2: Zach Steffen

TItle: TBA

Abstract: TBA