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:
2025 (fall) Jared Dans (student)
2025 (spring) Jarryd Horn (student)
2024 (fall) Jared Erb (student)
2023 (fall) Jared Erb (student), Peter Czajka (postdoc)
2022 (fall) Sungha Baek (student), Keenan Avers (postdoc)
2020 (fall) Shukai Ma
2019 (spring) Rui Zhang (student), Tarapada Sarkar (postdoc)
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
Title: Quantifying Statistical Independence in ExperimentalEnsembles of Microwave Measurements
Abstract: Statistical ensembles underpinexperimental characterization in complex physical systems, yet objectivecriteria for assessing ensemble quality and determining sufficient sample sizeremain limited. In this work, we introduce a quantitative framework formeasuring statistical independence among frequency-dependent scattering matrixrealizations and demonstrate how it can be used to optimize experimental dataacquisition.
Wedefine an independence index derived from pairwise correlations betweenrealizations, formulated through both linear inner-product measures andnonlinear distance-based kernels. A fast, matrix-based implementation enablesefficient computation for large ensembles without explicit pairwise loops. Central to the framework is an interaction matrix that captures the statisticalrelationship between realizations and whose distribution provides insight intoensemble quality.
We applythis methodology to both Random Matrix Theory–generated ensembles andexperimental measurements from a reconfigurable electromagnetic cavity undermechanical, electronic, and mixed mode-stirring conditions. The independenceindex is shown to correlate with physically meaningful parameters such as lossfactor and coupling, while revealing deficiencies in poorly constructedensembles. Leveraging the interaction matrix, we demonstrate that reorderingrealizations to minimize mutual interaction can significantly improve ensembleindependence, reduce parameter estimation error, and enable early stopping ofexperiments with minimal loss of fidelity.
Thisframework provides a general, computationally efficient tool for assessingensemble quality, guiding adaptive experiment design, and balancing accuracyagainst measurement cost, with potential applications across complex andchaotic physical systems.
Advisor: Steve Anlage