Talks

The astrophysical stochastic gravitational wave background (SGWB) originates from numerous faint sub-threshold  gravitational wave (GW) signals arising from the coalescing binary compact objects. This background is expected to be discovered from the current (or next-generation) network of GW detectors by cross-correlating the signal between multiple pairs of GW detectors. However, detecting this signal is challenging and the correlation is only detectable at low frequencies due to the arrival time delay between different detectors. In this work, we propose a novel technique, Spectrogram Correlated Stacking (or SpeCS), which goes beyond the usual cross-correlation (and to higher frequencies)  by exploiting the higher-order statistics in the time-frequency domain which accounts for the chirping nature of the individual events that comprise SGWB.

We show that SpeCS improves the signal-to-noise for the detection of SGWB by up to an order of magnitude, compared to standard optimal cross-correlation methods which are tuned to measure only the power spectrum of the SGWB signal. SpeCS can probe beyond the power spectrum and its application to the GW data available from the current and next-generation GW detectors would speed up the SGWB discovery. 

based on work with Ramit Dey, Luis Longo, and Suvodip Mukherjee

Zoom link:   https://pitp.zoom.us/j/97091817158?pwd=MHNkdjFQT0plVzJJY2lsOHRxdDdwdz09

I will first review the construction of quiver Yangians for the quiver and superpotential from string theory on general toric Calabi-Yau threefolds; they serve as BPS algebras of these systems and their characters reproduce the unrefined BPS indices. I will then explain how to generalize the construction to arbitrary quivers and how to incorporate refined BPS indices. The entire construction allows for straightforward generalizations to trigonometric, elliptic, and generalized cohomologies. Time permitting, I will discuss some applications, such as on BPS counting and on Gauge/Bethe correspondence. 

Zoom link:  https://pitp.zoom.us/j/96812116687?pwd=RjlsSVhDaUVBREtUUnd5S04rQjhpZz09

Causal reasoning is vital for effective reasoning in many domains, from healthcare to economics. In medical diagnosis, for example, a doctor aims to explain a patient’s symptoms by determining the diseases causing them. This is because causal relations, unlike correlations, allow one to reason about the consequences of possible treatments and to answer counterfactual queries. In this talk I will present two recent causal inference projects done with my collaborators deriving new algorithms to solve problems that arise when applying causal inference in the real world.

"Linear structural equation models relate random variables of interest via a linear equation system that features stochastic noise. The models are naturally represented by directed graphs whose edges indicate non-zero coefficients in the linear equations. In this talk I will report on progress on combinatorial conditions for parameter identifiability in models with latent (i.e., unobserved) variables. Identifiability holds if the coefficients associated with the edges of the graph can be uniquely recovered from the covariance matrix they define. Paper: https://doi.org/10.1214/22-AOS2221 or https://arxiv.org/abs/2201.04457"