Science Collaboration Mentors

Informatics and Statistics (ISSC)

Eric Feigelson He/Him Contact \| Website I am interested in variable star surveys (as a member of the TVS Science Collaboration) and cross-disciplinary interests associated with statistical methodology (as a core member of the ISSC). Both are challenged by the analysis of LSST lightcurves with sparse and irregular cadences. I am currently engaged in ARIMA modeling of 2 million HATSouth lightcurves in the search for transiting exoplanets, and periodicity search with ZTF photometry in the study of rotation and magnetic dynamos in young stars. I lead a proposal for a LSST micro-survey of an intensive study of one field during one - More -	Matthew J. Graham He/Him Contact \| Website I am interested in the application of machine learning and other advanced statistical techniques to astrophysical time series, and particularly the study of stochastic/aperiodic variablility in astronomical populations, e.g., AGN. In recent years, I have employed RNNs, Bayesian blocks, and Slepian wavelets, for example, to study extreme AGN optical variability and SMBH binaries, significant flaring events, changing-look quasars, and candidate EM counterparts to compact object mergers in AGN disks. - More -	Hermine Landt She/Her Contact \| Website I use the reverberation echo-mapping technique in order to study the inner structure of AGN, in particular the centrally obscuring dust emitting at infrared wavelengths. Such work involves modelling of optical photometric and spectroscopic light-curves, which are subsequently smoothed and shifted in order to determine the time delay between time series observed in different frequency bands. Currently preferred models are based on Gaussian Processes, which we have used successfully and extensively. - More -
Tom Loredo He/Him Contact \| Website I'm an astrostatician who works mainly on cosmic demographics problems and time series problems, across many subareas of astrophysics—minor planets (TNOs/KBOs), exoplanets, extragalactic astronomy, high-energy astrophysics (GRBs, X-ray pulsars, supernova neutrinos, UHE cosmic rays), and cosmology. I helped introduce modern Bayesian methods into astronomy in the late 1980s and 1990s, including use of hierarchical Bayesian methods for cosmic demographics. - More -	Gordon Richards Contact \| Website Drexel has been an institutional member of LSSTC since 2009 and I have been very actively engaged in the AGN Science Collaboration -- organizing the telecons since January 2019 and acting as co-chair since summer 2021. I am interested in understanding the physics of accretion disks and what LSST can do to help understand the diversity of AGN properties and the physics that drives those differences. - More -	Ricardo Vilalta He/Him Contact \| Website Our research laboratory intends to provide state-of-the-art techniques in analyzing scientific data, emphasizing the use of machine learning to analyze astrophysics data—the laboratory centers mainly on developing machine learning tools tailored to astrophysics and cosmology problems. Specifically, we aim at developing physics-informed machine learning models that incorporate domain knowledge as bias into the induction of predictive models. We consider specialized deep neural networks and differential equations as part of the model-building process. - More -