Research themes
Large Astronomical Surveys & AGN/Galaxy Evolution
Building and studying large multiwavelength samples of quasars and AGN to understand active galaxies, their hosts and environments, and their cosmic evolution.
Astronomical Data Science & Trustworthy AI
Developing scalable methods for spectroscopy, representation learning, multimodal inference, similarity retrieval, and anomaly discovery, with reliability as part of the scientific measurement.
Cosmological Applications of Large Surveys
Using quasars, galaxies, reliable redshifts, and cross-survey datasets as tracers for large-scale-structure and cosmological studies.
Selected research highlights
Euclid spectroscopy of nearly 3500 quasars
Space-based slitless spectroscopy, redshift determination, and public data products. Gaia-based quasar samples and an all-sky census
Large multi-survey catalogues built from astrometry, optical and infrared photometry, and machine learning. AI for astronomical spectroscopy: AIMS-z
Reliable redshift inference using spectral representations, astrophysical information, and multimodal data. AGN environments and cosmological applications
Studies of radio-galaxy environments, quasar hosts, and cosmological applications of large source samples.
Space-based slitless spectroscopy, redshift determination, and public data products. Gaia-based quasar samples and an all-sky census
Large multi-survey catalogues built from astrometry, optical and infrared photometry, and machine learning. AI for astronomical spectroscopy: AIMS-z
Reliable redshift inference using spectral representations, astrophysical information, and multimodal data. AGN environments and cosmological applications
Studies of radio-galaxy environments, quasar hosts, and cosmological applications of large source samples.
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