Resumen:

In the pursuit of unraveling the mysteries of our universe, the discovery and characterisation of extrasolar planets have been among the most captivating endeavours in modern astronomy. The processes of exoplanet formation and evolution have come into focus thanks to nearly 30 years of observational and theoretical efforts. Amid this backdrop of discovery and exploration, the James Webb Space Telescope (JWST), with its spectroscopic and imaging instruments working across the infrared spectrum with unrivalled capabilities, is already providing an unprecedented window into the atmospheres of exoplanets, allowing us to probe their physical and chemical properties. This in turn sheds light on many aspects of the formation and evolution of planets. At the same time, the data revolution is changing radically the way research is done in diverse fields of the physical sciences from particle physics to molecular dynamics to cosmology. The realm of exoplanetary science is no exception. Advancements in data science and machine learning are becoming indispensable tools to fully exploit the available data sets and to prepare for future instruments. Indeed, exoplanetary science resides at the intersection of two waves of transformation, where the dynamic interplay between cutting-edge observational technology and data-driven analytical tools promise to usher a new era of discoveries and revelations. In this talk, I present and overview of the latest advances in the field, with particular emphasis in the areas where the combination of data-driven techniques and new instrumentation has the potential to drastically advance the field. I also present part of the work conducted at the University of San Martín, where an interdisciplinary team of physicists, astronomers and computer scientists is harnessing the power of big data and innovative analytical techniques to extract valuable insights from an ever-expanding dataset.