BEGIN:VCALENDAR VERSION:2.0 PRODID:Icfo X-PUBLISHED-TTL:P1W BEGIN:VEVENT UID:69d4b2205a8e8 DTSTART:20240424T090000Z SEQUENCE:0 TRANSP:OPAQUE LOCATION:Auditorium and Online (Teams) SUMMARY:ICFO | BORJA REQUENA POZO CLASS:PUBLIC DESCRIPTION:The integration of artificial intelligence into research is pro pelling progress and discoveries across the entire scientific landscape. A rtificial intelligence tools boost the development of novel scientific ins ights and theories by processing extensive data sets\, guiding exploration and hypothesis formation\, enhancing experimental setups\, and even enabl ing autonomous discovery. In this thesis\, we harness the power of machine learning\, a sub-field of artificial intelligence\, to study non-determin istic systems\, which are amongst the hardest to characterize.\nOn one han d\, we address problems inherent to the study of quantum systems and the d evelopment of quantum technologies. Quantum physics presents formidable ch allenges due to the associated exponential complexity with the size of the system at hand\, as well as its intrinsic stochastic nature and the prese nce of intricate correlations between its components. We employ reinforcem ent learning\, a machine learning technique that excels at dealing with va st hypothesis spaces\, to address some of these challenges. Notably\, rein forcement learning has demonstrated super-human performance in multiple co mplex games like Go\, which present similar characteristics to the problem s encountered in the study of quantum physics. We use it to systematically simplify complex common problems in condensed matter and quantum informat ion processing tasks\, as well as to implement robust calibration schemes for quantum computers.\nOn the other hand\, we focus on the characterizati on of complex stochastic processes\, such as diffusion. Understanding diff usion processes is crucial to unravel the complex underlying physical and biological mechanisms governing them. This involves extracting meaningful parameters from the analysis of stochastic trajectories described by track ed particles. However\, accurately capturing and analyzing the trajectorie s presents multiple challenges\, stemming from the combination of their ra ndom nature\, complex dynamics\, and experimental drawbacks\, such as nois e. We develop machine learning algorithms to accurately extract such param eters\, even when they vary with time\, and demonstrate their applicabilit y in experimental scenarios. Furthermore\, we apply similar techniques to study the diffusion of internet users browsing an e-commerce website\, pre dicting their likelihood to make a purchase before closing the session.\n& nbsp\;\nWednesday April 24\, 11:00 h. ICFO Auditorium  \;\nThesis Dire ctor: Prof Dr. Maciej Lewenstein and Dr. Gorka Muñ\;oz Gill DTSTAMP:20260407T072832Z END:VEVENT END:VCALENDAR