All in all, my academic track record includes 12 scientific peer-reviewed papers in journals such as Nature Communications and Nature Biomedical Engineering. Among these, I have produced 4 1st author papers (3 corresponding author), accruing over 375 citations an h-index of 7. I have also been a guest speaker in many institutions and have presented my work in many prestigious conferences. In addition, my work has been recognized through several awards and fellowships such as “la Caixa” doctoral and a Stanford postdoctoral fellowship. My training spans signal processing, statistics and probability, machine learning, molecular biology, and genomics. Since the start of my research career, I had the ambition to “break the code” of highly dimensional and complicated biological data to understand complex biological systems. Even though my academic background is in Engineering and Mathematics (PhD and MSc in Electrical & Computer Engineering / MSc in Applied Mathematics and Statistics), I have attended numerous courses on molecular biology and genomics and have accrued a lot of experience in different areas throughout my career.  During my PhD under the supervision of Prof. Goutsias at Johns Hopkins University, I co-developed successfully several computational and analysis pipelines for a variety of complex models of epigenetics and integrations of different genomics data modalities. My PhD work was funded by a "la Caixa" fellowship that I was awarded in 2015. To this end, I collaborated with leading scientists, including Dr. Ha and Dr. Feinberg, the latter being one of the pioneers in the study of epigenetic alterations in cancer. Two highlights of my work are the development of the first epigenetic landscape model capable of quantifying differences in methylation entropy from whole-genome bisulfite data and the first method for haplotype-dependent allele-specific methylation. My ambition to develop meaningful methods is mirrored in the fact that this work led to many novel biological insights, including a previously unreported relationship between methylation stochasticity and DNA sequence. My academic drive to bridge computational sciences and biology to detect underlying patterns in challenging biological phenomena was continued during my postdoc at Stanford University under the supervision of Prof. Salzman (2021-2023). Here, I developed the first reference-free framework for de novo mobile genetic element discovery directly from sequencing data, being awarded a Postdoctoral Fellowship from the Stanford Center for Computational, Evolutionary and Human Genomics. Additionally, I collaborated with Prof. A. Fire and Prof. Ioannidis in various projects. Lastly, as a Postdoctoral Researcher in the Biomedical Sciences Department at Universitat de Barcelona in the Canals lab, I am actively developing computational methods for single-cell genomics and calcium imaging analysis using (deep) generative modeling. We are using these methods to characterize the developmental alterations in Huntington's disease, as well as in other neurodegenerative diseases, hoping that these in silico models will pave the way to reduce the need for animal experimentation.B.1. Breve descripción del Trabajo de Fin de Grado (TFG) y puntuación obtenida Summary: My TFG consisted in the development of a power inverter for a high-performance electric vehicle that participated in several Formula Student competitions. The discussion delves into key theoretical aspects crucial for the proper functioning of the converter, with special emphasis on signal processing and control theory. The work also describes the various calculations undertaken to determine the dimensions of key components of the converter, such as the DC bus (600V) and the semiconductors. Additionally, we touch upon the mechanical design of the assembly and the water cooling system. These designs are meticulously crafted considering the specific constraints imposed by the end application—the competition vehicle. Following this, we provide an in-depth exploration of the core elements of the code implemented in the converter, including position and speed calculations, as well as torque control. Lastly, we showcase the outcomes of experimental tests conducted on the bench, presenting tangible results obtained during the validation process.  Grade: Honors (Best TFG award). B.2. Breve descripción del Trabajo de Fin de Máster (TFM) y puntuación obtenida Summary: The inherent noise in sequencing data requires careful consideration for reliable conclusions. Hidden Markov models (HMMs) prove ideal in this context. Leveraging a Markov chain, the model captures genetic patterns, incorporating emission probabilities to address process-related noise. This thesis applies HMM theory for two purposes: (1) evaluating sequencing data reliability and (2) correcting potential sequence errors. Results demonstrate HMM's ability to identify genetic patterns and rectify errors, enhancing data reliability for downstream analyses. The developed tool, HiMMe, is accessible at https://github.com/jordiabante/HiMMe. Grade: Highest grade possible.