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Eneko Uruñuela, PhD
Machine Learning | Medical Imaging | Open Source Development
Professional Background
Eneko Uruñuela is a distinguished Biomedical Engineer with an impressive academic and professional trajectory, particularly in the field of machine learning for medical imaging. As a researcher and PhD candidate at the prestigious Universidad del País Vasco/Euskal Herriko Unibertsitatea, Eneko has dedicated his career to advancing the understanding and application of emerging technologies in neuroscience. His invaluable contributions to biomedical engineering are evident through his innovative work on deconvolution algorithms, which are designed to estimate neuronal-related activity using functional MRI data. These algorithms hold significant potential for clinical applications, especially for conditions where timing data of neuronal events are limited, such as resting state analysis or specific clinical cases.
Eneko's career began with a solid educational foundation in Biomedical Engineering, where he earned his Engineer's degree from Universitat Politècnica de Catalunya. This was further complemented by a Master's degree in Bioinformatics and Data Analysis from the University of Navarra, focusing on data analysis within the biomedical domain. His deep-seated interest in neuroscience and machine learning solidified during his Master's thesis, where he explored the deconvolution of fMRI signals at the Basque Center on Cognition, Brain and Language.
Eneko's vast industry experience is multifaceted, as he has held pivotal roles at Neuroelectrics, a company known for its pioneering work in brain stimulation technologies. Here, he developed an automated firmware testing system capable of working with up to 64-channel devices, enhancing the efficiency and reliability of crucial brain stimulation signals such as tACS, tDCS, and tRNS. Furthermore, as part of his endeavors, he created sophisticated algorithms for analyzing and monitoring electroencephalography (EEG) data in neonates, focusing on the early detection of seizures, thus contributing valuable insights into neonatal healthcare.
In addition to his research and technical expertise, Eneko has also taken on teaching responsibilities, serving as a Teaching Assistant at Neuromatch Academy, where he shared his knowledge and passion for neuroscience with fellow learners. His commitment to the open-source community is reflected in his contribution to signal denoising software for fMRI, which has been developed in Python and is readily available for use by his peers and the broader research community.
Education and Achievements
Eneko Uruñuela's academic pathway showcases not only his dedication to biomedical engineering but also his commitment to continuous learning and exploration in the field. He obtained his Doctor of Philosophy (PhD) in Machine Learning for Neuroimaging from Universidad del País Vasco/Euskal Herriko Unibertsitatea, where he engaged in cutting-edge research that melds technology with neuroscience. This advanced degree enables him to contribute meaningfully to the intersection of machine learning and biomedical applications.
His Master's degree in Bioinformatics and Data Analysis from the University of Navarra equipped him with the essential skills for effective data interpretation and analysis, which are crucial in the field of biomedical engineering. This specialization has undeniably positioned him as an expert in leveraging complex data sets to advance research outcomes. His earlier academic achievement came from earning his Engineer's degree in Biomedical/Medical Engineering from Universitat Politècnica de Catalunya, which laid the groundwork for his technical competencies in the sector.
Noteworthy is Eneko's entire thesis journey, notably his Master's thesis conducted at the Basque Center on Cognition, Brain and Language. This project significantly contributed to his foundation in the analysis of fMRI signals and paved the way for his doctoral research focused on developing robust machine learning models applicable to neuroimaging.
Key Competencies and Areas of Expertise
Eneko possesses a well-rounded skill set pertinent to today's fast-evolving biomedical landscape. His competencies include a proficiency in programming languages such as Python, Matlab, and R, making him adept at developing complex algorithms and conducting data analysis. He is particularly skilled at signal processing, image processing, and addressing inverse problems, which are fundamental to medical imaging and neuroinformatics. Additionally, he has hands-on experience with C++, adding versatility to his coding skills.
Eneko’s research endeavors are complemented by his collaborative spirit and dedication to the open-source community. His contributions to the development of signal denoising software for functional MRI in Python demonstrates his commitment to both innovation and sharing solutions with fellow researchers and scientists.
Achievements
Throughout his career, Eneko Uruñuela has amassed a wealth of accomplishments that reflect his capability and dedication to the field of biomedical engineering. As a key player at Neuroelectrics, he developed pivotal technologies that enhanced the functionality and reliability of devices used for brain stimulation. His efforts in creating a firmware testing system and algorithms for real-time EEG analysis have played a significant role in improving patient outcomes in clinical settings.
His dissertation work, focusing on deconvolution algorithms to estimate neuronal activity from fMRI data, stands as a testament to his innovative thinking and problem-solving abilities, ultimately driving forward advancements within neuroimaging research. Eneko is actively engaged in sharing knowledge and fostering collaboration, as exemplified by his role as a Teaching Assistant at Neuromatch Academy, where he effectively communicates complex scientific concepts to budding engineers and neuroscientists.
tags:[
biomedical engineering
machine learning
medical imaging
deconvolution algorithms
functional MRI
EEG analysis
signal processing
data analysis
inverse problems
bioinformatics
Highlights
I'm thrilled to announce that just three months into my new role, our team's paper has been accepted at MICCAI's ISLES challenge!
🚀 We've also been invited to give an oral presentation.
A huge thanks to our amazing team for making this possible. Excited for what's next!
🎉 Thrilled to share I've received the Alberta Innovates fellowship!
I'll be working on AI models for stroke care, focusing on personalized treatments & data privacy.
Excited to push the boundaries in health tech!
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