Cilcare
Hugo Laullier is an experienced professional in the fields of data and artificial intelligence, currently serving as the Head of Data & AI and previously as a Senior Data Scientist at Cilcare since January 2023. Prior to this role, Hugo contributed to GE Healthcare in prescriptive analytics, utilizing statistics and machine learning to enhance the use of medical imaging equipment in hospitals. Experience also includes developing machine learning models for energy optimization at Hager Group and managing the development of an internship management website for Télécom Physique Strasbourg. Additional projects involved investigating the relationship between sleep and voice at Reykjavik University and completing a professional observation internship at the Observatoire volcanologique et sismologique de Guadeloupe. Educational qualifications include a master's degree from the University of Strasbourg and an engineering degree from Télécom Physique Strasbourg.
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Cilcare
CILCARE is a biotech dedicated to harnessing auditory sciences to reshape the future of care via early diagnosis and targeted treatment. We believe hearing acts as a valuable indicator of our health and must be monitored and preserved accordingly. As awareness of the link between hearing and chronic health conditions grows, auditory sciences are paving the way for the early detection of chronic neurocognitive, inflammatory, and age-related diseases. CILCARE External Innovation supports its partners in the health and life-sciences sector in the development of new therapies to prevent and treat auditory-related diseases including inherited hearing loss, synaptopathy, ototoxicity, sensorineural hearing loss and tinnitus. CILCARE Therapeutics has forged a portfolio of proprietary drug candidates and co-developed programs, while pioneering an unprecedented clinical approach to developing compounds targeting cochlear synaptopathy. CILCARE Auditory Analytics aims to position hearing as a key asset of preventive medicine and global healthcare monitoring. We aim to utilize patients' auditory measurements to define digital auditory signatures that will serve to characterize different forms of hearing impairments or other pathologies with precision.