Université de Montréal / Faculté de médecine vétérinaire

Dr. Pablo Valdes Donoso has over a decade of experience in using data analytics to drive solutions to animal health and production issues. He is an IVADO assistant professor at the FVM and focuses on accelerating AI research in Veterinary Medicine. He leads the Platform AI-Agrosanté (PIAAS), co-leads the AI axis of the Center for Expertise and Clinical Research in Animal Health and Welfare (CERCL), and collaborates with the UdeM AI network, including the Consortium en Santé Numérique and the Institute for Data Valorization (IVADO). Pablo holds a DVM from the Universidad de Chile, an MPVM, an MSc in Applied Economics, and a PhD in Epidemiology from the University of California, Davis. 

Julie holds a Bachelor's degree in microbiology from Laval University and a Master's degree in Veterinary Sciences, Microbiology option, from the University of Montreal. Since 2010, Julie works within the Faculty of Veterinary Medicine (FMV) of the University of Montreal. She has held various positions in two of the FMV's Research Centers, namely the Research Group on Infectious Diseases in Animal Production (GREMIP) and the Reproduction and Fertility Research Center (CRRF), but also in the of Clinical Sciences of FMV as a manager.

Since 2021, she has been the assistant to the Vice-Dean for research. In parallel with her duties at the vice-deanship, she coordinates the administrative aspects of the activities of PIAAS.

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Miguel Sautié Castellanos has more than 15 years of experience in the development of tools for the management and analysis of biological data. He works within PIAAS as a data specialist. He holds a bachelor's degree in biochemistry and two master's degrees, one in Medical Informatics and the other in Computer Science.

Expertise: Machine Learning, Spatial and Temporal Statistics, Bayesian Statistics, Computational Biology, Genetics, Software and Database Development, Optimization Algorithms.

Rezwan is a graduated Computer Engineer with several years of experience using Machine Learning techniques to solve problems with different data types in academia and industry. Since winter 2024, she has embarked on a journey as an M.Sc. student in Veterinary Science, focusing on an emerging field that utilizes Artificial Intelligence for animal health and welfare. At PIAAS, her research primarily focuses on using sensor data and machine learning to evaluate early disease detection to optimize broiler production and minimize antimicrobial consumption.

Kiara recently graduated from McGill University with a Bachelor’s degree in Cognitive Science. Throughout her academic journey, she progressively specialized in artificial intelligence and machine learning, focusing on their application to social good. Alongside her position as a research technician at PIAAS, she is a research assistant at the RAISE lab, where she studies the risks of human-AI and human-robot interactions. She is interested in the ethical aspects of AI systems as well as their potential impact on social causes, and is enthusiastic about exploring how they can contribute to improving animal health.

Dr. Younès Chorfi is a full professor at the Faculté de médecine vétérinaire (FMV) of the Université de Montréal (UdeM) and since 2023, he serves as Associate Dean of Research and Graduate Studies at the FMV. He is a member of several research units, and was previously Director of the Department of Veterinary Biomedicine.

Dr. Chorfi completed his DMV at the Institut Agronomique et Vétérinaire Hassan II, in Morocco, before completing a master's degree in microbiology and a PhD in veterinary biomedicine at UdeM.

Dr. Jean-Pierre Lavoie is a full professor at the Faculté de médecine vétérinaire (FMV) of the Université de Montréal (UdeM) and he served, from 2018 to 2023, as Associate Dean of Research at the FMV. It is in this context that he undertook to set up PIAAS to promote the insertion of artificial intelligence in the agri-food and animal health industry and thus, contribute to improving the efficiency and competitiveness of Quebec and Canada in these fields.

After completing his doctorate in veterinary medicine and his internship at UdeM, Dr. Lavoie completed a residency and a postdoctoral fellowship at the University of California, Davis.

Expertise: equine internal medicine, asthma, equine respiratory diseases, equine model of bronchial asthma, respiratory physiology.

Dr. Majda Moussa is a professional in data science and artificial intelligence. She is a Computer Engineer and holds a Master's and a Doctorate in Software Engineering from École Polytechnique de Montréal. She has worked in various practice settings in statistics and machine learning for the past few years. She specializes in the development and industrialization of learning models and/or data pipelines in various platforms (on site or in the cloud). She has a versatile background in information technology and is in the process of consolidating a unique experience to transpose AI technologies into practical applications in the fields of agriculture, biofood and animal health.

With a master's degree in data science for biology from the Institut Agro de Rennes, Solène joined the PIAAS team in July 2023 as a data scientist. She has 2 years' experience in data analysis applied to the agri-food sector, mainly in sensory analysis and exploratory statistics on sales data or problems linked to research and development such as experimental designs. She appreciates the 360° vision of data projects, from the gathering of requirements to the development of decision-making tools and user support. Solène also has a particular attachment to issues linked to agriculture and animal health.

Lucas is a health sciences graduate at Collégial international Sainte-Anne. His academic and professional interests focus on the application of artificial intelligence and data analysis to improve health, particularly in the areas of veterinary medicine and human medicine. During his internship at PIAAS, he would analyze the clinical data of CHUV patients with difficult-to-diagnose diseases.