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Plateforme IA-Agrosanté


Mission statement :


PIAAS' Mission:

Develop and adapt artificial intelligence (AI) tools for research and offer services that will improve agri-food production and animal health in Quebec and Canada.

Its strategy :

  • Rapidly promote the insertion of AI in the agri-food and animal health industry.
  • Provide support in the development of data science and artificial intelligence projects.
  • Contribute to the training of the next generation of highly qualified personnel in animal health and data sciences

Its philosophy

  • Focuses on the issues that animal health and production industries consider to be priorities to increase the competitiveness of Quebec companies and help achieve food autonomy for the province and the country.
  • Focuses on innovation through research and rapid transfer of discoveries to industry to improve efficiency and competitiveness in the agri-food and animal health sectors.
  • In addition to our local team of data science professionals, the PIAAS benefits from the expertise of other data-driven groups, such as the Consortium in Digital Health of the Université de Montréal and IVADO.


Do you have a project to submit to the IA-Agrosanté platform?

Send a brief description of the project or the problem you are encountering to our coordinator Julie Blouin ( and we will contact you as soon as possible.


Pablo Valdes Donoso recently published a paper in PLOS ONE on the use of epidemiology and economics to evaluate control measures for endemic diseases....
Pablo Valdes Donoso was invited to participate in the "9th International Symposium on Aquatic Animal Health (ISAAH9)" and the "OECD Workshop on Food...

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About us

Logo Prompt
Logo Vice-rectorat à la recherche


Thanks to funding from the Prompt-IA program - University Propulsion Component, the Faculty of Veterinary Medicine (FMV) of the Université de Montréal (UdeM) has developed an innovative platform of artificial intelligence (AI) services in the field of agri-food and animal health: the Plateforme IA-Agrosanté (PIAAS). This platform was set up in August 2020 and is located at the FMV of the UdeM, in Saint-Hyacinthe, in the heart of the largest agri-food zone in Quebec. PIAAS is built on a unique network of experts in animal health and data science working to serve industry in these fields.

In return for the Prompt-IA funding, the Vice-Rector for Research, Discovery, Creation and Innovation (VRRDCI) of the Université de Montréal has agreed to contribute additional funding to the project. This VRRDCI funding is matched by a contribution from the Institut de valorisation des données (IVADO).

The emergence of new technologies that allow for the collection of comprehensive large data sets has created a growing need for AI research that provide tools to improve animal health and production.

Furthermore, existing research databases are often underutilized due to the inability to manage these data, either because they are too large, not standardized, or require novel analytic tools.

PIAAS aims to bring together the expertise of animal health and data science professionals to adapt and use AI tools to improve data management and analysis. Today, PIAAS is composed of a unique network of animal health and data science experts at the service of agri-food and animal health stakeholders on the development of data science and AI projects.


  1. To become a leader in the development of AI for animal health in Quebec and Canada.
  2. To increase the efficiency and competitiveness of the Quebec and Canadian agri-food industries by improving animal health and production sustainability.
  3. To support researchers, students, and other highly qualified personnel in developping robust databases and data analysis to contribute to emerging AI knowledge on animal health and production.
  4. To Educate animal health stakeholders on the collection of relevant data to advance innovative AI research on animal health and production

Notre équipe

Jean-Pierre Lavoie

Jean-Pierre Lavoie


Dr. Jean-Pierre Lavoie is a full professor at the Faculté de médecine vétérinaire (FMV) of the Université de Montréal (UdeM) and since 2018, he serves 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.


Julie Blouin

Julie Blouin


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.

Miguel Sautié Castellanos

Miguel Sautié Castellanos

Data Scientist


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.

Dalia Belaid

Dalia Belaid

Data Science Intern

Dalia Belaid is a computer science engineering student at the National Engineering School of Carthage focused on Data Science and Artificial Intelligence. Her IT background has equipped her with analytical skills and a critical mind to solve complex problems. She is contributing to PIAAS as a dedicated Data Science Intern. Her main objective is to utilize cutting-edge AI and machine learning models to benefit animal health and agriculture.

Pablo Valdés Donoso

Pablo Valdés Donoso


Dr. Pablo Valdes Donoso is a scientist with over a decade of experience in using data analytics to drive solutions to production and health challenges across different food production systems. He is an assistant professor of AI at FVM and is on a mission to help accelerate AI research in animal production and health. He co-leads the AI-Agrosanté Platform (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 Institute for Data Valorization (IVADO) and the Consortium en Santé Numérique.

Pablo holds a veterinary degree from the Universidad de Chile, a Master's degree in Preventive Veterinary Medicine (MPVM), a Master's degree in Applied Economics, and a PhD in Epidemiology from the University of California, Davis. His research focuses on understanding the dynamics of animal diseases, their economic impact, and the benefits of using control strategies through intensive data use and analysis.

Expertise: machine learning, network analysis, observational study design, regression models, linear optimization, longitudinal analysis, spatial and temporal analysis, system dynamics, time series, game theory.

Majda Moussa

Majda Moussa

Data Scientist

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.

Pauline Gauthier

Data Science Intern

Pauline Gauthier is a student in agricultural engineering school at the Institut Agro Dijon in France. She focuses mainly on livestock and new technologies. She is currently working as a data science intern at PIAAS. Her tasks mainly involve data processing and its use to improve animal health and well-being."

We are hiring!

We are always looking for students with an interest in animal health data science and AI, so do not hesitate to contact us if you wish to join our team:

Making a project a reality

How to realize an AI project in agri-food and/or animal health? :

  1. Planning a first meeting to present the idea to be developed.
  2. Assess project feasibility.
  3. Estimating the cost and the number of hours required to develop the project, therefore, its cost.
  4. Development of the project according to a Gantt chart.
  5. Project deployment.
  6. Project follow up.

For each project, our experts use the CRISP-DM (Cross-Industry Standard Process for Data Mining) methodology, which consists of 6 interdependent steps:

  1. Understanding the problem - What is the nature of the problem and how could it be solved using data? During this step, we define the main sources of the problem.
  2. Understanding the data - We inspect data sources and quality and assess whether other data types will be required.
  3. Preparation of the data - We organize and correct data for analysis.
  4. Modeling - We evaluate the modeling techniques that we can apply depending on the sources of the data and the problem needing to be solved.
  5. Evaluation - We evaluate using the best model that meets the project objectives.
  6. Delivery and deployment of results - We present the results according to the needs of the project (report, dashboard, software, etc.).

Declaration of confidentiality

We are very concerned about sensitivity and data protection. In order to assure our partners that their data will never be shared with third parties (unless authorized in writing by our partners), we provide a legal data confidentiality agreement.

At the end of the project, we ensure that all data is returned to the client and that no copies are preserved.

The PIAAS follows the protocol put in place by UdeM (



1. Database design and development

Base de données

  1. Data Modernization: Implement modern data architectures that will monetize your data, accelerate decision making, and deliver better customer experiences.
  2. Data Qualification: Evaluate, qualify, and restructure your datasets to fully leverage the business benefits of AI.
  3. Cloud Adoption: Use the synergy of AI and the cloud as a lever for transformation and innovation.
  4. Database design and development: MySQL, MariaDB and Microsoft Access. Frequentist and Bayesian statistics. Generalized linear mixed effects models. Generalized additive models. ...R and IBM SPSS





2. Exploratory statistical analysis

l'analyse des données

  1. Analysis of time series and spatial distributions. R, Python, Matlab and IBM SPSS
  2. Development of scripts and pipelines for the processing and analysis of biological sequences. R (Bioconductor, ...), Python (Biopython, ...), Java, C#, C++ and Matlab.
  3. Processing and statistical analysis of MALDI-TOF mass spectra
  4. Risk assessment analysis
  5. Network analysis
  6. Statistical genetics
  7. Development of scripts and pipelines for the processing and analysis of biological sequences. R (Bioconductor, ...), Python (Biopython, ...), Java, C#, C++ and Matlab.

    • Search in biological databases,
    • Structural and functional annotation of DNA and protein sequences,
    • Gene set enrichment analysis,
    • Prediction of the phenotypic effect of SNPs,
    • Multiple alignment,
    • Phylogenetic analysis,
    • Metagenomic data analysis,

  8. Cost-benefit analysis
  9. Cost-effectiveness analysis


3. Machine learning models

Intelligence artificielle

  1. Customized AI solutions from ideation to production: Choose the right AI solution with our experts in computer vision, NLP, and data analysis.
  2. Predictive model generation :

    • Training
    • Testing
    • Implementation

  3. Use the synergy of cloud and AI algorithms as a lever for transformation and innovation.
  4. Digitizing handwritten data from a predefined printout.



4. Deep learning models

Apprentissage profond

  1. Development of deep learning models: Python (Tensorflow/Keras, Scikit-learn,...), R, Java and Matlab.
  2. Use of convolutional neural networks (CNN), artificial neural networks (ANN), etc. for :

    • image recognition
    • audio recognition
    • Disease prediction



5. Software development

Développement de logiciels

  1. Development of software applications for digital image processing and analysis. Python, C++ and C#
  2. Desktop application development: C++/C/C#, Java and Python
  3. Web application development: PHP, Python, HTML, CSS and JavaScript
  4. VBA macro programming (Microsoft Access and Microsoft Excel)
  5. Development of web/mobile applications whose backend is based on artificial intelligence





6. Cloud-based solutions/services

  1. Solutions/services basés sur le cloud Cloud-based AI cognitive solutions to accelerate advanced decision-making.
  2. Online data storage
  3. Custom big data pipelines to efficiently consolidate and transform data.
  4. Deploy and manage applications with cloud services to guarantee high availability and efficient monitoring.
  5. Cloud-connected solutions to collect data at the edge using IOT technology.




7. Training

FormationContinuing Education:

  • AI Strategy Workshops,
  • data science, and
  • machine learning and deep learning.
  • Educational capsules:

    • Introduction to artificial intelligence: basic concepts, applications and issues.



Collaborators and partners

Data Science







Saint-Hyacinthe Technopole
Training and knowledge transfer


CEGEP de Saint-Hyacinthe

Contact Us



Do you have a project to submit to the IA-Agrosanté platform?

Send a brief description of the project or the problem you are encountering to our coordinator Julie Blouin ( and we will contact you as soon as possible.

Contact us

Plateforme IA-Agrosanté
Faculté de médecine vétérinaire
Université de Montréal

(450) 773-8521, poste 8437
(514) 343-6111, poste 8437

Julie Blouin

3200, rue Sicotte
Bureau 1106-1
Saint-Hyacinthe (Qc)
J2S 2M2


@Plateforme IA-Agrosanté