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Jihao You


Position/Title: Postdoctoral Fellow
email: jyou03@uoguelph.ca
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I'm currently working as a postdoctoral fellow supervised by Dr. Jennifer Ellis (supervisor) and Dr. Dan Tulpan (co-supervisor). My research projects as a postdoctoral fellow inlucde:

1. "Mechanistic Modelling of Feeding and Exercise Dynamics for Precision Horse Management". The objective of this project is to model feed intake as dynamic within-day meal events and to incorporate exercise duration, intensity, timing, and fitness-related modifiers into the equine metabolism model. We are collaborating with MadBarn on this project, which is jointly funded by MadBarn and Mitacs. (In progress)

2. "Development of Dynamic Mechanistic Model of Metabolism and Growth for Poultry". The objective of this project is to develop a dynamic mechanistic model to simulate growth and metabolism in different poultry species. The goal of the first stage is to rebuild a dynamic mechanistic model for modern male turkey genetic lines based on previous publications. We are collaborating with Hendrix Genetics to complete this work. (In progress)

3. "Connecting Feeding Behaviour with Feed and Digestive Efficiency in Chicken". This France–Canada collaborative project aims to improve feed efficiency and sustainability in poultry production through the integration of precision feeding technologies, machine learning, and mechanistic modeling. The objective is to identify feeding behavior profiles associated with digestive and feed efficiency, quantify their relationships with growth performance across genotypes and environments, and enhance the prediction of animal performance through biologically informed data-driven models. We are collaborating with the BOA (Avian Biology and Poultry Research) research unit at INRAE (National Research Institute for Agriculture, Food and Environment) on this project. (In progress)

4. "Automation of the Modern Commercial feed mill - Optimization of Pellet Quality using Machine Learning- 2.0". The objective of this project is to further investigate the relationship between nutrition, manufacturing and environmental factors and pellet quality using machine learning and data analysis approaches. This project builds on our previous project ("Optimization of Pellet Quality at the Mill Level using Machine Learning") by taking the next major step: expanding involvement to include additional feed mills from multiple companies across Ontario. We aims to create a robust and versatile machine learning system to predict PDI across Ontario mills and develop an optimization algorithm to support decision-making when using the model. To achieve the goal, we are collaborating with partners from feed industry, including Trouw Nuturition Canada, Molesworth Feed Supply, MasterFeeds (Canada), and Jones Feed Mills. (Completed)

 

First-author Publications

  1. You, J., Tulpan, D., Diao, J., & Ellis, J. L. (2026). Impact Range Assessment (IRA): An Interpretable Sensitivity Measure for Regression Modelling. arXiv preprint arXiv:2602.05239.
  2. You, J., Tulpan, D., Krziyzek, C., & Ellis, J. L. (2026). Learning to Predict Pellet Quality: A Machine Learning and Feature Engineering Approach. Journal of Animal Science, skag079. https://doi.org/10.1093/jas/skag079
  3. You, J., Hall, K., Civiero, J., Malpass, M. C., Tulpan, D., & Ellis, J. L. (2025). Evaluating variables affecting Pellet Durability Index (PDI) in pelleted corn-soy-based feeds for swine and poultry: A meta-analysis. Animal Feed Science and Technology, 116566. https://doi.org/10.1016/j.anifeedsci.2025.116566
  4. You, J., Tulpan, D., Krziyzek, C., & Ellis, J. L. (2025). Prediction of Pellet Durability Index (PDI) in a Commercial Feed Mill Using Multiple Linear Regression with Variable Selection and Dimensionality Reduction. Journal of Animal Science, skaf021. https://doi.org/10.1093/jas/skaf021
  5. You, J., Ellis, J. L., Tulpan, D., & Malpass, M. C. (2024). Review: recent advances and future technologies in poultry feed manufacturing. World's Poultry Science Journal, 1-13. 
  6. https://doi.org/10.1080/00439339.2024.2323536
  7. You, J., Ellis, J. L., Adams, S., Sahar, M., Jacobs, M., & Tulpan, D. (2023). Comparison of imputation methods for missing production data of dairy cattle. animal, 17, 100921. https://doi.org/10.1016/j.animal.2023.100921
  8. You, J., Tulpan, D., Malpass, M. C., & Ellis, J. L. (2022). Using machine learning regression models to predict the pellet quality of pelleted feeds. Animal Feed Science and Technology, 293, 115443. https://doi.org/10.1016/j.anifeedsci.2022.115443
  9. You, J., Lou, E., Afrouziyeh, M., Zukiwsky, N. M., & Zuidhof, M. J. (2021). Using an artificial neural network to predict the probability of oviposition events of precision-fed broiler breeder hens. Poultry Science, 100(8), 101187. https://doi.org/10.1016/j.psj.2021.101187
  10. You, J., Lou, E., Afrouziyeh, M., Zukiwsky, N. M., & Zuidhof, M. J. (2021). A supervised machine learning method to detect anomalous real-time broiler breeder body weight data recorded by a precision feeding system. Computers and Electronics in Agriculture, 185, 106171. https://doi.org/10.1016/j.compag.2021.106171
  11. You, J., van der Klein, S. A., Lou, E., & Zuidhof, M. J. (2020). Application of random forest classification to predict daily oviposition events in broiler breeders fed by precision feeding system. Computers and Electronics in Agriculture, 175, 105526. https://doi.org/10.1016/j.compag.2020.105526

 

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