Team Leader
Adeolu Adekunle
Texas A&M University
Animal Science
adeolu.adekunle@tamu.edu
Project Type
Research
Who Can Join
Graduate Students, Masters Students, Undergraduate Students
Project Description
This project focuses on translating already-developed Digital Twin and Machine Learning models for livestock disease prediction and management into fully operational, user-facing decision-support platform through the design of intuitive user interfaces, interactive dashboards, and rigorous validation pipelines. The core objective is to bridge the gap between advanced modeling and real-world usability by enabling decision makers to interact with, interpret, and act on model outputs in real time. This will involve building dynamic dashboards that visualize animal health status, disease risk trajectories, environmental drivers, and simulated intervention outcomes generated by the digital twin and ML models, alongside tools that allow users to run scenario analyses and compare management strategies. In parallel, the project will implement robust validation pipelines to continuously assess model performance using incoming field and clinical data, ensuring reliability, transparency, and regulatory confidence. The project will operationalize existing models into a scalable, decision-ready platform that supports proactive, data-driven BRD management and accelerates adoption in production settings.
Team Needs
Open to undergraduate and graduate students in Animal Science, Veterinary Medicine, Data Science, Computer Science, Engineering, Information Systems, and related disciplines. The project is especially suited for individuals with interests in artificial intelligence applications, machine learning, digital twin systems, data visualization, and software development. Ideal team members should be motivated, detail-oriented, and comfortable working with data-driven systems, with a willingness to learn or apply Python programming, dashboard development (e.g., Streamlit, Dash, or similar tools), and model validation workflows. Prior experience with AI/ML, systems modeling, or livestock production systems is an advantage but not required, as mentorship and technical guidance will be provided throughout the project.
Special Opportunities
Participants will gain hands-on experience working with cutting-edge Digital Twin systems, Machine Learning pipelines, and interactive AI-driven dashboards applied to real-world livestock health challenges. The project offers strong professional development in data-driven decision systems and computational modeling, positioning participants for competitive careers in AI, data science, veterinary informatics, and precision agriculture. Students will also be trained in modern research methodologies, model validation techniques, and scientific communication, with opportunities to contribute to peer-reviewed publications, technical reports, and conference presentations. In addition, the project fosters interdisciplinary collaboration across institutions and domains, providing valuable exposure to cross-functional teams working at the intersection of AI, animal health, and systems engineering. Participants will benefit from structured mentorship and professional networking, opening pathways to internships, research partnerships, and long-term career opportunities in academia, industry, and applied AI innovation.