We are thrilled to announce that our journal article, 'WeedSwin Hierarchical Vision Transformer with SAM-2 for Multi-Stage Weed Detection and Classification' has been acepted by the Scientific Reports (Nature). Congratulations to the team!

We are excited to announce that our review article, 'Carbon Emission Quantification of Machine Learning: A Review' has been published in IEEE Transactions on Sustainable Computing. This collaborative work with SHIELD Lab, SIU represents an important contribution to sustainable computing research. Congratulations to the entire team!

We are pleased to announce that our research proposal, 'AI-Driven Discovery of Sustainable Ionic Liquids: A Multimodal Deep Learning Approach for Enhanced Lignin and Plastic Deconstruction,' has been awarded funding by the IIN Sustaining Illinois program. The period of performance is May 16, 2025, to May 15, 2026

Our lab's latest research "Optical gas imaging and deep learning for quantifying enteric methane emissions from rumen fermentation in vitro" published in IET Image Processing explores a potential new approach to greenhouse gas monitoring in the livestock industry. By combining Optical Gas Imaging (OGI) with deep learning, we've investigated a promising alternative to traditional methane quantification methods. Our interdisciplinary collaboration between Animal Science and Computer Science shows how we can make environmental sustainability more achievable for farmers and researchers alike. While further research is needed, this initial work represents an important step in exploring more accessible approaches to livestock greenhouse gas monitoring.