BASE: Bridging AI, Systems, and Environment
Advancing AI frontiers through innovative research in agriculture and beyond
Graduate Research Assistant (50% FTE) position in AI, Machine Learning, Deep Learning, and Computer Vision — immediate joining for Fall 2026 and Spring 2027.
View Details
Conference Presentation at CVPR 2026 in Denver, CO
PhD student Taminul Islam presented 'TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock' at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026 in Denver, CO.
Research Focus
Computer Vision
Developing advanced techniques for visual data interpretation, including object detection, recognition, and image processing, to enhance automated visual understanding in diverse applications.
Deep Learning
Designing and optimizing state-of-the-art neural network architectures and algorithms to address complex problems across various domains, with a focus on improving performance and scalability.
AI for Agriculture
Applying artificial intelligence to agricultural challenges such as precision livestock management, crop health monitoring, and yield prediction, aiming to improve efficiency and sustainability in agriculture.
Federated Learning
Creating robust federated learning frameworks to handle non-iid data and resource constraints, ensuring effective and secure distributed learning across heterogeneous environments.
Recent Publications
Reference-Conditioned Distance Intervals on Unseen Camera Traps
Computer Vision for Ecology (CV4E) Workshop at the European Conference on Computer Vision (ECCV), 2026
Toqi Tahamid Sarker, Taminul Islam, Seth J. Morelock, Guillaume Bastille-Rousseau, Khaled R. Ahmed
VLMDual: A Vision-Language Distillation Framework for Joint Classification and Plume Segmentation in Rumen Acidosis Detection
Smart Agricultural Technology, 2026
Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby, Khaled R. Ahmed, Amer AbuGhazaleh
Mask-Guided Multi-Task Learning: Real-Time RGB Prediction of Plant Photosynthetic Efficiency at the Edge
International Conference on Pattern Recognition (ICPR), 2026
Abdellah Lakhssassi, Taminul Islam, Cristiana Bernardi Rankrape, Naoufal Lakhssassi, Karla Gage, Khaled R. Ahmed
Latest News
Paper Accepted at the CV4E Workshop at ECCV 2026
August 2026
We are pleased to announce that our paper 'Reference-Conditioned Distance Intervals on Unseen Camera Traps' has been accepted at the Computer Vision for Ecology (CV4E) workshop at the European Conference on Computer Vision (ECCV) 2026. Authored by Toqi Tahamid Sarker, Taminul Islam, Seth J. Morelock, Guillaume Bastille-Rousseau, and Khaled R. Ahmed. Congratulations to the team!
Journal Article Published: VLMDual in Smart Agricultural Technology (Q1)
July 2026
We are pleased to announce that our journal article 'VLMDual: A Vision-Language Distillation Framework for Joint Classification and Plume Segmentation in Rumen Acidosis Detection' has been published in Smart Agricultural Technology (Elsevier), a Q1 journal. Authored by Taminul Islam, Toqi Tahamid Sarker, Mohamed Embaby, Khaled R. Ahmed, and Amer AbuGhazaleh. Congratulations to the team!
Two Papers Presented at CVPR 2026 in Denver, CO
June 2026
PhD student Taminul Islam presented two of our papers at the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) 2026 in Denver, CO. He presented 'TRACE: Thermal Recognition Attentive-Framework for CO2 Emissions from Livestock' and 'FryNet: Dual-Stream Adversarial Fusion for Non-Destructive Frying Oil Oxidation Assessment'. Congratulations to the team!
Two Papers Accepted at ICPR 2026
June 2026
We are pleased to announce that two of our papers have been accepted at the International Conference on Pattern Recognition (ICPR) 2026: 'Mask-Guided Multi-Task Learning: Real-Time RGB Prediction of Plant Photosynthetic Efficiency at the Edge' and 'WeedRepFormer: Reparameterizable Vision Transformers for Real-Time Waterhemp Segmentation and Gender Classification'. Congratulations to the team!
Join Our Lab
Interested in pushing the boundaries of AI? We are always looking for talented individuals to join our team.
PhD Students
Conduct cutting-edge research in AI and Computer Vision.
Masters Students
Gain hands-on experience in advanced AI projects.
Our Sponsors and Collaborators
