BASE: Bridging AI, Systems, and Environment

Advancing AI frontiers through innovative research in agriculture and beyond

Our 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.

Generative Models

Advancing generative model techniques for data augmentation and realistic image synthesis, with a focus on improving the quality and diversity of synthetic data for various applications.

Real-time Systems

Implementing real-time AI solutions to enable immediate and accurate decision-making in critical applications, ensuring timely responses and actions in dynamic and high-stakes environments.

Lab Highlights




Research Projects


Team Members


Years of Experience

Latest Works and Activities

Conference Presentation at CVPR 2024

Conference Presentation at CVPR 2024

Our team presented research on segmenting in vitro methane emissions in cattle using optical gas imaging and deep learning.

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Our Sponsors and Collaborators

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Recent Publications

Porosity Prediction of 3D Printed Components Using U-Net and Its Variants

Aluri Manoj, Khaled R. Ahmed, Ghada Omar

2024 3rd International Conference on Artificial Intelligence For Internet of Things (AIIoT)View Publication

Gasformer: A Transformer-based Architecture for Segmenting Methane Emissions from Livestock in Optical Gas Imaging

Toqi Tahamid Sarker, Mohamed G Embaby, Khaled R. Ahmed, Amer AbuGhazaleh

Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern RecognitionView Publication

Cannabis Seed Variant Detection using Faster R-CNN

Toqi Tahamid Sarker, Taminul Islam, Khaled R. Ahmed

arXiv preprint arXiv:2403.10722View Publication

Advancing Generative Model Evaluation: A Novel Algorithm for Realistic Image Synthesis and Comparison in OCR System

Majid Memari, Khaled R. Ahmed, Shahram Rahimi, Noorbakhsh Amiri Golilarz

arXiv preprint arXiv:2402.17204View Publication

Latest News and Achievements

New Illinois Soybean Center Grant Awarded

August 2024

Awarded a one-year research grant from Illinois Soybean Center for "Smart Farming Soybean: The use of AI to detect and estimate early insect infections in Soybean"


New USDA-NIFA Grant Awarded

October 2023

Awarded a two-year research federal grant from USDA-NIFA for "Detecting Subacute Ruminal Acidosis using a real-time Deep Learning Algorithm"


PhD Student Defense Success

September 2023

PhD student Memari Majid successfully defended his dissertation and will graduate in Fall 2023


Illinois Innovation Network Grant

July 2023

Granted a one-year seed research grant from ILLINOIS INNOVATION NETWORK.


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.