BASE: Bridging AI, Systems, and Environment

Developing advanced techniques for visual data interpretation, including object detection, recognition, and image processing, to enhance automated visual understanding in diverse applications.

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.

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.

Creating robust federated learning frameworks to handle non-iid data and resource constraints, ensuring effective and secure distributed learning across heterogeneous environments.

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.

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.