Secure Prediction, Edge AI, and LLM Lab

We build robust, privacy-preserving learning systems for LLMs/VLMs, federated learning, and cyber defense.

NSF-fundedDHS-fundedA* conference publications

Federated Learning

LLMs and VLMs

CyberSecurity

Artificial Intelligence

Interdependent Networks

Data Science

Our Vision

At the Secure Prediction, Edge AI, and LLM Lab, our vision is to drive transformative research in the domain of AI, federated learning, large language models, vision-language models, cybersecurity, and multi-modality. We strive to place strong emphasis on edge intelligence, tailoring the potential of distributed computing to enable efficient decision-making. We aspire to pioneer advancements that redefine the boundaries of AI and machine learning, while ensuring the security and privacy of distributed systems. Through our interdisciplinary research, we strive to develop robust, efficient, and intelligent solutions that enhance decision-making in edge environments, driving innovation that positively impacts society in an increasingly interconnected world.

Security & Privacy

AI & Intelligence

Edge Computing

Global Impact

Announcement

A fully funded Ph.D. position is available! We are looking for strongly motivated candidates having experience in Large Language Models, Federated Learning or Computer Vision. We are always looking forward to work with SIUC undergraduate and MS students.

Our Sponsors

Proudly supported by leading organizations

Highlights

Celebrating our achievements and milestones

Achievement

NSF CRII Grant of $167,500 Awarded to Dr. Imteaj! Thanks to NSF!

Grant Award

Dr. Imteaj's project with FIU won nearly $100,000 DHS grant!

Conference

Zarif presented paper at IEEE SmartComp!

Award

Saika won the Graduate Research Prize at Tapia'23 Conference.

Recognition

Dr. Imteaj received the Prestigious FIU Real Triumph Award!

Best Paper

Best Paper Award at IEEE CSCI conference!

Recognition

Dr. Ahmed Imteaj Named in Stanford's Top 2% Scientists for Research Impact

Featured Projects

Showcasing innovative research and cutting-edge solutions

Ongoing Project

Developing Robust and Accurate Visual Language Model

Our project focuses on developing visual language model that can accurately interpret and generate natural language descriptions for visual content. Through innovative techniques, we aim to create a model that not only improves the understanding of visual data but also enhances the generation of meaningful and contextually relevant textual descriptions. This research has applications in image captioning, visual question answering, and various other tasks that require a deep understanding of both visual and textual data.

Featured

SkyWatch: Unveiling Criminal Activity via Keypoint Analysis with Video-Streaming of Drones (Aerial Perspectives)

This research delves into the realm of crime detection through a pioneering integration of drone technology and developing a novel keypoint detection algorithm. The ongoing project focuses on the acquisition of criminal activity data using drones and the application of advanced keypoint detection algorithms to decipher intricate patterns and anomalies. The overarching goal of this initiative is to provide law enforcement agencies with a powerful tool to elevate their situational awareness and proactively address criminal behavior, leveraging a unique aerial perspective.

Additional Projects

FLAMINGO: Adaptive and Resilient Federated Meta-Learning against Adversarial Attacks

Federated Meta-Learning (FML) can produce personalized model with limited training data which is suitable for resource-constrained edge devices. This project proposes a novel FML algorithm that enhances meta-learners robustness against adversarial attacks.

WatchOverGPT: Real-Time Crime Detection Framework

A framework that combines wearable cameras, smartphone location data, and LLM-based conversational AI to promptly identify and respond to potential life-threatening criminal activities and safety concerns.

Skyward Insights: Drone-Powered Land Cover Detection

This project leverages federated learning to analyze aerial images obtained via drone surveillance, developing a sophisticated system for real-time analysis and classification of land cover patterns.

Driving Smarter Roads: Real-time Traffic Insights

A cutting-edge approach to traffic management leveraging Federated Learning and Social Media Analysis for real-time insights, enabling smarter road networks with efficient traffic management.

Robust, Lightweight and Secure Distributed Machine Learning Algorithms

Developing cutting-edge algorithms that dynamically adapt to diverse data distributions, ensuring consistent performance while prioritizing lightweight solutions ideal for resource-constrained environments.

Ongoing Research

Exploring cutting-edge technologies and innovative solutions

Ongoing

Generative AI

Responsible Development and Deployment of Generative AI

The responsible development and deployment of Generative AI (Artificial Intelligence) is a critical imperative as this technology continues to advance. Generative AI, particularly exemplified by models like GPT (Generative Pre-trained Transformer), has demonstrated remarkable capabilities in generating human-like text, images, and more. However, with great power comes great responsibility. Our focus in responsible development lies in ethical considerations, transparency, and mitigating potential risks.

AI EthicsGPT ModelsTransparency

Distributed Learning

Learning across Interdependent Cyber-Physical-Societal Networks

This project is dedicated to develop a tailored Distributed Machine Learning Framework designed specifically for interconnected Cyber-Physical-Societal Networks. Our focus is on enabling interdependent decision-making processes by extracting profound insights from the interconnected networks. This framework aims to comprehend the complex relationships between these networks, facilitating a deeper understanding of their interdependence and empowering informed decision-making for enhanced resilience and efficiency across interconnected systems.

Federated LearningCyber-Physical SystemsDistributed ML

Active

Innovation

Drone Technology

Drone Swarming, Distributed Streaming and Learning

This project involves orchestrating a fleet of DJI Tello drones to operate collectively. Through distributed streaming, these drones exchange real-time data and insights. This data sharing facilitates a collaborative learning environment where each drone contributes its observations and experiences to a shared knowledge base. This collective intelligence enables the swarm to exhibit synchronized behavior and adaptive decision-making, enhancing their ability to navigate complex environments, perform tasks efficiently, and respond dynamically to changing scenarios.

Drone SwarmsReal-time LearningCollaborative AI