AI Engineering Center of Excellence

Artificial Intelligence at Clark’s School of Engineering

The AI Engineering Center of Excellence at the University of Maryland, College Park is a hub for pioneering research, innovation, and talent development. Our mission is to advance the science and engineering of next-generation AI systems while ensuring breakthroughs become engineered solutions—reliable, scalable, and impactful in the real world.

AI Research

Summary of ongoing AI-related projects

Projects involving physical systems integrated with AI for perception, decision-making, and action.

ArtIAMAS – AI and Autonomy for Multi-Agent Systems

ISR faculty develop embodied AI platforms that operate in contested environments. This includes human-robot teaming, drone and ground robot collaboration, and multi-modal sensing for adaptive behavior.

USDA OysterNet – AI for Underwater Robotics

ISR researchers developed convolutional neural networks (OysterNet) for underwater robots to estimate oyster populations. This project integrates computer vision with real-time robotic sensing and control.

CogniSense (JUMP 2.0)

Develops AI-embedded sensing systems using neuromorphic and multispectral technologies. ISR contributions include integration of sensor intelligence into physical devices for context-aware data collection.

Medical Microrobotics and Biointegrated AI Systems

ISR researchers are developing AI-guided ingestible devices for gut monitoring, including serotonin detection and drug delivery, integrating biosensing with embedded learning and adaptive actuation.

NIFTI – Bio-Inspired Flight and Sensing Systems

Incorporates embodied AI in nature-inspired micro air vehicles. ISR’s role includes sensor fusion, neural computation, and learning-based control architectures for physical flight systems.

Projects focused on descision-making agent soperating independently in dynamic environments.

ArtIAMAS

Central to ISR’s leadership in autonomy. Covers swarm coordination, decentralized learning, adversarial robustness, and safe autonomous operation across land, air, and sea platforms.

BLUES – Boundary Layer Under-Ice Environmental Sensing

Led by Miao Yu, this project develops autonomous underwater vehicles for Arctic environmental monitoring, using AI to adapt to extreme, data-sparse conditions.

Human-Robot Collaborative Autonomy (ONR)

Focuses on human-in-the-loop autonomy, multisensory perception, and collaborative planning between people and machines.

DARPA – Robust Swarm Tactics

ISR researchers are developing decision-making systems for swarms of autonomous drones operating under uncertain and adversarial conditions.

ONR – Intelligent and Learning Autonomous Systems

Explores composability and correctness in autonomy. ISR researchers are designing systems that learn while maintaining guarantees of performance and safety.

Autonomous Vehicle Traffic Flow Optimization

Research into optimizing urban traffic using connected autonomous vehicles (CAVs), with reinforcement learning and predictive modeling for real-time coordination.

Projects focused on trustworthy, resilient, and adaptive AI systems with provable performance.

Trusted Decentralized Learning and Verification

ISR faculty are advancing model checking, temporal logic, and formal methods for AI systems operating in embedded and distributed environments.

Mean-Field Game Theory for Scalable AI

This AFOSR MURI project, with ISR participation, applies game-theoretic frameworks to ensure convergence, stability, and scalability of multi-agent learning systems.

Machine Learning for Mental Health Monitoring

ISR-led projects develop speech-based diagnostic tools with robustness to real-world variability in patient data and device conditions.

Deterministic Annealing and Risk-Sensitive Reinforcement Learning

ISR is developing learning algorithms that perform reliably under uncertainty, with applications in control systems and autonomous decision-making.

Secure and Resilient Wireless AI Networks

ISR researchers are applying AI to secure communication protocols, focusing on robust performance in federated and edge learning environments.

AI Lab Profiles

The MRC Robotics and Autonomy Laboratory is a multi-functional space on the 3rd floor of the E.A. Fernandez IDEA Factory. RAL functions as a shared research and education facility for all MRC faculty and affiliated students. It supports mobile robotics research, robotics prototyping and manufacturing, and optical inspection equipment. Questions regarding lab usage should be addressed to the lab manager (Ivan Penskiy, ipenskiy@umd.edu).

Located in the University System of Maryland at Southern Maryland (USMSM) Southern Maryland Autonomous Research and Technology (SMART) Building, the MATRIX Lab features state-of-the-art facilities for autonomous vehicle research including an 80’ by 60’ open air-land lab with an amphibious pool, a hydrology lab featuring a circulating water channel with a 80 cm by 130 cm cross-section, an AR/VR capable research space, roof-top antenna farm, and outdoor ground and air vehicle testing.

The Center for Advanced Life Cycle Engineering (CALCE), at the University of Maryland, has been serving the electronics industry as a resource and knowledge base for the development of reliable, safe, and cost effective products for more than 35 years. Supported by over 300 of the world’s leading companies, CALCE conducts fundamental reliability science research in the areas of failure mechanism identification and modeling, accelerated test methods, prognostics and health management approaches, supply chain management techniques, as well as the application of artificial intelligence for remaining life and fault prediction of electronic devices and assemblies. In addition to its active research, CALCE provides test and failure analysis services. CALCE also provides continuing education opportunities to practicing engineers through our open website, monthly webinars, industry symposia, and professional development courses.

ArtIAMAS: AI and Autonomy for Multi-Agent Systems

A $68 million cooperative agreement with the U.S. Army Research Laboratory, ArtIAMAS focuses on safe, intelligent, and cooperative autonomous systems. Research spans AI, machine learning, robotics, cybersecurity, modeling and simulation, and operations research. Led by ISR faculty across aerospace engineering, mechanical engineering, and computer science, ArtIAMAS builds on a longstanding UMD–ARL partnership in autonomy and systems science.

CogniSense: Center on Cognitive Multispectral Sensors

Part of the DARPA/SRC JUMP 2.0 initiative, CogniSense develops AI-enabled sensor systems that interpret complex environments in real time. ISR researchers contribute to energy-efficient architectures, sensor fusion, and embedded learning for intelligent perception systems. The center is co-led by Pamela Abshire, with collaborators across 13 institutions.

AIM-HI: AI + Medicine for High Impact

An interdisciplinary initiative applying machine learning to behavioral health. ISR faculty develop speech-based AI tools to detect and monitor mental health symptoms outside of clinical settings. Current projects include mobile systems for early detection and personalized monitoring of depression and other conditions, led by Carol Espy-Wilson in collaboration with computer science and medical researchers.

NIFTI: Air Force Center of Excellence on Nature-Inspired Flight Technologies and Ideas

This center integrates bio-inspired sensing and control with AI for flight systems. ISR researchers contribute to the design of intelligent micro air vehicles, swarm robotics, and autonomous decision-making systems for challenging environments. Faculty from electrical engineering, mechanical engineering, and bioengineering lead UMD’s participation.

NEXTOR III: FAA Center of Excellence in Aviation Operations Research

ISR is a core partner in NEXTOR III, applying AI, optimization, and decision science to challenges in air traffic management and transportation systems. The group develops models and algorithms for collaborative decision-making, traffic flow prediction, and system-wide performance analysis.