Labs & Groups

Helena Mentis demonstrates tools that she uses in her HCI lab. Photo by Marlayna Demond ’11 for UMBC.


Research Labs

There are many research labs at UMBC that are focused on artificial intelligence or use AI technologies in their projects. These labs are associated with the UMBC AI Center and can be found in several departments.



The AI & Theory-Oriented Molecular Science Lab is headed by Tyler Josephson in the CBEE department. The fundamental building blocks in chemistry are atoms; from their interactions emerge molecules, materials, and their properties. In mathematical logic, basic assumptions are also called atoms; proofs, lemmas, and theorems are derived from these. The ATOMS Lab brings together chemical engineers, computer scientists, and mathematicians to equip computers to reason about and discover theories in molecular science.

Big Data Analytics Lab

The Big Data Analytics Lab is led by Jianwu Wang in Information Systems and studies different aspects of big data analytics, including scalable causality analytics, scalable data aggregation, and anomaly detection, with current applications focusing on climate and manufacturing. We integrate techniques in distributed computing, data mining, and machine learning and work with academic and industry collaborators to achieve multidisciplinary research and social impacts.

Causal AI Lab Lab

Understanding causality from data is a fundamental element of human-level intelligence. Headed by Md Osman Gani, the CAIL lab studies causal inference, machine learning, and AI methods to contribute to a deeper understanding of the cause and context in data-intensive healthcare and ubiquitous computing environments. Our work is collaborative and interdisciplinary and focuses on societal impacts with applications in healthcare, pervasive computing, rehabilitation engineering, occupational science, and more.

Computational Mechanics Laboratory

Professor MeIlin Yu’s Computational Mechanics Lab works on computational fluid dynamics, scientific machine learning, and high-performance computing. A current project, for example, uses reinforcement learning to develop computational fluid dynamics algorithms for large eddy simulation of turbulent flows.


Computing Compass Lab

Prof. Chenchen Liu leads the Computing Compas Lab, which develops novel computing paradigms for high-performance intelligence computing and explores innovations in AI’s algorithm, architecture, system, and circuit designs. Its research topics cover novel computer architecture and hardware for AI, neuromorphic computing, deep learning, edge computing, and VLSI design with emerging technologies.

Data Management & Semantics Group

DAMS focuses on four main areas of research: Data Management, AI, Privacy, and the Internet of Things. We deal with research challenges in bridging the gap between raw data (e.g., sensor data) and semantically meaningful data easily understood by people (e.g., inferences extracted from sensor observations). We incorporate semantics and privacy awareness into data management to design smarter and more responsible systems, develop prototypes and deploy them in the real world.

Dream Lab

The DREAM lab led by Charles Nicholas looks at ways of applying machine learning and AI to cybersecurity, and malware analysis in particular. Recent projects involve using tensor decomposition and innovative measures of string similarity to solve practical problems. The lab is also home to UMBC’s very active cyberdefense competition team, the ‘CyberDawgs’

Ebiquity Research Group

The Ebiquity Research Group consists of faculty and students from the CSEE and IS departments. Its research covers the application of AI to many areas, including natural language understanding, information extraction from text, mobile and pervasive computing, knowledge representation and reasoning, knowledge graphs, multi-agent systems, privacy, and cybersecurity.

Estimation, Control & Learning Laboratory

ECCL research is focused on developing data-driven, learning-based control and estimation techniques for mechanical and aerospace engineering problems. We use tools from linear and nonlinear system theory, linear and nonlinear control theory, optimization, and learning theory to design and develop novel algorithms for control and estimation problems in complex dynamic systems, including applications in robotics, autonomous systems, and UAVs.

Informatics for Human Flourishing Lab

The Informatics for Human Flourishing Lab works to create data-enabled human-centered technology and systems that promote human flourishing. It explores the fundamental data analytics/machine learning/AI methods that could solve practical problems, complemented by human-centered approaches using a mixture of quantitative and qualitative methods.

Interactive Robotics and Language Lab

IRAL is a research laboratory in the CSEE department led by Cynthia Matuszek. It studies robotics and natural language processing to combine the fields: developing robots that everyday people can talk to, telling them to do tasks or about the world around them. This approach to learning to understand language in the physical space that people and robots occupy is called grounded language acquisition. Its goal is to build robots that can perform tasks in noisy, real-world environments instead of being pre-emptively programmed to handle a fixed set of predetermined tasks.

Knowledge, Analytics, Cognitive, and Cloud Computing lab

The KnACC lab is led by Karuna Joshi from the Information Systems department and aims to address challenging issues at the intersection of Data Science and Cloud Computing.

KAI² – Knowledge-infused AI and Inference

KAI² is led by Manas Gaur and works on the integration and uplifting of AI with human knowledge representable in different forms: Structural Knowledge Graphs, Flattened Lexicons, Process Knowledge in Questionnaires, and Commonsense in General-purpose unstructured content, to design human-centered systems and applications for sensitive domains. It aims to make the next-generation neuro-symbolic AI approach inspired by human’s ability to combine data and knowledge to induce Explainable, Interpretable, and Safety aspects in statistical AI.

Language, Aid, and Representation AI Lab

The Language, Aid, and Representation AI Lab is led by professor Lara Martin. Its research focuses on human-centered artificial intelligence and natural language processing applications. Recent work has included automated story generation, augmentative and alternative communication tools, AI for tabletop roleplaying games, speech processing, and affective computing.

The Latent Lab

James Foulds heads the Latent Lab in the IS department. It carries our research in the area of socially conscious machine learning and artificial intelligence. Its work aims to improve AI’s role in society regarding fairness and privacy and to promote the practice of computational social science using probabilistic models and Bayesian inference.

Machine Learning for Signal Processing

Tulay Adali’s Machine Learning for Signal Processing Lab works on the development of theory and tools for processing signals that arise in today’s growing array of different applications and pose challenges for traditional signal processing techniques. It applies techniques from statistical and adaptive signal processing as well as machine learning to develop effective methods that address challenges in applications with a focus on medical image analysis and fusion.

Mobile, Pervasive & Sensor Computing Lab

Nirmalya Roy directs the Mobile, Pervasive, and Sensor Computing (MPSC) Lab, which analyzes data from sensors and mobile devices in an efficient manner, uncovers those hidden patterns, gauges the activity, behavior, and interaction of the users, and presents this to the users, society, or application to understand the human and system behavior better.

Multi Data Lab

The MData Lab is led by Vandana Janeja/. Real-world data is seldom compartmentalized. Data from one phenomenon at a location (e.g., health disparities) often intersects with, relates to, or impacts other phenomena (e.g., food deserts) in the same place. This raises the question: why analyze these phenomena in separate data silos? Tackling this question and crossing over silos into a messy heterogeneous world is at the heart of the research in the Lab. Multi-domain relevance is evident in all application areas, such as healthcare informatics, road traffic, and cybersecurity.

NLP and Social Computing Lab

The NLP and Social Computing is led by Shimei Pan and focuses on natural language processing (NLP), large-scale social media analytics, Human-AI interaction and societal Impact, and Intelligent Interactive systems

Perception, Prediction, and Reasoning Lab

The Perception, Prediction, and Reasoning Lab is led by Tejas Gokhale and focuses on the design of robust and reliable systems that can understand the visual world. It draws inspiration from principles of perception, communication, learning, and reasoning.

Remote Sensing, Signal & Image Processing

The RSSIPL Lab was founded by Professor Chein-I Chang in 1992 with research focus on remote sensing, signal and image processing, specifically, hyperspectral imaging, medical imaging, automatic target recognition.


The Security and Optimization for Networked Globe Lab is led by Houbing Herbert Song and has the mission to advance research and education through discovery and innovation at the confluence of AI/machine learning/data science, cybersecurity, and cyber-physical systems (CPS).

Vinjamuri Lab

The Vinamuri Lab studies Brain-Machine Interfaces (BMIs) that control upper-limb prostheses. A current research objective is understanding how the brain controls complex hand movements. The human hand has about 30 dimensions in contrast to a human arm, which has only seven dimensions. BMIs that control human arms have already been demonstrated with decent accuracy.


The primary research areas include Electronic Design and Automation, Brain-inspired computing, Neural network architecture exploration, Autonomous driving and CAN security, digital, analog, and mixed-signal CMOS ICs/SOCs for a variety of applications, Verification and testing techniques, CAD tools for design and analysis of microprocessors and FPGAs, as well as interdisciplinary research projects.