Multimodal Environments for Twin Research & Immersive eXperiences

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Mixed Extended-Reality and Twin Research and Immersive X-Systems (METRIX) Lab

Pushpita Chatterjee, Ph.D.

Assistant Professor of Computer Science and Data Science

puspita.chatterjee@mmc.edu

METRIX Lab Vision:

Smart Education: METRIX is committed to redefining education through immersive, personalized, and secure learning environments powered by AI and extended reality (XR). The goal is to empower learners with the tools and resources necessary for success in a connected, data-driven world.
Secure Healthcare Delivery: By advancing technologies such as UAV-assisted healthcare and AI-driven security frameworks, METRIX ensures that healthcare systems are both efficient and secure. This helps healthcare providers deliver timely, data-backed services to improve patient care.

Team

Pushpita Chatterjee, Ph.D.

Assistant Professor of Computer Science and Data Science

Eugene Levin, Ph.D., CP

Professor, Spatial Data Science
Director of International Programs

Student Research

We look forward to discussion opportunities for student research. Interested students will need to have completed the courses below for preparation for research in this lab. Contact Dr. Chatterjee, puspita.chatterjee@mmc.edu for more information.

The following courses are in the M.S. Data Science program. View program’s course page for more information.

MSDS 525: Data Management Foundations for Data Science
MSDS 535: Further Mainstream Programming Languages for Data Science
MSDS 555 Big Data Management and Analytics
MSDS 580: Research Methods
MSDS 590: Capstone Projects

The following course is in the Ph.D. Data Science program. View program’s course page for more information.

MSDS 715: Big Data Modeling

Education Workforce Development

Collaborative Interactive Data Science Academy

The Collaborative Interactive Data Science Academy is a residential, week-long summer experience for high school students. The program implements virtual augmented, and mixed reality control of robotic systems using NASA geospatial and extra-terrestrial big data. Students will build statistical and critical thinking skills through exposure to NASA research and data science tools.

Research

The METRIX Lab pursues advancements at the convergence of intelligent systems, security and immersive technologies to transform education and healthcare delivery. By leveraging geospatial intelligence, AI-driven security frameworks, and digital twins, the lab develops secure, adaptive solutions to meet the evolving needs of these sectors. METRIX integrates AI-based security with geospatial intelligence to enhance UAV-assisted healthcare systems and real-time environment monitoring.

In education, the lab pioneers immersive learning by using XR technologies including virtual reality (VR), augmented reality (AR), and mixed reality (MR) to create personalized, engaging environments with gamification and real-time analytics. Digital twins play a key role, providing data-driven solutions for optimizing operations in both educational and healthcare settings.

The lab also focuses on improving healthcare security and efficiency through immersive technologies and AI, particularly with UAVs for remote services. By fostering smart campus environments, METRIX ensures effective, real-time integration of learning and healthcare technologies, driving better outcomes through enhanced resource management, security and data analytics.

Research Areas

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Research Areas

AI-Driven Geospatial Intelligence for Human Security

Integrating AI with Geospatial Intelligence (GEOINT) offers transformative capabilities to address critical human security challenges, including disaster response, border security and situational awareness. This research focuses on developing AI-driven decision-support systems that enhance predictive analytics, real-time threat detection and crisis management. By leveraging AI-powered computer vision models, satellite imagery, and LiDAR data, the project enables automated object detection and classification for monitoring infrastructure, tracking human activity and identifying security risks. Key research areas include:

AI-Powered Threat Detection: Developing machine learning models to analyze geospatial data for identifying potential threats, such as unauthorized activity or infrastructure damage, in real-time.
Predictive Analytics: Employing advanced AI techniques like recurrent neural networks (RNNs) and reinforcement learning to forecast security risks, such as migration patterns or potential border breaches, supporting proactive security measures.
Real-Time Data Integration: Creating AI-powered dashboards that integrate data from multiple sources, such as UAVs, IoT sensors, and satellite imagery, to provide decision-makers with comprehensive, real-time situational awareness.
AI-Enhanced Surveillance: Utilizing AI to automate the analysis of geospatial data from surveillance systems, providing critical insights for law enforcement, defense agencies and security personnel.

This research aims to develop robust, AI-driven geospatial intelligence systems that enhance human security efforts, improve crisis management and enable more informed decision-making in real-world security operations. By leveraging AI, the initiative supports more effective, efficient and proactive responses to security threats.

AI-Enabled Health Digital Twin

AI-Enabled Health Digital Twin Research aims to address critical healthcare challenges such as delayed diagnoses, fragmented care and inefficient resource management. Our innovative approach leverages virtual models of patients and healthcare systems, integrating real-time clinical data with Social Determinants of Health (SDOH), including socioeconomic, environmental and educational factors. This integration enhances the accuracy of health risk predictions and the personalization of care plans, fostering improved early interventions, resource optimization and proactive health management. By optimizing patient outcomes and healthcare system efficiency, our work aims to transform the healthcare landscape.

At the core of our research is the development of dynamic, AI-driven health models that utilize real-time clinical data, patient-specific information, and SDOH. These models are powered by machine learning algorithms to forecast health risks, simulate treatment outcomes and tailor care strategies. We integrate IoT devices and health records to enable continuous health monitoring, while predictive analytics help improve early diagnoses and optimize resources. Additionally, AI simulations support healthcare decision-making, enhancing the overall efficiency and effectiveness of healthcare delivery. Our research objectives include:

Data Collection: We integrate data from electronic health records (EHRs), wearables, and environmental sensors to build comprehensive, real-time health profiles.
Model Development: Machine learning algorithms will be applied to create predictive models that simulate potential treatment outcomes and health trajectories.
Predictive Analytics: AI techniques will be employed for early disease detection, risk assessments and the optimization of personalized treatment plans.
User Interface: We will design clinician-friendly interfaces that allow healthcare providers to interact seamlessly with digital twins, visualizing patient-specific data and actionable insights.
Security and Privacy: We will implement robust security protocols, including encryption and blockchain technologies, to ensure safe handling and sharing of sensitive healthcare data.

Through our innovative approach, we aim to revolutionize healthcare delivery by improving patient outcomes, reducing healthcare costs, and fostering a more efficient and effective healthcare system for all.

AI-Driven Security for UAV-Assisted Healthcare Delivery

Unmanned Aerial Vehicles (UAVs) are revolutionizing healthcare by enabling rapid delivery of medical supplies, vaccines and emergency services in remote or underserved areas. However, as UAVs become integral to healthcare systems, ensuring their security against cyber threats is essential. The AI-Driven Security for UAV-Assisted Healthcare Delivery research focuses on developing advanced, AI-powered security frameworks to protect UAV operations and sensitive healthcare data.

UAVs in healthcare delivery face risks such as unauthorized access, signal interference, GPS spoofing and system vulnerabilities. Addressing these challenges requires AI-driven solutions that provide real-time threat detection, dynamic responses and secure communication protocols. This research develops machine learning algorithms to continuously monitor UAV systems for anomalies and security breaches, ensuring timely threat detection and mitigation. Key areas of focus include:

AI-Powered Threat Detection: Machine learning algorithms to detect and address cybersecurity threats in real time.
Adaptive Encryption and Secure Communications: AI-driven encryption to protect sensitive healthcare data transmitted between UAVs and healthcare providers.
Autonomous Security Response: AI systems that enable UAVs to autonomously respond to threats like signal jamming or GPS spoofing.
Resilient Navigation and Path Planning: AI-driven systems that optimize UAV flight paths, ensuring reliable deliveries even in challenging conditions.

By developing these advanced security solutions, this research will enhance the safety, efficiency and reliability of UAV-assisted healthcare delivery, enabling secure and uninterrupted service in critical healthcare environments.

AI-Driven Personalized Learning in Immersive Environments

The convergence of AI with immersive technologies such as augmented reality (AR), virtual reality (VR), and extended reality (XR) is revolutionizing educational methodologies by enabling personalized, engaging and adaptive learning experiences. This research aims to develop AI-driven frameworks that leverage immersive environments to enhance student engagement, performance and satisfaction. The key research areas are:

AI-Powered Personalized Learning: Developing adaptive learning systems that utilize AI to analyze individual learner profiles, preferences, and performance data, thereby delivering customized educational content within AR/VR/XR platforms. This approach ensures that learning experiences are tailored to meet the unique needs of each student, fostering a more effective and engaging educational environment.
Gamification in Smart Learning Environments: Investigating the integration of gamification elements, such as points, badges, and leaderboards, into smart learning spaces to enhance motivation and engagement. By incorporating game-like mechanics, students are encouraged to actively participate and persist in their learning journeys, leading to improved educational outcomes.
Smart Learning Spaces: Designing and implementing intelligent learning environments that leverage AI and immersive technologies to create dynamic, interactive and responsive educational settings. These spaces adapt to the evolving needs of learners, providing a seamless integration of physical and virtual learning experiences.
Extended Reality (XR) for Ubiquitous Learning: Exploring the application of XR technologies to facilitate ubiquitous learning experiences, allowing students to access educational content anytime and anywhere. This approach breaks down traditional learning barriers, offering flexible and accessible learning opportunities.
Learning Analytics and Behavior Prediction: Utilizing AI-driven analytics to monitor and predict student behaviors and learning patterns within immersive environments. By analyzing data from various sources, educators can gain insights into student progress and engagement, enabling timely interventions and support to enhance learning outcomes.

By advancing these areas, the research seeks to establish a comprehensive framework for AI-driven personalized learning within immersive environments, aiming to transform educational practices and outcomes through the strategic integration of AI and immersive technologies.

Grant Support: Publications

A list of publications can be found at Google Scholar or ORCID for the following faculty.

Pushpita Chatterjee, Ph.D.
Sajid Hussain, Ph.D.
Eugene Levin, Ph.D.

Mixed Extended-Reality and Twin Research and Immersive X-Systems (METRIX) Lab

Pushpita Chatterjee, Ph.D.

Assistant Professor of Computer Science and Data Science

puspita.chatterjee@mmc.edu

METRIX Lab Vision:

Team

Pushpita Chatterjee, Ph.D.

Assistant Professor of Computer Science and Data Science

puspita.chatterjee@mmc.edu

Eugene Levin, Ph.D., CP

Professor, Spatial Data Science
Director of International Programs

elevin@mmc.edu

Student Research

Education Workforce Development

Research

Research Areas

Research Areas

Grant Support: Publications

Invest in Knowledge

With Your Support We Can Change the World.

Mixed Extended-Reality and Twin Research and Immersive X-Systems (METRIX) Lab

Pushpita Chatterjee, Ph.D.

Assistant Professor of Computer Science and Data Science

puspita.chatterjee@mmc.edu

METRIX Lab Vision:

Team

Pushpita Chatterjee, Ph.D.

Assistant Professor of Computer Science and Data Science

puspita.chatterjee@mmc.edu

Eugene Levin, Ph.D., CP

Professor, Spatial Data Science Director of International Programs

elevin@mmc.edu

Student Research

Education Workforce Development

Research

Research Areas

Research Areas

Grant Support: Publications

Invest in Knowledge

With Your Support We Can Change the World.

Professor, Spatial Data Science
Director of International Programs