Director: Dr Seung-Jong Jay Park
We are a multidisciplinary research center dedicated to the research and development of advanced AI.
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Research Areas
Systems and Networking
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Cybersecurity & Responsible AI
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Artificial Intelligence & Data Science
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High-Performance and Cloud Computing
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Research Laboratories
Director: Dr. Sajal Das
The mission of the CReWMaN Laboratory is to conduct innovative research in networking (core, wireless, sensors), mobile and pervasive computing, distributed and grid computing, privacy and security, biological networks, and social networks. This is accomplished by creating a stimulating learning environment through teaching, research, mentoring and service excellence, with focus on teaching cutting-edge courses and establish multi-disciplinary collaborations.
Director: Dr. Sid Nadendla
The mission of CPHS laboratory is to conduct cutting-edge research to design and develop smart and trustworthy cyber-physical human systems. Our focus is on multi-agent trust (e.g. due to security/privacy concerns, selfish interaction, social discrimination and lack of transparency), machine learning, and human decision modeling in the context of diverse application domains such as transportation, mining and healthcare. The laboratory is also equipped with a prototype transportation testbed which is equipped with autonomous robots (e.g. Nvidia Jetbots), Jetson Nano cards to mimic road side units and diverse sensors (e.g. cameras, EEG headsets to monitor driver’s mental state).
Director: Dr. Sanjay Madria
The mission of the Web and Wireless Computing (W2C) & Pervasive and Mobile Computing Laboratory is designed to carry out cutting edge research in different aspects of data management (security, compression, replication, caching, query processing, aggregation, fusion) in wireless networks and cloud computing environment. Our focus is on scientific research to advance the state of art in these areas. The current projects are supported by NSF, DOE, ARL, ARO, AFRL, NIST, UM System, etc. The current researchers in the lab are pursuing their PhD/MS degree in different areas of interest. The lab is well-equipped with over 50 3.2 Ghz PCs, 5 Dell Server, linux machines, laptops etc. The lab also has sensor network test-beds consists of Crossbow sensor motes like Telosb, Mica2 and Missouri S&T motes. Lab has also developed a DTN testbed for disseminating information securely for battlefield environment.
Director: Dr. Md Arifuzzaman
The Computer Systems & Networking Lab (CSNL) advances next-generation computing and communication infrastructures with a focus on high-performance systems, networking, and large-scale distributed computing. Our work develops intelligent methods to manage massive data flows, including online optimization for high-speed file transfers, improved end-to-end throughput measurement, and enhanced reliability in large-scale storage environments.
At CSNL, we also explore emerging technologies at the intersection of systems, machine learning, and quantum networking. Current efforts include secure and communication-efficient Federated Learning, as well as proactive and adaptive techniques to enable scalable quantum routing for future quantum internet architectures. CSNL aims to build resilient, high-performance infrastructures for data-intensive science to accelerate scientific discoveries.
Director: Dr. Satish Puri
Scalable HPC and Architetures for Retrieval and Patterns (SHARP) lab at Missouri S&T is currently engaged in three research projects. In the first project, we are revisiting spatial data analytics on heterogeneous systems comprised of data processing units (DPU), GPUs and CPUs. The DPUs are a new class of programmable processors made by NVidia (and other manufacturers). Similar to a modern smart network interface card, DPUs can be used to filter unnecessary data from overwhelming the CPU and memory bandwidth. DPUs can be used by CPUs to offload computations; thereby reducing the load on CPU and increasing the capability of the compute node. The second project is “Nearest Neighbor Similarity Search for Polygons and Trajectories”. Research thrust of the third project is to design communication-efficient spatial analytics algorithms for data-intensive computations by leveraging Processing-In-Memory (PIM) Paradigm. In this new computational model, code execution happens near DRAM memory instead of CPU.
Research publications are focused on leveraging GPUs and HPC compute clusters for speeding up geospatial analytics workloads like polygon overlay, spatial join, Voronoi diagram, geometric intersection, spatial autocorrelation, etc.
Director: Dr. Shubham Chatterjee
The IRIS Lab is dedicated to advancing the frontier of intelligent information systems research. We explore the fascinating challenge of teaching machines to move beyond simple data retrieval toward genuine understanding, reasoning, and natural interaction with knowledge repositories.
Our work sits at the convergence of Information Retrieval (IR), Natural Language Processing (NLP), and Large Language Models (LLMs), creating systems that evolve alongside users’ increasingly sophisticated information needs. Rather than treating search as a static process, we envision it as an adaptive dialogue between humans and machines.
Director: Dr. Xiaowei Yu
The Cognitive and Biomedical AI (CoBAI) Lab focuses on developing artificial intelligence that advances both biomedical discovery and our understanding of human cognition. Our research spans two interconnected directions: AI for Biomedical and Health Sciences and Brain-Inspired AI.
In the biomedical domain, we design intelligent systems for medical imaging, multi-omics, and healthcare text analysis to improve diagnosis, prognosis, and treatment decision-making. We explore methods such as multimodal learning, foundation models, and zero-shot generalization to enable AI systems to operate effectively even with limited labeled data.
From the cognitive perspective, we draw inspiration from the brain’s organizational principles—such as core-periphery structure and hierarchical processing—to build more efficient and human-like AI architectures. This cross-disciplinary approach bridges neuroscience, machine learning, and biomedical engineering, aiming to create AI that not only serves medicine but also reflects the way humans think and learn.
Director: Dr. Mia Mohammad Imran
At LENS, we investigate how software systems evolve, operate, and interact within complex technological and human environments. Through empirical research, we seek to uncover patterns, principles, and behaviors that advance understanding of software reliability, maintainability, and design. LENS aims to unite expertise across software engineering, analytics, and human-centered computing to transform observation into knowledge and knowledge into practice.
Director: Dr. Huiyuan Yang
Welcome to the Human-Centered AI (HCAI) Lab.
Our research operates at the intersection of machine learning and the human condition, focusing on developing artificial intelligence for and about people. We specialize in utilizing a rich variety of sensory data—including video, wearable sensors, Electronic Health Records (EHRs), fMRI, and human biomedical and behavioral signals—to make sense of complex human states, such as mental health and physical wellbeing. We develop and deploy advanced, robust, and interpretable AI models, including Large Language (LLMs) and Vision-Language Models (VLMs), to support all stakeholders in healthcare. Our goal is to improve accessibility, flexibility, accuracy, and efficiency in areas ranging from mental health diagnostics and patient-provider communication to hospital operations. A core pillar of our mission is to safeguard these AI models, ensuring they are safe, fair, reliable, and secure for critical healthcare applications.
Director: Dr. Junjie Xiong
The Cyber Intelligence and Resilience Laboratory is dedicated to building secure, robust, and explainable systems across the web, cloud, and AI ecosystems. Our research lies at the intersection of cybersecurity, large language model safety, and resilient cloud architectures.
CIR Lab investigates advanced attack and defense mechanisms, including prompt-injection detection, web spoofing prevention, secure multi-cloud orchestration, and trust modeling for AI agents. We aim to create a future where cyber systems are not only powerful but also responsible and trustworthy.
Through collaboration with academia, industry, and government partners, the lab’s mission is to advance both fundamental understanding and real-world security solutions in cyber domains.
Director: Dr. Sejun Song
The Secure and Semantic AI Lab (SAIL) in the Department of Computer Science at Missouri S&T, directed by Dr. Sejun Song, Cynthia Tang Endowed Professor of Computer Science, develops privacy-preserving, secure, and context-aware AI systems for health, agriculture, transportation, and public safety. His work spans semantic AI, responsible AI, context recognition, DeepFake and presentation-attack detection, secure communication, and smart sensor systems. Current projects include:
- EyeDentity: eye-based zero-trust continuous authentication with liveness, DeepFake, and AI-cheating attack detection (DoD, NSF IUCRC).
- CARE-AI: a semantic AI sensor that monitors risk, health, and behavioral patterns for elderly care without capturing human-recognizable images (NSF I-Corps).
- AlertBLE: an AI-enabled, GPS-free BLE system for bidirectional work-zone hazard alerts indoors and outdoors (MoDOT; 2 U.S. patents).
- Halo: dynamic geo-fencing and LLM-driven schedule automation for mobile-zone safety of vulnerable individuals (NSF I-Corps, NSF PFI; 2 U.S. patents).
- ASaaS: Smart Agri-Sense as a Service, covering precision irrigation, AI-based weed and fruit detection, and mobile soil-moisture sensing (NSF SCC-PG).
- SANFRAN: secure, reliable private 5G and semantic communication over UAV-based flying RAN (NIST, NSF IUCRC).
- Counting the Uncounted: correcting occlusion-caused bias in crowd-counting benchmarks.
NSF, AFOSR, Cisco Systems, ETRI, NIST, AFRL, NASA, ONR, CDC, TAMU, KT Research, MoDOT, and the Cynthia Tang Endowment have supported SAIL’s research.
Director: Dr. Baek-Young Choi
Director: Dr Suman Maity
Director: Dr. Ce Zhou
SASAI lab integrates AI security and autonomous systems security to ensure the safety and reliability of intelligent cyber-physical systems. The lab focuses on three thrusts: (1) securing multi-sensor perception through digital/physical attack modeling and defense, (2) fortifying control and communication in EV and AV ecosystems, and (3) investigating attack and defense problems in emerging LLM-driven autonomous driving agents.
Director: Dr. Marion Sudvarg
The Real-Time Cyber-Physical Systems Lab (RT-CPS-L) investigates computing systems that interact with the physical world and must therefore satisfy stringent timing requirements for correct and safe operation. We address these challenges in real-world applications, including autonomous vehicles, drones, and networks of telescopes (from space-based high-energy detectors to robotic observatories) for coordinated, time-critical observations of astrophysical phenomena. We design and analyze scheduling models, algorithms, and optimization techniques that capture the complexity of modern cyber-physical systems, and we develop firmware, operating systems, and software platforms that provide theoretically grounded temporal guarantees.
RT-CPS-L collaborates with researchers in physics, control theory, and aerospace and mechanical engineering to address real-world scientific challenges. Our work is published in leading systems venues, including RTSS, RTAS, and OSDI, and has been presented at astrophysics conferences. Research is supported by NASA and industry partners.