Ph.D. in Computer Science
University of Memphis, United States
Academic profile
Dr. Kishor Datta Gupta
Assistant Professor, Clark Atlanta University
Atlanta, Georgia
Dr. Gupta is a tenure-track Assistant Professor whose work focuses on artificial intelligence, machine learning, and cybersecurity. His competitive awards include the 2025 NASA MUREP Partnership Learning Annual Notification award, two Department of Defense research grants, and a Nuclear Regulatory Commission award.
He has served as a Visiting Faculty Researcher at the Air Force Research Laboratory and has advanced work in multimodal data fusion, physics-guided neural networks, trustworthy AI, and autonomous systems. He is an IEEE Senior Member with journal and conference publications, editorial service, research leadership, and a record of mentoring students toward national and international achievements.
Academic preparation
Education
Degrees in computer science completed in the United States and Bangladesh.
M.S. in Computer Science
Lamar University, United States
B.Sc. in Computer Science
KUET, Bangladesh
Research background
Research experience
Academic and laboratory roles spanning trustworthy AI, intelligent systems, cybersecurity, and autonomous technologies.
Assistant Professor, Clark Atlanta University
Leads AFRL-funded work involving physics-guided neural networks, PGCNN, and trustworthy AI.
Visiting Researcher, Air Force Research Laboratory
Postdoctoral Researcher, North Carolina A&T State University
Worked on context-aware intelligent systems and projects connected with ARL and NASA.
Graduate Research Assistant, University of Memphis
Conducted research on adversarial AI defense, NSA-funded multifactor authentication, and DHS cybersecurity.
Sponsored activity
Research grants
The grant summary below retains the titles, sponsors, roles, and amounts reported on the source website.
HBCU AI–Quantum Innovation Network (HAQIN)
National Science Foundation; Co-Principal Investigator; led by Clark Atlanta University
Close to $2 million in total intended funding
Connecting HBCUs and partners to advance research, education, and workforce development in artificial intelligence and quantum science.
Adaptive Knowledge Graph-Based Zero-Shot Learning
Department of Defense / AFRL
$145,000
Physics-Guided Multimodal Data Fusion
Department of Defense / AFRL
$161,000
Trustworthy AI for UAV Controller Security
Georgia Tech HBCU RCF Award
$35,000
Adaptive Zero-Trust Cybersecurity Architecture
U.S. Nuclear Regulatory Commission; PI for CAU
$500,000
NASA MPLAN Award
Principal Investigator
$50,000
AFRL VFRP Extension Award
Air Force Research Laboratory
$25,000 total
Army Research Lab xTechHBCU
Army Research Laboratory
$20,000
Google GenAI Consortium
Co-Principal Investigator
$150,000
NASA USRC Award
Co-Investigator
$40,000
Large Language Models for Mission-Critical Airborne Radar Systems
Georgia Tech HBCU RCF Award
$40,000
Gupta Lab
Ongoing research projects
Clark Atlanta University project portfolio, reported by the source page as updated August 28, 2025.
01
Physics-Guided Multimodal Data Fusion and Clustering for Enhanced Image Retrieval
Combines physics priors with LiDAR, thermal, RGB, and IoT signals to improve retrieval precision and clustering. The work also uses rule-set layers, retrieval-augmented generation, and large language models for context-aware search.
- K. D. Gupta et al., “Physical Rule Guided CNN for Debris Detection,” SPIE DCS, 2025.
- M. Kamal et al., “Physical Fuzzy Rule Based News Article Clustering,” IEEE COMPSAC, 2025.
- M. A. Haque et al., “Utilizing Structural Metrics from KGs to Quantify LLM Robustness,” IJDSA, 2024.
- K. D. Gupta et al., “Physics Guided Neural Networks with Knowledge Graph,” Digital, 2024.
02
Adaptive Zero-Trust Cybersecurity for Advanced Reactors
Develops adaptive, AI-driven, real-time cybersecurity for nuclear reactor control systems, with the goal of strengthening national energy security and regulatory readiness.
- P. Biswas et al., “Review of Smart Grids with AI & Renewable Integration,” IEEE Access, 2025.
03
PHILIA: Physics Informed and LLM Integrated Autonomy
Integrates physics-informed neural networks and large language models to support explainable, trustworthy, and resilient autonomous systems, including UAVs, digital twins, and mobility systems.
04
Trustworthy AI for UAV Controller Security
Secures UAV controllers through adversarial robustness, anomaly detection, knowledge-graph trust metrics, and AI-enabled monitoring.
- M. M. Rahman et al., “UAV Datasets: Segmentation, Classification, Detection, Tracking,” Algorithms, 2024.
05
Assured Autonomy for Human-Assisted Drone Operations
Studies UAV autonomy in GPS-denied and constrained settings while maintaining human-in-the-loop safety.
06
Physics-Guided Digital Twin for Smart Cities
Builds a resilient urban digital twin from LiDAR, thermal data, photogrammetry, IoT, weather, and social sensing to support real-time monitoring and decision-making.
07
MonitorLLM: Continuous Evaluation of LLMs
Benchmarks and monitors large language models for hallucination, bias, robustness, and reliability through structured measures identified as ICR, IPR, CI, SPA, SPI, and IMI.
The source website reports that Ariful Haque’s poster received an award at DFRWS USA 2025 and that a related paper was accepted at the AAAI Fall Symposium.
Selected work
Publications
Books, recent first-author journal papers, and project-associated publications listed on the source website.
Securing Industrial Control Systems: Advanced Strategies and Technologies
Springer, author, 2026.
Data Driven Approaches for Medical Imaging
Springer, co-editor, 2024.
- “Physics Guided Neural Networks with Knowledge Graph,” Digital, 2024.
- “Adversarial AI Threats on Nuclear Power Plants,” ANS Transactions, 2023.
- “Adversarial Attacks & Defenses for Deployed AI Models,” IEEE IT Professional, 2022.
- “Negative Selection Algorithm Research & Applications,” IEEE TAI, 2022.
- “Physical Rule Guided CNN for Debris Detection,” SPIE DCS, 2025.
- “Physical Fuzzy Rule Based News Article Clustering,” IEEE COMPSAC, 2025.
- “Utilizing Structural Metrics from KGs to Quantify LLM Robustness,” IJDSA, 2024.
- “Review of Smart Grids with AI & Renewable Integration,” IEEE Access, 2025.
- “UAV Datasets: Segmentation, Classification, Detection, Tracking,” Algorithms, 2024.
Clark Atlanta University
Teaching
Courses in computer vision, machine learning, operating systems, and advanced algorithms, combining foundations with practical and research-oriented work.
CPS/CS XXXX
Computer Vision: From OpenCV to Vision-Language and World Models
Hybrid, laboratory-centered course covering OpenCV, CNNs, YOLO, ResNet, vision transformers, vision-language models, and predictive world models. Each assignment includes a GitHub repository and narrated demo video workflow.
CCIS 434
Machine Learning
Introduces supervised, unsupervised, and reinforcement learning. Students use Python, scikit-learn, and Google Colab to analyze data and develop predictive models.
CCIS 634
Advanced Machine Learning
Covers contemporary deep-learning and AI methods, including CNNs, RNNs, transformers, and generative models, with theoretical and implementation work across vision, NLP, and multimodal tasks.
CCIS 473
Introduction to Operating Systems
Examines processes, scheduling, memory management, file systems, and virtualization, emphasizing secure and efficient resource management.
CCIS 673
Advanced Operating Systems
Studies distributed systems, concurrency, security, and cloud or edge operating-system design through research-oriented projects.
CCIS 653
Advanced Algorithms
Graduate study of approximation and randomized algorithms, advanced graph theory, network flows, computational geometry, and complexity theory, with applications to AI, data science, and cybersecurity research.
Teaching experience listed on the introductory page: Operating Systems at CAU from 2022 to 2024, and graduate-level Applied Machine Learning at CAU from 2023 to 2024.
Gupta Lab
Research opportunities
Current student opportunities at Clark Atlanta University in AI, cybersecurity, machine learning, digital twins, multimodal systems, and autonomous technologies.
Undergraduate research opportunities
Undergraduates can support graduate students on active projects, gain hands-on experience in AI, cybersecurity, machine learning, and digital twins, and prepare work for conference presentation.
Graduate research positions
Graduate students can participate in projects supported by NASA, AFRL, NSF, and NRC. Topics include physics-guided neural networks, multimodal data fusion, UAV autonomy, and trustworthy AI.
Apply to Clark Atlanta UniversityProfessional engagement
Community and service
Conference leadership, editorial work, peer review, tutorials, grant-panel participation, and invited panel presentations.
Conference
Co-Chair, IEEE SSCI, 2022 and 2023.
Editorial
Associate Editor, Journal of King Saud University Computer and Information Sciences, Springer Nature.
Peer review
Reviewer for AAAI, IJCNN, SSCI, and other venues.
Grant panels
Reviewer for NASA and NSF grant and award panels.
Tutorials
SSCI 2021 on adversarial machine learning; SSCI 2022 on machine learning and defense; SSCI 2023 on generative AI; NICE 2026 on Lite VLM.
Panel talks
P3DL Conference at GEOINT 2025 in Washington, D.C., presenting “Digital Twins for Smart Cities: Physics-Guided, Knowledge-Graph–Driven Situational Awareness”; AI in Gaming at BEAon 2024; and Nuclear Digitization at ANS 2023.