Dr. Khandaker Mamun Ahmed

Assistant Professor | Artificial Intelligence & Cybersecurity

Dakota State University

khandakermamun.ahmed [at] dsu.edu

I am currently a tenure-track Assistant Professor in the Beacom College of Computer and Cyber Sciences, at Dakota State University. My research focuses on Computer Vision, Federated and Distributed Learning, Multimodal AI, Cybersecurity, Trustworthy AI, and Edge/IoT Intelligence, with an emphasis on developing secure, privacy-preserving, and intelligent AI systems.

I received my Ph.D. in Computer Science from Florida International University (FIU) in 2024, where I was recognized with the Best Graduate Student Research Award. My research has resulted in peer-reviewed publications, a granted U.S. patent, and funded research, including a $175,000 project on secure AI-driven crop anomaly detection.

View my publications on Google Scholar.

🏆 $175K funded project | 📜 U.S. Patent | 📚 Recent selected publications | 👥 Graduate research mentorship

Research Areas

👁️ Computer Vision & Multimodal AI

🌐 Federated & Privacy-Preserving AI

🧩 Cybersecurity and Information Operations

📡 Edge AI & Intelligent Systems

News

Aug 2026, Paper Accepted, "Synthetic Thermal Image Generation for Real-Time Animal Detection Under Low-Visibility Conditions", The 6th IEEE Cyber Awareness Research Symposium (CARS), 2026.
Aug 2026, Paper Accepted, "Hybrid Ensemble Learning for EEG-Based Epileptic Seizure Forecasting", The 6th IEEE Cyber Awareness Research Symposium (CARS), 2026.
Jul 2026, Paper Accepted, "Machine Learning-Based CPU Burst Time Prediction for Grid Workload Scheduling" 25th International Conference on Machine Learning and Applications (ICMLA), 2026.
Mar 2026, Paper Accepted, "From Pixels to Semantics: A Multi-Stage AI Framework for Structural Damage Detection in Satellite Imagery" to CVPR Workshop 2026.
Mar 2026, Paper Accepted, "Privacy–Utility Tradeoffs in Federated Learning for Hyperspectral Crop Imagery", The 2026 International Conference on Applied Computing: Bridging Theory, Innovation, and Real-World Impact (CAC), 2026.
Mar 2026, Paper Accepted, "Advancing Food Security Through Hyperspectral Crop Anomaly Detection Using Deep Unsupervised Learning", The 2026 International Conference on the AI Revolution: Research, Ethics, and Society, 2026.
Mar 2026, Dr. Ahmed accepted the role to become PC member of ICTAI 2026 conference.
Dec 2025, Paper Accepted, "𝐒𝐦𝐚𝐥𝐥-𝐎𝐛𝐣𝐞𝐜𝐭 𝐃𝐞𝐭𝐞𝐜𝐭𝐢𝐨𝐧 𝐚𝐭 𝐭𝐡𝐞 𝐄𝐝𝐠𝐞: 𝐀 𝐏𝐚𝐫𝐞𝐭𝐨-𝐄𝐟𝐟𝐢𝐜𝐢𝐞𝐧𝐭 𝐁𝐞𝐧𝐜𝐡𝐦𝐚𝐫𝐤 𝐨𝐟 𝐋𝐢𝐠𝐡𝐭𝐰𝐞𝐢𝐠𝐡𝐭 𝐘𝐎𝐋𝐎 𝐌𝐨𝐝𝐞𝐥𝐬 𝐨𝐧 𝐔𝐀𝐕 𝐚𝐧𝐝 𝐎𝐯𝐞𝐫𝐡𝐞𝐚𝐝 𝐃𝐚𝐭𝐚𝐬𝐞𝐭𝐬", IEEE Access Journal, 2025.
Nov 2025, Paper Accepted, "Synthetic Data in Education: Empirical Insights from Traditional Resampling and Deep Generative Models", AAAI AI4EDU workshop, 2026.
Nov 2025, Paper Accepted, "Structural Damage Detection Using AI Super Resolution and Visual Language Model", The 24th International Conference on Machine Learning and Applications (ICMLA), 2025. (Acceptance Rate: 20%)
Jun 2025, Paper Accepted, "Toxicity in State Sponsored Information Operations", ACM HT ’25: The 36th ACM Conference on Hypertext and Social Media, 2025.
May 2025, Paper Accepted IEEE 5th Cyber Awareness and Research Symposium 2025 ( CARS'25 ).
May 2025, Paper Accepted, "The Language of Influence: Sentiment, Emotion, and Hate Speech in State Sponsored Influence Operations ", The 18th Conference on PErvasive Technologies Related to Assistive Environments (PETRA' 25), 2025.
Dr. Ahmed received DSU CyberAg grant as Co-PI of amount $175,000 🏆 .
Mar 2025, Paper Accepted, "Advancing DevSecOps in SMEs: Challenges and Best Practices for Secure CI/CD Pipelines", 13th International Symposium on Digital Forensics and Security, 2025.
Feb 2025, Poster Presentation: 𝐂𝐚𝐭𝐡𝐞𝐫𝐢𝐧𝐞 𝐇𝐨𝐢𝐞𝐫 𝐚𝐧𝐝 𝐊𝐡𝐚𝐧𝐝𝐚𝐤𝐞𝐫 𝐌𝐚𝐦𝐮𝐧 𝐀𝐡𝐦𝐞𝐝, "AI Super Resolution for Structural Damage Detection from Low-Quality Sources", SDSU Data Science Symposium 2025.🏅(3rd place award). Link
Feb 2025, Poster Presentation: 𝐓𝐚𝐩𝐢𝐰𝐚 𝐀𝐦𝐢𝐨𝐧 𝐂𝐡𝐢𝐧𝐨𝐝𝐚𝐤𝐮𝐟𝐚 𝐚𝐧𝐝 𝐊𝐡𝐚𝐧𝐝𝐚𝐤𝐞𝐫 𝐌𝐚𝐦𝐮𝐧 𝐀𝐡𝐦𝐞𝐝, "Generative AI for Synthetic Data Creation: Building Mastery-Focused Educational Datasets", SDSU Data Science Symposium 2025. Link
Feb 2025, Poster Presentation: 𝐍𝐧𝐚𝐞𝐦𝐞𝐤𝐚 𝐂𝐡𝐚𝐫𝐥𝐞𝐬 𝐈𝐠𝐰𝐞 𝐚𝐧𝐝 𝐊𝐡𝐚𝐧𝐝𝐚𝐤𝐞𝐫 𝐌𝐚𝐦𝐮𝐧 𝐀𝐡𝐦𝐞𝐝, "Machine Learning and SHAP Interpretability for Chronic Disease Understanding", SDSU Data Science Symposium 2025. Link
Feb 2025, Poster Presentation: 𝐌𝐮𝐡𝐚𝐦𝐦𝐚𝐝 𝐁𝐡𝐮𝐭𝐭𝐚, 𝐊𝐡𝐚𝐧𝐝𝐚𝐤𝐞𝐫 𝐌𝐚𝐦𝐮𝐧 𝐀𝐡𝐦𝐞𝐝, 𝐀𝐛𝐢𝐝 𝐌𝐞𝐡𝐦𝐨𝐨𝐝, 𝐘𝐨𝐮𝐬𝐬𝐞𝐟 𝐇𝐚𝐫𝐫𝐚𝐭𝐡, 𝐚𝐧𝐝 𝐉𝐢𝐡𝐞𝐧𝐞 𝐊𝐚𝐚𝐛𝐢, "Enhancing Crop Yield Through Efficient Anomaly Detection Using Transfer Learning and Multispectral Satellite Imagery", SDSU Data Science Symposium 2025. Link
Jan2025, Travel grant received from SDSU Data Science Symposium'25.
Jan2025, Serving as a Guest Editor of a Journal Special Issue "Role of Artificial intelligence in Natural Language Processing".
Oct 2024, Master's student Khanh Nguyen received Graduate Research Initiative Award (GRI) award. 🏅

Featured Research Projects

Anomalous Activity Detection

This research develops a privacy-preserving, agent-based edge framework for anomaly detection in surveillance videos, integrating deep feature extraction and human-in-the-loop validation to enable efficient, real-time crime prevention. Key focus of this research:

🤖 AI-Driven Anomaly Detection 🔒 Privacy & Centralization Challenges 🧑‍⚖️ Human-in-the-Loop Decision Support

Publications: Patent 1 (granted - US11875566B1), Patent 2 (Submitted- Docket No. FIU.563)

Structural Damage Detection

Our key focus of this research:

🧠 AI-Enhanced Video Super-Resolution 🤖 Advanced Visual Language Model 📊 Validation & Performance

Publications: ICMLA '25, CVPRW '26

Influence Operations

This research investigates how state-sponsored influence operations strategically deploy toxic language and differentiated emotional-rhetorical tactics across nations to manipulate discourse, amplify engagement, and advance geopolitical objectives. Key focus areas are:

😡😇 Emotional & Rhetorical Strategies 🧪 Toxic Language Analysis 🌐 Global Impact of IOs on Social Media 💻 Tools, Code & Transparency

Publications: ACM PETRA '25, ACM HT '25

Federated Deep Learning for Edge Computing

We are developing privacy-preserving learning techniques in heterogeneous federated environments. Our key focus of this research:

📡 Edge Devices & IoT Data ⚙️ Resource Constraints FL 🔄 Federated Heterogeneity
Publications:

ICMLA '21, Springer Nature '22, ICCCN '22

Small Object Detection

Our key focus of this research:

🔍 Small Object Detection Challenge 📊 Benchmarking & Evaluation 🚀 Improved Detection Performance ⚡ Lightweight Models for Edge Devices

Publications:

IEEE Access '25, IEEE Access (Submitted)

Vision2FIM: Computer Vision Aided Curvilinear Topographic Feature Detection from LiDAR Data for Better Flood Inundation Modeling

Agent-based Learning to Utilize Local Data for Anomalous Activity Recognition (Previously Funded by the U.S. Department of Homeland Security)

We aim to detect anomalous events or suspicious activities such as assault, explosion, and shooting in surveillance videos.
We plan to improve the accuracy of decisions of human agents by reducing the manual work of monitoring of human agents.
We focus to provide better visualization to locate anomalous event and act accordingly.

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