Dr. Khandaker Mamun Ahmed

Assistant Professor

Dakota State University

Email: khandakermamun [dot] ahmed [at] dsu [dot] edu

Hi! I am currently a tenure-track Assistant Professor at the Beacom College of Computer and Cyber Sciences, Dakota State University. I received my PhD in Computer Science from Florida International University in 2024 with the recognition of Best Graduate Student in Research Award, where I received my M.Sc. degree as well. Prior to that, I received my B.Sc. degree in Software Engineering from University of Dhaka. My research interests span Federated Learning, Computer Vision, Cybersecurity, Explainable AI, and Optimization and Learning Algorithms. My contributions have significantly impacted computer vision for anomaly detection, privacy-preserving distributed machine learning, reflected in numerous top-tier conference presentations and peer-reviewed journal publications. My efforts have received multiple recognitions including "2022 Best Graduate Student Research Award" from KFCIS at FIU, Travel Grant from FIU GPSC, and Travel Grant from IEEE BIBM. I am also a co-inventor of an invention disclosure, and authored two book chapters, and published numerous peer-reviewed journals and conference papers.

Research Focus

Artificial Intelligence

👁️ Computer Vision

🌐 Federated & Distributed Learning

🧩 Multimodal AI Systems

🔍 Large Vision-Language Models

📡 Edge & IoT Intelligence

Cybersecurity

🤝 Trustworthy & Ethical AI

🔐 Privacy-Preserving Intelligence

🏗️ Critical Infrastructure Resilience

🌍 State-Sponsored Influence & Information Operations

🎯 Adversarial AI

Featured Research Projects

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 (Submitted)

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

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

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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