PhD Candidate (AI/ML) • Graph ML Researcher • Builder of Intelligent Learning Systems • 🏆 Best Paper Award
I develop AI systems that learn from evolving networks, detect anomalies in real time, and make intelligent decisions from complex graph-structured data.
- 🧠 Graph Machine Learning: dynamic graph streams, anomaly detection, graph neural networks, representation learning
- 🚀 AI Systems: multimodal AI, self-supervised learning, scalable experimentation pipelines
- 🌎 Geospatial AI: spatial representation learning, climate AI, graph foundation models
- 🚗 Autonomous Systems: perception security, anomaly detection, trustworthy AI
- 🔐 Cybersecurity AI: vulnerability assessment, cyber risk evaluation, intelligent monitoring
📍 Based in Tennessee, USA
🎓 Currently completing a PhD in Computer Science at Tennessee Technological University.
- 🏆 Best Paper Award Winner (College of Engineering, Tennessee Tech)
- 📚 Published researcher with work in ACM TKDD, IEEE, FLAIRS, Applied Sciences, and other venues
- 🔬 Creator of Adaptive-DecayRank, Adaptive-GraphSketch, GeoModRank, and ViGAT
- 🤖 Research spanning Graph ML, GNNs, Multimodal AI, Geospatial AI, and Autonomous Vehicle Security
- 🐍 Strong experience in Python, PyTorch, C++, graph analytics, and scalable AI systems
If you need someone who can design algorithms, build AI systems, and publish impactful research, that's my lane.
Ph.D. Candidate, Computer Science (AI & Machine Learning) Tennessee Technological University, USA (2023–Present)
M.Sc., Applied Mathematics & Informatics Moscow Institute of Physics and Technology (MIPT), Russia (2020–2022)
📈 GraphSketch :Scalable real-time anomaly detection for dynamic streams using probabilistic sketching.
🧠 Adaptive-DecayRank: Real-time Node anomaly detection in evolving graph networks.
🌎 GeoModRank: Self-supervised spatial representation learning for multimodal geospatial and climate data.
🚗 ViGAT: Vision-to-Graph anomaly detection for autonomous vehicles and intelligent perception systems.
My research focuses on building intelligent systems capable of learning from dynamic environments and making decisions under uncertainty. Core research themes include:
- Dynamic Graph Learning
- Graph Neural Networks
- Multimodal & Geospatial AI
- Self-Supervised Learning
- Autonomous Vehicle Security
I am particularly interested in scalable AI systems that operate in real-world environments where data evolves continuously and intelligent adaptation is required.
- 📄 Adaptive GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching (IEEE ICKG 2025)
- 📄 Adaptive DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian Updates (Applied Sci)
- 📄 Anomaly Detection in Dynamic Graphs: A Comprehensive Survey (ACM TKDD)
- 📄 Cyber Risk Evaluation for Android-Based Devices (IEEE DSC)
- 📄 Machine Learning Enhanced Categorization of Cybersecurity Vulnerabilities (IEEE UEMCON)
- 📄 Low-Resource Neural Machine Translation Using Transfer Learning (MIPT Research Thesis)
ViGAT: Vision-to-Graph Anomaly Detection for Autonomous Vehicles — CVPR 2026 (Under Review)
Adaptive-GraphSketch: Real-Time Edge Anomaly Detection via Multi-Layer Tensor Sketching — IEEE ICKG 2025
Adaptive-DecayRank: Real-Time Anomaly Detection in Dynamic Graphs with Bayesian Updates — Apl. Sci 2025
Anomaly Detection in Dynamic Graphs: A Comprehensive Survey — ACM TKDD 2024
Dynamic PageRank with Decay — FLAIRS 2024
Cyber Risk Evaluation for Android-Based Devices — IEEE DSC 2023
Machine Learning Enhanced Categorization of Cybersecurity Vulnerabilities — IEEE UEMCON 2024
Low-Resource Neural Machine Translation for English–Igbo — arXiv 2025
- 🥇 Doctoral Best Paper Eminence Award — Tennessee Tech College of Engineering
- 🥇 Best Graduate Poster Award
- 🎓 Fully Funded PhD Fellowship
- 🌍 Open Doors International Olympiad Winner
- 🎓 University of Milan-Bicocca PhD Scholarship Recipient
- 💻 MIPT Phystech-Alpha Competition Award Recipient
Recognized for excellence in AI, Machine Learning, and Graph-Based Research.
| 🤖 Machine Learning | 🧠 Generative AI | ⚙️ AI Systems & MLOps | 💻 Programming & Infrastructure |
|---|---|---|---|
| • PyTorch, TensorFlow, Scikit-Learn | • LLMs, LangChain, Hugging Face | • Real-Time Inference | • Python, C++, SQL |
| • Deep Learning (CNNs, Transformers) | • RAG & Embeddings | • Streaming Analytics | • JavaScript, React |
| • Graph Neural Networks (GCN, GAT, GraphSAGE) | • Vector Databases | • Model Evaluation & Optimization | • Linux, Git, Docker |
| • Graph Machine Learning | • Knowledge Graphs (Neo4j) | • Distributed Training | • Conda, Jupyter |
| • NLP & Multimodal Learning | • Agentic AI & AI Agents | • AI Infrastructure & AIOps | • NVIDIA A100, Jetson |
| • Anomaly Detection & Representation Learning | • Diffusion Models & Foundation Models | • Scalable ML Systems | • GPU-Accelerated Computing |
- President, African Student Union (Tennessee Tech University)
- President, Computer Science Graduate Student Club
- Research Lead for Graph ML, Dynamic Graph Analytics, and Autonomous AI Systems projects
- Led interdisciplinary AI/ML initiatives spanning cybersecurity, geospatial AI, autonomous systems, and software engineering
- NSF REU Research Mentor, guiding undergraduate researchers in AI, Machine Learning, and Data Science
- Peer Reviewer for ACM TKDD, IEEE SMC, Pattern Recognition, and Springer Nature journals
- Technical leader experienced in cross-functional collaboration, research communication, and large-scale AI project execution
I'm passionate about building intelligent systems that bridge cutting-edge AI research and real-world impact.
I'm actively interested in opportunities involving:
- 🧠 Research Scientist & Applied Scientist Roles
- 🤖 Artificial Intelligence & Machine Learning
- 📈 Graph Machine Learning & Graph Neural Networks
- 🌎 Geospatial AI & Spatial Foundation Models
- 🚗 Autonomous Systems & Multimodal AI
- 🔐 Trustworthy AI, Cybersecurity, and Anomaly Detection
- ⚡ Scalable AI Systems, Real-Time Inference, and Intelligent Decision-Making
I enjoy collaborating on ambitious research, open-source projects, and AI systems that solve challenging real-world problems. If you're interested in collaboration, research partnerships, or discussing innovative AI ideas, feel free to reach out.