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Name: Can Ozkan

Type: User

Company: University College London

Bio: PhD Student @SpaceTimeLab @UCL

Location: London

Welcome to Can's GitHub Home

👋 Hi there! I'm Can OZKAN, a PhD student at University College London (UCL) and a guest lecturer at the London School of Economics (LSE). My research revolves around dynamic network representations, delving into the intricate interplay within road networks and telecommunication networks. I'm particularly intrigued by understanding the repercussions of disruptions such as road segment closures or collapses in telecom links on the entire network.

Research Focus 🧠

My primary focus lies at the intersection of graph neural networks and reinforcement learning, leveraging these powerful tools to model and analyze dynamic networks. I'm passionate about uncovering the hidden patterns and dependencies in complex systems, seeking innovative solutions to address real-world challenges in network modeling.

Academic Journey 📚

Ph.D. at UCL: Exploring dynamic network representations and their implications on network resilience.
Guest Lecturer at LSE: Having fun with teaching data science and machine learning

Technical Toolbox 🛠️

Graph Neural Networks (GNNs): Harnessing the power of GNNs to capture complex relationships in dynamic networks.
Reinforcement Learning: Applying RL techniques to optimize network behavior in response to disruptions.
Numpy, PyTorch, PyG: My go-to tools for efficient numerical computation, deep learning, and graph-related tasks.
Stable Baseline: Employing stable baseline methods for reinforcement learning applications.

Previous Endeavors 🎓

Master's at Imperial College London: Explored short-term traffic prediction using Kalman filter, PCA, and ICA techniques.

Connect with Me 🌐

Feel free to reach out if you're interested in collaborative research, have questions about my work, or just want to chat about the exciting world of dynamic networks!

Can Ozkan's Projects

academic-kickstart

📝 Easily create a beautiful website using Academic, Hugo, and Netlify

accel-brain-code

The purpose of this repository is to make prototypes as case study in the context of proof of concept(PoC) and research and development(R&D) that I have written in my website. The main research topics are Auto-Encoders in relation to the representation learning, the statistical machine learning for energy-based models, adversarial generation networks(GANs), Deep Reinforcement Learning such as Deep Q-Networks, semi-supervised learning, and neural network language model for natural language processing.