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Exploring Spiking Neural Networks for temporal pattern recognition, using biologically inspired architectures and advanced training techniques.
Investigated the Information Bottleneck method for analyzing trade-offs between representation compression and prediction accuracy in machine learning models.
Developed simulations and models for vehicular networks using game theory to optimize communication and resource allocation strategies.
Implemented CSRNet in PyTorch for crowd counting tasks, focusing on high-density population datasets and improving model accuracy and scalability.
If you’d like to connect, discuss collaborations, or have questions about my projects and research, feel free to reach out. I’m always open to interesting opportunities and conversations in the field of data science, artificial intelligence, and computational physics.
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