Exploring frontiers in AI, Cryptography, and Robotics

Artificial Intelligence & Machine Learning

Deep Reinforcement Learning for Complex Systems

This research explores the application of deep reinforcement learning algorithms to complex multi-agent systems. We propose a novel approach that combines model-based and model-free methods to achieve better sample efficiency.

Publications

Sample Efficiency in Multi-Agent RL

NeurIPS 2024

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Cryptography

Post-Quantum Cryptography Analysis

Analysis and implementation of post-quantum cryptographic algorithms resistant to quantum computing attacks. Focus on lattice-based cryptography and its practical applications.

Publications

Lattice-Based Encryption Schemes

Crypto 2024

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Robotics

Visual SLAM for Dynamic Environments

Development of robust visual SLAM algorithms that can handle dynamic environments with moving objects. Combines traditional geometric methods with deep learning for object detection and tracking.

Publications

Dynamic SLAM with Deep Learning

ICRA 2024

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