This repository showcases applied research and engineering work at the intersection of artificial intelligence, machine learning, robotics, and complex systems science.
Developing and optimizing large language models through custom training pipelines, parameter-efficient finetuning methods, and rigorous evaluation frameworks. Focus on both technical performance metrics and behavioral alignment.
Building sophisticated multi-agent environments where autonomous agents interact, learn, and evolve. Exploring emergent behaviors, coordination mechanisms, and scalable architectures for agentic systems.
Developing robot control systems, perception pipelines, and motion planning algorithms. Integrating AI with physical systems for manipulation, navigation, and human-robot interaction. Exploring embodied intelligence through simulation and real-world robotics platforms.
Investigating the intersection of artificial intelligence, economic mechanisms, and tokenized systems. Designing and analyzing incentive structures, market dynamics, and decentralized coordination through computational modeling and simulation.
Advanced analytics and modeling for complex, high-dimensional datasets with temporal dependencies. Applying dimensionality reduction, time-series analysis, and causal inference to understand evolving systems.
Each project directory contains:
- Research objectives and methodology
- Implementation code and experiments
- Results and analysis
- Documentation for reproducibility
Projects leverage modern ML/AI frameworks including PyTorch, Transformers, JAX, robotics frameworks such as ROS/ROS2, MoveIt, Gazebo, PyBullet, and specialized tools for agent-based modeling, simulation, and distributed training.
Work emphasizes:
- Rigorous experimental design and evaluation
- Scalable, production-ready implementations
- Bridging theoretical insights with practical applications
- Open research and reproducible results
Exploring the frontiers of intelligent systems, robotics, and complex adaptive behaviors