Machine Learning Research Engineer
Working on efficient learning systems, neuromorphic computation, and large-scale decision models.
Email • Google Scholar • Kaggle • Blog • X
My work focuses on methods that enable efficient, adaptive, and interpretable intelligence, with emphasis on:
- Neuromorphic and spiking architectures
- Reinforcement learning and sequential decision-making
- Multimodal systems and generative models
- Large-scale optimization and systems-level efficiency
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Neuromorphic Decision Transformer: A spiking-neuron formulation of Decision Transformers designed for energy-efficient sequential control. → Repository • arXiv
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AgroSense: A multimodal framework integrating visual and tabular signals for crop and soil analysis in precision agriculture. → Repository • arXiv
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SmartTraffic-RL: A reinforcement learning system for adaptive control of urban traffic signals. → Repository
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AgenticCyberOps: An autonomous pipeline for security analysis, penetration testing, and threat triage. → Repository
Programming: C, C++, Python
Frameworks: PyTorch, JAX, TensorFlow
Systems & Tools: Docker, Linux, Git
ML Stack: Transformers, RL, multimodal modeling, RAG systems
Other repositories include prototype systems, model re-implementations, exploratory agents, and utility tools spanning machine learning, optimization, and systems programming.