GPTree
Explainable decision-making via LLM-powered decision trees, designed for practical, human-in-the-loop workflows.
Research
My research investigates trustworthy artificial intelligence for decision-making, focusing on the intersection of deep reinforcement learning, embodied control, and large language models. My core research interests include:
- Deep Reinforcement Learning: Developing robust algorithms for sequential decision-making in complex, high-dimensional spaces.
- Robotics & Control: Investigating learning-based control strategies and representation learning for embodied agents.
- Agentic AI: Designing reasoning frameworks and human-in-the-loop learning pipelines for autonomous systems.
- Quantum Machine Learning (Actively learning): Exploring the theoretical and practical intersections of quantum computing methodologies and artificial intelligence.
Selected Work
Think Reason Learn
Open-source library combining large language models with interpretable ML to build transparent decision systems.
Dual Approach in Autonomous Directional Drilling
Research on autonomous directional drilling, combining Drillbotics hardware innovations with a virtual rig simulator.
Publications & Preprints
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From Stochastic Answers to Verifiable Reasoning: Interpretable Decision-Making with LLM-Generated Code
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GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees
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GPT-HTree: A Decision Tree Framework Integrating Hierarchical Clustering and Large Language Models for Explainable Classification
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VCBench: Benchmarking LLMs in Venture Capital
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From Limited Data to Rare-event Prediction: LLM-powered Feature Engineering and Multi-model Learning in Venture Capital
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Dual Approach in Autonomous Directional Drilling: Innovations with Drillbotics 1.5 Inch Automated RSS and Virtual Rig Platforms
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LLM-AR: LLM-powered Automated Reasoning Framework
Open-source & Projects
News
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2025
New benchmark released for evaluating LLMs in venture capital.
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2025
VCBench accepted for a computing conference (2026), London.
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2024
Best Student in Software Engineering - UMaT Excellence Awards.
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2023
UMaT tops Virtual Category of Drillbotics - JoyNews.
