Research
========

Think Reason Learn is the culmination of cutting-edge research spearheaded by 
Vela Research in close collaboration with Oxford University. This project 
integrates advanced AI techniques with interpretable machine learning to 
create transparent, high-performance models for complex classification tasks.
For inquiries or collaboration opportunities, contact us at research@vela.partners.

Papers
------

- **GPTree: Towards Explainable Decision-Making via LLM-powered Decision Trees**
  
  *Explores LLM-guided decision trees for dynamic feature generation.*
  
  **Research Team:**
    `Sichao Xiong <https://www.linkedin.com/in/sichao-xiong-2595a3258/>`_ 
    `Yigit Ihlamur <https://yigitihlamur.com>`_  
    `Fuat Alican <https://fuatalican.com/>`_  
    `Aaron Ontoyin Yin <https://aaron-ontoyin.github.io/>`_
  
  📄 **Paper:** `arXiv:2411.08257 <https://arxiv.org/abs/2411.08257>`_


- **Random Rule Forest (RRF): Interpretable Ensembles of LLM-Generated Questions for Predicting Startup Success**
  
  *Develops ensembles of LLM-generated rules for transparent predictions.*
  
  **Research Team:**
    Ben Griffin
    Diego Vidaurre  
    Ugur Koyluoglu
    `Joseph Ternasky <https://www.linkedin.com/in/ternasky/>`_
    `Fuat Alican <https://fuatalican.com/>`_
    `Yigit Ihlamur <https://yigitihlamur.com>`_
  
  📄 **Paper:** `arXiv:2505.24622 <https://arxiv.org/abs/2505.24622>`_