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ashioyajotham/README.md

Hi 👋, I'm Jotham Ashioya

ML Researcher | Data Scientist

🔭 Currently Working On

  • Building AI Agents for Bible Study, research and coding
  • Algorithmic Trading bots
  • ML Systems Design & Implementation
  • Data Science Projects

🌱 Research Interests

  • Foundation Models (confabulations ["hallucinations"], reasoning, knowledge-encoding)
  • Video generation models (physical world modeling)
  • MLOps & System Design
  • AI Safety (alignment, red-teaming) and formal verification

🛠️ Skills & Technologies

python pytorch docker

Web Development & Deployment

Streamlit Flask FastAPI Docker

Featured Deployments

Streamlit App

📝 Latest Blog Posts

▶ Read more on Substack

GitHub followers GitHub User's stars

Typing SVG

📈 GitHub Analytics

⚡ Recent GitHub Activity
Ashioya's Activity Graph

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  1. fingpt_trader fingpt_trader Public template

    A quant trading system platform based on FinGPT, demonstrating new applications of large pre-trained Language Models in quantitative finance.

    Python 12 5

  2. web_research_agent web_research_agent Public

    Research implementation exploring dynamic task analysis and multi-strategy synthesis in the ReAct paradigm. Demonstrates how systems can adapt their reasoning approach based on question structure w…

    Python

  3. bible bible Public

    An intelligent tool for biblical study and analysis, combining modern AI with scriptural wisdom for spiritual insights and biblical research.

    Python

  4. Value-Aligned-Confabulation-VAC-Research Value-Aligned-Confabulation-VAC-Research Public

    Research framework for evaluating value-aligned confabulation in LLMs - distinguishing beneficial speculation from harmful hallucination.

    Python

  5. exploring_saes exploring_saes Public

    Implementation and analysis of Sparse Autoencoders for neural network interpretability research. Features interactive visualization dashboard and W&B integration.

    Python 1 2

  6. transformers_from_scratch transformers_from_scratch Public

    Transformer model from scratch using PyTorch.

    Python