Advancing low-dose CT imaging for early cancer detection

At AAK Tele-Science, I’m working at the intersection of AI, machine learning, and scientific research, developing cutting-edge recommendation systems and data-driven solutions to enhance researcher, project, and investor matchmaking.

Spearheaded the development of VR educational games as a scientist-educator-game developer, enhancing STEAM learning for children while contributing to neurodevelopmental condition research, including autism and ADHD, to support medical professionals and researchers.

AI engineering work to collect, analyze, interpret, and present data for optimizing gameplay.

AI4MathSci / AI4Quant – Open Source Contributor

Jan 2025 – Present

Remote / Independent Research Initiative

🔬 Building next-gen quant trading and research pipelines with AI and agentic reasoning.

Summary:

AI4MathSci is an open-source project bridging quantitative finance with modern AI/LLM tools, including LangGraph, LlamaIndex, and custom strategy ensembles. Our goal is to create an explainable, modular research framework for trading, financial modeling, and document analysis.

Key Contributions:

🧠 Designed and implemented the Shortm@k ensemble logic (majority-vote-based decision engine for financial strategies).

📊 Developed Python-based trading strategies and metrics evaluation tools (Sharpe Ratio, drawdown, win/loss, etc.).

🧱 Restructured core repository into modular pipelines: shortmak-demo, pynescript_strategy, llama_module, and CLI runners.

📄 Collaborated on integrating LlamaIndex for unstructured financial document ingestion into strategy workflows.

⚙️ Prototyped CLI-based unified runner (run_pipeline.py) for end-to-end demos, plotting, and backtests.

🔍 Conducted research into LangGraph/MCP agentic workflows for future integrations with quant pipelines.

Stack:

Python · Backtrader · Pandas · LlamaIndex · LangGraph · LangChain · PyPortfolioOpt · Matplotlib · CLI Tooling · GitHub · AI x Finance

Outcomes:

✅ Laid the foundation for a modular, explainable AI-driven quant platform.

✅ Demonstrated potential for academic-grade integration of AI reasoning + trading pipelines.

✅ Built collaboration channels for scaling contributions and preparing for future release/publication.

Led advanced research and system development at OREL, applying AI/ML to cognitive science, neuroscience, and philosophy of mind. Contributed to breakthrough projects modeling reasoning, memory, and perception using virtual environments, simulation theory, and neural data.

Developed computational frameworks for simulating cognitive phenomena — including perception, decision-making, and developmental learning — with high fidelity to real-world behavior.

Built tools and algorithms for modeling neural dynamics, supporting data analysis pipelines across cognitive neuroscience and virtuality studies.

Published interdisciplinary work exploring realism vs. anti-realism in cognitive science, bridging philosophical insight with empirical modeling.

Pioneered simulations exploring how virtual environments shape cognition, informing new paradigms in XR-based neuroscience and digital learning theory.

Collaborated across neuroscience, philosophy, and AI communities to design experiments and models redefining how cognition is studied in simulated systems.