Autonomous AI research team: write a plan.md, spawn agents, train, verify, and deliver a clean ML repo.

Real-time TUI for monitoring cloud GPU training instances

A gated memory layer for trustworthy AI-assisted research workflows.

C++/Python microstructure research engine for event-driven limit order book prediction and reproducible quantitative experiments.

Automates hermetic environments (macOS/HPC) to eliminate drift. Provisions offline RAG (Gemma 2), compiles LaTeX manuscripts, and indexes local knowledge. Unifies infrastructure, writing, and inference into a single, audit-ready artifact.

Research prototype for trace-based observability and failure analysis in retrieval-augmented generation.

Selected LLM, NLP, retrieval and vision-language research-engineering projects.

Open ML systems platform for training, profiling, evaluating, and serving AI models.

Event-driven quantitative research evidence engine with real-data risk studies, leakage-safe execution, corrected inference, and reproducible reports.

AI-powered Research Assistant using Groq, Llama 3.1, LangChain, and Tool Calling to autonomously search the web, read webpages, and generate structured research reports.

Regime-aware portfolio using machine learning, financial time-series features, risk research engine with dynamic allocation, stress testing, factor diagnostics, reproducible research packages, and a Streamlit dashboard.

Research-engineering lab for LLM post-training, behavior evaluation, regression detection, and reliability reporting.

Reproducible evaluation suite for LLM behavior research: epistemic pathology, delegated introspection, and temporal consciousness diagnostics

Empirical study of neural scaling laws using transformers trained on a corpus of Python standard library modules. Investigates how model size, dataset size, and compute influence language modeling loss through Chinchilla-style scaling law fitting and small-scale transformer experiments.

Controlled mini-benchmark for context visibility, shortcut regimes, and composition in tiny causal transformers.

Adnane Arharbi

Quantitative research and research engineering profile

GitHub profile README for Nicholas Kashani Motlagh

Empirical study of evaluation robustness in large language models. Compares benchmark style evaluation prompts with realistic deployment prompts across multiple open-weight LLMs, measuring evaluation–deployment divergence using automated LLM-as-a-Judge scoring, statistical analysis, and reproducible safety benchmarks.

Portfolio landing page for compact AI evaluation artifacts.