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drkianmaleki/README.md

Kian Maleki GitHub Banner

Hi, I’m Kian Maleki, PhD 👋

I’m a PhD physicist turned AI/ML builder who enjoys turning mathematical ideas into useful, testable, and well-documented software.

My work sits at the intersection of machine learning, scientific computing, document intelligence, retrieval-augmented generation, and model evaluation. I like building systems that are not only accurate, but also interpretable, reproducible, and useful in real-world workflows.


What I build

I build intelligent systems with math in the engine room and usability on the dashboard.

  • AI and machine learning systems for classification, prediction, and evaluation
  • RAG and document intelligence tools for extracting, searching, and reasoning over complex documents
  • Model-evaluation frameworks that go beyond single-number metrics
  • Scientific computing projects inspired by physics, optimization, and numerical modeling
  • Portfolio-ready software with clean structure, documentation, and practical demos

Selected projects

PharmaDoc-AI

A document-intelligence and RAG system for pharmaceutical-style documents, combining extraction, retrieval, OCR-aware workflows, and chatbot-style interaction.

Ambiguity Framework

A classifier-evaluation framework that studies ambiguity across decision thresholds instead of relying on one fixed threshold.

Sequence Acceleration Benchmark

A research-style benchmark exploring convergence acceleration and finite-horizon learning-curve prediction for gradient boosting models.


Current focus

I am currently focused on:

  • Retrieval-augmented generation
  • Document AI and OCR robustness
  • Applied machine learning systems
  • AI agents and tool-using workflows
  • Model reliability, evaluation, and uncertainty
  • Turning research ideas into clean, reusable software

Background

Before moving deeply into AI and machine learning, I worked in theoretical and computational physics, including quantum materials, molecular transport, open quantum systems, and numerical modeling.

That background shapes how I approach AI: I care about mechanisms, assumptions, failure modes, and whether a model actually behaves the way we think it does.


Connect

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  1. ambiguity-framework ambiguity-framework Public

    Python 1

  2. Heart-Disease-Prediction Heart-Disease-Prediction Public

    Python 1

  3. PharmaDoc-AI PharmaDoc-AI Public

    A multi-format RAG system for pharmaceutical and regulatory documents, with OCR, table extraction, chart digitization, deterministic answer routing, and source-traceable AI answers.

    Python 1

  4. sequence-acceleration-benchmark sequence-acceleration-benchmark Public

    Python 1

  5. LumberLex LumberLex Public

    Lumber product name normalization engine — maps inconsistent retail strings to canonical species with fuzzy matching, confidence scoring, and LLM explanations

    Python