Why this combination?
Linguistics gives me a structured understanding of language and communication. Programming and machine learning let me turn that understanding into experiments, models, tools, and applications.
Open to AI & NLP opportunities
Hi, I'm
Erfan Jafarzadeh
Computational Linguist · NLP Engineer · AI Developer
I build language-aware AI systems, machine learning applications, and research tools at the intersection of linguistics, computation, and human communication.
Python
Primary language
NLP + ML
Technical focus
4+
Working languages
AI + LANGUAGE
Building at the intersection
PYTHON / NLP
Research × Engineering
01 / ABOUT
A linguistics background with an engineering direction.
My work combines computational linguistics, machine learning, NLP, language evolution research, and practical software development.
Current technical stack
MLPyTorch
NLPspaCy · NLTK
DATAscikit-learn
VISIONOpenCV · MediaPipe
LANGUAGEPython
AILLMs · RAG
02 / PROJECTS
Selected AI, NLP and research projects.
Each project links to a dedicated case study where you can document the problem, methodology, technical decisions, results and visuals.
Project screenshot / visualization
Add image later: assets/persian-ai.jpg
Add image later: assets/persian-ai.jpg
AI / NLP / IN PROGRESS
Persian Personality Assessment AI
A machine-learning-oriented project for exploring automated scoring and analysis of Persian personality assessment data.
Simulation visualization
Add image later: assets/language-evolution.jpg
Add image later: assets/language-evolution.jpg
RESEARCH / SIMULATION
Language Evolution Simulation
Computational experiments exploring iterated learning, cultural transmission, and the emergence of linguistic structure.
Application screenshot
Add image later: assets/rag-agent.jpg
Add image later: assets/rag-agent.jpg
LLM / RAG / IN PROGRESS
Local AI Research Assistant
A local research environment designed to connect books, papers, retrieval pipelines and language-model-based assistance.
Your next project
Duplicate this card and edit it.
Duplicate this card and edit it.
NEW PROJECT
Project Title
Write a short explanation of the problem, what you built, and why it matters.
03 / CASE STUDIES
How I approach problems.
Detailed project documentation. Keep these sections practical: explain what the problem was, what you did, and what changed.
CASE STUDY / 01
Persian Personality Assessment AI
01 / ACTIVE
Replace this paragraph with the short overview of the project. Explain the dataset, the research or product problem, and the practical purpose of the system.
01. Problem
Describe the specific problem you are solving. Avoid generic statements and explain what makes the problem technically or practically difficult.
02. Approach
Describe the pipeline: data preparation, feature extraction, model selection, training, evaluation and iteration.
03. Technical Decisions
- Why this dataset?
- Why this model architecture?
- How is Persian text processed?
- What evaluation metrics are used?
04. Results / Next Steps
Add measurable results here when available. If the project is still in progress, explain the next engineering milestone.
Dataset / pipeline screenshot
Model architecture / chart
Application screenshot
CASE STUDY / 02
Language Evolution Simulation
02 / RESEARCH
Use this section to explain the research question, theoretical assumptions and computational implementation.
01. Research Question
State the exact question the simulation is designed to investigate.
02. Model Design
Explain agents, input, transmission, learning and output.
03. Experiment
Describe variables, conditions, repetitions and evaluation.
04. Findings
Add visualizations, observations and limitations here.
Agent architecture
Simulation output
Research chart
CASE STUDY / 03
Local AI Research Assistant
03 / BUILDING
Document the architecture of the local RAG environment, including document ingestion, chunking, retrieval, models and interface.
01. Problem
Explain why ordinary chat interfaces are insufficient for your research workflow.
02. Architecture
Explain ingestion, embeddings, vector storage, retrieval and generation.
03. Challenges
Document local hardware limits, context quality, hallucination and retrieval issues.
04. Outcome
Add benchmarks, screenshots and future improvements.
System architecture
Interface screenshot
RAG pipeline
04 / EXPERIENCE
Research, language and technology.
2025 — 2026
Grade 5 Homeroom & English Teacher
Nikoo International School · Tehran
PYP curriculum delivery and English instruction in a bilingual educational environment.
2022 — 2025
UX Researcher & Web Developer
HiPersia & RasamPlus · Tehran / Karaj
Worked with design, user testing and web development while developing an interest in cognition, interaction and AI systems.
05 / SKILLS
Tools I use to build and investigate.
Machine Learning
- Python
- PyTorch
- scikit-learn
- NumPy · Pandas
- Statistical analysis
- Model experimentation
NLP & Linguistics
- spaCy · NLTK
- Corpus analysis
- Computational linguistics
- Language evolution
- Syntax & semantics
- Multilingual language analysis
Software & AI Systems
- OpenCV · MediaPipe
- LLMs & local AI
- RAG systems
- Git & GitHub
- Linux
- HTML · CSS · JavaScript
06 / CONTACT
Let's build something useful.
I am interested in AI, NLP, machine learning, research engineering and computational linguistics opportunities.