Cagri Temel
AI/ML Engineer · Researcher · Co-Founder & CTO, Hezarfen LLC
I build trustworthy, explainable AI: safe Chain-of-Thought reasoning for autonomous robots, and interpretable machine-learning systems for high-stakes domains.
About
I am an AI and machine-learning engineer and researcher, and the Co-Founder and Chief Technology Officer of Hezarfen LLC, where I lead Vardenus, an AI-driven real-estate (PropTech) and LegalTech platform built on artificial intelligence, blockchain, and modern cloud infrastructure. My work sits at the intersection of explainable AI, AI safety, and applied large-language-model systems, with a focus on making advanced models trustworthy: grounded, auditable, and safe enough to deploy in high-stakes settings.
As an IEEE Senior Member and a Senior Member of the IEEE Computational Intelligence Society, my current research centers on safe and interpretable Chain-of-Thought reasoning for autonomous robots. This work has been presented at IEEE venues including the IEEE Conference on Artificial Intelligence (CAI 2026) and the IEEE New Era AI World Leaders Summit. I serve the community as a reviewer and program-committee member for several IEEE and AAAI/ACM venues.
I hold an M.S. in Computer Science from Grand Canyon University (GPA 3.89, Alpha Chi Honor Society) and a B.S. in Electrical and Electronic Engineering from Istanbul Aydın University. I am an inventor on patents in both the United States and Turkey, and I am based in Redmond, Washington.
Teaching is the other half of the work. I built and wrote ML Academy (mltraining.org), a free and open-source curriculum of 123 interactive lessons that teaches machine learning, deep learning, and large language models entirely in the browser, in English and Turkish. It is built on one idea carried over from my research: a course should not just show machine learning, it should make the student prove it.
Experience
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2020–
Co-Founder & Chief Technology Officer
Lead the design and implementation of the PropTech and LegalTech systems behind Vardenus, an AI-driven rental platform connecting landlords, tenants, and contractors: an LLM-driven legal-automation module (R-Law) for landlord-tenant mediation, compliance, and dispute resolution, and security-first MLOps infrastructure. Tokenized fractional ownership and escrow automation on Polygon PoS were designed and are the subject of a U.S. patent application.
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2025–
Machine Learning Engineer
Ship LLM-powered features end-to-end, from data pipelines to training, evaluation, and inference. Built retrieval-augmented generation with guardrails (FAISS / Pinecone) for higher answer quality and reliability; MLOps with MLflow / DVC, model registry, CI/CD, canary and A/B releases, and drift monitoring; and APIs at scale with FastAPI and Docker / Kubernetes, with latency and cost optimization.
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2020–
Software Development Engineer in Test
Designed and automated test suites with Java, Selenium WebDriver, TestNG, JUnit, and Cucumber (BDD / Gherkin); data-driven testing, the Page Object Model pattern, and API testing with Postman and REST Assured.
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2019–20
Maker Engineer
Led STEM and maker-space initiatives and hands-on curricula, mentoring 100+ students and organizing community showcase events.
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2018–19
Quality Assurance Engineer
Architected QA and test-automation frameworks (Java, Selenium, TestNG; TDD/BDD) for web-based educational platforms with video streaming and student-tracking systems.
Education
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2025
M.S., Computer Science
Specialization in advanced machine learning and artificial intelligence.
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2018
B.S., Electrical and Electronic Engineering
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2017
Space Studies Program
Certifications: Machine Learning (MIT) · Machine Learning Professional (IBM) · Deep Learning & Neural Networks with Keras (IBM) · Generative AI with LLMs (AWS / DeepLearning.AI)
Skills
AI & Machine Learning
Deep learning and neural networks (computer vision, NLP, generative AI, LLMs), explainable AI, predictive modeling, robotics AI and Chain-of-Thought reasoning.
Software Engineering & Testing
Python, Java, C/C++; test automation (Selenium, TestNG, JUnit); TDD/BDD; CI/CD (Jenkins, GitHub Actions).
Cloud & MLOps
AWS (EC2, Lambda, S3, RDS); Docker, Kubernetes, MLflow; SQL and NoSQL; API design and integration.
Leadership
Cross-functional and global team leadership, strategic technology planning, and STEM program development.
By the Numbers
Affiliations & Recognition
IEEE Senior Member· IEEE Computational Intelligence Society· AAAI· International Space University· Grand Canyon University
News
- Aug 2026Released ML Academy at mltraining.org: 123 free interactive lessons on ML, deep learning, and LLMs, open source and in two languages.
- 2026Organizing a special session on Trustworthy & Explainable AI at IEEE Telepresence 2026, Bristol.
- May 2026CT-SAFR presented at the IEEE Conference on Artificial Intelligence (CAI 2026) and published in IEEE Xplore.
- May 2026Taught the hands-on Data & Analytics workshop on explainable neural trees at Washington State University.
- May 2026Joined the Program Committee of AAAI/ACM AIES 2026 (AI, Ethics & Society).
- Apr 2026Paper published in Dentistry Journal (MDPI, Q1, IF 3.1): a blinded comparison of AlimGPT against GPT-4o, Gemini, and Llama.
- Feb 2026TRACE published at IEEE SoutheastCon 2026, where I also chaired the AI and Predictive Modeling session.
- Feb 2026Spoke on “AI That Matters: Trust Over Power” at Louisville AI Week 2026.
- Dec 2025Invited speaker at the IEEE New Era AI World Leaders Summit, Seattle.
Teaching: ML Academy
mltraining.org is a free, open-source curriculum I wrote from scratch: 123 interactive lessons that run entirely in the browser, with no videos, no installation, and no payment. Other courses show machine learning. This one makes you prove it.
Predict, then see
The student commits to an answer before any animation runs, and the hit rate becomes a calibration score that exposes where intuition fails.
Feel the need for the tool
Re-split the data with a new seed, watch the model ranking flip, and only then meet the 5×2cv F-test that settles it.
Run your own answer
Lessons end in executable code. A wrong sign is not rejected, it runs, and the student watches gradient ascent blow the loss up.
Every number verified
123 lessons, 401 steps, 165 visualizations, 387 cited references, and a script that re-derives every figure before release.
Research Areas
Explainable AI (XAI)
Making model decisions inspectable and auditable through traceable reasoning, grounding, and governance for AI used in regulated, high-stakes settings.
Safe Chain-of-Thought Reasoning
Multi-layered verification that treats LLM reasoning as a checkable control artifact, detecting unsafe or hallucinated steps before any action.
Trustworthy Autonomous Systems
Decision frameworks for robots that trace every action back to sensor evidence, built for auditability under the EU AI Act and ISO 13482.
Interpretable ML / Neural Trees
Architectures that combine neural networks with decision-tree transparency for robust, explainable predictions under noise and missing data.