Cagri Temel
AI/ML Engineer · Researcher · Co-Founder & CTO, Vardenus
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 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.
Experience
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2020–
Co-Founder & Chief Technology Officer
Lead the design and implementation of PropTech and LegalTech systems at the intersection of AI, blockchain, and real estate: an LLM-driven legal-automation suite (R-Law) for landlord-tenant mediation, compliance, and dispute resolution; tokenized fractional ownership and escrow automation on Polygon PoS; and security-first MLOps and Web3 infrastructure.
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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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2018
Space Studies Program (SSP)
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· NASA Space Apps· Grand Canyon University
News
- Feb 2026Paper accepted in Dentistry Journal (MDPI, Q1, IF 3.1): a blinded comparison of AlimGPT vs GPT-4o, Gemini, and Llama.
- 2026Presented CT-SAFR at the IEEE Conference on Artificial Intelligence (CAI 2026), Granada.
- May 2026Joined the Program Committee of AAAI/ACM AIES 2026 (AI, Ethics & Society).
- 2026Organizing a special session on Trustworthy & Explainable AI at IEEE Telepresence 2026, Bristol.
- 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.
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.