I am Peyman Kor, AI Consultant with experince buidng end-to-end AI agents for business problems.
Originally from Iran, I’m now based in Norway, completed my PhD in Reinforcement Learning.
Most recently at Antire, I built bristIQ — a first-of-its-kind, expert-verified AI agent for Google Ads optimization.
My doctoral work focuses on Reinforcement Learning, investigating both classical approaches like Q-learning and innovative applications such as RL for LLMs Reinforcement Learning from Human Feedback.
Prior to my PhD, as a Data Scientist, I was part of a successful industry collaboration project focused on optimizing, resulting in developing an innovative Bayesian Optimization workflow.
Previously, I studied at the Technical University of Denmark and worked on diverse computational projects. In addition, I hold an M.Sc in Engineering from the University of Stavanger, specializing developing practical ML solutions for real-world problems.
I’m actively engaged on GitHub and regularly share findings through my blog.
Experience
Antire
• Building AI agent for Google Ads optimization using Python, LangChain, and Google Ads API
• Driving AI adoption across the organization through the “AI Value Center” initiative
• Collaborating with C-suite leadership on AI strategy leveraging Azure OpenAI, Vertex AI, and BigQuery
decisionclarity
• Research on LLM applications for enterprise “Deep Research” solutions
• Rapid software prototyping and data-driven marketing analytics
• Consulting with CEO/Founder on no-code and AI-powered business solutions
NORCE & UiS
• Developed novel machine learning model for production optimization in subsurface applications
• Designed and tested algorithms for probabilistic modeling, improving computational efficiency by 500%
• Collaborated with stakeholders from eight Norwegian energy companies, ensuring seamless integration of analytical insights into decision-making
Decarbonify
AKER BP
• Developed a probabilistic reserve estimate dashboard tool using R Shiny/RStudio to provide real-time data-driven insights for decision-making
• Analyzed over 10,000 scenarios using Monte Carlo Simulations, allowing data-driven decision-making and improved risk assessment
Education
University of Stavanger
Technical University of Denmark
University of Stavanger
