Evolutionary LLM Systems for Code Optimization
Hosted by Amir Feizpour and Dmytro Nikolaiev
In this video
What you'll learn
Literature review of LLMs for code optimization
Productive exploration of code optimization
Where LLMs can improve perfomance
Evaluation as the engine: writing task-aligned metrics
Why this topic matters
You'll learn from
Amir Feizpour
Founder @ Aggregate Intellect
Amir Feizpour is the founder, CEO, and Chief Scientist at Aggregate Intellect building a generative business brain for service and science based companies. Amir has built and grown a global community of 5000+ AI practitioners and researchers gathered around topics in AI research, engineering, product development, and responsibility. Prior to this, Amir was an NLP Product Lead at Royal Bank of Canada. Amir held a research position at University of Oxford conducting experiments on quantum computing resulting in high profile publications and patents. Amir holds a PhD in Physics from University of Toronto. Amir also serves the AI ecosystem as an advisor at MaRS Discovery District, works with several startups as fractional chief AI officer, and engages with a wide range of community audiences (business executives to hands-on developers) through training and educational programs. Amir leads Aggregate Intellect’s R&D via several academic collaborations.
Dmytro Nikolaiev
Dmytro Nikolaiev is a Machine Learning Scientist at ChainML (Theoriq)
Dmytro Nikolaiev is a Machine Learning Scientist at ChainML (Theoriq), where he builds and scales agentic LLM systems - from orchestration to evaluation and production monitoring. He’s especially interested in evolutionary algorithms, automated improvement loops, and “systems that get better with feedback.” Dmytro likes bridging research ideas with practical implementations: turning papers into repeatable engineering patterns, robust evaluation harnesses, and measurable system upgrades.
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