Our Purpose
Recent breakthroughs in AI-driven algorithm discovery - including AlphaEvolve, CodeEvolve, OpenEvolve, and ShinkaEvolve - have shown that AI systems can outperform decades of human effort. From breaking a 56-year-old record in matrix multiplication to achieving 5× speedups on systems research problems, these advances signal a future where AI-driven methods will be a key part of algorithmic discovery.
The AIDDA Institute accelerates this transition by building a global community that fosters collaboration and knowledge sharing:
Speaker events
Featuring leading researchers and organizations at the cutting edge of AI-driven discovery.
Focused working groups
Addressing research questions and/or developing tooling.
Industry connections
Enabling companies to turn AI-driven algorithm discoveries into tangible results.
Who should join
Anyone interested in exploring the frontier of AI-driven algorithm discovery, whether researchers, engineers, industry practitioners, or enthusiasts.
Events Calendar
Join our conference, reading groups, technical discussions, speaker events, and community gatherings. To add your event to the calendar, please reach out to us at events@algorithmdiscovery.org.
Upcoming Events
AIDDA 2026
A half-day remote conference of curated talks from the people defining this field. Speakers announced soon.
Automated Discovery at Scale
Frontier AI models have produced novel insights in mathematics, physics, and other domains. How do we expand these trickles of insight into a firehose?
Committee Members
The AIDDA Institute is seeking additional committee members. If you would like to get involved, reach out to us at committee@algorithmdiscovery.org.
Dr. Daniel Adams
Dr. Daniel Adams is a Mathematics Researcher at The Innovation Game (TIG). He holds an MSc in Mathematics from the University of Bristol and a PhD from the University of Edinburgh, where he specialised in Stochastic Analysis and Optimal Transport. He was awarded a prestigious Maxwell Research Fellowship jointly by the University of Edinburgh and Heriot-Watt University, and later held a postdoctoral position at Université Paris Dauphine.
Henrique Assumpção
Henrique Assumpção is a Machine Learning Researcher at Inter and a Master's student in Computer Science at Universidade Federal de Minas Gerais (UFMG). He holds a BSc in Computer Science from UFMG, with a minor in Pure Mathematics. He is the lead developer of CodeEvolve, an open-source implementation of Google DeepMind's AlphaEvolve for automated algorithmic discovery and optimization. His research spans representation learning, language models, and algebraic graph theory.
Shayan Chashm Jahan
Shayan Chashm Jahan is a PhD student in Theoretical Computer Science at the University of Maryland, College Park. His research spans algorithms and algorithmic game theory, with work appearing at venues including NeurIPS 2024. He is the founder of Repovive, a platform for algorithmic coding competitions with over 7,000 users, and runs a YouTube channel with over 30,000 subscribers dedicated to teaching algorithms.