Enhancing AI Agents with Causal AI

Hosted by Amir Feizpour and Ali Madani

204 students

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Build Multi-Agent Applications - A Bootcamp
Amir Feizpour, PhD and Abhimanyu Anand

What you'll learn

How cause-effect relationships improve agent decision-making

Applications of causal recommendations in healthcare

Designing advanced, context-aware agents via causal learning

Why this topic matters

This talk focuses on how causal learning enhances AI agents, enabling them to make smarter, context-aware decisions. By understanding cause-effect relationships, AI agents can provide actionable recommendations in healthcare and finance, going beyond traditional prediction models. We’ll also explore future directions, such as causal representation learning and generative modeling.

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. 

Ali Madani

Staff ML Scientist @ Recurssion

Ali is a machine learning specialist with a focus on Biotechnology and Techbio. Holding a PhD from the University of Toronto, his research has applied machine learning to cancer genomics complemented by master's degrees in Mathematics. Ali’s scholarly output includes over 30 publications in prestigious journals and conferences, highlighting his expertise in developing machine learning and computational models for complex biological and chemical data.


As a Staff Machine Learning Scientist at Recursion Pharmaceuticals, a leading Techbio company, Ali has focused on the biological facets of drug discovery, leveraging causal learning to uncover novel therapeutic insights. Previously, his tenure as the Director of Machine Learning at Cyclica was marked by significant contributions to the chemistry aspect of drug discovery, showcasing his adaptability in applying computational methods across the drug development spectrum.


Ali is also committed to educating others about machine learning. He authored Debugging Machine Learning Models with Python and has been active in mentorship in the field. Ali exemplifies the integration of machine learning with biotechnology, driving innovations in drug discovery.

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