Dr. Andre Franca

Dr. Andre Franca

CTO at connectedFlow

Andre is the co-founder and CTO of connectedFlow, developing the next generation of AI co-pilots to help e-commerce/D2C operators make better decisions, without the pain of data analytics. He's previously the VP of R&D at causaLens, where he was applying cutting edge Causal AI research to solve business-critical problems in global enterprises. Prior to that he was an executive director at Goldman Sachs, developing and validating quantitative models used by the business. Andre received his PhD in theoretical physics from the University of Munich, where he studied the interplay between quantum mechanics and general relativity in black-holes.

All Sessions by Dr. Andre Franca

Causal AI: from Data to Action

Machine Learning | Intermediate

In this talk, we will explore and demystify th world of Causal AI for data science practitioners, with a focus on understand cause-and-effect relationships within data to drive optimal decisions. In this talk, we will focus on: * from shapley to DAGs: the dangers of using post-hoc explainability methods as tools for decision making, and how tranditional ML isn't suited in situations where want to perform interventions on the system. * discovering causality: how do we figure out what is causal and what isn't, with a brief introduction to methods of structure learning and causal discovery * optimal decision making: by understanding causality, we now can accurately estimate the impact we can make on our system - how to use this knowledge to derive the best possible actions to make? This talk is aimed at both data scientists and industry practitioners who have a working knowledge of traditional statistics and basic ML. This talk will also be practical: we will provide you with guidance to immediately start implementing some of these concepts in your daily work.

Causal AI: from Data to Action

Machine Learning | Intermediate

In this talk, we will explore and demystify th world of Causal AI for data science practitioners, with a focus on understand cause-and-effect relationships within data to drive optimal decisions. In this talk, we will focus on: * from shapley to DAGs: the dangers of using post-hoc explainability methods as tools for decision making, and how tranditional ML isn't suited in situations where want to perform interventions on the system. * discovering causality: how do we figure out what is causal and what isn't, with a brief introduction to methods of structure learning and causal discovery * optimal decision making: by understanding causality, we now can accurately estimate the impact we can make on our system - how to use this knowledge to derive the best possible actions to make? This talk is aimed at both data scientists and industry practitioners who have a working knowledge of traditional statistics and basic ML. This talk will also be practical: we will provide you with guidance to immediately start implementing some of these concepts in your daily work.

Open Data Science

 

 

 

Open Data Science
One Broadway
Cambridge, MA 02142
info@odsc.com

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