🇮🇳Knowledge Elicitation & Application

Zabi Ulla S, Sr. Director Advanced Analytics, Course5 Intelligence

Presented at the 7th Annual BayesiaLab Conference at the North Carolina Biotechnology Center.

Abstract

The discussion focuses on knowledge elimination in causal modeling, especially transfer learning, and how Course5 uses these concepts to solve complex problems in marketing measurement and optimization. Course5 marketing applications are primarily built around Bayesian Science, leveraging BayesiaLab at various levels in its development. In the specific application of BayesiaLab in its solution ‘Integrated Marketing Measurement,’ I demonstrate how Course5 infers knowledge from one model and transfers it into other related models to handle various challenges in marketing measurement today. The solution is exclusively built leveraging the Bayesian Inference engine from BayesiaLab and will talk about the flexibility and agility of the technology along with the robustness of Bayesian Inference Modeling.

Presentation Video

Presentation Slides

About the Presenter

Zabi Ulla S, Sr. Director Advanced Analytics, Course5 Intelligence

Zabi has 15 years of experience in data analytics, machine learning, and applied artificial intelligence, primarily in the business consulting domain. He has worked for marque clients such as Lenovo, Intel, Microsoft, YouTube, Del Monte, Wrigley, T-Mobile, etc., solving complex business problems related to customer monetization and marketing optimization.

In his current role, Zabi leads advanced analytics and data science practice with Course5 Intelligence. In his previous stint with other companies, he gained experience in designing and executing machine learning models and developed teams to develop niche solutions.

Zabi comes from an applied statistics background. He has a master's in statistics and was recently selected as a Top-40 data scientist in India by Analytics India Magazine. Zabi carries an acute interest in machine reasoning, causal inference, and experimental designs, along with machine learning and data science.