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EVENT DETAILS |
In this talk, Jen Burningham, Jorge Castan & Rakshith Dasenahalli will discuss the applications of Machine Learning & AI Fairness techniques in credit risk models for banking institutions. They will cover the typical use cases & the approaches the Data Science Elite (DSE) team used to address the challenges & necessity in mitigating model bias. This session will introduce the Credit Risk accelerator that was developed based on the experience of the DSE team.
Jorge Castan
Jorge Castan hails from Mexico City & received his Ph.D. in Computational & Applied Mathematics from Rice University. He has a genuine passion for data science & machine learning business applications. For 15+ years he has been developing data science & AI models as well as algorithms to solve numerical optimization & regularized inverse problems. At IBM, Jorge is the Lead Data Scientist of the Machine Learning Hub North America, a team that empowers organizations to create business value through data science & AI. In 2020, Jorge was certified as a Level 3 Thought Leader & Distinguished Data Scientist by The Open Group.
Rakshith Dasenahalli
Rakshith is a Data Scientist with the IBM Data Science Elite (DSE) Team & has 3 years of experience on the team. He has worked with various enterprises across different industries helping them understand & use their data to improve their businesses. He has experience working in Machine Learning, Data Visualization, & AIfairness customer engagements. He holds a M.S. in Electrical Engineering from University of Southern California.
Jen Burningham
Jen Burningham is a manager, Senior Data Scientist, & Machine Learning Engineer in the IBM Data Science & AI Elite Team. She has a strong background in healthcare, tech, & startups. Jen is passionate about using AI to improve businesses & lives. She received her Bachelor's Degree in Applied Mathematics with Concentration in Statistics from Yale University & earned her MBA from Yale School Of Management.
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