Enabling collaboration: Accelerate cutting-edge innovations in climate modeling

Overview

Climate change have both immediate impacts and long terms impacts that need to be modeled. Global organizations such as the IPCC, TCFD, Bank of England, the European Central Bank and network of central banks (NGFS) have been working hard to provide us insights, frameworks, mathematical models, and data to tackle this global problem. This talk will focus on empowering you to better understand the landscape of climate modeling through the lens of 2 key case studies driven by large financial institutions. The first case study will dive into the real-world challenge of El Nino and its impact on GDP. The second case study will focus on the impact of transition risks as seen through the eyes of global banks. In addition to a focus on climate, we will learn about how MathWorks is working closely with large financial institutions and regulators to accelerate model development and drive more collaboration.

Highlights

• Overview of climate modeling and regulatory landscape through the eyes of (IPCC, TCFD, Bank of England, the European Central Bank, NGFS)
• Case Study #1: Forecasting GDP based on El Nino
• Case Study #2: Modeling the impact of climate stresses on loan portfolios.
• Accelerating model development with Modelscape
• Drive collaboration with WebAPP Server

About the Presenter

Marshall Alphonso
Principal Global Lead Engineer – MathWorks
Faculty at Columbia University

Marshall Alphonso specializes in explainable Artificial Intelligence and Climate modeling. He has over 10+ years’ experience training clients at over 250 companies, including top hedge funds, banks, and other financial institutions around the world. Currently he is faculty at Columbia University teaching artificial intelligence for the enterprise risk management master’s program. As a prior advisor to the CRO of McKinsey & Co. Investment Office, Marshall was responsible for the design and implementation of the fund liquidity framework, stress testing framework and a multitude of quantitative risk and investment tools, enabling evaluation of exposures for risk and attribution.

He holds a B.S. in electrical engineering and mathematics from Purdue University and an M.S. in electrical engineering from George Mason University. Additional significant graduate work at Harvard University Innovation Lab and KISR-sponsored data science research at MIT.