Using Generative AI & Machine Learning in the Enterprise
September 19, 2023
Eva He
Sr Quant at JPMorgan Chase & Co
As a senior quant & analytics at JP Morgan & Chase, Eva provides insights and designs solution to improve consumer’s digital banking experience. Previously Eva worked as Data Scientist at Rivian, where he provided ML and Analytics solutions for electric vehicle testing & validation process and battery insights. With background in Applied Math, Eva pursued her Master Degree in ML and Data Science from Columbia University. On her spare time, Eva collaborates with academia researchers to explore ML/AI’s capability in dataset enrichment and model improvement – her particular research interest and passion is in AI’s application in earth science and generative modeling.
Watch live: September 19 @ 4:05PM – 4:35PM ET
AI Disruption: Deep Generative Modeling in Finance
Generative modeling is a rapidly growing field with a wide range of applications including image and audio synthesis, text generation, and dialogue systems. At its core, generative models are capable of learning the underlying structure and patterns data, enabling them to generate new data points that resemble the original distribution. One area where generative modeling has shown particular promise is in the generation of synthetic financial data. This talk will focus on the application of generative models in financial trading strategy. This talk plans to give a high level walk through on Conditional Generative Adversarial Networks (cGANs) for trading strategies calibration and aggregation: (i) the training and selection of a cGAN for time series data; (ii) how each sample is used for strategies calibration; and (iii) how all generated samples can be used for modelling. At the end, will touch base on the benefit of using generative models in finance – data privacy and security etc.