Machine Learning in Finance Workshop: 2020 Virtual Edition
Hosted by Bloomberg, The Fu Foundation School of Engineering & Applied Science (SEAS), The Data Science Institute at Columbia University
Achintya Gopal: Applications of Deep Generative Modeling in Finance
Abstract:
Generative modeling techniques have experienced a resurgence in the machine learning research community over the last five to ten years with Generative Adversarial Networks (GANs), Variational Autoencoders (VAEs), and Normalizing Flows as only a few examples. While publications have largely focused on showing progress in the domains of images and natural language processing, the underlying innovations have implications outside of those narrow applications. In this talk, we will share concrete applications of these techniques that resolve real-world challenges in financial modeling that arise from the difficulties inherent in financial datasets.
Bio:
Achintya Gopal is a Machine Learning Quant Researcher at Bloomberg, where he was previously a Software Engineer. He earned his Masters and Bachelor's degrees in Computer Science at Johns Hopkins University. His research interests are focused on machine learning and financial modeling, particularly the applications of normalizing flows.
