Bayan Bruss

Bayan Bruss, Senior Director of Applied ML Research at Capital One, explores the intricacies of applying deep learning to tabular data in the financial sector. He addresses the challenges faced, such as messy data and fraud detection, emphasizing the underappreciated significance of this domain. The discussion highlights the need for modern techniques like transformers and transfer learning, aiming to boost model performance and interpretability. Additionally, they delve into the potential of multimodal deep learning for enhancing predictive models.

Bayan Bruss, Vice President of Applied ML Research at Capital One, dives into groundbreaking research on applying machine learning in finance. He discusses two key papers presented at ICML, focusing on interpretability in image representations and the innovative global graph transformer model. Listeners will learn about tackling computational challenges, the balance between model sparsity and performance, and the significance of embedding dimensions. With insights into advancing deep learning techniques, this conversation opens new avenues for efficiency in machine learning.

Bayan Bruss, VP of Applied AI at a major consumer bank building AI for autonomous financial decision-making. He explores why money is a uniquely hard ML problem. They discuss perception-belief-action frameworks for finance. They debate foundation models versus purpose-built encoders, why synthetic time-series data helps, limits of explainability, and hybrid latent vs language reasoning for financial systems.