Achintya Gopal (Bloomberg): "Using Graph Neural Networks to Discover Supply Chain Edges"

Link to Paper: https://arxiv.org/abs/2111.01878 Abstract: One of the key components in analyzing the risk of a company is understanding a company's supply chain. Supply chains are constantly disrupted, whether by tariffs, pandemics, severe weather, etc. In this presentation, I'll discuss how we can use graph neural networks to tackle the problem of predicting previously unknown suppliers and customers of companies and show strong performance in finding these connections by combining the predictions of our model and the domain expertise of supply chain analysts. Speaker Bio: Achintya Gopal is a Machine Learning Quant Researcher in the Quantitative Research group in the Office of the CTO at Bloomberg, where he works on applying machine learning within the financial domain. Prior to that, he worked on estimating carbon emissions using machine learning, developing new models in normalizing flows, and exploring new methods to evaluate statistical models with model uncertainty. More recently, he has been working on a variety of projects ranging from volatility modeling using neural networks, causal inference for investing, generative models in differential privacy, active learning for NLP, and the interpretability of large language models.