Speakers & Panelists

Keynote Speakers

Andrew Gordon Wilson

Andrew Gordon Wilson is an Associate Professor at the Courant Institute of Mathematical Sciences and Center for Data Science at NYU. Prof. Wilson wishes to develop a prescriptive foundation for building intelligent autonomous systems, with work involving Bayesian inference, distribution shifts, scientific discovery, and generalization in deep learning. He has been Workshop Chair, Tutorial Chair, EXPO Chair, and Senior Area Chair for major machine learning conferences, and has received numerous awards, including the NSF CAREER Award, the Amazon Research Award, and best paper, reviewer, area chair, and dissertation awards.

Title: Why Are Language Models A Good Idea for Time Series Forecasting?

We show language models that have only been trained for next word prediction can outperform purpose-built time-series models trained on time series training data. How is this result possible? What does it mean for the future of time series forecasting? In this talk, I'll tell the story of how we proposed the first large language model for time series forecasting, how this field has since been evolving, and explain why this class of models has promise in domains outside of language.

James Zhang

Dr. James Zhang is the Managing Director of AI Prediction and Strategy Platform of Ant Group. Dr. Zhang obtained his Ph.D. degree from Univ. of Ottawa, Canada in Electrical Engineering, and both his Master’s and Bachelor degrees from Zhejiang Univ., China. Before he joined Ant Financial, Dr. Zhang worked on finance-related AI at Bloomberg, helped setting up the AI branch of Bloomberg Labs and initiating the GPU computation farm of Bloomberg. Dr. Zhang worked in various areas including image processing, natural language processing, pattern recognition, high-speed hardware development, optical networks, operations research, biometrics, and financial systems.

Title: The exploration on Time-Series Foundation Models

In this presentation, Dr. Zhang will provide an overview of AI research on foundational models for time series data. The talk will include an industrial perspective on time series research and its applications in facilitating real-world business solutions. Dr. Zhang will also explore Ant Group's innovative work in this area, focusing on advancements such as TimeLLM, TimeMixer, and iTransformer, along with the philosophical considerations underpinning these developments. The presentation will provide a discussion on real-world applications and the potential implications for future academic and industrial endeavors in this field.

Panellists

Mononito Goswami

Mononito Goswami is currently a Robotics Ph.D. student at the Auton Lab in the School of Computer Science at Carnegie Mellon University, where he is advised by Prof. Artur Dubrawski. His research focuses on building foundation models for time series and tabular data. He is also interested in exploring how data is used to (pre-)train machine learning models and in developing effective model evaluation techniques. Currently, he is a Student Researcher at Google Research. Previously, he was an Applied Scientist Intern at Amazon Web Services (AWS) AI Labs during the summers of 2022 and 2023. Prior to beginning his Ph.D., [Name] earned his bachelor's degree in computer engineering from Delhi Technological University (formerly Delhi College of Engineering) in India.

Antigoni Polychroniadou

Antigoni Polychroniadou, Executive Director at J.P. Morgan AI Research, heads the J.P. Morgan AlgoCRYPT Center of Excellence. Holding a Ph.D. from Arhus University and a postdoc from Cornell University, she's received several awards, such as the junior Simons fellowship, and is named Prolific JPMorgan inventor. Her cryptographic tools, leveraging new privacy-preserving algorithms for time series data and beyond, are reshaping the landscape of data utilization in finance.

Ayub Hanif

Ayub Hanif is an Executive Director and Head of European Quantitative Strategy in the Global QDS team, whose research covers views on Investment styles, sectors and stocks. He takes an active role in producing AI/Machine Learning research for investment management. The team consistently ranks in Institutional Investor’s Top 3. His research covers views on styles, sectors, systematic stock selection, systematic ESG and on the intersection of machine learning and portfolio management. Ayub holds a Master's in Computer Science from Queen Mary University of London and, from UCL, a Master's of Research in Financial Computing and a PhD in Computational Finance. He has developed AI models in applied mathematics, computational finance and astrophysics, and has written top-tier research publications.

Page updated

Google Sites

Report abuse