🔥 News
- Aug, 2025Tutorial on Non-Euclidean Foundation Models. International Conference on Knowledge Discovery and Data Mining (KDD).
- May, 2025Keynote on AI agent for science. WebConf 2025.
- May, 2025Organized Workshop on Graph Signal Processing (GSP 2025).
- May, 2025Organized Workshop on Non-Euclidean Foundation Models. WebConf 2025.
- April, 2025Invited Talk on Hyperbolic Foundation Models. University of Illinois Urbana Champaign.
- April, 2025Invited Talk on Hyperbolic Large Language Models. Queen's University.
- Dec, 2024Invited Talk on Time Series Representation Learning. Amazon.
- Nov, 2024Organized Workshop on Time Series Foundation Models. International Conference on AI in Finance (ICAIF 2024)
- Oct, 2024Invited Talk on Hyperbolic Representation Learning. University of Rochester.
- Sep, 2024Keynote speech on Hyperbolic Representation Learning and Foundation Models. IMS-NTU Joint Workshop on Applied Geometry for Data Sciences.
- Aug, 2024Invited Talk on LLM for Telecom.Ericsson, Sweden.
- Aug, 2024The lab has received an NSF core program award on building foundation models for scientific discovery.
- Jun, 2024Rex gave a tutorial on Machine Learning in Network Science at NetSci 2024.
Vision
We are a group of data-driven machine learning enthusiasts who are primarily interested in building unified approaches to integrate and learn from complex real-world data. Beyond just text and images, we also build novel deep learning models that consider graphs, time series, geometry and tabular data, and use them to solve a wide array of applications in domains such as biology, medicine, chemistry, physics, neuroscience, social networks, science of science and supply chain.
Motivated by real-world use cases, we focus on efficient and scalable techniques that combine relational reasoning, multimodal learning, geometric deep learning and foundation models. Furthermore, we are actively doing research in trustworthy deep learning to allow safe, transparent and reliable deployment of such models.
