Organizers

Abdul Fatir Ansari is a Senior Applied Scientist at Amazon Web Services, focusing on time series forecasting. He is the lead developer of the Chronos family of time series foundation models, which ranked among the most downloaded models on HuggingFace in 2024. He received his PhD in 2022 from the National University of Singapore under the supervision of Prof. Harold Soh. During his PhD, he worked on deep generative models for images and time series, receiving the Dean’s Award for his research. He has published in and reviewed for top machine learning conferences including ICML, ICLR, and NeurIPS, and was twice recognized as a top reviewer. He co-organized the “Time Series in the Age of Large Models” and “Foundation Models for Structured Data” workshops at NeurIPS 2024 and ICML 2025, respectively.

Arjun Ashok is a PhD Student at the University of Montreal and Mila-Quebec AI Institute, advised by Irina Rish and Alexandre Drouin. He is also a Visiting Researcher at ServiceNow Research, Montreal, Canada. He is the co-creator of the Lag-Llama foundation model for forecasting and the Context-is-Key benchmark for context-aided forecasting. His research interests lie in time series forecasting and decision-making, with a focus on designing scalable general-purpose models for time series prediction tasks. He has given multiple talks on his work on flexible time series prediction models and time series foundation models. He was a co-organizer of the “Time Series in the Age of Large Models” workshop at NeurIPS 2024. He serves as a regular reviewer for machine learning and forecasting conferences and has been recognized as a top reviewer.

Elizabeth Fons is a Research Lead in the AI Research Group at JP Morgan, specializing in modeling and understanding time series data, with an emphasis on generative methods, imputation, and leveraging large language models for time series interpretation and reporting. She is also a Lecturer at University College London. Elizabeth received her PhD in Computer Science from University of Manchester, where her research, conducted in collaboration with AllianceBernstein, focused on machine learning applications for time series analysis in finance.

Mononito Goswami is an Applied Scientist in the Agentic AI organization at Amazon Web Services, focusing on building pragmatic agents for structured data. He recently earned his PhD from Carnegie Mellon University, where his research focused on developing foundation models for structured data. He has led the development of MOMENT, a foundation model for time series with over 2 million downloads on HuggingFace. His research has been published at NeurIPS, ICLR, and ICML, where he has been recognized as a top reviewer. He co-organized the AAAI 2024 Spring Symposium on Clinical Foundation Models and the Foundation Models for Structured Data workshop at ICML 2025.

Xinyu Li is a PhD student at Carnegie Mellon University, advised by Prof. Artur Dubrawski. Her research centers on LLM-based agents, with a focus on developing scalable benchmarks and general agentic frameworks for time series machine learning tasks. She has gained industry research experience at Microsoft and Amazon, and her work has been presented at multiple ML conferences and workshops. Xinyu co-organized the AAAI 2024 Spring Symposium on Clinical Foundation Models.

Chenghao Liu is a Staff Research Scientist at Datadog AI Research, where he focuses on time series research and AI for observability. Previously, he was a Lead Applied Scientist at Salesforce AI Research, working on time series foundation models. He received B.S. and PH.D. degrees in Computer Science and Technology from Zhejiang University. His research interests include machine learning, data mining and their applications. He has published over 90 top-ranked AI conference and journal papers, had multiple Oral/Spotlight Papers at NeurIPS, ICML, KDD, ICLR, AAAI and IJCAI, etc.

Xiyuan Zhang is an Applied Scientist at Amazon Web Services working on time series forecasting, especially on pre-training and multimodal analysis. She is the co-author of Chronos, one of the most downloaded time series foundation models on HuggingFace. Xiyuan earned her PhD in Computer Science from the University of California, San Diego. She has published and received Top Reviewer Award at top machine learning conferences such as NeurIPS, and has co-organized the “Foundation Models for Structured Data” workshop at ICML 2025. Xiyuan is a recipient of the Qualcomm Innovation Fellowship and has been recognized as a Cyber-Physical-System (CPS) Rising Star.

Yichen Zhou is a Research Engineer at Google Research. He received his PhD in Statistics and MS in Computer Science from Cornell University, and BS in Mathematics from Tsinghua University. His current research focuses on foundation models for structured data. He is the co-author of the TimesFM family of time series foundation models. He has published in and reviewed for top machine learning and statistics venues including ICLR, NeurIPS, ICML, and JMLR.