Speakers

Bernie Wang is a Principal Scientist at Amazon, where he leads research in automated machine learning (AutoML), time series forecasting, and foundation models. He focuses on making advanced AI/ML accessible and leads the development of widely used open-source tools such as AutoGluon, GluonTS, and Chronos. He has built scalable algorithms and forecasting systems for Amazon’s retail and AWS platforms, contributing to services like Amazon Forecast and SageMaker, and has led science teams across multiple AWS products. Bernie earned his Ph.D. in Computer Science from Tufts University and has published extensively in top-tier venues including NeurIPS, ICML, and JMLR.

Othmane Abou-Amal is an Applied AI & AI Research, Director at Datadog, where he drives the company’s AI direction across large language models, generative AI, and intelligent systems for observability. He leads the development of AI-powered capabilities that enable engineers to automatically detect, investigate, and resolve complex production issues at scale. His work sits at the intersection of research and production, spanning LLM-based applications, autonomous agents, and time-series modeling. Othmane focuses on building reliable, scalable AI systems that are deeply integrated into real-world engineering workflows, bringing cutting-edge advances in AI to practical use in modern infrastructure and observability platforms.

Rose Yu is an associate professor at UC San Diego department of Computer Science and Engineering and Amazon Scholar. She is a primary faculty with the AI Group. Her research interests lie primarily in machine learning, especially for large-scale spatiotemporal data. She is particularly excited about AI for scientific discovery. She has won Presidential Early Career Award for Scientists and Engineers (PECASE), DARPA Young Faculty Award, ECASE Award, NSF CAREER Award, Hellman Fellowship, Faculty Awards from Sony, JP Morgan, Meta, Google, Amazon, and Adobe, several Best Paper Awards, Best Dissertation Award at USC. She was named as MIT Technology Review Innovators Under 35 in AI and 2025 Samsung AI Researcher of the Year.

Pablo Montero-Manso is a Senior Lecturer in the Discipline of Business Analytics, University of Sydney. His research interests focus on Machine Learning and Statistics for Time Series Analysis: clustering, classification and forecasting. Pablo has developed several successful methodologies for automatic forecasting on large datasets. In 2018, he co-developed an award-winning forecasting methodology and during 2020 and 2021 his research was applied to the forecasting of the COVID-19 pandemic in both Australia and Spain. He has authored and maintains multiple open source data analysis tools. His research and software tools are being used in academia, industry, and the public sector. Pablo received his PhD from the University of A Coruna, Spain. Before joining the University of Sydney, he was a post-doctoral fellow at Monash University.