Organizers

Abdul Fatir Ansari is an Applied Scientist at Amazon Web Services working on time series forecasting and log analytics. He received his PhD in 2022 from the National University of Singapore where he was supervised by Prof. Harold Soh. During his PhD, Abdul Fatir worked on deep generative models for images and time series and was awarded the dean’s award for his PhD research. He has published in and served as a reviewer in top machine learning venues such as ICML, ICLR and NeurIPS. His broad research interests lie in the areas of time series analysis and generative modeling, encompassing probabilistic generative modeling (both implicit and explicit), variational inference, unsupervised learning, and representation learning.

Arjun Ashok is a Visiting Researcher at ServiceNow Research, Montreal, Canada and a PhD Student at Mila-Quebec AI Institute and University of Montreal advised by Irina Rish and Alexandre Drouin. 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 (forecasting, imputation, anomaly detection etc.). He has published and given talks in multiple machine learning and forecasting conferences in industry and academia on his work on flexible forecasting and time-series foundation models.

Imry Kissos brings over two decades of pioneering work in machine learning algorithm development, with a focus on enhancing industry standards through innovative research and applications. Currently as a Senior Applied Scientist at AWS Central Economics, Imry leverages cutting-edge machine learning techniques to optimize AWS productivity. He holds a Master of Science degree from Technion - Israel Institute of Technology. He has a notable track record in leading diverse teams and fostering collaboration to achieve exceptional results. His expertise is underscored by his significant contributions to computer vision and machine learning at Amazon Lab126, where he enhanced daily physical mobility with the Amazon Halo and improved Amazon Fashion’s catalog metadata through deep CNNs. Imry’s leadership skills extend to organizing scientific meetings, including the notable ECCV 2018 workshop on Computer Vision for Fashion, Art, and Design, and internal Amazon workshops in 2020.

Dr. Moshe Unger is an Assistant Professor at Tel Aviv University, Israel, in the Coller School of Management, where he conducts applied machine learning research in the fields of time series analysis and recommender systems. His research focuses on personalization, data mining, machine learning, and recommender systems, with an emphasis on time-series analysis using deep learning techniques and its application in various business domains, including recommender systems. Prior to his current position, he completed a postdoctoral researcher role at NYU Stern School of Business and served as an Associate Research Scientist at the NYU Stern Fubon Center of Technology, Business, and Innovation. Dr. Unger brings extensive experience in organizing successful workshops, notably including past Context-Aware Recommender Systems (CARS) workshops at RecSys’19-22, RecSys’09-12, and ComplexRec at RecSys’17-20.

Kashif Rasul is a research scientist at Morgan Stanley working in the areas of neural forecasting, reinforcement learning, and generative modeling for sequential data. He studied Mathematics at Monash University in Australia, and received his PhD from the Free University, Berlin, Germany and has worked in several industrial research labs over the years. He has published several highly cited papers on time series prediction, has further open-sourced several models for time series prediction and has been a prominent contributor to open-source libraries.

Pedro Mercado is an Applied Scientist in AWS, working on hierarchical time series and large-scale probabilistic machine learning models for time series forecasting, bridging the gap from scientific research to setting up models in production. Before this he received his PhD in Computer Science from the University of Saarland in Germany and holds a M.Sc. from the Max Planck Institute for Informatics, and a B.Sc. in Applied Mathematics from Instituto Tecnológico Autónomo de México.

Laurent Callot is a Principal Applied Scientist in AWS whose work currently focuses on the development, evaluation, and applications of foundation models. Prior to joining AWS, Laurent earned a PhD in Economics from the University of Aarhus focusing on the econometrics analysis of high-dimensional time-series and was a researcher at the Free University in Amsterdam focusing on developing machine learning methods for time-series analysis. He has published papers in the ML, Economics, and statistics conferences and journals.

Johannes Stephan is a Senior Applied Scientist and part of a team at Zalando SE that develops and ships Demand Forecasting models to steer pricing decisions. Before joining Zalando in 2016, Johannes earned a PhD in Computational Biology with a focus on developing stat. models that help identify genetic drivers for complex diseases. During his work for Zalando, he actively contributed to the scientific forecasting community by co-authoring several pre-prints and presenting at workshops and conferences.