Dr. Artur Dubrawski is Alumni Research Professor of Computer Science at Carnegie Mellon University where he directs the Auton Lab, a large research group focusing on fundamental research and applications of Artificial Intelligence. He received a Ph.D. in robotics and AI from the Institute of Fundamental Technological Research, Polish Academy of Sciences, and a M.Sc. in aircraft engineering from Warsaw University of Technology. A 35-year veteran of applied AI, Dr. Dubrawski combines academic and real-world experience having served in executive and research leadership roles in high tech industry. His team is the origin of the first foundation model for time series, TimeGPT-1, and the first family of open source / open weights time series foundation models MOMENT.

Dr. Luciana Ferrer is a researcher at the Computer Science Institute, affiliated to the University of Buenos Aires (UBA) and the National Scientific and Technical Research Council (CONICET), Argentina. Prior to her current position, Luciana worked at the Speech Technology and Research Laboratory at SRI International, USA. Luciana received her Ph.D. degree in Electronic Engineering from Stanford University, USA, in 2009, and her Electronic Engineering degree from the University of Buenos Aires, Argentina, in 2001. Her primary research interest is machine learning applied to speech and natural language processing tasks. Luciana leads a team of researchers working on projects funded by Argentinian institutions and international companies like Google Inc., Amazon, JPMorgan. She has been area chair for Interspeech, participates in program committees for international conferences like ICASSP and Interspeech, and is a regular reviewer for many journals, including IEEE/ACM TASLP, Speech Communication, and Computer Speech and Language. She is Technical Program Chair for Interspeech 2024. Luciana has published more than 160 research articles which received over 6500 citations. She has been invited speaker at international events like Khipu, the Speaker and Language Odyssey workshop and the Automatic Speech Recognition and Understanding workshop

Dr. Elizabeth Fons is a Research Lead in the AI Research Group at JP Morgan, specializing in synthetic time series data and foundation models for time series. 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. Prior to her PhD, Elizabeth obtained an MSc in Physics from the University of Buenos Aires and worked on pattern recognition in particle physics at the Max Planck Institute for Physics in Munich.

Dr. Svitlana Vyetrenko is an Executive Director in the AI Research group at JPMorgan Chase & Co., where she leads a team focusing on generative time series models, multi-agent simulations and reinforcement learning. She is also an Adjunct Lecturer at Stanford University and a Lecturer at the University of California at Berkeley. Dr. Vyetrenko holds a PhD in Applied and Computational Mathematics from California Institute of Technology, and has over 12 years of experience in the financial industry working on artificial intelligence and machine learning techniques for electronic trading. She previously co-organized a workshop on ‘Machine Learning for Investor Modeling and Recommender Systems’ at ICAIF 2023.

Dr. Qingsong Wen is the Head of AI & Chief Scientist at Squirrel AI Learning, leading a team (in both Seattle and Shanghai) working in EdTech area via AI technologies (like LLM, AI Agent, GenAI, Transformer, SSL, GNN, XAI, etc.). Before that, he worked at Alibaba, Qualcomm, Marvell, etc., and received his M.S. and Ph.D. degrees in Electrical and Computer Engineering from Georgia Institute of Technology, USA. His research interests include machine learning, data mining, and signal processing, especially AI for Time Series (AI4TS), AI for Education (AI4EDU), and Decision Intelligence. He has published over 100 top-ranked AI conference and journal papers, had multiple Oral/Spotlight Papers at NeurIPS, ICML, and ICLR, had multiple Most Influential Papers at IJCAI, received multiple IAAI Deployed Application Awards at AAAI, and won First Place of SP Grand Challenge at ICASSP. Currently, he serves as Organizer/Co-Chair of Workshop on AI for Time Series (AI4TS @ KDD, ICDM, SDM, AAAI, IJCAI) and Workshop on AI for Education (AI4EDU @ KDD, CAI). He also serves as Associate Editor for Neurocomputing, Associate Editor for IEEE Signal Processing Letters, Guest Editor for Applied Energy, and Guest Editor for IEEE Internet of Things Journal. In addition, he has regularly served as Area Chair of AI conferences, including NeurIPS, KDD, ICASSP, etc.

Georgios (George) Palaiokrassas is currently a Postdoctoral Associate at Yale University, specializing in the intersection of Blockchain, Machine Learning, and the Internet of Things. His research focuses on developing advanced algorithms and models for decentralized finance (DeFi), with an emphasis on fraud detection, credit risk assessment, and liquidation prediction.  George has made significant contributions to the understanding and implementation of smart contracts on blockchains, reinforcement learning in blockchain environments and the application of Large Language Models (LLMs) for blockchain use cases enhancing smart contracts and improving overall system reliability and efficiency through AI-driven insights. Previously, George conducted his undergraduate, graduate, and postdoctoral studies at the National Technical University of Athens, where he focused on machine learning and blockchain. He obtained his PhD in Computer Software Engineering from NTUA, with a thesis on social network data analysis.  He also earned an M.Eng. in Electrical and Computer Engineering from the same institution

Leandros Tassiulas  is Professor of Electrical Engineering & Computer Science at Yale University, USA. He obtained the Diploma in Electrical Engineering from the Aristotle University of Thessaloniki, Thessaloniki, Greece in 1987,and the M.S. and Ph.D. degrees in Electrical Engineering from the University of Maryland, College Park in 1989 and 1991, respectively. He has held faculty positions at Polytechnic University New York, University of Maryland College Park, and University of Thessaly in Greece. His current research is on intelligent services and architectures at the edge of next generation networks including Internet of Things, sensing & actuation in terrestrial and non terrestrial environments and quantum networks. He worked in the field of computer and communication networks with emphasis on fundamental mathematical models and algorithms of complex networks, wireless systems and sensor networks. His most notable contributions include the max-weight scheduling algorithm and the back-pressure network control policy, opportunistic scheduling in wireless, the maximum lifetime approach for wireless network energy management, and the consideration of joint access control and antenna transmission management in multiple antenna wireless systems. Dr. Tassiulas is fellow of IEEE (2007) and ACM (2020); he received several awards, including the IEEE Koji Kobayashi Computer and Communications Award (2016) for contributions to the scheduling and stability analysis of networks, the ACM Sigmetrics Achievement Award (2020), the inaugural  IEEE INFOCOM Achievement award (2007) for “Fundamental contributions to resource allocation in communication networks” a Bodosaki Foundation award (1999) and numerous best paper awards. More details can be found at his homepage: https://seas.yale.edu/faculty-research/faculty-directory/leandros-tassiulas

Rex Ying is an assistant professor in the Department of Computer Science at Yale University, with appointments in Yale Foundations of Data Science, Yale Biomedical Informatics and Data Science, Yale Computational Biology and Bioinformatics, and Yale Wu Tsai Institute. His research focus includes algorithms for foundation models, multimodal models, graph neural networks, geometric embeddings, and trustworthy deep learning. Rex has built multi-modal foundation models in engineering, natural science, social science and financial domains. He also developed the first billion-scale graph embedding services at Pinterest. He won the best dissertation award at KDD 2022, and the Amazon Research Award in 2024.