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Ming Zhu, is from University of Chinese Academy of Sciences. His research interests include deep reinforcement learning, machine learning, and non-convex optimization. He created several open-source projects, such as RLSolver, ElegantRL, FinRL, FinRL-Meta, and FinGPT. He is a Co-Founder of AI4Finance foundation and SecureFinAI foundation. He is also an organizer of the RLSolver contest. His research papers have been published in top AI journals and conferences, including NeurIPS, IJCAI, TNNLS, TITS, Springer Nature, etc. |
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Xiao-Yang Liu, Ph.D., Columbia University, faculty at Rensselaer Polytechnic Institute. His research interests include deep reinforcement learning, big data, and high-performance computing. He created several open-source projects, such as FinRL, ElegantRL, and FinGPT. He contributed chapters to a textbook on reinforcement learning for cyber-physical systems and a textbook on tensors for data processing. He serves as a PC member for NeurIPS, ICML, ICLR, AAAI, IJCAI, AISTATS, and ICAIF. He also served as a Session Chair for IJCAI 2019. He organized Financial Challenges in Large Language Models (FinLLM)@IJCAI 2024, FinRL Contest at ACM ICAIF 2023, the First/Second Workshop on Quantum Tensor Networks in Machine Learning (QTNML) at NeurIPS 2020/2021, IJCAI 2020 Workshop on Tensor Networks Representations in Machine Learning, and the NeurIPS 2019 Workshop on Machine Learning for Autonomous Driving. |