Organizers

Hung Le (Main Coordinator) — Senior Lecturer and ARC DECRA Fellow at Deakin Applied Artificial Intelligence Initiative (A2I2), Deakin University, Australia. At A2I2, he has supervised PhD students and led research on foundation models, agentic AI, reinforcement learning, memory-augmented learning, and multimodal AI, with publications in leading venues including NeurIPS, ICML, ICLR, AAAI, ACL, and KDD. He has served as Area Chair, Senior Program Committee member, Meta-Reviewer, and Reviewer for major conferences including AAAI, ICML, ICLR, ACL, and NeurIPS, and has organized tutorials at KDD, IJCAI, AJCAI, and AAMAS. His expertise is closely related to this workshop, particularly in efficient foundation models, memory-augmented agents, reasoning and planning, and resource-efficient AI systems. He is committed to supporting an inclusive research community through mentoring students, promoting international collaboration, and supporting early-career researchers.

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Haripriya Harikumar (Submission and Review Coordinator) — Research Fellow at the Centre for AI Fundamentals at University of Manchester, UK. She received her Ph.D. degree in Machine Learning from Pattern Recognition and Data Analytics at Deakin University, Australia. Her research focuses on developing secure, safe, and trustworthy AI systems that can make robust decisions and reliable predictions, even in uncertain and resource-constrained environments. She aims to enhance the reliability of AI-driven technologies, including healthcare tools, online platforms, and autonomous systems, making them safer and more dependable for widespread use. Her papers were accepted at reputed conferences in Machine Learning, Data Mining and Computer Vision. She has served as a reviewer in top conferences and workshops, including NeurIPS, ICML, UAI, CVPR, ECCV, BMVC, ICCV, and AAAI. She also co-organized and successfully executed the accepted NeurIPS 2023 workshop on Backdoors in Deep Learning.

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Khanh-Binh (Calvin) Nguyen (Coordinator for Invited Talks) — Associate Research Fellow at Deakin Applied Artificial Intelligence Initiative (A2I2) at Deakin University, Australia. He earned his Ph.D. in Electrical and Computer Engineering from Sungkyunkwan University. His core research investigates advanced computer vision, Vision-Language Models (VLMs), and the engineering of intelligent AI agents. He is particularly focused on developing open, memory-efficient frameworks tailored for resource-constrained environments. Through his work on memory-friendly designs, Multi-Agent Debate (MAD) systems, and latent reasoning pipelines, he actively aims to advance the accessibility of AI beyond traditional frontier models. His research is published in top-tier venues, and he regularly serves as a reviewer and Program Committee member for premier conferences and journals, including NeurIPS, CVPR, AAAI, DAC, ICCAD, DATE, and IEEE Transactions on Multimedia. He is committed to supporting an inclusive research community through mentoring students and promoting international collaboration.

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Dung Nguyen (Poster Session Organizer) — Research Fellow at Deakin Applied Artificial Intelligence Initiative (A2I2), Deakin University, Australia. His research focuses on cooperation and coordination between artificial agents, with publications in venues in artificial intelligence such as AAMAS, AAAI, ACML, and IJCAI. He has served as a reviewer for NeurIPS and ICLR. His expertise is closely related to this workshop, particularly in multi-agent systems and modelling other agents. He is committed to supporting an inclusive research community through mentoring students.

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Manh Nguyen (Webmaster) — Senior PhD student at Deakin Applied Artificial Intelligence Initiative (A2I2), Deakin University, Australia. His research focuses on uncertainty estimation, hallucination mitigation, and multi-agent systems, with publications in venues such as AAAI and ACL. His work explores how collaborative and trustworthy agentic systems can improve the efficiency, reliability, and accessibility of AI models, particularly in resource-constrained environments where deploying large frontier models is impractical. His expertise closely aligns with the workshop’s focus on open and efficient agentic AI beyond frontier models. He is also committed to fostering an inclusive research community through mentoring and supporting students.

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Long Hoang Dang (Submission and Review Coordinator) — Assistant Professor and Head of the Computer Science Department at the Posts and Telecommunications Institute of Technology (PTIT). He received his Ph.D. in Computer Science from Deakin University in 2024. His research focuses on vision-language reasoning, with publications in leading artificial intelligence venues including AAMAS, AAAI, and IJCAI. He has served as a reviewer for NeurIPS, ECCV, EMNLP, and ICLR. He is also committed to fostering an inclusive research community through mentoring and supporting students.

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