Keynote speaker
LLMs Meet Optimization: Reinventing Intelligent Scheduling with Language Models and Reinforcement Learning
Qian Zhou is currently an Associate Professor at Nanjing University of Posts and Telecommunications (NJUPT), China, where she also serves as the Director of the System Security and Availability Engineering (SAVAGE) Application Technology Institute. Prof. Qian Zhou has long been engaged in research on basic theories and applications such as applied cryptography algorithm design, network security and privacy, Internet of Things technology, and databases. She have successively undertaken vertical projects such as the National Natural Science Youth Fund project, National Laboratory Open Projects, and the National Key Demonstration Project. In the past five years, more than thirty academic papers have been published in high-level journals, and multiple patents and soft works have been authorized. At the same time, she actively participates in various scientific research and service activities. She is a member of the Chinese Association for Cryptologic Research, a senior member of the CCF, and an Executive Member of the Information Systems Committee of the China Computer Federation. She also provides long-term information security consulting services for the government and enterprises in China.
Abstract
As intelligent systems become more complex, traditional optimization methods
often struggle with dynamic, uncertain, and large-scale scheduling tasks. In this keynote, we explore how Large Language Models (LLMs) can be used together with Reinforcement Learning (RL) to improve task planning, path optimization, and multi-agent coordination. We introduce LLM-QL, a new approach where LLMs provide adaptive guidance to speed up RL learning in complex routing problems like multi-fleet task planning. We also highlight applications in social network analysis and evolutionary multi-objective optimization, showing how language models help generate faster, safer, and more personalized decisions. The talk will share real-world examples and experiments, demonstrating how LLMs can act not only as natural language tools, but also as intelligent decision-makers in optimization systems.
Qian Zhou
Associate Professor at Nanjing University of Posts and Telecommunications (NJUPT), China.
Nicos Maglaveras
Professor of Medical Informatics Aristotle University of Thessaloniki Greece
Personalised health driven by digital health systems and multi-source health/environmental data, ML/AI/DL analytics and predictive models
Nicos Maglaveras received the diploma in electrical engineering from the Aristotle University of Thessaloniki (A.U.Th.), Greece, in 1982, and the M.Sc. and Ph.D. degrees in electrical engineering with an emphasis in biomedical engineering from Northwestern University, Evanston, IL, in 1985 and 1988, respectively. He is currently a Professor of Medical Informatics, A.U.Th. He served as head of the graduate program in medical informatics at A.U.Th, as Visiting Professor at Northwestern University Dept of EECS (2016-2019), and is a collaborating researcher with the Center of Research and Technology Hellas, and the National Hellenic Research Foundation.
His current research interests include biomedical engineering, biomedical informatics, ehealth, AAL, personalised health, biosignal analysis, medical imaging, and neurosciences. He has published more than 500 papers in peer-reviewed international journals, books and conference proceedings out of which over 160 as full peer review papers in indexed international journals. He has developed graduate and undergraduate courses in the areas of (bio)medical informatics, biomedical signal processing, personal health systems, physiology and biological systems simulation.
He has served as a Reviewer in CEC AIM, ICT and DGRT D-HEALTH technical reviews and as reviewer, associate editor and editorial board member in more than 20 international journals, and participated as Coordinator or Core Partner in over 45 national and EU and US funded competitive research projects attracting more than 16 MEUROs in funding. He has served as president of the EAMBES in 2008-2010. Dr. Maglaveras has been a member of the IEEE, AMIA, the Greek Technical Chamber, the New York Academy of Sciences, the CEN/TC251, Eta Kappa Nu and an EAMBES Fellow.
Abstract
The last years saw a steep increase in the number of wearable sensors and systems, mhealth and uhealth apps both in the clinical settings and in everyday life. Further large amounts of data both in the clinical settings (imaging, biochemical, medication, electronic health records, -omics), in the community (behavioral, social media, mental state, genetic tests, wearable driven bio-parameters and biosignals) as well as environmental stressors and data (air quality, water pollution etc.) have been produced, and made available to the scientific and medical community, powering the new AI/DL/ML based analytics for the identification of new digital biomarkers leading to new diagnostic pathways, updated clinical and treatment guidelines, and a better and more intuitive interaction medium between the citizen and the health care system.
Thus, the concept of connected and translational health has started evolving steadily, connecting pervasive health systems, using new predictive models, new approaches in biological systems modeling and simulation, as well as fusing data and information from different pipelines for more efficient diagnosis and disease management.
In this talk, we will present the current state-of-the-art in personalized health care by presenting cases from COVID-19 and COPD patients using advanced wearable vests and new technology sensors including lung sound and EIT, new outcome prediction models in COVID-19 ICU patients fusing X-Rays, lung sounds, and ICU parameters transformed via AI/ML/DL pipelines, new approaches fusing environmental stressors with -omics analytics for chronic disease management, and finally new ML/AI-driven methodologies for predicting mental health diseases including suicidality, anxiety, and depression.
