Prof. Ali Emrouznejad
Professor and Chair in Business Analytics
Director, Centre for Business Analytics in Practice
Surrey Business School, University of Surrey, UK
Speech Title: Computational Intelligence and Optimization Techniques for Performance Prediction in Information Systems
Speech Abstract: The integration of computational intelligence and optimization techniques has become essential for evaluating and predicting performance in information systems. These advanced methodologies enable the analysis of large datasets, the identification of inefficiencies, and the enhancement of decision-making processes. By combining artificial intelligence (AI) algorithms, such as neural networks, alongside optimization models like Data Envelopment Analysis (DEA), predictive frameworks can forecast system performance based on current data. This facilitates proactive decision-making and optimized resource allocation, ultimately improving efficiency and effectiveness in information-driven environments.
In this talk, we will explore the application of DEA in measuring the efficiency of information systems. Furthermore, we will examine how the generated efficiency scores can be utilized to enhance AI models, enabling the identification of performance bottlenecks, explaining inefficiencies, and leveraging these insights for predictive analytics. By combining computational intelligence with optimization techniques, we aim to advance performance evaluation and forecasting in complex information environments.
Bio: Ali Emrouznejad is a Professor and Chair in Business Analytics at Surrey Business School, UK. He is also director of the Centre for Business Analytics in Practice, where he leads research efforts in a variety of areas, including AI and big data as well as performance measurement and management, efficiency and productivity analysis. He serves as an editor, associate editor, or member of the editorial boards for multiple scientific journals and has published over 250 articles in top-ranked journals. With H-index of over 80, he has been named as one of the top 2% most influential scientists in the world by Stanford University. He is a Fellow of the Institute of Mathematics and its Applications (FIMA) Fellow of the Institute Sustainability as well as Fellow of the Institute for People-Centred Artificial Intelligence. [see: https://emrouznejad.com/].
Assoc. Prof. Pavel Loskot
IEEE Senior Member
Zhejiang University-University of Illinois at Urbana-Champaign Institute (ZJUI), China
Areas of Expertise: Statistical signal processing, Probabilistic modeling, Networked systems
Bio: 25+ years of experience in design, analysis, implementation and deployment of telecommunication systems through numerous academic and industrial collaborative projects and consultancy contracts. Expert level knowledge of digital and statistical signal processing, algorithms and methods. Solid background in applied probability and statistics. Avid Linux programmer and user since 1996. In 2014/2015, as a Visiting Researcher at CSRC of the Chinese Academy of Engineering Physics started working on computational molecular biology. In 1999-2001, Research Scientist and Project Manager at CWC, Oulu, Finland. A Fellow of the Higher Education Academy of the UK, and the Recognised Research Supervisor of the UK Council for Graduate Education. A Senior Member of the IEEE since 2013.
Speech title: From Sensitivity Analysis to Bayesian Optimization
Speech abstract: A typical situation in designing data processing systems including the algorithms for signal processing and the models for machine learning is the presence of a large number of parameters. This constitutes a very challenging configuration problem in many dimensions how to choose the values of the parameters yielding the optimum or at least the desirable performance. For instance, the ablation experiments in machine learning assess the influence of each feature, but removing the features individually one by one. While this can provide at least some insight into the effect of each feature on the performance, it is well known that one-at-a-time (OTA) analysis can lead to misleading conclusions. In this talk, I will outline basic methods of sensitivity analysis from sensitivity indices, to different methods of decomposing the output variance, to creating surrogate models, and finally explain how the Bayesian optimization has been successfully applied to many high-dimensional optimization problems including finding the hyperparameters of machine learning models.
Prof. Xin Nie
Wuhan Institute of Technology, China
Areas of Expertise: Software engineering, intelligent optimization algorithms, machine vision, SoC Design
Speech title: Coordinated Task-planning for Multi-autonomous Satellites
Speech abstract: With the rapid development of remote sensing technology, multi-satellite autonomous task planning has become a key research direction in satellite Earth observation. The core challenge in this field lies in how to achieve efficient collaborative planning of Earth observation tasks under the conditions of limited resources and diversified observation requirements. Intelligent optimization algorithms, as an effective tool to solve this problem, are continuously promoting technological progress in this field. Firstly, an overview of the importance of multi-satellite collaborative task planning and its current research status at home and abroad will be presented. The observation task planning of satellite constellations not only needs to consider the observation capabilities of satellites, user requirements, and resource constraints but also has to deal with complex issues such as visibility constraints between stars and the Earth and spatial geometric transformations....more...
