教师主页移动版

Personal Informtion Dr. Yan Ke graduated from the Department of Computer Science of the National University of Singapore in 2013 (QS ranking 8th in the world and 1st in Asia in 2023). He then joined the Department of Built Environment, College of Design and Engineering, National University of Singapore (Department ranking 6th in the world) as a Tenure-Track Assistant Professor. In 2023, after receiving the funding from China's oversea-talents program, Dr. Yan Ke becomes a professor with the College of Mechanical and Vehicle Engineering, Hunan University, China. Dr. Yan Ke was ranked in the world's leading 2% scientists selected by Stanford University in 2020, 2021, 2022 (consecutive single years) and 2022 (whole career). He is an internationally recognized expert in the fields of AI, IoT, Smart Building and Smart Environment. Dr. Yan Ke worked in many overseas universities, institutes and research groups, gaining research experiences, and maintaining long-term cooperative relationships with professors from those universities and institutes. He currently serves on the editorial boards of several academic journals, including IEEE Transactions on Industrial Informatics, IEEE/ACM Transactions on Computational Biology and Bioinformatics, and Building and Environment, etc. As of January 30, 2023, he has published more than 70 SCI papers, with a total citation count of 3000+ and an H-index of 32. Dr. Yan Ke's main research interests include intelligent buildings, information-based building management systems, the design of automatic fault diagnosis systems for air-conditioning equipment, building total energy management, energy consumption prediction and photovoltaic (PV) power generation forecasting. Email: keddiyan@gmail.com 校内邮箱：keyan@hnu.edu.cn

教育背景

个人简介 严珂，湖南大学教授，博士生导师，国家海外高层次外籍人才专项，国家科技部外国专家交流项目，以及新加坡教育部TIER 1项目三项。曾任新加坡国立大学副教授（2023年QS全球排名第八，专业全球排名第六），日本早稻田大学客座教授，天津城建大学客座教授以及中国计量大学副教授。在2020、2021和2022年连续入选全球最具影响力科学家2%榜单，并在2022年入选全球最具影响力2%科学家终身榜单。担任多个国际知名期刊的副编辑和客座编辑，包括IEEE TRANSACTIONS ON INDUSTRIAL INFORMATICS，BUILDING AND ENVIRONMENT，APPLIED ENERGY等等。发表各级别论文百余篇，其中，论文发表在CS顶级会议AAMAS，AAAI，MFCS，能源领域顶级期刊APPLIED ENERGY，RENEWABLE ENERGY，建筑领域顶级期刊ENERGY AND BUILDINGS，BUILDING AND ENVIRONMENT，IEEE TRANSACTIONS TIER1级别期刊IEEE TRANSACTIONS ON SUSTAINABLE ENERGY，INDSUTRIAL INFORMATICS，INTERNET OF THINGS JOUNAL，AUTOMATION SCIENCE AND ENGINEERING等，被引次数超过3200次，H-index为 34.

工作履历

News 8-2-2024   Our first review paper titled "AI in HVAC Fault Detection and Diagnosis: A Systematic Review" is going to be published with Energy Reviews, which is a "High-Starting Point" journal lead by Prof Heping Xie.   1-12-2023   FEM obtains its first impact factor at 7.4 in 2023.   26-08-2023 We are organizing two special issues with SC-Indexed journals: Special Issue on Intelligent Sensoring and Advanced Fault Diagnosis of Complex Systems with Measurement and Control (Impact Factor: 2.0) (GEs: Xin Li and Ke Yan): https://journals.sagepub.com/page/mac/open-special-collections/intelligent-sensoring-and-advanced-fault-diagnosis-of-complex-systems Special Issue on Empowering the Future Generation of Data Mining and Knowledge Discovery in Bioinformatics with International Journal of Data Mining and Bioinformatics (Impact Factor: 0.3) (GEs: Zhao Li, Ke Yan, Ji Zhang and Saiping Jiang): https://www.inderscience.com/mobile/inauthors/cfp.php?id=5725   02-08-2023 We are organizing a Special Issue with Computer Communications (Impact Factor: 6.000) title: Special Issue on Artificial Intelligence Meeting Green Edge-Cloud Computing. CFP: https://www.sciencedirect.com/journal/computer-communications/about/call-for-papers#special-issue-on-artificial-intelligence-meeting-green-edge-cloud-computing   Edge-Cloud Computing (ECC) is an emerging distributed paradigm to implement edge offloading, in which complex tasks are migrated from IoT devices to edge-cloud servers. In particular, Green Edge-Cloud Computing (GECC) is a novel concept focused on the core problem of energy management in the whole architecture, that is closely related to task dispatch, resource scheduling, network communication and so on. It is obvious that GECC holds great promise in energy-sensitive domains, such as smart grid, Internet of vehicles, healthcare. With the great development of intelligent algorithms, AI is a powerful analysis technology that is well applied to dealing with the energy management in GECC. It is efficient to utilize deep learning to implement the task allocation of edge servers and reduce the energy consumption of the whole system. Technical scope of this special issue includes, but is not limited to:Design of AI-based energy-efficient architecture in GECCTheoretical analysis of energy management in GECCAI-based applications of GECC in energy managementAI for energy management in GECCDeployment of federated learning in GECCAI-based task Scheduling in GECCResource management for federated learning in GECCSecurity and privacy in GECCEnergy optimization of federated learning in GECC Guest editors:Xiaokang Zhou, Ph.DXiaolong Xu, Ph.DZheng Yan, Ph.DKe Yan, Ph.D Manuscript submission information:The journal's submission platform (Editorial Manager®) will be open for submissions to this Special Issue from December 30th, 2023. Please refer to the Guide for Authors to prepare your manuscript, and select the article type of “VSI: AI Meeting GECC” when submitting your manuscript online. Both the Guide for Authors and the submission portal could be found on the Journal Homepage: Computer Communications | Journal | ScienceDirect.com by Elsevier.   05-06-2023 We are organizing a Special Issue with Applied Energy (Impact Factor: 11.446) titled: Emerging AI Technologies in Energy Consumption and Carbon Emission Modelling, Evaluation and Forecasting. CFP: https://www.sciencedirect.com/journal/applied-energy/about/call-for-papers   The increment of carbon (CO2) emission levels for different scale societies, has become one of the top concerns for major countries across the world. Recently, AI-based carbon emission modelling provides an important clue tackling the above-mentioned issue and soon becomes a critical topic in the field of applied energy. The emerging artificial intelligence (AI) related technologies, including machine learning, data mining, time series data analytics, physics informed data models, data-driven prediction and forecasting, Internet of things (IoT), sensor networks and edge computing, provide unlimited possibilities and opportunities for new data-driven solutions for energy saving and forecasting, clean energy optimization and carbon emission minimization. There are existing obstacles and research gaps adopting AI, machine learning, including deep learning technologies for carbon emission evaluation and forecasting due to the uncertainty and unclear performance in current/future generation smart building and smart city environment. The special issue, therefore, provides a forum for researchers and scientists to exchange quality research solutions and results to tackle the carbon emission issues utilizing the emerging AI related technologies for a low-carbon society. Both data-driven and physical model-based forecasting techniques are interested, targeting on more interpretable and efficient carbon emission modelling. The detailed topics of interest include, but are not limited to, the following: Smart energy savings using AI related technologiesSustainable energy generation modelling and predictionEnergy consumption modelling and forecastingClean energy usage optimization with AITotal energy performance modelling and optimizationEnergy market trading with cyber-physical systemsSmart cities development with green energy technology Guest editors: Dr. Ke YanNational University of Singapore, Singapore Dr. Xiaokang ZhouShiga University, Japan Prof. Bin YangTianjin Chengjian University, ChinaUmeå University, Sweden Prof. Lu LiuUniversity of Leicester, United Kingdom Prof. Jin WenDrexel University, United States Manuscript submission information:All submitted papers must be clearly written in excellent English and contain original or review work, which has not been published or is currently under review by any other journals or conferences. A detailed submission guideline is available as “Guide to Authors” at: https://www.elsevier.com/journals/applied-energy/0306-2619/guide-for-authors.All manuscripts and any supplementary material should be submitted through Editorial Manager (EM): https://www.editorialmanager.com/apen/default2.aspx. Please kindly select “VSI: AI in Carbon Emission” when reaching the “Article Type” step in the submission process. All papers will be peer-reviewed by at least two independent reviewersKey dates:Portal Opening Date: 1 August 2023Final submission deadline: 31 March 2024Final acceptance deadline (for guest editors): 31 July 2024   03-03-2023 We are currently recruiting post-doctoral researchers in Hunan University. The annual salary is generally greater than 470,000 China yuan (rmb). More details can be found at: https://mp.weixin.qq.com/s/NV0jBxZX5xR15FcO9MoLeg Contact Email: keddiyan@gmail.com (Current deadline is 15/04/2023) 21-02-2023 Master/PhD candidates are welcome to join the team. We are looking for students who are intersted in Artificial Intelligence, Time Series Data Analysis and Forecasting, Fault Detection and Diagnosis and Total Energy Control in Buildings. Contact Email: yanke@qq.com 12-02-2023. We are organizing a mini-symposium of “AI Applications in the Built Environment” with The 29th International Conference on Computational & Experimental Engineering and Sciences (ICCES2023) May 26-29, 2023. Shenzhen, China: https://www.iccesconf.org/symposia/ 10-02-2023. We are organizing two special issues with the journal Sensors (Impact Factor: 3.847), including: AI and Big Data Analytics in Sensors and Applications: https://www.mdpi.com/journal/sensors/special_issues/ABDASA Application of AI-Based Enabled Cyber Resilience in Sensor Networks for Infrastructure Management: https://www.mdpi.com/journal/sensors/special_issues/0EQPU60742

研究领域

Academic Qualifications Postdoctoral Researcher (System Engineering and Management, 2014), Masdar Institute of Science and Technology, Khalifa University, Abu Dhabi, United Arab Emirates, Supervisor: Dr. Afshin Afshari. Ph.D. (Computer Science, 2013), National University of Singapore, Singapore, Supervisor: Dr. Alan, Cheng Ho-Lun. Bachelor of Computer Science (w. Hons, 2006), National University of Singapore, Singapore.

学术成果

Career Path Full Professor, Mechanical and Electrical Engineering, Hunan University. Jan 2023 - Present Deputy Program Director, Cross-Disciplinary Degree Programme, NUS. Aug 2020 – Jan 2023 Visiting Professor, Waseda University, Japan. July 2019 - Present Assistant Professor, Tenure-track, National University of Singapore. Dec 2017 - Jan 2023 Associate Professor, China Jiliang University, Hangzhou, China. Sep 2015 - Dec 2017 Post-Doc Researcher, Masdar Institution, Abu Dhabi, UAE. Mar 2013 - Dec 2014 Research Assistant, National University of Singapore, Singapore. Sep 2010 - Sep 2012 Teaching Assistant, National University of Singapore, Singapore. Jan 2008 - Aug 2010