The data science talks, hosted by the School of Data Science at CityU, bring to you a unique opportunity to re-connect with fellow alumni, faculty and soon-to-be graduates through a virtual networking platform. Participants will receive latest updates on two up-to-date data science topics shared by our professors. Don’t miss this!
The HK Tech Forum on Data Science and AI (DSAI) gathers world-renowned scholars in data science and AI to address challenging issues in driving data science and AI technology for the benefit of the society. Featured speakers include Turing award winner Prof. John Hopcroft, world-class AI entrepreneur Dr.
Professor Phil Jones will present his experiences of Research Assessment Exercises, mainly from a UK Built Environment perspective. Professor Jones is a leading building scientist with achievements spanning half a century. Professor Jones has been involved with the Research Excellence Framework (REF, the UK equivalent of RAE) as an active researcher and during his tenure as the Head of the Welsh School of Architecture in Cardiff University, during which Architecture at Cardiff was ranked 1st in the UK. He was a member of the 2001 Built Environment and 2008 Architecture and Built Environment RAE panels in the UK, and the 2006 Built Environment panel in Hong Kong. He will talk in general terms about the RAE and REF, and then focus on specific features relating to outputs, impact and environment.
Members of the CityU community are welcome to join us! Apart from taking this opportunity to unite with fellow alumni, students, and faculty members, participants will receive updates on the latest progress of our School from our Dean and Programme Leaders. In the data science talk, we will share our research on using various data science approaches to confronting the COVID-19 pandemic.
Deep learning has resulted in breakthroughs in dealing with big data, in speech recognition, computer vision, natural language processing, and many other domains. It is based on deep neural networks with structures designed for various purposes. A mathematical foundation is needed to help understand modelling, and the approximation or generalization capability of deep learning models with network architectures and structures. In this talk, Professor Zhou will consider deep convolutional neural networks (CNNs) that are induced by convolutions. The convolutional architecture identifies essential differences between deep CNNs and classic neural networks. Professor Zhou will describe a mathematical theory for deep CNNs associated with rectified linear unit activation. In particular, Professor Zhou will discuss approximation and learning capability of deep CNNs dealing with functions of many variables.