CityU Homecoming 2023 - SDSC’s Data Science Talks
CityU Homecoming 2023 - SDSC’s Data Science Talks

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!

All members of the CityU community are welcome to join us! For registration, please click HERE!

 

Time Programme Presenter(s)
4:00 pm Welcome Remarks

Dr Zijun ZHANG, Associate Dean, SDSC

4:10 pm – 4:50 pm

Data Science Talk – Session 1

Data Science Powered Emerging Solutions for Our Society

 

 Abstract:

With the development of over a decade, deep learning and big data have introduced an emerging data science paradigm of deriving solutions for our society, which enables the adaptive and automated large-scale data processing and modelling process. This talk will revisit the application-oriented breaking-throughs generated by data science and envision the possible further impacts introduced by recent data science development to our life and career.

 

Dr Zijun ZHANG, Associate Dean, SDSC

4:50 pm – 5:30 pm

Data Science Talk – Session 2

Uncertainty Quantification in Computer Simulations

 

Abstract:

Computer simulations, which are performed based on a computer model (also called simulator), are increasingly used in science and engineering to achieve various simulation objectives. However, due to the increasing complexity of simulators employed in practice, computer simulations can be costly and time-consuming. Consequently, simulation objectives often need to be achieved with few simulation runs, which creates significant uncertainty about the results. In this talk, I will give an overview of the emerging field of uncertainty quantification, which focus on the development of efficient methods that take into account various sources of uncertainty to achieve multiple types of simulation objectives with few simulation runs.

 

Dr Matthias TAN, Associate Professor, SDSC