Professor Kah Phooi Seng

Adjunct Professor
School of Engineering and Information Technology
  • ABOUT
  • PUBLICATIONS

Dr Jasmine K. P. Seng is currently an adjunct professor in School of Engineering & IT at University of New South Wales (UNSW). She also previously worked at University of Tasmania, Griffith University, Monash University, Nottingham University, Sunway University, Edith Cowan University and Charles Sturt University.

 

She received her PhD and Bachelor of Engineering with First Class Honours degrees from the School of Engineering, University of Tasmania in Australia. She has a strong record of publications and published 3 books and over 250 papers in journals, book chapters and international refereed conferences. Her research strengths and interests include machine learning and data analytics, AI and intelligent system, Internet of Things (IoT), computer vision/ image processing, reconfigurable embedded systems, mobile software development, multimodal information processing, intelligent sensing and sensor networks, human computer interaction (HCI) and affective computing, innovative technologies for agriculture, viticulture and environment, etc.

 

She has received over AUD$2m in competitive grant funding on machine learning and data analytics after returning to Australia. These include three ERA ranked Category 1 grants. Prior to relocating to Australia, she has received over $1.8m in competitive grant funding including 7 government grants (counterpart to ARC Discovery). For research supervision, she has supervised and co-supervised more than 25 postgraduate (PhD/ MPhil/ MSc in research) students to completion. 

Journal articles

Ang LM; Seng KP; Ijemaru GK; Zungeru AM, 2019, 'Deployment of IoV for Smart Cities: Applications, Architecture, and Challenges', IEEE Access, vol. 7, pp. 6473 - 6492, http://dx.doi.org/10.1109/ACCESS.2018.2887076

Seng KP; Ang LM, 2018, 'A Big Data Layered Architecture and Functional Units for the Multimedia Internet of Things', IEEE Transactions on Multi-Scale Computing Systems, vol. 4, pp. 500 - 512, http://dx.doi.org/10.1109/TMSCS.2018.2886843