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Fang Chen

Senior Principal Researcher


Previous Positions

 

Dr. Fang Chen was employed with Beijing Jiaotong University in China from 1995-1999. She was appointed as the Associate Professor of the Faculty of Electronic and Information Engineering in 1995, as the Deputy Director of the Institute of Information Science in 1996, and then as the Dean of Faculty of Electronic and Information Engineering in 1997.

Dr. Chen began her career in industry in 1999 as senior researcher and Team Leader in Intel China Research Centre. She joined Motorola in 2000 as Principal Researcher and founding manager of the Speech and Language Generation Research Lab of Motorola China Research Centre, where she also acted as the account manager of business relationships for the Motorola China Research Centre.

Dr. Chen moved to Australia in 2002 to work for the Motorola Australian Research Centre, where she chaired the Patent and Publication Committees.

She joined NICTA in 2004 and is currently the Research Group Manager at the ATP Laboratory, in the Machine Learning Research Group.

Qualifications

Dr. Chen holds a PhD in Signal and Information Processing, a MSc and BSc in Telecommunications and Electronic Systems respectively, and an MBA.

Research Interests

Dr. Chen's main research interests are behaviour analytics (especially in using signal processing), machine learning, and pattern recognition in human and system performance prediction and evaluation. She has done extensive work on human-machine interaction and cognitive load modelling. She pioneered the theoretical framework of measuring cognitive load through multimodal human behaviour, and provided much of the empirical evidence on using human behaviour signals (such as speech, language, eye movement and manual/pen gesture), and physiological responses from people’s EEG, skin conductance (GSR), to measure and monitor cognitive load.


She leads many taskforces in transforming industry through technologies in data analytics, and is engaged in projects of national and international scale and impact. Her effort now focuses on helping industries by making better use of data they have, increasing productivity and innovation through business intelligence. She has achieved great success in several projects on advanced data analytics in infrastructure modelling and failure prediction. The “Data Driven Pipe Failure Prediction” project led to the creation of the most accurate prediction method in the world for urban water infrastructure pipe failure prediction. It has been validated worldwide through datasets coming from more than 20 utilities. She is also leading the effort on Data Driven Traffic Modelling - to use machine learning and data analytical algorithms in solving real-world transport problems, leading to solutions for predicting traffic patterns, providing decision support, ensuring road safety, validating performance metrics , and creating social benefits.


She has more than 150 refereed publications and filed more than 30 patents in Australia, the US, Europe, Canada, China, Japan, Korea and Mexico. She has supervised more than 20 PhD students in last the five years, including ongoing students. She has also been awarded various research grants, including many years of support from US Air Force Research.

NICTA Projects

Dr.Chen is leading the following projects:

Making Machine Learning Transparent

Advanced Data Analytics in Transport

Decision Support for Incident Management

Data Driven Pipe Failure Prediction

BrainGauge

 

NICTA Past Projects

Dr. Chen was the Research Group Manager for the Making Sense of Data (MSD) research theme at the ATP Laboratory. Projects under this theme:

  • The Decision Support for Incident Management (DSIM) project aims to research and develop core technologies for a new generation of human computer interfaces that support critical decision-making for users in high-performance and time critical environments. The research focuses on i) measuring the cognitive load experienced by human operators of large volume and complex information systems, in real-time and in an unobtrusive way; ii) optimising joint human-system integration via adaptive decision support based on human cognitive status and situational context.
  • Human Performance Improvement (HPI). When elite athletes become fatigued their competitive edge may be lost. In Emergency Services and Defence, fatigue may be fatal. Monitoring human performance is critical to ensure safety and peak competitive output. Coaches and commanders need to monitor personnel in training and in the field, and to have instant feedback on performance. At present, athletes and soldiers are monitored under special laboratory conditions, have data returned to base after several days of logging, and may fill out surveys to determine their own performance. Coaches and commanders want real-time feedback of performance: the challenge is to interpret, transmit and display the information. There are two work packages from HPI is under MSD theme at ATP lab.
  • The STaR-UI project proposes to introduce multimodal input technologies and the fusion of these inputs and cognitive load based multimodal output generation to address the need of control rooms. The project is developing technologies that efficiently guide control room operators through processes such as incident response, to automatically generate reports and present other relevant information to the operators as they proceed. The project is also developing and implementing cognitive load management techniques to streamline interaction with various traffic management centre applications enabling more effective application integration and more reliable traffic management operations.
  • The Braccetto project is a collaborative project in human-machine interaction. It aims to develop sophisticated information sharing technology that can help geographically distributed teams collaborate more effectively. The project is developing new methods for supporting simultaneous work on software applications between sites in conjunction with tightly linked, high-quality, multi-party telepresence technology. The project will evaluate how these methods improve the productivity of teams and team members’ awareness of co-workers.
  • The DMiST project combines the areas of computational geometry, data mining, data bases and algorithm design. The movement of objects (e.g. cars, people, animals) can be represented by trajectories in the plane. Analysing these provides and trying to find interesting patterns such as where animals meet to form a flock is the aim of the project.

Affiliations

Dr. Chen has received Conjoint Professor and Honorary Associate positions with the University of New South Wales and the University of Sydney. She is also an adjunct professor in Beijing Jiaotong University.

Selected Patents

  1. Text-to-Speech System with Prosodic Control, China patent application No. ZL02127007.4  Huang, J., Chen, F, 25 July 2002. Granted 14 June 2006. Patent number : 02127007.4
  2. Method For Chinese Word Segmentation, China patent application No. ZL02127005.8 Chen, G., Chen, F, 25 July 2002, granted 17 May, 2006. Patent number : 02127005.8
  3. Method for Synthesizing Speech, China patent application No. 03132698.6, Chen, F., Chen, G., 29 September 2003. Granted 14 June 2006. Patent number: 03132698.6
  4. Method for Synthesizing Speech, South Korea patent application, Chen, F., Chen, G., 17 September 2004. Granted 16 Oct, 2007. Patent Number: 769033
  5. Measuring Cognitive Load (Speech Content Analysis), Australian patent application AU2008905089, Sept 30, 2008., Fang CHEN; Muhammad Asif KHAWAJA, Eric CHOI
  6. Measuring Cognitive Load (Multimodal), Canada patent application CA2655189, Dec 12, 2008., Chen, F., Choi, E., Ruiz., N.
  7. Measuring Cognitive Load, US patent application WO2007000030, Jan 4, 2007, Chen, F., Choi, E., Ruiz., N.
  8. Multimodal Computer Navigation, US patent application WO2006128248, Dec 7, 2006, Taib, R., Chen, F., Shi, Y.
  9. Measuring Cognitive Load, Australia patent application AU2006264222, Jun 28, 2006; AU2005903441, Jun 29, 2005. Chen, F., Choi, E., Ruiz., N.
  10. Active Speech Cancellation, China patent application No. 03122115.7, Zhang, Y., Huang, J., Chen, F., 18 April 2003
  11. Text Summarisation, PCT application No. PCT/US04/36896, Chen, F., Han, K., 4 November 2004.
  12. Method for Synthesizing Speech, PCT application No. PCT/US04/30467, Chen, F., Chen, G., 17 September 2004
  13. Text-to-Speech System with Prosodic Control, Japan patent application No. 2004-524006,  Huang, J., Chen, F., 24 January 2005
  14. A method for extracting user preference and its application in search based on metadata annotation, US patent application No. 11/123351, Chen, F., Li, W. June 2005
  15. Method for Synthesizing Speech, Korea patent application No. 10-2005-7001367, Chen, F, Oct., 2005

Selected Publications
     

  1. B. Zhang, Y. Wang, and F. Chen, “Multilabel Image Classification via High-Order Label Correlation Driven Active Learning”, IEEE Transactions on Image Processing, vol. 23, no. 3, 2014.

  2. N.L.D. Khoa, B. Zhang, Y. Wang, F. Chen and S. Mustapha, “Robust Dimensionality Reduction and Damage Detection Approaches in Structural Health Monitoring”. In International Journal of Structural Health Monitoring (SHMIJ), vol. 13, issue 4, pp. 406-417, 2014.

  3. Z. Li, B. Zhang, Y. Wang, F. Chen, R. Taib, V. Whiffin, and Y. Wang, “Water pipe condition assessment: A hierarchical beta process approach for sparse incident data,” Machine Learning (ML), 2013.

  4. Z. Li, W. Wang, Y. Wang, F. Chen, and Y. Wang, “Visual tracking by proto-objects,” Pattern Recognition (PR), vol.46, pp. 2187-2201, 2013.

  5. M. S. Hussain, R. A. Calvo, F. Chen, Automatic cognitive load detecting from face, physiology, task performance and fusion during affective interference, Interacting with Computers, 25(4), 2013. Elsevier.

  6. S. Chen, J. Epps, “Blink Analysis for Cognitive Load Estimation: Towards Wearable Computing that Understands Your Current Task”, IEEE Pervasive Computing, vol.12, no.3, 56-65, 2013.

  7. G. Herman, B. Zhang, Y. Wang, G. Ye, and F. Chen, “Mutual information-based method for selecting informative feature sets,” Pattern Recognition (PR), 2013.

  8. Chen, F., Ruiz, N., Choi, E., Epps, J., Khawaja, A., Taib, R., Yin, B. and Wang, Y., “Multimodal Behaviour and Interaction as Indicators of Cognitive Load”, ACM Transactions on Interactive Intelligent Systems, 2012.

  9. Xu, J., Wang, Y., Chen, F., Choi, E., Li, G., Chen, S. and Hussain, S., “Pupillary Response Based Cognitive Workload Index under Luminance and Emotional Changes”, Proc. SIGCHI Conference on Human Factors in Computing Systems (CHI’11), Vancouver, Canada, May 2011, pp. 1627-1632.

  10. Ruiz, N., Chen, F. and Oviatt S., “Multimodal Input”, in Multimodal Signal Processing: Theory and Applications for Human-Computer Interaction. Edited by Thiran, J.P., Marques, F. and Bourlard, H., Academic Press, 2010, Chapter 12, pp. 231-255.

  11. P. Zarjam, J. Epps, F. Chen, and N. H. Lovell, “Estimating cognitive workload using wavelet entropy-based features during an arithmetic task”, Computers in Biology and Medicine, vol. 43, no.12, pp. 2186-2195, 2013. Elsevier.

  12. M. A. Khawaja, F. Chen, and N.Marcus, “Measuring Cognitive Load using Linguistic Features - Implications for Usability Evaluation and Adaptive Interaction Design”, International Journal of Human-Computer Interaction, 2013.

  13. Khawaja, M. A., Chen, F., Marcus, N., “Analysis of Collaborative Communication for Linguistic Cues of Cognitive Load”, International Journal of Human Factors and Ergonomic Society, 2011.

  14. Knoll, A., Wang, Y., Chen, F., Xu, J., Ruiz, N., Epps, J. and Zarjam, P., “Measuring Cognitive Workload with Low-Cost Electroencephalograph,” Proc. IFIP International Conference on Human-Computer Interaction (INTERACT’11), Lisbon, Portugal, September 2011, pp. 568-571.

  15. Chen, S., Epps, J., Ruiz, N and Chen, F., “Eye Activity as a Measure of Human Mental Effort in HCI”, Proc. International Conference on Intelligent User Interfaces (IUI’11), Palo, Alto, U.S.A., February 2011, pp. 315-318.

  16. Yu, K., Epps, J. and Chen, F., “Cognitive Load Evaluation of Handwriting Using Stroke-level Features”, Proc. International Conference on Intelligent User Interfaces (IUI’11), Palo, Alto, U.S.A., February 2011, pp.423-426.

  17. Khawaja, M. A., Chen, F. and Marcus, N., “Using Language Complexity to Measure Cognitive Load for Adaptive Interaction Design”, Proc. International Conference on Intelligent User Interfaces (IUI’10), Hong Kong, China, February 2010, pp. 333-336.

  18. Yin, B., Chen, F., Ruiz, N. and Ambikairajah, E., “Exploring Classification Techniques in Speech based Cognitive Load Monitoring”, Proc. InterSpeech 2008, Brisbane, Australia, September 2008, pp2478-2481.

  19. Yin, B., Chen, F., Ruiz, N. and Ambikairajah, E., “Speech-based Cognitive Load Monitoring System”, Proc. IEEE International Conference on Acoustic, Speech and Signal Processing (ICASSP’08), Las Vegas, March/April 2008, pp. 2041-2044.

  20. 18. Shi, Y., Ruiz, N., Taib, R., Choi, E. and Chen, F., “Galvanic Skin Response (GSR) as an Index of Cognitive Load”, Proc. SIGCHI Conference on Human Factors in Computing Systems (CHI’07), San Jose, April/May 2007, pp. 2651-2656.




 

Contact: Dr Fang Chen

Email: fang.chen@nicta.com.au