Distinguished Professor Fang Chen

Biography

Distinguished Professor Fang Chen is an award-winning, internationally-recognised leader in artificial intelligence (AI) and data science. She is passionately innovative in her work, architecting and implementing data-driven solutions to problems met in industry and governments. Her experience in solving these complex real-life problems in large-scale networks span transport, water, energy, health, agriculture and many more sectors. She is also actively promoting ethical, human-centred AI.

Fang won the "Oscar" of Australian science – the Australian Museum Eureka Prize 2018 for Excellence in Data Science. She is the "Water Professional of the Year", awarded by the Australian Water Association in 2016. In 2021, she won the Australia and New Zealand "Women in AI" Award in Infrastructure and the NSW Premier's Prize of Science and Engineering.

Fang leads multidisciplinary teams of experts, together with whom she has won major scientific and industry awards on the national level. These include the Intelligent Transport Systems Australia National Award 2014, 2015 and 2018, the NSW iAwards 2017, the VIC iAwards 2019 and 2020, and the National Award and NSW "Research and Innovation Award" 2018 from the Australian Water Association.

She has built up a career in creating innovations, developing digital transformation strategy, and executing them with leadership and passion. With vast experience in many segments of industry, governments, and academic environments, it allows her to formulate strategies for innovation, development, products and business growth.

Distinguished Professor Chen also has extensive global experience with more than 100 different entities across North America, Europe and many parts of Asia, under extremely varied circumstances from early-stage R&D, to product development and deployment. She has done exceptional work to bridge customer requirements and innovative technological solutions, creating value in business with innovations.

Professor Chen has created much ground-breaking research evidenced by 300+ refereed publications in science and engineering, including several books. In addition, she has filed 30+ patents in Australia, US, Canada, Europe, Japan, Korea, Mexico and China.

Fang is a member of the inaugural NSW Government AI Advisory Committee. She also serves on the expert panel of the Singapore National Science Foundation, and on several boards, including ITS Australia.

In addition, she serves as co-chairperson on the National Transport Data Community of Practice (NTD-CoP) at ITS (Intelligent Transport Systems) Australia, the Expert Panel for National Science Foundation (NSF) Singapore, Public Interest Technology Advisory Committee for Economic Development of Australia (CEDA).

Currently, Fang is the Executive Director of Data Science at the University of Technology Sydney (UTS) and the Executive Director of the UTS Data Science Institute.

Strength and Uniqueness

In the digital era, big data analytics helps industries to harness the power of data – more efficient operations, more cost savings, higher profits and more customer satisfaction. Given the considerable impact of data science in real world applications, it has the capability to rapidly revolutionise traditional solutions and ways of thinking in the industry.

To this end, Professor Fang Chen is a prominent leader in AI and data science with international reputation and industrial recognitions. She has created many innovative research and solutions, transforming industries that utilise data science with ethical approaches.

Extensive experience with strategy; leadership in creation and execution

Professor Chen is passionate about digital transformation, particularly on using data science to influence evidence-based decision making in industry and governments. She has actively led in developing new strategies with innovative forward thinking, and led teams to achieve outstanding outcomes.

With vast experience in many segments of industry and governments, she is also dextrous with external factors shaping advances in technology. A keen understanding of trends in technology, and where technology fits within the social, political, and economic landscape, allow her to formulate strategies for research, development, products and adoption with impact.

Track record in research and innovation

Professor Chen's track record in science and engineering is well-documented – she has more than 300 refereed publications, including books and top journals and conferences. She has filed more than 30 patents (in eight countries and regions, including Australia, US, Canada, Europe, Japan, Korea, Mexico and China).

She has created world-class solutions and products throughout her career. She leads many task-forces with the goal of utilising data analytics and computational platforms in a manner where, their scales and impacts extend to the national and international. She has helped many industries worldwide advance towards excellence in better, newer solutions.

Professor Chen and her team has won 2018 Australian leading science prize Australian Museum Eureka Prize for Excellence in Data Science, on using new Bayesian nonparametrics and Nonhomogeneous stochastic processes methods with proven impact of a suite of applications in water industry.

Transformations to industry with practical impact and industrial recognition

Professor Chen's work is in providing practical solutions of tremendous impact, utilising cutting edge scientific and engineering discoveries. Her task-forces have been on national and international scales – focusing on helping industries increase their productivity, innovation, profitability, and customer satisfaction. She has in many ways pioneered and spearheaded the data analytics effort that awarded her a track record in Transformations to industry with practical impact and industrial recognition, especially in in water, transport and civil infrastructure.

Global experience

Professor Chen’s extensive experience means she has worked with more than 80 different entities cross North America, Europe and many parts of Asia. She has worked in many industries, but as a water professional alone, she has achieved great success in the creation of advanced data-driven analytic solutions, serving more than 30 worldwide water utilities.

Her large-scale data collections and projects across different continents and judiciaries also gives her considerable understanding in global regulations and policy. She has been actively working with research communities and serving various roles in top international conferences – within industrial and technological events, she has extensive involvement. In particular, for 6 years Professor Chen has been the main rapporteur of the ITS (Intelligent Transport Systems) – a world congress which summaries major technology trends in transport industry, and publishes global industrial guides, particularly on big data and its innovative use and disruption in the transport industry.

Multidisciplinary approaches within different career experiences

Professor Chen has had the opportunity over her career to increase her skills in multiple settings, having excelled in work across academia, industry, and government. This means she is a highly adaptive and skilled individual, having had success working in extremely varied circumstances, from early stage R&D, to product development and deployment, from education, to policy evaluation.

She has broad experience working from and Professor awing from various disciplines – AI/Machine learning/Data mining, IoT, civil engineering (structure engineering, transport engineering, and so on), material science, chemical engineering, design, brain science, human machine interaction, and cognitive/organisational psychology.

The extensive experience of running multidisciplinary teams formulated her strong base of architecting innovative solutions to resolve complex problems in large-scale complicated systems or networks.

Public engagement and industrial relationships

Professor Chen is socially savvy when it comes to working with people, establishing relationships and maintaining them.

She has done exceptional work to bridge industrial requirements and innovative technological solutions, creating value in research and business that is accessible to government and industry users. Some example clients are Sydney Water, Yarra Valley Water, Queensland Urban Utilities, Transport NSW, Transurban, VicRoads, MainRoads (WA), Telstra, Sydney Trains, V/Line, Western Water, Telstra, NBN, ANZ, AEMO, Salmat; Energy Safe Victoria, NSW Public Service Commission; multinational clients include GE, Acer, Downer, Cubic, WSP, AECOM, Thales, CAE, Boeing, Data Spark, Navantia, Isle Utilities and Dolby. She has led many scoping workshops with relevant business owners to sketch out business requirements, data availability, technical approaches and avenue to impact. Those discussions then turned into industry transformational practice.

She has also established relationships and served many government agencies, including the Department of Education, Department of Planning Industry and Environment, NSW Infrastructure, various transport agencies, the Department of Immigration and Border protection, NSW Public Service Commission, ATO and the DSTg. She is also a sought after speaker for international events and technological advertisers for SMEs. She delivered a TEDx talk on “How can we design AI that we trust” 2019 for more than 5,500 audience.

She is an active advocator for AI and its impact, potential issues and ethical approaches through numerous public speaking keynotes and panels. She has also had many media presence.

Employment History

UNIVERSITY OF TECHNOLOGY SYDNEY 2018 – Present

Executive Director Data Science/Distinguished Professor

Selected projects:

• Reduce Leaks and Breaks in Water

• Predicting Electrical network trigged fire incident

• Sydney Trains Passenger Flow

• Timetable delay analysis

• Automatic Train Track Inspections

• Spare Parts Inventory Planning

• Using Machine Learning for Student Learning

• Ethical AI: Assessment of non-biased talent shortlisting algorithm

NATIONAL ICT AUSTRALIA (NICTA), Data61 (CSIRO) 2004 – 2018

Research Group Leader/Senior Principal Researcher

Selected projects:

• NSW Premier's Innovation Initiative on congestion priority

• On-demand transport trial

• RMS Roads Report System

• Sydney CBD mobility

• Sydney M4 Smart Motorway Evaluation

• Kwinana Freeway widening (Perth)

• VicRoads

• Traffic Watch

• Decision support for incident management

• Large Scale Traffic Simulation for Sydney Metropolitan Area

• Structural Health Monitoring for Sydney Harbor Bridge

• Water pipe failure prediction

• Predictive Analytics for Sewer Corrosion

• Intelligent Network Operation

• Water Demand Analysis

• Water leakage detection

• Wastewater Pipe (Sewerage) Blockage Prediction

• Long-term Reticulation Water Pipe Failure Prediction

• NBN Demand Forecast

• Telstra Predictive Maintenance

• Power Load Disaggregation

• Solar photovoltaic (PV) gross generation estimation

• Fault detection and diagnosis (FDD)

• Residential and occupancy analysis

• Manuka Smart Parking Data Analysis

• Dwelling Production Prediction

• Department of infrastructure

• Cognitive load in emergency management centres and bushfire management centres

• Human-machine interaction trust calibration

• Gas pipe predictive maintenance

• Predictive maintenance for naval engines

MOTOROLA 2000 – 2004

Lab Manager/Chair of Patent and Publication Committee/ Principal Researcher

INTEL 1999 – 2000

Team Leader/Senior Researcher

BEIJING JIAOTONG UNIVERSITY, CHINA 1995 – 1999

Dean of Faculty of Electronic and Information Engineering/ Director of Institute of information science

Qualifications

Doctor of Philosophy (Computer Science)

Beijing Jiaotong University, China

Master of Science (Electrical Engineering)

Beijing University of Aeronautics and Astronautics, China

Bachelor of Science (Electrical Engineering)

Beijing University of Aeronautics and Astronautics, China

Recognition and Awards

• NSW Premier's Prizes for Science & Engineering: Excellence in Engineering and Information and Communications, 2021

• Women in AI Australia and New Zealand Award, AI in Infrastructure, 2021

• Best Overall Paper at the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), May 11-14, 2021, Delhi, India. Title “Weak Supervision Network Embedding for Constrained Graph Learning”.

• iAward Victoria for Government and Public Sector Solution of the Year: “nbn Broadband demand forecasting”, 2020

• Research and Innovation Merit Award, Australian Water Association (AWA) VIC 2019, for collaborative project on data driven water pipe failure prediction with Western Water.

• Australian Museum Eureka Prize for Excellence in Data Science. 2018

• NSW and National Research and Innovation Award, Australian Water Association (AWA) NSW 2018, for collaborative projects on data analytics with Sydney Water.

• "Brian Shackle Award" in 2017 in recognition of the most outstanding contribution with international impact in the field of human interaction with, and human use of, computers and information technology.

• iAwards, 2017 on Water Pipe Failure Prediction

• Water Professional of the Year 2016, a Water Award from the Australian Water Association (AWA) NSW. As a water professional, she spent years of her career in promoting data science in the water sector and has achieved great success in the creation of advanced data-driven analytic solutions, serving more than 30 worldwide water utilities. With extensive experiences and influences spanning the technical and organizational aspects, she is helping the water industry achieve ground-breaking approaches for the improvement of risk analysis and reactive maintenance, as well as significant economic benefits. Through the integration of a wide range of creations, she is now actively leading efforts that are expand the contributions beyond the water industry to the wider community, providing increased social benefits. The efforts include energy saving, environmental sustainability maintenance, and community health improvement nationally and internationally.

• 2014 & 2015 intelligent Transport Systems (ITS) Australia National Research Award. This is a recognition of the NICTA team’s advanced position in utilising machine learning in conjunction with transport science to achieve new cutting edge approaches on transport modelling, which brings efficiency in transport management and safety for communities.

• 2014 NICTA impact award.

• CeBIT2011 Innovation Awards Runner Up, 2011

• Highly Commended at Engineers Australia Awards, 2011

• OZCHI best Paper Award, 2007

• Innovation Award from Motorola, 2002 & 2003

• Engineering Award for Commercial Secret Motorola,2002

• Bravo Award for excellent performance, Motorola 2001

• Long-term Retention Award Motorola, 2001

• Award of “Next Generation Leader” Motorola, 2000

• Golden Award of Women Star, Motorola 2000

• Award for outstanding contribution, Intel 1999

• Award for youth academic leader in higher education, Beijing Municipal Commission of Education, 1997 (30 leaders elected from over 100 universities)

Media

• 1/2/2022: UTS, Data science keeps the trains on track

• 19/10/2021: Two cities one community, Policy another cross-border first

• 20/09/2021: Create, Teaching artificial intelligence to spot when people need space to think

• 16/09/2021: UTS, A data deep dive to deliver for Regional Australia

• 06/04/2021: Winners of Women in AI Awards demonstrate how a “diverse workforce equals diverse solutions”

• 29/03/2021: Winner of the 𝗔𝗜 𝗶𝗻 𝗜𝗻𝗳𝗿𝗮𝘀𝘁𝗿𝘂𝗰𝘁𝘂𝗿𝗲 Category for Women in AI Awards 2021 (Australia –New Zealand)

• 26/03/2021: Women in AI Awards ANZ: Winners 2021

• 17/03/2021: Itnews NSW govt names inaugural AI committee members

• 16/12/2020: ZDet, Australia gets a national guide to help assess effectiveness of STEM initiatives

• 14/12/2020,The Australian, Afterpay’s growth ‘easier in Australia’

• 09/12/2020, News8Plus World first for moral AI and office fairness

• 08/12/2020: EurekAletert, World first for ethical AI and workplace equity

• 08/12/2020: TechXplore, World first for ethical AI and workplace equity

• 08/12/2020: Scimex, A world first for ethical AI addresses equity in HR hire

• 08/12/2020, Techstreet, World first for ethical AI and workplace equity

• 08/12/2020, Ethical Editor, World first for ethical AI and workplace equity

• 07/12/2020: The Australian, World first for Ethical AI

• 14/10/2020: iTnews, UTS trains AI to predict Sydney Trains' pandemic performance

• 03/09/2020: The Mandarin, Eight experts on the risks and benefits of artificial intelligence in government and the public sector

• 18/08/2020: ABC News, Are you really making your own decisions? The influence of algorithms, explained. | The Drum

• 22/05/2019: The Australian, Student tracking to reveal keys to learning.

• 11/04/2019: itNews, Acer, UTS partner for attention span boosting analytics pilot.

• 10/04/2019: ZDNet, Acer and UTS trial classroom program that monitors student movements.

• 10/04/2019: WHICH-50, UTS and Acer to track students eyes in education engagement study.

• 21/03/2019: Fortescue, Fortescue to establish Future of Mobility Centre in Karratha.

• 21/11/2018: SouthStart2018, AI with Impact.

• 08/09/2018: news.com.au, Tech guru says Sydney Harbour Bridge is way smarter than most people think.

• 01/05/2018: AFR Smart Cities, Population growth the challenge.

• 03/09/2018: Water Source, Water pipe failure prediction tool takes out Eureka Prize.

• 03/09/2018: Roads & Infrastructure, Predictive water pipes project wins top science award.

• 24/07/2018: Australian Museum, Smart Infrastructure Team - WINNER, 2018 Eureka Prize for Excellence in Data Science

• 15/06/2018: TEDxSydney 2018, How can we design AI that we trust.

• 09/11/2017: NSW Government, Education for A Changing World Symposium 2017.

• 03/10/2017: itnews, Australian Navy trials sensor analytics on ship engines.

• 22/10/2015: Roads & Infrastructure, New prediction system targets water pipeline failure.

• 21/05/2015: Global Water Intelligence, NICTA’s A$700 million promise to utilities.

• 26/03/2015: ComputerWorld, Turnbull embraces the Internet of Things.

• 2015: Deloitte Access Economics, "The implementation of the Smart Pipes technology could deliver $2.6 billion in economic benefits over the next ten years. The benefits from the cost reductions was be approximately $298 million a year in 2013-14."

• 11/08/2014: The Australian, A big data fix for leaky pipes.

• 11/06/2012: The Sydney Morning Herald, Technology gives voice to worker brain strain.

• 22/06/2011: The Coversation, I um therefore I ah: workplace stress and how to manage it.

Program Committees

• 2021- Advisory Board of Data for Social Good Hackathon, Data Science and Ai Association of Australia | DSAi

• 2021- Board Director, ITS (Intelligent Transport Systems) Australia

• 2021- Member, NSW Government AI Advisory Committee

• 2020 Associate Editor, ACM Transactions on Interactive Intelligent Systems (TiiS)

• 2020 Chair, National Transport Data Community of Practice (NTD-CoP ), ITS Australia 2020. It is to foster collaboration and shared query into the potential opportunities and challenges transport data analytics offers our industry and community. The NTD-CoP includes representatives from government, industry and academia to investigate research opportunities, pilots and tests, and analysis of deployments.

• 2020 Editorial Board Member, MDPI. A pioneer in scholarly open access publishing, MDPI has supported academic communities since 1996 based in Switzerland, MDPI has 283 diverse, peer-reviewed, open access journals

• 2020 Australian Delegate representing Standard Australia, International working group on Smart City

• 2018 IJCAI Program Committee

• 2017 Program Co-Chair, International Conference on Intelligent User Interfaces (IUI)

• 2015 Senior Program Committee Member, International Conference on Intelligent User Interfaces (IUI)

• 2014 Senior Program Committee Member, International Conference on Intelligent User Interfaces (IUI)

• 2013 Senior Program Committee Member, International Conference on Intelligent User Interfaces (IUI)

• 2013 General Chair, International Conference on Multimodal Interfaces (ICMI)

• 2012 Area Chair, International Conference on Intelligent User Interfaces (IUI)

• 2011 Program Committee Member, International Conference on Intelligent User Interfaces (IUI)

• 2007 Publicity Chair, International Conference on Intelligent User Interfaces (IUI)

• 2007 Corporate Liaison Chair, Int. Conf. on Multimodal Interfaces (ICMI )

Keynote Speeches Since 2017

• Data driven future, Global woman in data science conference organized by Stanford University. March 2017

• Data driven Utilities, OzWater leadership forum, Apr 2017 Sydney

• Transformation of asset management, International Asset Management Conference, June 2017, Melbourne

• AI in transport, AI and machine Leaning Summit, Aug 2017, In conjunction with ICML 2017

• Big Data Analytics, Big Data Forum, in conjunction with IJCAI 2017, Aug 2017.

• AI and our future life, opening keynote, AI for education forum organized by NSW department of education Nov 2017

• Big Data and infrastructure management, Asset Management Council Annual National Symposium 17th November 2017 Melbourne

• Machine learning with impact, Downer group leaders innovation forum, Dec 2018

• Automation and AI, Opening keynote speech, Applying Artificial Intelligence and Deep Learning for Enterprises Conference | 5 - 6 February 2018, Melbourne Convention and Exhibition Centre.

• AI and smart city, WSP global leadership forum, Feb 2018

• The impact of new wave technologies, TEDx, March 2018

• AI and autonomous transport, WSP global innovation forum, March 2018

• AI technologies for the future transport, the 2018 Australian Academy of Technology and Engineering (ATSE) National Technology Challenges Dialogue on the theme of Shifting gears: Preparing for a transport revolution, Melbourne on 9 May (to be held)

• AI and productivity, CeBIT's Artificial Intelligence & Machine Learning conference, May 2018

• AI with impact, OSX Forum, Transport for NSW, May 2018

• Advanced Data Analytics, International conference of Innovation in infrastructure and asset management, June 20 2018

• AI in transport, Driverless and Intelligent Mobility Conference in June 2018

• AI and Trust, TEDx Sydney annual conference 2018

• AI and Digital Economy, Entrepreneurs' Programme Annual Forum, Organised by The Department of Industry, Innovation and Science, Australian Government, July 2018

• Using Data Analytics For Better Infrastructure Management Decisions, Smart Infrastructure Summit, Aug 2018

• AI in transport, ITS summit, Aug 2018

• Monitoring the Health of Structures with AI, AI and ML summit, Sep 2018

• AI - research trend and future business, Australian Institute of Company Directors, Oct 2018

• AI and Future, South Start-up annual conference, South Australian Government, Nov 2018

• “ Disrupt the gender gap in Artificial Intelligence”, Hall & Wilcox 26 Feb, 2019

• “Harnessing AI for future insights & informed decision making”, Data Management and Security for Government Summit 27-28 Feb, Canberra, 2019

• “AI for Insurance”, HiVE 2019, Hong Kong Apr 2019

• “AI and our future”, Asia Business Link Annual Conference Apr 2019

• “Impact of AI”, Knowledgexchange Annual Conference (for accounting and advisory practices), Gold coast June 2019

• “AI for smart infrastructure”, 3rd Annual Emerging Technologies for Public Infrastructure Conference 19-20 June

• “AI for future education”, Annual conference on Blended Learning & Digital Campus, July 2019

• “AI and our future”, Cyber Security Network Annual Event, 26 July

• “AI for Health”, to NHMRC board July 2019

• “AI in Education”, AI for government 2019 conference, August 12-14.

• "Big Data and Cities" , "The Shadow of Big Data - An Interdisciplinary Colloquium on Big Data’s Human Impact", September 10th

• “Data Driven Future for Water”, Horizon 2020, Water Research Australian Annual Planning Conference 11 Sep 2019

• “Data trust & privacy”, CEBIT Australia Oct 2019

• “AI for education”, CIVICA Forum, Sydney Oct 2019

• “AI and our Future”, AI in Libraries and Museums Symposium, Melbourne Oct 2019

• “IoT and AI with Impact”, Industrial Internet Consortium Annual Conference, Nov 2019

• Data Driven Predictive Maintenance for Water Utilities, Digital Maintenance and Field Service Automation Forum, March 2020, Melbourne.

• “Data Science with Impact”, Communicating Uncertainty Conference, Sydney July 2020.

• Data Driven Urban Design, 12th International Smart City Expo Mar 2020, Sydney.

• “Data and Data Science for Mobility”, Mobility 2020 conference, September 2020.

• “Intelligent Water Management Systems Using AI and IoT”, ASEAN Australia Smart Cities Trust Fund, November 2020

• “AI and our future”, AI for public sector, November 2020.

• “Ethical AI: from principles to practice”, UTS Ethical AI Webinar, Dec 2020.

• Ethical AI Assurance Framework, NSW Government AI Summit, Feb 2021

• Technologies in Secondary Education, ACCE annual conference, Digital Learning and Teaching Victoria, March 2021

• AI and Women in AI, Women in AI APAC Datathon

• Machine Learning with Impact, Mysuru IEEE Student Branch in association with IEEE Bangalore Section and IEEE Mysore Sub Section

• Rise and risks of Talent AI, REEJIG and ATC Event & Media, Apr 2021

• AI in reinventing construction, Webinar by UTS Boral Centre for Sustainable Buildings, Apr 2021.

• Machine Learning for Infrastructure Maintenance, Field Service ANZ conference, Sydney July 2021

• Evidence Based Decision Making with Big Data and Machine Learning, AI for Government Summit, Oct 2021

• Machine Learning for infrastructure maintenance, ANZ field services annual conference, Oct 2021

• Engaging with Industry, ATSE Industry Mentoring Network in STEM NSW annual event, Oct 2021

• AI with Impact, Thought Leaders Series: Engineering smart cities leveraging AI innovation, Engineers Australia, Feb 2022.

Selected Patents (30+ granted and filed patents in total)

• J. Huang and F. Chen, “Concatenative Text-to-speech Conversion”, application No. CN1471025A, granted on Jun. 14, 2006 (CN1259631C).

• 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

• F. Chen and G. Chen, “Chinese Segmenting Method”, application No. CN1471024A, granted on May. 17, 2006 (CN1256688C).

• F. Chen and G. Chen, “Method for Synthesizing Speech”, application No. CN1604182A, granted on Jun. 21, 2005 (CN1260704C).

• F. Chen and G. Chen, “Method for Synthesizing Speech”, application No. KR20060066121A, granted on Oct. 22, 2007 (KR100769033B1)

• F. Chen, B. Yin, and K. MacDonald, “Verifying a User,” application filed on Dec. 18, 2012 (AU 2012265559).

• F. Chen, K. Yu, “Measuring Cognitive Load”, application filed on Feb. 13, 2012 (AU 20122008812).

• F. Chen, B. Yin, and K. MacDonald, “Verifying a user,” Application No. US13551313, filed on Jul. 18, 2013 (US20130185071A1).

• F. Chen, N. Ruiz, and E. Choi, “Measuring cognitive load,” Application no. US20100217097A1, granted on Nov. 17, 2015 (US9189596B2).

• F. Chen, N. Ruiz, and E. Choi, “Measuring cognitive load,” Application no. CA2655189A1, granted on Jan. 26, 2016 (CA2655189C).

• F. Chen and K. Yu, “Measuring cognitive load,” Application No. US20120282577A1, granted on May. 16, 2017 (US9652996B2).

• F. Chen, M. Asif Khawaja, and E. Choi, “Measuring cognitive load,” Application No. US20110207099A1, granted on Aug. 22, 2017 (US9737255B2).

• F. Chen, M. Asif Khawaja, and E. Choi, “Measuring cognitive load,” Application No. 2008905089 AU, granted on Aug. 22, 2017 (AU 2009299102).

• B. Li, Y. Wang, F. Chen, and Y. Wang, “Group Infrastructure Components,” application filed on Jan. 18, 2018 (US20180018640A1).

• B. Zhang, Y. Wang, and F. Chen, “Infrastructure working behaviour characterisation,” application filed on Sep. 21, 2017 (US20170270442A1).

• Bang Zhang, Yang Wang, and Fang Chen, “Extended Hawkes process for infrastructure failure prediction,” application filed (N14 012-PROVAU), 2014.

• Zhidong Li, Yang Wang, and Fang Chen, “Bayesian nonparametric method for infrastructure failure prediction,” WO 2014/085849 A1, 2014.

• Bang Zhang, Zhidong Li, Yang Wang, and Fang Chen, “Determining a health condition of a bridge,” application filed (N12 023-PCT), 2012.

• Z. Li, Y. Wang, and F. Chen, “Bayesian nonparametric method for infrastructure failure prediction,” application filed on Oct. 29, 2015 (US20150310349A1)

• Hoang Nguyen, Wei Liu, Paul Rivera, Chen Cai, Fang Chen, “Traffic Watch: Real-time traffic incidents detection from Twitter”, N15 011-PROVAU, 09 Oct 2015

• “System and method for determining a service demand in a service network”, Yan Xu, Chen Cai, Fang Chen, N15 010-PROVAU, 11 Nov 2015

Selected Refereed Publications Since 2015

(300+ career publications in total)

Books and Refereed journal papers:

1. Fang Chen and Jianlong Zhou. Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership. Springer, 2022. ISBN: 978-3-030-72187-9.

2. Jianlong Zhou and Fang Chen, “Towards humanity-in-the-loop in AI lifecycle”, in Humanity Driven AI: Productivity, Well-being, Sustainability and Partnership. F. Chen and J. Zhou eds. Springer, 2022.

3. J. Zhou, A. Holzinger, and F. Chen, “Towards Explainability for AI Fairness”, in xxAI - Beyond explainable Artificial Intelligence, Springer, 2022.

4. Iman Rahimi, Amir H. Gandomi , Kalyanmoy Deb, Fang Chen, and Mohammad Reza Nikoo, “Scheduling by NSGA-II: Review and bibliometric analysis”, Processes, 10(1), 98, 2022.

5. Z. Zhang, L. Wang, Y. Wang, L. Zhou, and F. Chen, “Dataset-driven Unsupervised Object Discovery for Region-based Instance Image Retrieval”, IEEE Transactions on Pattern Analysis and Machine Intelligence, 2022.

6. L. Wen, J. Zhou, W. Huang, and F. Chen, “A Survey of Facial Capture for Virtual Reality", IEEE Access, 2022.

7. Z. Zhang, Q. Wu, Y. Wang, and F. Chen, “Exploring Pairwise Relationships Adaptively from Linguistic Context in Image Captioning”, IEEE Transactions on Multimedia, 2021.

8. Hongda Tian, Jessie Nghiem, and Fang Chen, “Electrical Network-Related Incidents Prediction Based on Weather Factors”, in “Advances in Data Science and Analytics: Concept and Paradigm”, Wiley, 2021.

9. B. Liang, Z. Li, H. Tiang, S. Liang, Y. Wang, and F. Chen, “Optimising Water Quality with Data Analytics and Machine Learning”, In “Advances in Data Science and Analytics: Concept and Paradigm”, 2021.

10. F. Zhou, S. Luo, Z. Li, X. Fan, Y. Wang, A. Sowmya, and F. Chen, “Efficient EM-Variational Inference for Nonparametric Hawkes Process”, Statistic and Computing, 2021.

11. J. Xu, J. Luo, H. Tian, Z. Wang, Y. Wang, F. Chen, and W. Kang, “Joint Input and Output Space Learning for Multi-Label Image Classification”, IEEE Transactions on Multimedia, vol. 23, pp.1696-1707, 2021.

12. Kastrati Z, Ahmedi L, Kurti A, Kadriu F, Murtezaj D, Gashi F. “A Deep Learning Sentiment Analyser for Social Media Comments in Low-Resource Languages”, Electronics. 2021; 10(10):1133.

13. T. Mao, A.-S. Mihaita, F. Chen, and H.L. Vu, "Boosted Genetic Algorithm using Machine Learning for traffic control optimization", IEEE Transactions on Intelligent Transportation Systems, 2021.

14. J. Zhou, S. Yang, C. Xiao, and F. Chen, “Examination of community sentiment dynamics due to COVID-19 pandemic: a case study from a state in Australia”, SN Computer Science, 2021.

15. J. Zhou, A. H. Gandomi, F. Chen, and A. Holzinger, "Evaluating the Quality of Machine Learning Explanations: A Survey on Methods and Metrics", Electronics, 10(5), 593, 2021.

16. I. Rahimi, A. H. Gandomi, P. G. Asteris, and F. Chen, “Analysis and Prediction of COVID-19 using SIR, SEIR, and Machine Learning Models: Australia, Italy, and UK Cases”, Information, 2021.

17. I. Rahimi, F. Chen, and A. H. Gandomi, “A review on COVID-19 forecasting models”, Neural Computing and Applications, 2021.

18. T. T. Khuat, F. Chen, and B. Gabrys, “An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network”, IEEE Transactions on Fuzzy Systems, vol. 29, no. 2, pp. 427-441, 2021.

19. J. Zhou, H. Zogan, S. Yang, S. Jameel, G. Xu, and F. Chen, “Detecting Community Depression Dynamics Due to COVID-19 Pandemic in Australia”, IEEE Transactions on Computational Social Systems, 2021.

20. B. Liang, Z. Li, H. Tiang, S. Liang, Y. Wang, and F. Chen, “Optimising Water Quality with Data Analytics and Machine Learning”, In “Advances in Data Science and Analytics: Concept and Paradigm”, 2021.

21. F. Zhou, S. Luo, Z. Li, X. Fan, Y. Wang, A. Sowmya, and F. Chen, “Efficient EM-Variational Inference for Nonparametric Hawkes Process”, Statistic and Computing, 2021.

22. Amir H Gandomi, Iman Rahimi, and Fang Chen, “A Review on COVID-19 Forecasting Models”, Neural Computing and Applications, 2020.

23. Y. Ou, A. S. Mihaita, and F. Chen, “Big Data Processing and Analysis on the Impact of COVID-19 on Public Transport Delay”, in Data Science for COVID-19, Elsevier, 2020.

24. J. Zhou, S. Yang, C. Xiao, and F. Chen, “Examination of community sentiment dynamics due to COVID-19 pandemic: a case study from Australia”, SN Computer Science, 2020.

25. T. Mao, A.-S. Mihaita, F. Chen, and H, L. Vu, “Boosted Genetic Algorithm using Machine Learning for traffic control optimization”, IEEE Transactions on Intelligent Transportation Systems, 2020.

26. R. Nikoloska, L. Bykerk, D. Vitanage, J. Valls Miro, F. Chen, Y. Wang, B. Liang, and S. Verma, “Enhancing Sydney Water’s leak prevention through acoustic monitoring”, AWA Water e-Journal, 5 (2), 2020.

27. S. Yang, J. Jiang, A. Pal, K. Yu, F. Chen, and S. Yu, “Analysis and Insights for Myths Circulating on Twitter during the COVID-19 Pandemic”, IEEE Open Journal of the Computer Society, 2020.

28. J. Zhou, S. Luo, and F. Chen, “Effects of Personality Traits on User Trust in Predictive Decision Making”, Journal on Multimodal User Interfaces, 2020.

29. J. Xu, J. Luo, H. Tian, Z. Wang, Y. Wang, F. Chen, and W. Kang, “Joint Input and Output Space Learning for Multi-Label Image Classification”, IEEE Transactions on Multimedia, 2020.

30. S. Luo, V. W. Chu, Z. Li, Y. Wang, J. Zhou, F. Chen, and R. Wong, “Multi-Task Learning by Hierarchical Dirichlet Mixture Model for Sparse Failure Prediction”, International Journal of Data Science and Analytics, 2020

31. F. Zhou, Z. Li, X. Fan, Y. Wang, A. Sowmya, and F. Chen, “Fast Multi-resolution Segmentation for Nonstationary Hawkes Process Using Cumulants”, International Journal of Data Science and Analytics, 2020.

32. T. T. Khuat, F. Chen, and B. Gabrys, “An Effective Multi-Resolution Hierarchical Granular Representation based Classifier using General Fuzzy Min-Max Neural Network”, IEEE Transactions on Fuzzy Systems, 2019.

33. Z. Zhang, Q. Wu, Y. Wang and F. Chen, "High-Quality Image Captioning with Fine-Grained and Semantic-Guided Visual Attention," IEEE Transactions on Multimedia, vol. 21, no. 7, pages 1681-1693, July 2019.

34. F. Chen, C. Duarte, and W.-T. Fu, eds, “Highlights of ACM IUI 2017”, ACM Transactions on Interactive Intelligent Systems (TiiS), 2019.

35. F. Chen and J. Zhou, "AI in the public interest", in Close to The Machine: Technical, Social, and Legal Aspects of AI, Office of the Victorian Information Commissioner, Australia, 2019. https://ovic.vic.gov.au/wp-content/uploads/2019/08/closer-to-the-machine-web.pdf

36. J. Xu, Z. Wang, Y. Wang, F. Chen, J. Gao, and D. Feng, “Affective Audio Annotation of Public Speeches with Convolutional Clustering Neural Network”, IEEE Transactions on Affective Computing, 2019.

37. F. Chen and J. Zhou, eds. “Trustworthy AI and visual analytics for big data”, Journal of Computer Languages, 2019.

38. Jianlong Zhou and Fang Chen, “Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent”, Springer, 2018. ISBN: 978-3-319-90403-0.

39. S. Oviatt, K. Hang, J. Zhou, K. Yu, and F. Chen, “Dynamic Handwriting Signal Features Predict Domain Expertise”, ACM Transaction on Interactive Intelligent Systems, vol. 8, no. 3, article no. 18, 2018.

40. F. Chen, J. Zhou, and K. Yu, “Multimodal and Data-Driven Cognitive Load Measurement”, in R. Zheng, editor, Cognitive Load Measurement and Application: A Theoretical Framework for Meaningful Research and Practice, chapter 10, pages147-163, Routledge, New York and London, 2018.

41. T. Wen, C. Cai, L. Gardner, S.T. Waller, V. V. Dixit, and F. Chen, “Estimation of Sparse O-D Matrix Accounting For Demand Volatility”, IET Intelligent Transport Systems, 2018.

42. J. Zhou, K. Yu, F. Chen, Y. Wang, and S. Z. Arshad, “Multimodal Behavioural and Physiological Signals as Indicators of Cognitive Load”, in S. Oviatt, editor, Handbook of Multimodal-Multisensor Interfaces, Morgan & Claypool Publishers, 2018. In press.

43. J. Zhou and F. Chen, “2D Transparency Space – Bring Domain Users and Machine Learning Experts Together”, In J. Zhou and F. Chen eds. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, chapter 1, pages 3-19, Springer, 2018.

44. J. Zhou, K. Yu, and F. Chen, “Revealing User Confidence in Machine Learning-Based Decision Making”, In J. Zhou and F. Chen eds. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, chapter 11, pages 225-244, Springer, 2018.

45. B. Zhang, T. Guo, L. Zhang, P. Lin, Y. Wang, J. Zhou, and F. Chen, “Water Pipe Failure Prediction: A Machine Learing Approach Enhaced By Domain Knowledge”, In J. Zhou and F. Chen eds. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, chapter 18, pages 363-383, Springer, 2018.

46. K. Yu, S. Berkovsky, D. Conway, R. Taib, J. Zhou, and F. Chen, “Do I Trust a Machine? Differences in User Trust Based on System Performance”, In J. Zhou and F. Chen eds. Human and Machine Learning: Visible, Explainable, Trustworthy and Transparent, chapter 12, pages 245-264, Springer, 2018.

47. T. Wen, A.S. Mihăiţă, H. Nguyen, C. Cai, and F. Chen, “Integrated Incident Decision Support Using Traffic Simulation and Data-Driven Models”, Transportation Research Record, 2018.

48. M. Ebrahimi, E. ShafieiBavani, R. Wong, and F. Chen, “Twitter User Geolocation by Filtering of Highly Mentioned Users”, Journal of the Association for Information Science and Technology (JASIST), 2018. [A*]

49. N. Nourbakhsh, F. Chen, Y. Wang, R. A. Calvo. “Detecting Users’ Cognitive Load by Galvanic Skin Response with Affective Interference”, ACM Transactions on Interactive Intelligent Systems (TiiS), vol. 7, no. 3, article no. 12, 2017.

50. L. Luo, W. Liu, I. Koprinska, and F. Chen, “DAAR:A Discrimination-Aware Association Rule Classifier for Decision Support”, in Transactions on Large-Scale Data- and Knowledge-Centered Systems XXXII, LNCS volume 10420, Springer, pages 47-68, 2017.

51. Tao Wen, Chen Cai, Lauren Gardner, Vinayak Dixit, S. Travis Waller, and Fang Chen, “Two Methods to Calibrate the Total Travel Demand and Variability for a Regional Traffic Network”, Computer-Aided Civil and Infrastructure Engineering, 2017. DOI: 10.1111/mice.12278. [A*]

52. T. Wen, C. Cai, L. Gardner, V.V. Dixit, S.T. Waller, and F. Chen, “A Strategic User Equilibrium for Independently Distributed Origin-Destination Demands”, Computer-Aided Civil and Infrastructure Engineering, 2017. DOI: 10.1111/mice.12292. [A*]

53. J. Zhou and F. Chen, “DecisionMind: Revealing Human Cognition States in Data Analytics-Driven Decision Making with a Multimodal Interface”, Journal on Multimodal User Interfaces, (Springer), 2017.

54. J. Zhou, S. Z. Arshad, X. Wang, Z. Li, D. Feng, and F. Chen, “End-User Development for Interactive Data Analytics: Uncertainty, Correlation and User Confidence”, IEEE Transactions on Affective Computing, 2017.

55. T. Wen, C. Cai, L. Gardner, V. Dixit, S. T. Waller, and F. Chen, “A Strategic User Equilibrium For Independently Distributed Origin-Destination Demands”, Computer-Aided Civil and Infrastructure Engineering, 2017.

56. E. ShafieiBavani, M. Ebrahimi, R. Wong, and F. Chen, “A Semantically Motivated Approach to Compute Rouge Scores”, arXiv preprint arXiv:1710.07441, 2017.

57. A. Hamzehei, S. Jiang, D. Koutra, R. Wong, and F. Chen, “Topic-based social influence measurement for social networks”, Australasian Journal of Information Systems, vol. 21, 2017.

58. Hoang Nguyen, Chen Cai, and Fang Chen, “Automatic classification of traffic incident’s severity using machine learning approaches”, IET Intelligent Transport Systems, 2017.

59. J. Zhou, J. Sun, Y. Wang, and F. Chen, “Wrapping Practical Problems into A Machine Learning Framework – Using Water Pipe Failure Prediction as A Case Study”, International Journal of Intelligent Systems Technologies and Applications, vol. 16, No. 3, pp.191-207, 2017.

60. Fang Chen, Jianlong Zhou, Yang Wang, Kun Yu, Syed Z. Arshad, Ahmad Khawaji, and Dan Conway, “Robust Multimodal Cognitive Load Measurement”, Springer, ISBN: 978-3-319-31698-7, 2016.

61. A. Diez Olivan, N.L.D. Khoa, M. Makki Alamdari, Y. Wang, F. Chen, and P.Runcie, “A Clustering Approach for Structural Health Monitoring on Bridges”, Journal of Civil Structural Health Monitoring, Vol 6, Issue 3, pp. 429-445, Springer, 2016.

62. V. W. Chu, R. K. Wong, F. Chen, S. Fong, and P. C. K. Hung “Self-Regularized Causal Structure Discovery for Trajectory-based Networks”, Journal of Computer and System Sciences, vol. 82, no. 4, pp. 594-609, 2016. [A*]

63. J. Zhou, M. A. Khawaja, Z. Li, J. Sun, Y. Wang, and F. Chen, “Making Machine Learning Useable by Revealing Internal States Update – A Transparent Approach”, International Journal of Computational Science and Engineering, Vol.13, No. 4, pp.378-389, 2016.

64. Y. Wang, B. Li, Y. Wang, F. Chen, B. Zhang and Z. Li, “Robust Bayesian Non-parametric Dictionary Learning with Heterogeneous Gaussian Noise”, Journal of Computer Vision and Image Understanding (CVIU), Vol. 150, pp. 31-43, 2016. [A].

65. E. ShafieiBavani, M. Ebrahimi, R. K. Wong, and F. Chen, "On Improving Informativity and Grammaticality for Multi-Sentence Compression", arXiv preprint arXiv:1605.02150.

66. E. ShafieiBavania, M. Ebrahimi, R. K. Wong, and F. Chen, "An Efficient Approach for Multi-Sentence Compression", Journal of Machine Learning Research (JMLR), pp. 414-429, 2016 [A].

67. J. Zhou, J. Sun, F. Chen, Y. Wang, R. Taib, A. Khawaji, and Z. Li, “Measurable Decision Making with GSR and Pupillary Analysis for Intelligent User Interfaces”, ACM Transactions on Computer-Human Interaction (ToCHI), vol. 21, no. 6, article no. 33, 2015. [A*]

68. B.-W. Hsu, M.-J. J. Wang, C.-Y. Chen, and F. Chen, “Effective Indices for Monitoring Mental Workload While Performing Multiple Tasks”, Perceptual and Motor Skills, vol. 121, no. 1, pp. 94-117, 2015.

69. J. Zhou and F. Chen, “Making Machine Learning Useable”, International Journal of Intelligent Systems Technologies and Applications, vol. 14, no. 2, pp. 91-109, 2015.

70. A. Menon, C. Cai, W. Wang, T. Wen, and F. Chen, “Fine-Grained OD Estimation with Sparsity Regularisation”, Transportation Research Part B,vol. 80, pp. 150-172, 2015. [A*]

71. Y. Uemura, Y. Kajiwara, J. Zhou, F. Chen, and H. Shimakawa, “Estimating Human Physical States from Chronological Gait Features Acquired with RFID Technology”, Sensors & Transducers, vol. 194, no. 11, pp. 76-83, 2015.

Refereed conference papers:

1. Ting Guo, Xingquan Zhu, Yang Wang, and Fang Chen, “Graph Compression Networks”, 2021 IEEE International Conference on Big Data, 2021.

2. Seunghyeon Lee, Fang Chen, “Identifying the effective restriction and vaccination policies during the COVID-19 crisis in Sydney: A machine learning approach”, The 34th Australasian Joint Conference on Artificial Intelligence (AJCAI’21), Australia, 2021.

3. K. Saleh, K. Yu, and F. Chen, “Video-based Student Engagement Estimation via Time Convolution Neural Networks for Remote Learning”, The 34th Australasian Joint Conference on Artificial Intelligence (AJCAI’21), Australia, 2021.

4. T. Guo, X. Zhu, Y. Wang, and F. Chen, “Weak Supervision Network Embedding for Constrained Graph Learning”, Proceedings of the 25th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD-2021), May 11-14, 2021, Delhi, India. (Best Overall Paper Award)

5. Y. Zhang, F. Zhou, Z. Li, Y. Wang, and F. Chen, “Bias-Tolerant Fair Classification”, Asian Conference on Machine Learning 2021 (ACML2021), 2021.

6. J. Zhou, S. Verma, M. Mittal, and F. Chen, " Understanding Relations between Perception of Fairness and Trust in Algorithmic Decision Making", International Conference on Behavioral and Social Computing (BESC 2021), 2021.

7. S. Liang, Z. Li, B. liang, Y. Ding, Y. Wang, and F. Chen, “Failure Prediction for Large-scale Water Pipe Networks Using GNN and Temporal Failure Series”, CIKM 2021, 2021.

8. J. Zhou, F. Chen, A. Berry, M. Reed, S. Zhang, and S. Savage, “A Survey on Ethical Principles of AI and Implementations”, 2020 IEEE symposium on the ethical, social and legal implications of artificial intelligence (ETHAI), Canberra, Australia, 2020.

9. B. Liang, S. Verma, J. Xu, S. Liang, Z. Li, Y. Wang, and F. Chen, “A Data Driven Approach for Leak Detection with Smart Sensors”, in ICARCV 2020, 2020.

10. Sunny Verma, Rujia Shen, Jiwei Wang, Zhefeng Ge, Fan Jin, Yang Wang, Chen Wang, Fang Chen, Liming Zhu, and Wei Liu, “Deep-HOSeq: Deep Higher Order Sequence Fusion for Multimodal Sentiment Analysis”, ICDM 2020, 2020.

11. Y. Ou, S. Mihaita, and F. Chen, “Dynamic Train Demand Estimation and Passenger Assignment”, IEEE Intelligent Transportation Systems Society Conference (IEEE ITSC 2020), 2020.

12. T. T. Khuat, F. Chen and B. Gabrys, “An Improved Online Learning Algorithm for General Fuzzy Min-Max Neural Network”, 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow, UK, 2020.

13. J. Zhou, W. Huang, and F. Chen, "A Radial Visualisation for Model Comparison and Feature Identification", PacificVis 2020, Tianjin, China, 2020.

14. Z. Zhang, Q. Wu, Y. Wang and F. Chen, "Visual Relationship Attention for Image Captioning", IJCNN 2019, 2019.

15. J. Zhou, Z. Li, H. Hu, K. Yu, F. Chen, Z. Li, and Y. Wang, “Effects of Influence on User Trust in Predictive Decision Making”, CHI 2019 – LBW, 2019.

16. F. Zhou, Y. Zhang, Z. Li, X. Fan, Y. Wang, A. Sowmya, and F. Chen, “Hawkes Process with Stochastic Triggering Kernel”, PAKDD 2019, 2019.

17. N. L. D. Khoa, H. Tian, Y. Wang, and F. Chen, "Online Data Fusion Using Incremental Tensor Learning," PAKDD 2019, 2019.

18. Y. Chang, Z. Li, B. Zhang, L. Luo, A. Sowmya, Y. Wang, and F. Chen, “Recovering DTW Distance between Noise Superposed NHPP”, PAKDD 2019, 2019.

19. S. Luo, V. Chu, Z. Li, Y. Wang, J. Zhou, F. Chen, and R. Wong, “Multitask learning for sparse failure prediction”, PAKDD 2019, 2019.

20. K. Yu, S. Berkovsky, R. Taib, J. Zhou, and F. Chen, “Do I trust my machine teammate?: an investigation from perception to decision”, In Proceedings of the 24th International Conference on Intelligent User Interfaces (IUI2019), pp. 460-468, Los Angeles, USA, March, 2019.

21. J. Zhou, H. Hu, Z. Li, K. Yu, and F. Chen, “Physiological Indicators for User Trust in Machine Learning with Influence Enhanced Fact-Checking”, International IFIP Cross Domain Conference for Machine Learning & Knowledge Extraction (CD-MAKE 2019), Canterbury, UK, August, 2019.

22. J. Zhou and F. Chen, “Towards Trustworthy Human-AI Teaming under Uncertainty”, IJCAI 2019 Workshop on Explainable AI (XAI), Macau, China, 2019.

23. Z. Zhang, Q. Wu, Y. Wang, and F. Chen, “Visual Relationship Attention for Image Captioning”, IJCNN 2019, 2019.

24. T. Guo, X. Zhu, Y. Wang, and F. Chen, “Discriminative Sample Generation for Deep Imbalanced Learning”, IJCAI 2019, 2019.

25. Q. Do, S. Verma, F. Chen, W. Liu, “Multiple Knowledge Transfer for Cross-Domain Recommendation”, 16th Pacific Rim International Conference on Artificial Intelligence (PRICAI 2019), Cuvu, Yanuca Island, Fiji, August 26-30, 2019.

26. Yu, K., Taib, R., Butavicius, M. A., Parsons, K., & Chen, F. (2019, September). Mouse Behavior as an Index of Phishing Awareness. In IFIP Conference on Human-Computer Interaction (pp. 539-548). Springer, Cham.

27. B. Liang, Z. Li, R. Taib, G. Mathews, Y. Wang, S. Lu, F. Chen, T. Hua, R. Ius, A. Peters, D. Vitanage, and C. Doolan, “Predicting Water Quality for Whole Woronora Delivery Network with Sparse Samples”, ICDM 2019, 2019.

28. H. Tian, N. L. D. Khoa, A. Anaissi, Y. Wang, and F. Chen, “Concept Drift Adaption for Online Anomaly Detection in Structural Health Monitoring”, CIKM 2019.

29. M. Ebrahimi, E. ShafieiBavani, R. Wong, and F. Chen, “A unified neural network model for geolocating Twitter users”, the 2018 SIGNLL Conference on Computational Natural Language Learning (CoNLL 2018), Brussels, Belgium, 2018.

30. V. W. Chu, R. K. Wong, C.-H. Chi, and F. Chen, “Extreme Topic Model for Market eAlert Service”, IEEE SCC 2018, 2018. [A]

31. J. Zhang, B. Li, X. Fan, Y. Wang, and F. Chen, “Corrosion Prediction on Sewer Networks with Sparse Monitoring Sites: A Case Study”, The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018), 2018, Melbourne, Australia. [A]

32. F. Zhou, Z. Li, X. Fan, Y. Wang, A. Sowmya, and F. Chen, “A refined MISD algorithm based on Gaussian process regression” , The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018), 2018, Melbourne, Australia. [A]

33. M. Ebrahimi, E. ShafieiBavani, R. Wong, and F. Chen, “Leveraging Local Interactions for Geolocating Social Media Users”, The 22nd Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2018), 2018, Melbourne, Australia. [A]

34. Z. Zhang, L. Wang, Y. Wang, L. Zhou, J. Zhang, and F. Chen, “Instance Image Retrieval by Aggregating Sample-based Discriminative Characteristics”, ACM International Conference on Multimedia Retrieval (ACM ICMR2018), Yokohama, Japan, 11-14 June 2018.

35. J. Zhou, S. Z. Arshad S. Luo and F. Chen, “Effects of Uncertainty and Cognitive Load on User Trust in Predictive Decision Making”, the 16^th IFIP TC.13 International Conference on Human-Computer Interaction (INTERACT 2017), 2017. [A] (Reviewer’s Choice Award), (“The Brian Shackel Award” in recognition of the most outstanding contribution with international impact in the field of human interaction with, and human use of, computers and information technology)

36. S. Z. Arshad, J. Zhou, S. Berkovsky, and F. Chen, “Human-In-The-Loop Machine Learning with Intelligent Multimodal Interfaces”, ICML2017 Workshop on Human-In-The-Loop Machine Learning, Sydney, 2017.

37. L. Luo, B. Li, I. Koprinska, S. Berkovsky and F. Chen, “Tracking the Evolution of Customer Purchase Behavior Segmentation via a Fragmentation-Coagulation Process”, in Proceedings of the International Joint Conference on Artificial Intelligence, 2017. [A*]

38. S. Dang, X. Cai, Y. Wang, J. Zhang, and F. Chen, “Unsupervised Matrix-Valued Kernel Learning for One Class Classification”, in the 26th ACM International on Conference on Information and Knowledge Management (CIKM 2017), 2017. [A]

39. A. Menon, Y. Lee, C. Cai, and F. Chen, “Predicting Short Term Public Transport Demand via Inhomogeneous Poisson Processes”, in the 26th ACM International on Conference on Information and Knowledge Management (CIKM 2017), 2017. [A]

40. D. Conway, R. Taib, M. Harris, S. Berkovsky, K. Yu, and Fang Chen, “A Qualitative Investigation of Bank Employee Experiences of Information Security and Phishing”, Symposium on Usable Privacy and Security (SOUPS), 2017.

41. K. Yu, S. Berkovsky, R. Taib, D. Conway, J. Zhou, and F. Chen, “Do I Trust My Machine Teammate? An Investigation from Perception to Decision”, BHCI 2018, 2018.

42. E. ShafieiBavani, M. Ebrahimi, R. Wong, and F. Chen, “A Graph-Theoretic Summary Evaluation for Rouge”, EMNLP 2018, 2018.

43. W. Wang, J. Xu, C. Cai, Y. Wang, and F. Chen, “DualBoost : Handling Missing Values with Feature Weights and Weak Classifiers that Abstain”, CIKM 2018, 2018. [A]

44. E. ShafieiBavani, M. Ebrahimi, R. Wong, and F. Chen, “Summarization Evaluation in the Absence of Human Model Summaries Using the Compositionality of Word Embeddings”, COLING 2018, 2018, Santa Fe, New Mexico, USA. [A]

45. K. Yu, S. Berkovsky, R. Taib, D. Conway, J. Zhou, and F. Chen, “User Trust Dynamics: An Investigation Driven by Differences in System Performance”, IUI2017. [A]

46. A.S. Mihaita, P. Tyler, A. Menon, T. Wen, Y. Ou, C. Chen, and F. Chen, “An investigation of positioning accuracy transmitted by connected heavy vehicles using DSRC”, In Proceedings of Transportation Research Board 96th Annual Meeting, Washington DC, 2017. [A]

47. J. Zhou, S. Z. Arshad, S. Luo, K. Yu, S. Berkovsky, and F. Chen, “Indexing Cognitive Load Using Blood Volume Pulse Features”, Proceedings of CHI 2017 Conference Extended Abstracts on Human Factors in Computing Systems, pp. 1861-1868, Denver, USA, 2017. [A*]

48. S. Luo, J. Zhou, H. Duh, and F. Chen, “BVP Feature Analysis for Intelligent User Interface”, Proceedings of CHI 2017 Conference Extended Abstracts on Human Factors in Computing Systems, pp. 2269-2275, Denver, USA, 2017. [A*]

49. Q. Do, W. Liu, and F. Chen, “Discovering both Explicit and Implicit Similarities for Cross-Domain Recommendation”, PAKDD 2017. [A]

50. Z. Chen, J. Zhou, X. Wang, J. Swanson, F. Chen, and D. Feng, “Neural Net-Based and Safety-Oriented Visual Analytics for Time-Spatial Data”, The 2017 International Joint Conference on Neural Networks (IJCNN2017), pages 1133-1140, 2017. [A]

51. Q. Do, W. Liu, and F. Chen, “LiST: Lightning-fast and Scalable Coupled Tensor Factorization for Analyzing Large-scale Data”, IJCNN2017. [A]

52. M. Ebrahimi, E. ShafieiBavani, R. K. Wong, F. Chen , “Exploring Celebrities for Twitter User Geolocation Inference”, The Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), 2017. [A]

53. A. Anaissi, N.L.D. Khoa, S. Mustapha, M. M. Alamdari, A. Braytee, Y. Wang, and F. Chen, “Adaptive One-Class Support Vector Machine for Damage Detection in Structural Health Monitoring”, PAKDD 2017. [A]

54. M. Ghanavati, R. Wong, F. Chen, and S. Fong, “Boosting financial forecasting returns based on metric learning and hierarchical beta process”, ACSW 2017.

55. S. Luo, V. W. Chu, J. Zhou, F. Chen, and R. K. Wong, “A Multivariate Clustering Approach for Infrastructure Failure Predictions”, Proceedings of IEEE Big Data Congress (IEEEBigData 2017), Honolulu, Hawaii, USA, June 25-30, 2017.

56. B. Li, X. Fan, J. Zhang, Y. Wang, F. Chen, S. Kodagoda, T. Wells, L. Vorreiter, D. Vitanage, G. Iori, D. Cunningham, and T. Chen, “Predictive analytics toolkit for H2S estimation and sewer corrosion”, Australia’s International Water Conference (OzWater 2017), 16-18 May 2017.

57. Dammika Vitanage, Corinna Doolan, Lucinda Maunsell, Bronwyn Cameron, Fang Chen, Yang Wang, and Zhidong Li, “Success in data analytics – Sydney Water and Data61 collaboration”, Australia’s International Water Conference (OzWater 2017), 16-18 May 2017.

58. L. Luo, B. Li, S. Berkovsky, I. Koprinska and F. Chen, “Online Engagement for a Healthier You: A case Study of Web-Based Supermarket Health Program”, in Companion Proceedings of the 2017 World Wide Web Conference, pp. 1053-1061, 2017. [A*]

59. M. Ebrahimi, E. ShafieiBavani, R. Wong, and F. Chen, “Exploring Celebrities on Inferring User Geolocation in Twitter”, In The 21st Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2017), 2017. [A]

60. L. Luo, B. Li, I. Koprinska, S. Berkovsky, and F. Chen, “Who will be Affected by Supermarket Health Programs? Tracking Customer Behavior Changes via Preference Modeling”, in Proceedings of the 20^th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2016), Auckland, New Zealand, 19-22 April, 2016. [A]

61. X. Fan, B. Li, Y. Wang, Y. Wang, F. Chen, “The Ostomachion Process”, AAAI, 2016. [A*]

62. T. Wen, C. Cai, L. Gardner, V. V. Dixit, S.T. Waller, and F. Chen, “A Strategic User Equilibrium for Independently Distributed Origin-Destination Demands”, in TRB 2016 Compendium of Papers DVD, presented at Transportation Research Board 95th Annual Meeting, Washington, DC, 10 - 14 January 2016. [A]

63. P. Lin, B. Zhang, T. Guo, Y. Wang, F. Chen, “Interaction Point Processes via Infinite Branching Model”, AAAI, 2016 [A*]

64. T. Guo, B. Zhang, Y. Wang, F. Chen, C. Jawanda, S. Thapa, N. Karunatilake, “Improved Data-Driven Reticulation Watermain Failure Prediction”, OzWater, 2016.

65. B. Li, X. Fan, Y. Wang, F. Chen, F. Spaninks, “Data-Driven Customer Segmentation for Water Demand Analysis”, OzWater, 2016.

66. D. Conway, F. Chen, K. Yu, J. Zhou, and R. Morris, “Misplaced Trust: A Bias in Human-Machine Trust Attribution – In Contradiction to Learning Theory”, CHI2016 Late-Breaking Work, San Jose, CA, USA. [A*]

67. K. Yu, S. Berkovsky, D. Conway, R. Taib, J. Zhou, and F. Chen, “Trust Profiles on System Accuracy”, UMAP2016 .

68. P. Cheema, N.L.D. Khoa, M. Makki Alamdari, W. Liu, Y. Wang, F. Chen, P. Runcie, “On Structural Health Monitoring Using Tensor Analysis and Support Vector Machine with Artificial Negative Data”, in the 25th ACM International on Conference on Information and Knowledge Management (CIKM 2016), pp. 1813—1822, Indianapolis, USA, 2016. [A]

69. J. Zhou, Z. Li, Z. Zhang, B. Liang, and F. Chen, “Visual Analytics of Relations of Multi-Attributes in Big Infrastructure Data”, IEEE International Symposium on Big Data Visual Analytics 2016 (BDVA2016), pp. 31-32, 2016.

70. J. Zhou, S. Z. Arshad, K. Yu and F. Chen, “Correlation for User Confidence in Predictive Decision Making”, OzCHI2016.

71. H. Nguyen, W. Liu, P. Rivera and F. Chen, “TrafficWatch: real-time traffic incident detection and network monitoring using social media”, Proceedings of the 20th Pacific Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2016), Auckland, New Zealand, 19-22 April, 2016. [A]

72. Y. Wang, B. Li, X. Fan, Y. Wang, F. Chen, "Bayesian Optimization of Partition Layouts for Mondrian Processes", IJCAI, 2016. [A*]

73. V. W. Chu, R. K. Wong, F. Chen, and C.-H. Chi, “Service Selection based on Dynamic QoS Networks,” SCC 2016 (13th IEEE International Conference on Services Computing). [A]

74. E. ShafierBavani, M. Ebrahimi, R. K. Wong, and F. Chen, “A Query-based Summarization Service from Multiple News Sources,” SCC 2016 (13th IEEE International Conference on Services Computing). [A] (Best student paper)

75. M. Ghanavati, R. K. Wong, F. Chen, Y. Wang, and S. Fong, “A Generic Service Framework for Stock Market Prediction,” SCC 2016 (13th IEEE International Conference on Services Computing). [A]

76. L. Luo, B. Li, I. Koprinska, S. Berkovsky, and F. Chen, “Discovering Temporal Purchase Patterns with Different Responses to Promotions”, CIKM 2016. [A]

77. V. W. Chu, R. K. Wong, F. Chen, and C.-H. Chi, “Interrelationships of Service Orchestrations”, International Conference on Advanced Data Mining and Applications 2016 (ADMA2016), pp. 95-110, 2016.

78. E. ShafieiBavani, M. Ebrahimi, R. Wong, and F. Chen, “Appraising UMLS Coverage for Summarizing Medical Evidence”, In the 26th International Conference on Computational Linguistics (COLING2016), pp. 513-524, 2016. [A]

79. T. Guo, B. Zhang, Y. Wang, F. Chen, C. Jawanda, S. Thapa, N. Karunatilake, “Data-Driven Long-Term Failure Prediction for Reticulation Water Main”, World Water Congress and Exhibition 2016.

80. T. Wen, C. Cai, L. Gardner, V.V. Dixit, S.T. Waller, and F. Chen, “A Maximum Likelihood Estimation of Trip Tables for The Strategic User Equilibrium Model”, in TRB 94th Annual Meeting Compendium of Papers, presented at The 94th Annual Meeting of the Transportation Research Board, Washington D.C, 11 - 15 January 2015. [A].

81. J. Zhou, J. Sun, F. Chen, X. Wang, and X. Miao, “Safety-Oriented Visual Analytics of People Movement”, Proceedings of IEEE Conference on Visual Analytics Science and Technology (VAST) 2015, Chicago, IL, USA, pages 181-182, 25-30 Oct, 2015.

82. J. Zhou, C. Bridon, F. Chen, A. Khawaji, and Y. Wang, “Be Informed and Be Involved: Effects of Uncertainty and Correlation on User’s Confidence in Decision Making”, in Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI2015), 2015, Seoul, Korea, pp. 923–928. [A*]

83. A. Khawaji, J. Zhou, F. Chen, and N. Marcus, “Using Galvanic Skin Response (GSR) to Measure Trust and Cognitive Load in the Text-Chat Environment”, in Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI2015), 2015, Seoul, Korea, pp. 1989–1994. [A*]

84. S. Arshad, Y. Wang, and F. Chen, “Interactive Mouse Stream as Real-Time Indicator of User’s Cognitive Load”, in Proceedings of the 33rd Annual ACM Conference Extended Abstracts on Human Factors in Computing Systems (CHI2015), 2015, Seoul, Korea, pp. 1025–1030. [A*]

85. N.L.D. Khoa, B. Zhang, Y. Wang, W. Liu, F. Chen, S. Mustapha and P. Runcie, “On Damage Identification in Civil Structures Using Tensor Analysis”, in the 19th Pacific-Asia Conference on Knowledge Discovery and Data Mining (PAKDD 2015). [A]

86. J. Zhou, J. Y. Jung, and F. Chen, “Dynamic Workload Adjustments in Human-Machine Systems Based on GSR Features”, J. Abascal et al. Eds., Human-Computer Interaction - INTERACT 2015, Part I, LNCS 9296, pp. 550–558, 2015, Springer. [A]

87. S. Oviatt, K. Hang, J. Zhou, and F. Chen, “Spoken Interruptions Signal Productive Problem Solving and Domain Expertise in Mathematics”, Proceedings of the 17th ACM International Conference on Multimodal Interaction (ICMI2015), Seattle, USA, pp. 311-318, November, 2015.

88. B. Li, B. Zhang, Z. Li, Y. Wang, F. Chen, and D. Vitanage, “Prioritising Water Pipes for Condition Assessment with Data Analytics,” OzWater, 2015.

89. B. Li, B. Zhang, Z. Li, Y. Wang, F. Chen, D. Zhang, and D. Vitanage, “Multi-level Data Analytics for Risk Water Pipe Selection”, LESAM, 2015.

90. Y. Wang, B. Li, Y. Wang, and F. Chen, “Metadata Dependent Mondrian Processes”, ICML, 2015. [A*]

91. P. Lin, B. Zhang, Y. Wang, Z. Li, B. Li, Y. Wang, F. Chen, “Data Driven Water Pipe Failure Prediction: A Bayesian Nonparametric Approach”, The 24th ACM International Conference on Information and Knowledge Management (CIKM) 2015, pp. 193-202, 2015. [A]

92. S. Arshad, J. Zhou, C. Bridon, F. Chen, and Y. Wang, “Investigating User Confidence for Uncertainty Presentation in Predictive Decision Making”, In Proceedings of the Annual Meeting of the Australian Special Interest Group for Computer Human Interaction (OzCHI ’15), pp. 352-360, Melbourne, Australia, 2015.

93. L. Luo, W. Liu, I. Koprinska, and F. Chen, “Discovering Causal Structures from Time Series Data via Enhanced Granger Causality”, in Proceedings of Australasian Joint Conference on Artificial Intelligence (AI 2015), pp. 365-377, Canberra, Australia, 1-4 December, 2015.

94. M. Ghanavati, R. K. Wong, F. Chen, and Y. Wang, “A Generic Ranking Service on Scientific Datasets”, IEEE SCC 2015 (12th IEEE International Conference on Services Computing). [A]

95. A. Hamzehei, M. Ebrahimi, E. ShafieiBavani, R. K. Wong, and F. Chen, “Scalable Sentiment Analysis for Microblogs based on Semantic Scoring”, IEEE SCC 2015 (12th IEEE International Conference on Services Computing). [A]

96. L. Luo, W. Liu, I. Koprinska, and F. Chen, “Discrimination-Aware Association Rule Mining for Unbiased Data Analytics”, International Conference on Big Data Analytics and Knowledge Discovery (DaWaK) 2015.

97. Z. Yu, R. K. Wong, C.-H. Chi, and F. Chen, “A Semi-supervised Learning Approach for Microblog Sentiment Classification”, IEEE International Conference on Smart City/SocialCom/SustainCom (SmartCity) 2015.

98. Z. Li, Y. Xu, C. Cai and F. Chen, “A Bayesian Approach for Travel Time Data Fusion”, ITS World Congress, 2015.

Current Affiliations

The University of New South Wales, Australia

Conjoint Professor