Professor Fang Chen is a prominent leader in AI/data science with international reputation and industrial recognition and the leader of the Data Science Institute at UTS. She is the winner the 'Oscars' of Australian science, 2018 Australian Museum Eureka Prize for Excellence in Data Science.
She has created many innovative research and solutions, transforming industries that utilise AI/data science. She has helped industries worldwide advance towards excellence in increasing their productivity, innovation, profitability, and customer satisfaction. The transformations to industry with practical impact won her many industrial recognitions including being named as “Water Professional of The Year” in 2016.
She has actively led in developing new strategies, which prioritise the organisation’s objectives, and capitalise on any growth opportunities. She has built up a career in creating research and business plans, and executing with leadership and passion.
In science and engineering, Professor Chen has 300+ refereed publications, including several books. She has filed 30+ patents in Australia, US, Canada, Europe, Japan, Korea, Mexico and China.
Dr. Mihaita is currently a Senior Lecturer in the University of Technology in Sydney, Faculty of Engineering and IT, leading the newly created UTS Future Mobility Research lab. Before joining UTS, she was a Senior Research Scientist and team leader in the ADAIT group from NICTA (now Data61) and continues to act as an affiliated Senior Researcher.
Her main research focus is how to engage traffic simulation and optimization using machine learning and artificial intelligence to improve traffic congestion, predicting the duration of traffic accidents and estimating their urban impact, while also leveraging smart analytics for connected and autonomous vehicles in a smart city environment. She is highly engaged in smart city modelling and worked on traffic plan optimization inside ecological neighbourhoods using evolutionary algorithms.
Dr. Mihaita holds several leadership roles in various initiatives such as: currently C.I. in the ARC Linkage Project LP180100114 under the Australian-Singapore Strategic Collaboration Partnership (a $2.4 mil program for collaborations between the two countries on solving congestion problems), and previously: transport leader and scrum master in the “Premiere’s Innovation Initiative” (a $3.9mil program and sole winner of the TfNSW congestion program), the “On-Demand Mobility” trials in Northern Beaches in partnership with Keolis Downer, as well as “the Investigation of positioning accuracy of connected vehicles” operated by the Road Safety Centre in Transport for NSW (TfNSW).
Dr. Yuming Ou is a Senior Lecturer in the Data Science Institute, University of Technology Sydney. He has expertise in data mining, machine learning and big data. Dr Ou has more than ten years of experience in the area of big data analytics and has a demonstrated track record of transforming cutting‐edge research innovation into real life impact. His research interest in the transport domain includes travel time prediction, public transport delay propagation, travel demand estimation and emergency event impact analysis.
Dr. Ou has successfully delivered many research projects as a leading CI or CI leveraging data analytics and machine learning, such as the Premier’s Innovation Initiative for intelligent congestion management with TfNSW, on‐demand public transport service trial with Keolis Downer, learner attention analysis via computer interaction behaviours with Acer Australia, intelligent data analytics for property management with PIA, data analytics for Albury and Wodonga economic zone with Albury City Council, automated risk analytics for controlled exports with DHL, the smart parking data analytics with Mornington Peninsula Shire Council. In 2021, he led the development of a next generation of digital twins system which was the finalist of the IoT Australia Awards 2021 in Smart Cities category and the finalist of the ITS Australia Awards 2021 in Excellence in Transport Data category.
Dr. Tuo Mao is a Ph.D. graduated from University of New South Wales (UNSW), a senior engineer in University of Technology, Sydney (UTS). He is also a visiting scientist at the Intelligent Mobility group at Data61 CSIRO.
He has experience in motorway modelling and coordinated ramp metering optimization; Vehicle to infrastructure (V2I) communication wireless connection system modelling and simulation; Traffic signal control plan optimization using Genetic algorithm; General machine learning (especially reinforcement learning); Bus signal priority modelling and simulation in a corridor; Public transport assignment modelling and simulation.
Dr Seunghyeon Lee is working as a lecturer in the Faculty of Engineering and Information Technology at the University of Technology Sydney. Prior to this position, he had taken up a researcher position at the University of Canterbury and the University of Hong Kong. He has completed Ph.D. degree at the University of Hong Kong in 2016.
Dr Seunghyeon has about 30 publications as Q1 journals, peer-reviewed conference proceedings, and presentations. He has led most of the publications as the first and the corresponding authors. Contributing to the academic field of transportation engineering, he serves as the reviewers for 11 international journals, including Transportation Research Part B: Methodological, IEEE Transactions on Cybernetics, and Accident Analysis and Prevention. Moreover, he has performed about 20 funded research and consultancy projects in transport engineering with his supervisors in South Korea, Hong Kong, and New Zealand.
Dr Seunghyeon's major research interests are traffic management and control, traffic-flow theory, intelligent transportation systems, deep learning approach, and connected and autonomous traffic systems for multimodal transportation networks. He has carried out numerous research projects, including traffic signal control, real-time big-data surveillance and mining algorithms, car-following models, transport optimisations, traffic guidance and road marking, and preliminary feasibility studies on transport infrastructure, stochastic system modeling, and connected traffic systems structure. His research applies a range of approaches, such as empirical surveys, traffic simulation, data recursive process, mathematical programs, and stochastic differential equations.
Haowen Li is currently a research assistant at University of Technology, Sydney (UTS) and an honours-year undergraduate student from the Australian National University (ANU). He has strong programming and mathematical skill. He also has experience in transportation data analysis using deep learning, face encoding learning and object detection. He has worked on several individual and group projects, including face verification about disguised as well as imposter's faces, detecting as well as recognizing house numbers from street view images.
Artur Grigorev is currently a PhD student in Information Technology on the Faculty of Engineering and IT in the University of Technology Sydney, where he doing the research about traffic incident analysis and impact prediction under supervision of Dr. Adriana-Simona Mihaita and Dr. Sunghyeon Lee. Graduated as a first-class bachelor in Computer science and Engineering (ITMO University, Russia, Saint-Petersburg, 2017) and Master with distinction in the field of Applied math and computer science (Urban Supercomputing speciality, ITMO University, 2019). His master research was related to the application of computer vision methods to traffic analysis (to collect vehicle count, flow, direction and speed from video). Topics of interest include Traffic Analysis, Machine Learning, Deep Learning and Simulation modelling.
Dong Zhao is currently a PhD candidate of the Faculty of Engineering and IT at the University of Technology Sydney. She is a member of the UTS Future Mobility Research lab. Before she joins UTS in February 2021, she obtained her Bachelor's degree from the Beijing University of Civil Engineering and Architecture followed by a Master of Philosophy from the University of Sydney. Her research spans a range of transport problems majorly covering travel behaviour as well as public transport management and planning that matches the requirement of both urban and rural area toward effective mobility and movement. She contributes to the development of computational methods to cope with existing traffic problems from data obtaining and processing to information mining and mathematical modelling. Her two principal research areas cover the study on pedestrian walking behaviour and public transport reliability. Her current research interests are multi-modal public transport modelling, transport network modelling, disruption modelling, traffic engineering and sustainable travelling.
Mike Li is currently a Research Master student of the Faculty of Engineering and IT at the University of Technology Sydney. He is a member of the UTS Future Mobility Research lab. Before he joins UTS in August 2021, he obtained his Bachelor's degree from the University of Melbourne, and his Master’s degree from the University of Sydney. He has several years’ commercial IT development experience in companies from small startups to large enterprises. He has strong programming skills, and a solid understanding of various IT technologies and architectures. His research interests include machine learning, deep learning, data analytics, and their applications in traffic prediction.
Iman Rahimi, PhD , earned his BSc (Applied Mathematics) in 2009, MSc (Applied Mathematics – Operations Research) in 2011 and his PhD in the Department of Mechanical and Manufacturing Engineering, Faculty of Engineering, Universiti Putra Malaysia, Malaysia, in 2017. He is now working at the Faculty of Engineering and Information Technology, University of Technology Sydney, Australia, as a research scholar. His research interests include machine learning and multiobjective optimization. He also has edited three books entitled "Evolutionary Computation in Scheduling", "Big Data Analytics in Supply Chain Management: Theory and Applications", and "Multi-Objective Combinatorial Optimization Problems and Solution Methods" with Wiley, CRC Press (Taylor & Francis Group), and Elsevier, respectively. He has served as an editor for the following journals: International Journal of Renewable Energy Technology (IJRET) and International Journal of Advanced Heuristic and Meta-Heuristic Algorithms. Besides, he has acted as a reviewer for "International Journal of Production Research (Q1 JRC ranking)" and "Research in Transportation Business & Management (Q3 JCR ranking)". He also received several awards namely, research grants from "University of Tabriz (Iran)", "Iran National Science Foundation", "International Research Scholarship (Australia)", "Faculty of Engineering and Information Technology Scholarship (Australia)", and "Nicolas Baudin Scholarship" from Embassy of France in Australia.
Dr. Yang Wang is an associate professor at the University of Technology, Sydney. He received his Ph.D. degree in Computer Science from the National University of Singapore in 2004. Before joining Data61 (formerly NICTA) in 2006, he was with the Institute for Infocomm Research, Rensselaer Polytechnic Institute, and Nanyang Technological University. His research interests include machine learning and information fusion techniques, and their applications to asset management, intelligent infrastructure, cognitive and emotive computing, medical imaging, and computer vision.
Dr. Zhidong Li received his Ph.D. degree from the University of New South Wales, Sydney, Australia. He received the M.E. degree in computer science from the University of New South Wales in 2006, and the B.S. degree in computer science from the University of Xiamen in 2002. He is currently a senior lecturer in University of Technology Sydney. Before joining UTS, He was a senior engineer in Data61 at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) which is the federal government agency for scientific research in Australia. His research interests include machine learning, data mining, pattern recognition, image processing, and Human Computer Interaction.
Dr. Marian-Andrei Rizoiu is currently a Lecturer in Computer Science with the Faculty of Engineering and IT in the University of Technology Sydney. His main research interest is to develop behavioural models for human actions online, at the intersection of applied statistics, artificial intelligence and social data science, with an interdisciplinary focus on social influence and information diffusion in online communities. He has strong data science skills and has actively collaborating with the team on the incident and congestion prediction works.
Dr. Ting Guo received his Ph.D. degree from University of Technology, Sydney, Australia. His research mainly focuses on Data Mining and Machine Learning. Before joining UTS, he was a postdoctoral fellow in Data61 (CSIRO), the federal government agency for scientific research in Australia. His capacity includes predictive maintenance, factor analysis and building advanced machine learning models for industrial fields.