Professor Pierre Benckendorff is an award-winning researcher specialising in visitor behaviour, technology enhanced learning and tourism. He has held several teaching and learning leadership positions at The University of Queensland and James Cook University in Australia. His experience includes coordinating a team of teaching and learning staff, program quality assurance and accreditation, and curriculum reviews of undergraduate and postgraduate coursework programs in business, tourism, hospitality and event management. He has developed and taught undergraduate and postgraduate courses in introductory tourism management, international tourism, tourist behaviour, tourism and leisure futures, tourism transportation, tourism operations, tourism technologies, tourism analysis, business skills and marketing communications.
Pierre has been actively involved in a number of national teaching and learning projects totalling close to AUD 1 million in grant funding. In 2007, he received a national Carrick citation for outstanding contributions to student learning. Pierre was part of the national team that developed the Learning and Teaching Academic Standards for Tourism, Hospitality and Events and has continued to co-lead efforts to embed and measure these standards under the auspices of CAUTHE. He is currently the co-chair of knowledge creation for the BEST Education Network and in this capacity, has worked with the World Travel and Tourism Council to edit a book of international cases based on Tourism for Tomorrow award finalists and winners. He is the co-editor of the Handbook of Teaching and Learning in Tourism. Pierre serves regularly as an external reviewer of tourism programs in Australia and overseas institutions.
His research interests include visitor behaviour, tourism information technologies, and tourism education and training. He has authored over 80 publications in these areas in leading international journals and is a regular speaker at tourism research conferences. He is on the editorial board of several leading tourism journals and is a regular reviewer of papers. He has also co-authored one of the leading textbooks on tourism and information technology. He has served as a judge for the Queensland Tourism Awards as well as the Australian Tourism Awards. His passion for travel and tourism has taken him to some of the world’s leading theme parks and airports, the major cities of Europe and North America, the African Savannah and the bustling streets of Asia. He has also travelled extensively throughout Australia and New Zealand.
Dr Moni holds a PhD in Artificial Intelligence & Data Science in 2014 from the University of Cambridge, UK followed by postdoctoral training at the University of New South Wales, University of Sydney Vice-chancellor fellowship, and Senior Data Scientist at the University of Oxford. Dr Moni then joined UQ in 2021. He also worked as an assistant professor and lecturer in two universities (PUST and JKKNIU) from 2007 to 2011. He is an Artificial Intelligence, Computer Vision & Machine learning, Digital Health Data Science, Health Informatics and Bioinformatics researcher developing interpretable and clinical applicable machine learning and deep learning models to increase the performance and transparency of AI-based automated decision-making systems.
His research interests include quantifying and extracting actionable knowledge from data to solve real-world problems and giving humans explainable AI models through feature visualisation and attribution methods. He has applied these techniques to various multi-disciplinary applications such as medical imaging including stroke MRI/fMRI imaging, real-time cancer imaging. He led and managed significant research programs in developing machine-learning, deep-learning and translational data science models, and software tools to aid the diagnosis and prediction of disease outcomes, particularly for hard-to-manage complex and chronic diseases. His research interest also includes developing Data Science, machine learning and deep learning algorithms, models and software tools utilising different types of data, especially medical images, neuroimaging (MRI, fMRI, Ultrasound, X-Ray), EEG, ECG, Bioinformatics, and secondary usage of routinely collected data.
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