Wayne Wobcke is an Associate Professor in the School of Computer Science and Engineering
at the University of New South Wales, specializing in Artificial Intelligence and Data Science.
His research ranges from theory (intelligent agents), to practice (dialogue management,
personal assistants, data mining and recommender systems).
He has published over 90 papers and received over $6 million in research funding, winning a PAKDD best paper runner up award and an AAAI deployed application award for work on people-to-people recommender systems. He has served as Programme Committee Chair of two major Artificial Intelligence conferences (Australasian AI and PRIMA) and given two invited addresses at international workshops, on intelligent agents in healthcare and social media analytics.
His industry experience began at British Telecom Labs in the UK where he was part of a team that won a British Computer Society Medal for Innovation in Information Technology. Since joining UNSW in 2002, he has collaborated extensively with industry, serving for over 10 years as Programme Manager and Project Leader in two CRCs (Smart Internet Technology CRC and Smart Services CRC), leading a team of seven academic and research staff. A highlight of Smart Internet Technology CRC was the development in 2004 of a voice controlled mobile application for interaction with e-mail and calendar (a precursor to Apple's Siri). A major achievement through Smart Services CRC was the deployment of a people-to-people recommender system for suggesting suitable matches in online dating, fielded in 2012 on one of Australia's largest online dating sites.
He recently worked with Data to Decisions CRC, leading a multi-disciplinary team of data scientists and social scientists, with the aim of researching: (i) efficient methods for extracting information from high velocity streams of rich text data (such as social media and news feeds), (ii) methods for combining information from multiple asynchronous data streams to build up a knowledge base of information about entities, places and events, (iii) using insights from social science to build and evaluate predictive models for events of interest to analysts, and (iv) methods to determine sentiment and stance (orientation towards political issues) from social media posts.
His current work focuses on the use of data science in humanitarian contexts and the use of machine learning in official statistics, funded by an ARC Discovery Project led by Fleur Johns (UNSW Law), in collaboration with BPS (Statistics Indonesia), STIS (Politeknik Statistika, Indonesia), and UN Global Pulse through Pulse Lab Jakarta.