- With a background in Social Science and Technology studies, Einat’s research centers on the adoption of innovative technologies. Driven by a commitment to enhance individual well-being and advance societal welfare, Einat seeks to unlock the potential of technology for positive change by bridging the gap between technology and society. The central focus of Einat’s work revolves around understanding how individuals respond to emerging technologies, delving into the intricacies of users’ needs, motivations, concerns, and benefits when embracing ground-breaking innovations. This exploration extends to gaining insights into how these factors influence usage patterns and identifying the challenges users encounter during adoption and continued use. In CINTEL, Einat examines the evolving skillset necessary for effective Human-AI interaction
- Ben worked with the MLAI FSP from 2020 to 2022 when they moved to the CINTEL FSP. Ben joined CSIRO in 2020 as a Postdoctoral Fellow in the newly formed Machine Learning and Artificial Intelligence Future Science Platform. After on-boarding remotely from Melbourne, Ben is now located in Brisbane. Prior to this position, Ben studied Computer Science and Mechatronics Engineering at Monash University, where Ben then completed PhD in Computer Systems Engineering as part of the Australian Research Council Centre of Excellence for Robotic Vision. Ben’s doctoral thesis focused on the development of efficient algorithms for the representation and retrieval of high dimensional big data. Ben continues to have many research interests in related fields including multi-modal representation and retrieval, human-in-the-loop active learning and non-linear dimension reduction. Working at CSIRO Ben’s keen to continue investigating and developing fundamental data science tools, and is excited to leverage the breadth of scientific domains covered by the organisation in pursuit of seeing these tools utilised to their full potential. Outside of research work Ben also enjoys flexing creative muscles with board game design and recipe development.
- Pascal holds a PhD from the University of Tasmania (2019), and a Masters degree in Physics from the University of Bonn, Germany (2000). Besides having long standing interests in the natural sciences and signal / data analysis, including machine learning, he gained rich experience in scientific, industrial, and community / citizen science projects spanning diverse fields, from developing real-time control systems and working on numerical earth system simulations, over capturing and analysing ultrasonic bat echolocation calls, to industrial consulting, e.g. software development and data analysis in the chemical industry, telecommunications, and business intelligence. In his role, Pascal focuses on using machine learning / AI, particularly on remote embedded (‘edge’) acoustic sensing devices, to advance trust in collaborative decision support systems under very low bandwidth communication constraints.
- Fatemeh is working on human-centric cybersecurity, human machine collaboration for cybersecurity. Previously she worked as a research associate at Capability Systems Centre, School of Engineering and Information Technology, University of New South Wales (UNSW) Canberra. She also did her PhD at UNSW Canberra. Fatemeh is interested in the application of Machine Learning (ML), Artificial Intelligence (AI), Decision Analytics, Decision Optimisation, Decision-Making Support Systems, Multi-Criteria/Multi-Objective Decision Making, Incorporating Plural Human Views into Decision Making Analytics, Decision Making under Risk/Uncertainty, Systems Engineering, Systems Science, Systems Thinking and Modelling, Optimisation, Simulation, Simulation-Optimisation and Metaheuristics on the area of Human Machine Collaboration for Cybersecurity.
- Yanran received her BSc. Eng. Honors and PhD degree in Mechanical Engineering from Monash University (Australia) in 2018 and 2022. Yanran’s research interest generally lies in the area of human motion analysis, fatigue estimation, machine learning, and deep learning. Her PhD research topic is to develop predictive and proactive fatigue monitoring frameworks through machine learning/deep learning techniques. Currently, she is researching on a dynamic situational awareness in human-robot teams to build a richer, dynamic human-robot collaboration, enabling humans and robots to respond quicker to environmental changes and make better decisions.
- Maisie’s focus is Genomics and Bioinformatics. Maisie worked as a Research Associate at the University of South Australia (UniSA), developing drug repurposing methods for COVID-19 treatment. She obtained her PhD in Bioinformatics from UniSA, where she developed computational methods for breast cancer prognosis in precision medicine. Maisie is generally interested in developing and applying data mining/machine learning and causal inference methods to solve real-world problems. She has applied machine learning in various fields, such as preeclampsia biomarkers identification, RNA tertiary structure prediction, protein subcellular localization, and cell position prediction.
- Two central themes in Melanie’s research are perceptions of harm and the nature of moral evaluation. Melanie’s PhD in Social Psychology at the University of Melbourne explored individual and contextual factors that influence our perception of what is harmful and who is harmed. As an Emerging Scholar of the Center for the Science of Moral Understanding (University of North Carolina), Melanie extended this research to investigate the consequences of moral evaluation arising from these divergent concepts of harm, including implications for political polarisation. Prior to undertaking doctoral studies, Melanie completed a Postgraduate Diploma in Psychology at the University of Melbourne using moral communication as a device to probe the contents of moral reasoning. In her role, Melanie will be developing a framework identifying the factors that contribute to establishing and maintaining human trust in collaborative intelligence systems.
- Hashini received her PhD in Human-Centered Computing and Artificial Intelligence in 2022 from Monash University (Australia). Specifically, her PhD research involved detecting temporal patterns of anxiety using multimodal-multisensor analytics to support the future design of intelligent and clinically-meaningful assistive mobile technologies for anxiety. She holds a BSc. Eng. Honors in Computer Science from the University of Moratuwa, Sri Lanka. She also has a background in designing and developing robotic and electronic-based STEM educational toolkits for children and disability communities. Hashini’s broader research interests focus on discovering impactful human-centered AI technologies for multiple domains, using the techniques of domain exploration, multimodal analytics of physiological and behavioral signals, and employing AI techniques in an explainable manner. Currently, Hashini is researching how the situational awareness of humans in human-robot teams can be maintained at an optimal level throughout a mission, by designing and implementing adaptive interfaces integrated with user models.
- Sidra’s work centres around the use of mental models in Human-AI teams and seeks to understand how humans perceive machines with Artificial Intelligence during a collaborative task. Subsequently, she aims to understand how human skills and experiences are impacted during collaborative tasks with Artificial Intelligence. Her PhD thesis at University of Technology Sydney focused on the inadvertent influence of robots on humans. In particular, she investigated the interplay between emotion and logic in persuasive Human-Robot Interaction. She carried out experiments to understand if emotional and logical inducement through robot speech could lead to persuasive backfiring, i.e., create attitude change in the direction opposite to the one intended. She is currently awaiting conferral for her PhD thesis in Persuasive robotics at University of Technology Sydney. Sidra is a 2013 Computer Science graduate from Carnegie Mellon University in Qatar, with a minor in Robotics. Throughout her undergraduate years, she has been involved in a number of robotics projects involving multi-robot communication and coordination, distributed control and autonomous navigation of UAVs and visual place recognition. Her research interests involve developing algorithms that are at the intersection of robotics, cognitive science & psychology to optimize Human-Robot Collaboration.
- Alan completed his PhD in Ecology at the University of Adelaide with research on improving the quality of biodiversity data collected using citizen science projects. As part of his PhD research, he developed species observation recording apps, including for the echidnaCSI project which was one of the 3 finalists for the 2021 Eureka Prize for Innovation in Citizen Science. Prior to returning to Australia for his PhD, Alan had a wide-ranging career in software development internationally with a later focus on mobile applications. His interests include software development, human-computer interaction and usability, biodiversity conservation and ecology, citizen science, machine learning and the combination of these areas for positive contributions to our shared world and knowledge systems. In his role, Alan is researching using collaborative intelligence to assist with biological collection management.
- Shahroz’s research interests are Continual and Lifelong Learning, privacy & security, and fairness in machine learning-based systems. He likes to explore human-centered AI and the social impact of machine learning-based methods with respect to privacy, security, and fairness. Shahroz has also worked on the applications of machine learning in various fields with respect to domain adaptation & generalization such as: 1. Intrusion detection and Anomaly detection in time-series data from vehicles and satellites. 2. Deepfake and synthetic media detection for privacy & security applications.
- Zhuowei did his PhD at University of Technology Sydney in deep learning and artificial intelligence. His research interests focus on federated learning, noisy label learning, weakly supervised learning, few-shot learning, and reinforcement learning. During his PhD, he has applied his research to various real-world applications in medicine, agriculture, and computer vision. In his role, Zhuowei will use collaborative intelligence for data cleaning and anomaly detection in completed control systems. He will be designing human-centered machine learning algorithms and workflows to enable better human-computer interaction/communication. He is also aiming to design websites and apps which can be used for human-computer interaction.