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An experienced researcher with a multi-disciplinary training background, Ning is a Research Fellow in the Inclusive Education and Employment research group at ISSR.

Ning has strong expertise in delivering multi-year collaborative research projects, including complex mix-method evaluation projects (e.g., the national evaluation of the Australian Government’s Paid Parental Leave scheme and a follow-up ARC Linkage Project, funded by the Department of Social Services).

Ning has also significantly contributed to a number of projects funded by Department of Education at federal and state levels and though various research grant schemes, utilising data from LSIC, LSAC, LSAY and HILDA to investigate the impacts of a range of factors (e.g., individual, family, school and community) on disadvantaged students’ outcomes, such as socio-emotional wellbeing, academic achievement, university participation and graduate labour market outcomes.

Ning has specialist expertise in literature review, constructing theoretical framework to guide data analyses, data preparation, management, screening and analysis, which she has applied to great effect in these projects. Ning has a track record of successfully delivering accessible and policy informing documentation. She also specialises in longitudinal data analysis, and has published extensively on school climate, student engagement, and long-term education and labour market outcomes for students from equity groups.

Yuwei Xiang holds a bachelor's degree in Food Science and Technology (2019) from the University of Shanghai for Science and Technology (USST, China). She earned her PhD in Food Microbiology (2024) from the University of Queensland (Australia). After completing her PhD, she pursued an academic career as a postdoctoral researcher in the lab of Prof. Mark Turner. Her research specializes in lactic acid bacteria (LAB) and their applications in food fermentation and biotechnology, with a focus on exploring the genetic mechanisms of LAB to enhance osmoresistance, particularly under high c-di-AMP conditions.

Research Areas

• Lactic Acid Bacteria (LAB): I study the roles of LAB in food fermentation, with a focus on their genetic and metabolic responses to environmental stresses.
• Osmoresistance and c-di-AMP Pathways: My research investigates the genetic regulation of osmoresistance in LAB, aiming to improve their robustness in industrial fermentation processes.
• Current Projects
• Investigating LAB from plant-based sources for potential applications in food fermentation and innovative food ingredients production.

• Keywords: Lactic acid bacteria, food fermentation, osmoresistance, c-di-AMP, gene regulation, biotechnology.

My research has centred on elucidating the neural mechanisms underlying complex sensorimotor functions and motor learning through an interdisciplinary blend of cutting‐edge neuroscience and advanced computational methods. Early in my career, I pioneered the use of intraoperative microelectrode recording and stimulation to map the basal ganglia and thalamus, enabling precise modulation of motor functions via deep brain stimulation. Driven by my research interests, I embarked on a PhD journey in Prof. Tim Murphy’s lab at the University of British Columbia (UBC) where I was honoured with the Scholarships from the International Alliance of Translational Neuroscience. Prof Tim Murphy’s lab develops cutting-edge neurophysiological techniques to probe the complex information flow in the brain during sensorimotor processing. I co-led the development of intact skull chronic windows for mesoscopic wide‐field imaging in awake mice, a now widely adopted technique described in Silasi, Xiao et al., 2016 (co-first author, 241 citations). I also pioneered methodologies for mapping functional connectivity between cortical mesoscopic networks and subcortical single spiking neurons (Xiao et al., 2017. 151 citations). My research integrates artificial intelligence and computer vision to automate the exploratory analysis of the rich neural and behavioural video datasets. I have combined chronic, simultaneous wide‐field imaging, multi‐site electrophysiology using the Mesotrode (Xiao et al., 2023), and advanced machine learning tools (e.g., MesoNet, Xiao et al., 2021, Nat. Commun.) to capture and quantify large‐scale neuronal spatiotemporal patterns associated with a specific motor act, such as self-initiated running, reaching and orofacial movements. I also co-developed a 3D virtual mouse model that translates 2D behavioural videos into a 3D model space, enabling more detailed analysis of mouse behaviours, and established a standardised behavioural framework to disentangle movement dynamics from unrelated factors. This work was featured on the cover of Nature Methods (Bolaños, Xiao et al., 2021. co-first author). I contributed to the development of real-time systems for selectively tracking mouse body movements. This work paves the way for advanced "closed loop" brain-computer interfaces, facilitating understanding of the neural basis of behavioural control (Forys, Xiao et al., 2018, 2020. co-first author). My recent work in the Balbi lab at the University of Queensland on continuous auditory feedback demonstrates that real-time, movement-coded auditory cues can significantly accelerate fine motor skill learning in mice (Xiao and Balbi, eNeuro), exemplifying how augmented sensory input can promote motor performance. Similarly, my contributions to real-time EEG-based asynchronous error prediction in human–robot interaction using machine learning (Xiao et al., under revision) and the development of MesoGAN—a Generative Adversarial Network (GAN) framework that generates realistic behavioural videos from neural decoding of wide-field cortical calcium dynamics (Xiao et al., under revision)— highlighting my commitment to bridging neuroscience with adaptive robotics and AI.

Dr Zixi Xie is a Postdoctoral Research Fellow in the School of Chemical Engineering and leads a subgroup within the Functional Materials Engineering (FME) Lab. She earned her PhD in Chemistry from the University of Sydney, where she focused on metal-organic frameworks and stimuli-responsive materials, investigating properties of magnetism, negative thermal expansion, and negative linear compressibility.

Her current research explores a wide range of advanced materials, including amorphous and glassy functional membrane materials, light-emitting materials, and piezoelectric materials. These materials are being developed for diverse applications such as gas separation, energy storage, next-generation light-emitting devices, and sensors. Her work aims to combine fundamental material design with application-driven innovation. She was awarded the AINSE Early Career Researcher Grant in recognition of her promising research contributions.

Dr Xie is also actively engaged in research translation and commercialisation. Her innovative work on flexible thin-film lighting devices has received Pathfinder funding from UniQuest to support R&D, raise technology readiness levels (TRL), and advance the commercialisation of next-generation LED technologies. She participated in CSIRO’s ON Prime program to build industry connections and explore market pathways.

Yuan Xu completed a Bachelor of Engineering degree (Chemical and Material) from the University of Queensland in 2015. After that, he started his PhD in the research field of colloidal science, rheology and chemical engineering, supervised by Professor Jason Stokes. He has continued in UQ as postdoctoral research fellow since 2019, at which, he has contributed to multidisciplinary projects including viscoelastic lubrication of soft matter systems, and programming structural anisotropy in nanocellulose hydrogels. His research capability focuses on the area of rheology, colloidal science/ physical chemistry, material/physical science, soft matters/complex fluids, and tribology/lubrication.

Dongming Xu is Associate Professor in Business Information Systems at the UQ Business School. She holds a PhD from the City University of Hong Kong in Information systems.

Dr Xu’s research focuses on the confluence of information technology use and information technology innovation to reach a deep understanding of how information systems are used and how information systems influence the society. In recent years, she primarily works on IT entrepreneurship that focuses on the understanding of hi-tech start-ups development and the relationship between IT innovation and business performance. In the meantime, she has been working in the area of social media use in business, such as, disaster management, eFinance, eHealth. Thus research interests lay in the areas of message transmission, information quality control, and decision making infrastructure etc.

The other research interests include decision making and business intelligence on a variety of contexts, such as theoretical foundations, applications, and technologies, such as intelligent agents, data mining, etc. In particular, she is working on eFianance applications, web-server-agent-based family wealth management systems, decision support systems for securities exception management, knowledge management systems and disaster management systems. Her research usually combines theoretical model building, laboratory and field experiments and the development of prototype systems.

Dongming's previous research outputs have been published in more than 100 top tier journals and conference proceedings, such as IEEE Transaction on Knowledge and Data Engineering, Information and Management, Decision Support Systems, and the Expert Systems with Applications, International Conference on Information Systems, etc. Dr. Xu as a Chief-Investigator or a Co-Investigator received multiple grants from different research agencies, such as, Hong Kong Government Research Grant Council, The National Natural Science Foundation of China, The University of Queensland, City University of Hong Kong, Shenzhen (China) State Research Council.

Currently, she is an Associate Editor with Information & Management, Journal of Electronic Commerce Research, Australasian Journal of Information Systems, among others.

Seema Yadav is Postdoctoral Fellow with Centre for Animal Science at Queensland Alliance for Agriculture and Food Innovation. Her Ph.D. project was focused on implementing genomic selection to accelerate genetic gains in Australian sugarcane breeding programs. Before joining the UQ, she was working as an international consultant with the Quantitative Genetics cluster at the International rice research institute, Philippines. She has double master's degrees in Mathematics and Statistics. Her research interests include developing novel genomic prediction methods, specifically their ability to capture G x E interaction effects. She had deep interest in machine learning models and optimization techniques within this domain.