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Nicholas EvansView profile

Professor

• Linguistic Typology
• Historical Linguistics
• Contact Linguistics
• +12 more

Nicholas Evans is a Distinguished Professor in the Department of Linguistics at the School of Culture, History & Language, Australian National University. He is an ARC Laureate Fellow and serves as Director of the ARC Centre of Excellence for the Dynamics of Language (CoEDL), reflecting his leadership in integrating typology, descriptive linguistics, and cognitive science. His work spans linguistic diversity, endangered languages, and the interplay between language and culture. His research focuses on Australian and Papuan languages, with extensive fieldwork in remote communities. Key interests include linguistic typology, historical and contact linguistics, semantics, and the cognitive underpinnings of grammar. He has published foundational grammars and dictionaries of Kayardild, Bininj Gun-wok, and Dalabon, contributing significantly to the documentation of endangered languages. His recent publications reveal a strong trend in cross-linguistic studies of social cognition (SCOPIC), syntactic typology, and phylogenetic modeling of the Yam languages. These works integrate linguistic, cognitive, and genetic data, highlighting interdisciplinary approaches to understanding language evolution and human diversity. ARC Laureate Fellow Fellow of the Australian Academy of the Humanities (FAHA) Fellow of the Academy of the Social Sciences in Australia (FASSA) Fellow of the British Academy (FBA) Evans supervises research students and leads major projects such as 'The Wellsprings of Linguistic Diversity' and CoEDL, which involve extensive collaboration across institutions and disciplines. His grants include modeling Pacific creoles and phonemic analysis of Tok Pisin. He is also involved in digital archiving initiatives like PARADISEC and the Language Data Commons of Australia, ensuring long-term preservation of linguistic data. He leads and contributes to research teams focused on Southern New Guinea languages, paradigm syncretisms (PARABANK), and cultural heritage preservation. His work emphasizes reciprocal engagement with speech communities and training the next generation of language documenters.

• Linguistics
• Descriptive Linguistics
• Documentary Linguistics
• +7 more

Mark Post is a Senior Lecturer and Honours Coordinator in the Discipline of Linguistics at the Faculty of Arts and Social Sciences, The University of Sydney. He also serves as Deputy Head of School (Humanities) and is affiliated with the China Studies Centre, Sydney Southeast Asia Centre, and The Vere Gordon Childe Centre. His educational background includes a BA Hons from Wesleyan University, an MA from the University of Oregon, and a PhD from La Trobe University. Dr. Post's research focuses on describing and explaining the typologies and relationships among Greater Mainland Southeast Asian and Himalayan languages. His descriptive work relies exclusively on primary field data, with interests in improving fieldwork methodologies. He approaches linguistic explanation from a functional-diachronic perspective, viewing language as a dynamic process whose structures emerge from patterns of discourse and interaction. His extensive publication record reveals a strong focus on Eastern Himalayan languages, particularly Tani languages, with research spanning linguistic typology, historical linguistics, and language documentation. Key themes include classifiers, deixis, applicatives, and the ethnolinguistic prehistory of the Eastern Himalaya across more than 50 publications since 2004. His notable recognition includes: 2011 Recipient of the Association for Linguistic Typology's Pāṇini Award Dr. Post has supervised research students including Chintan SHETH on ethno-linguistic prehistory in the Eastern Himalaya. His grant history includes significant projects such as 'Pseudo grains and adaptiveness in the Eastern Himalayas' (ARC, 2023), 'Center for Cultural Linguistic Diversity - Eastern Himalayas' (Firebird Foundation, 2022), and 'A Documentation of the Upper Belt variety of Minyong (Adi)' (Endangered Languages Documentation Programme). He is a life member of the Galo Language Development Committee (Arunachal Pradesh, India) and actively participates in the Association for Linguistic Typology, Australian Linguistic Society, and North East Indian Linguistics Society.

Lexing XieView profile

Professor

• Machine Learning
• Social Media Analysis
• Computer Vision
• +7 more

Lexing Xie is a Professor in Computer Science at the Australian National University. He leads the ANU Computational Media Lab ( http://cm.cecs.anu.edu.au ) and the ANU Integrated AI Network. His work focuses on the intersection of machine learning, social media analysis, and multimedia understanding. Dr. Xie's research broadly focuses on innovative design and use of machine learning algorithms, especially on large-scale graph data and collective behaviour. His recent work spans several key areas: Popularity in social media -- understanding, predicting, and optimization Multimedia knowledge graphs, vision and language integration Humanising machine intelligence through better understanding of social dynamics His publications reveal a strong trend toward understanding information diffusion patterns in social media, particularly through visual content. He has made significant contributions to the study of visual memes, popularity prediction using point processes, and multimodal learning that connects vision with language. His work often bridges theoretical machine learning with practical applications in social media analysis. Dr. Xie has received recognition for his research, including an Honourable Mention at CSCW 2019 for his work on attention flow in online video networks. His research has been supported by collaborations with major institutions including IBM Research and Columbia University. As an advisor, Dr. Xie has mentored numerous students who have gone on to contribute significantly to publications in top-tier conferences. His lab, the ANU Computational Media Lab, serves as a hub for interdisciplinary research connecting computer science with social sciences.

Massimo PiccardiView profile

Professor

• Natural Language Processing
• Computer Vision
• Machine Learning
• +3 more

Massimo Piccardi is a Professor of Natural Language Processing (NLP), Computer Vision, and Machine Learning at the University of Technology Sydney (UTS) , where he has been since 2002. He currently serves as the Head of the School of Electrical and Data Engineering and leads the Big Data Analytics program at the Global Big Data Technologies Centre. His research focuses on advancing NLP, machine learning applications in healthcare, and cybersecurity in IoT systems. He has authored over 200 journal papers and conference proceedings, secured significant ARC and CRC grants, and holds the IEEE Computer Society Distinguished Contributor Award (2022). Education & Professional Roles: Joined UTS in 2002, progressing from Associate Professor (2002–2007) to Professor (2008–present). Serves as Associate Editor for IEEE Transactions on Big Data and Editor for Artificial Intelligence in Medicine. Active in professional societies including IEEE, ACL, and ALTA (President, 2023–2024). Research Interests: Core areas include NLP (translation, summarization, adversarial attacks), healthcare informatics (clinical NLP, health service analysis), and cybersecurity (IoT security, privacy-preserving systems). Cross-cutting themes include generative models, cross-lingual systems, and ethical AI. Grants & Projects: Principal Investigator on ARC Discovery/Linkage projects and CRC grants. Recent projects include controllable machine translation (Amazon), privacy-preserving digital agriculture, and STEM innovation (ASTRID project with NBN Co). Labs & Collaborations: Leads the UTS Global Big Data Technologies Centre, collaborating on projects like adversarial NLP attacks, medical machine translation, and secure IoT frameworks.

• Network Security
• Cryptographic Protocols
• Industrial Control System Security
• +7 more

Professor Ernest Foo is a distinguished academic at Griffith University's School of Information and Communication Technology, specializing in cybersecurity with a focus on industrial control systems and cryptographic protocols. With over 15 years of experience in computer networking, he has established himself as a leading expert in SCADA security and smart grid cybersecurity. His research has significant practical applications in critical infrastructure protection, and he has developed hands-on security training programs that have trained professionals from major Australian utilities and government agencies. Professor Foo's educational background includes: Bachelor of Engineering with Honours in Electronic and Computer Engineering from University of Queensland Doctor of Philosophy from Queensland University of Technology Professor Foo's research interests center around secure cryptographic protocols with specific applications in industrial control system security and cyber physical systems. His work spans SCADA security, smart grid protection, wireless sensor network security, and post-quantum cryptography. He has made significant contributions to understanding vulnerabilities in industrial protocols like Modbus and DNP3, and has pioneered the application of process mining and data mining techniques for attack detection in critical infrastructure systems. His research bridges theoretical security concepts with practical implementations in real-world industrial environments. Professor Foo's recent publications demonstrate a clear trajectory toward increasingly sophisticated security frameworks for critical infrastructure. His work shows a progression from foundational SCADA security research to advanced applications of artificial intelligence, machine learning, and formal methods in cybersecurity. A notable trend is the integration of zero trust principles with industrial control systems, alongside growing emphasis on post-quantum cryptographic solutions. His publications span high-impact journals and conferences in cybersecurity, with increasing focus on anomaly detection in cyber-physical systems and the application of graph-based machine learning techniques to network security challenges. Professor Foo's notable scientific achievements include: Best paper award at the 2nd International Cyber Resilience Conference for "Gap analysis of Intrusion Detection in Smart Grids" Professor Foo has secured significant research funding including an ARC Linkage grant with Powerlink Queensland focused on cyber security for electricity sub-stations. He currently leads multiple research projects including Westpac Micro-Credentials in Financial Crime Investigation, Digital Banking Micro-credentials with ANZ, and research on quantum-safe cryptography. As a dedicated educator, he serves as Program Director for multiple cybersecurity programs including the Master of Cyber Security, and has supervised numerous doctoral and masters students. His Cyber Security: Industrial Control System course, conducted annually from 2013-2018, featured innovative hands-on training with real-world participants from major Australian utilities. Professor Foo has been instrumental in establishing the SCADA security research laboratory with multiple vendor system miniatures running industrial PLCs. His work bridges theoretical security concepts with practical implementations, making significant contributions to the security of industrial control systems and critical infrastructure worldwide.

• Computer Vision
• Deep Learning
• Reinforcement Learning
• +7 more

Dr. Chandranath Adak is an Assistant Professor at the Department of Computer Science and Engineering, Indian Institute of Technology Patna (IIT Patna), and concurrently serves as a Visiting Fellow at the School of Computer Science, University of Technology Sydney (UTS), Australia. He holds a Ph.D. in Analytics from UTS (2019) and previously served as an Assistant Professor at Indian Institute of Information Technology Lucknow (IIITL) and the Centre for Data Science at JIS Institute of Advanced Studies, Kolkata. Education: Ph.D. (Analytics), University of Technology Sydney (2019) M.Tech., Computer Science and Engineering, University of Kalyani (2014) B.Tech., Computer Science and Engineering, West Bengal University of Technology (2012) Research Interests: His work spans Computer Vision, Deep Learning, Reinforcement Learning, Document Image Analysis, and AI-driven solutions for healthcare, forensics, and industrial automation. He has pioneered methods in biomarker detection using electrochemical sensors combined with ML models, handwriting analysis for educational and forensic applications, and anomaly detection in industrial systems. His research bridges theoretical advances with real-world applications, such as medical diagnostics and quality control systems. Publications: His recent work includes innovations in biosensor-based medical diagnostics, handwriting evaluation systems, and transformer networks for historical document analysis. These contributions reflect a focus on interdisciplinary applications of AI across healthcare, cultural heritage preservation, and industrial automation. Awards: Start-up Research Grant, SERB, India (2022) Dr. Kalam Doctoral Scholarship, UTS (2018) IEEE CIS Graduate Student Research Grant (2017) Senior Member, IEEE (2024) Teaching & Supervision: Taught courses at UTS including 'Introduction to Data Analytics' and supervised research in machine learning and computer vision. His mentorship emphasizes hands-on experience with AI tools and real-world problem-solving. Labs & Teams: Engaged in collaborative projects at UTS's CIBCI Centre and Griffith University's IIIS, focusing on computational intelligence and sensor-driven AI systems.

Sarath KodagodaView profile

Professor

• Robotics
• Mechatronics
• Sensor Fusion
• +6 more

Professor Sarath Kodagoda is a leading academic and researcher in robotics and mechatronics at the University of Technology Sydney (UTS), where he serves as Acting Director of the UTS Robotics Institute. He specializes in sensor fusion, data processing, and machine learning, with a focus on robotic solutions for infrastructure inspection and assistive technologies. His work includes developing the Robotic Remote Lab teaching facilities and founding the iPipes lab for wastewater infrastructure research. Affiliations: UTS Robotics Institute, Faculty of Engineering & IT Leadership Roles: President of the Australian Robotics & Automation Association, Ambassador for NSW Smart Sensing Network His research interests span robotics, sensor networks, and infrastructure robotics, with notable contributions to pipeline inspection and tactile sensing. He has published over 170 papers, attracted $6M+ in grants, and supervised 12 PhD students now working at Amazon, Google, and ABB. Awards include the UTS Medal for Teaching & Research Integration and multiple national/international innovation awards. Recent work emphasizes robotic systems for wastewater infrastructure, assistive robotics for vision-impaired individuals, and advanced sensor technologies. His articles highlight innovations in tactile sensing skins, 3D object detection (e.g., CaLiJD, LMIINet), and pipeline defect detection (PIPE-CovNet+). Grants: Contracts with Amplitel, NBN Co Ltd, and ARC Linkage Projects Labs/Teams: UTS Robotics Institute, iPipes Lab

• Natural Language Processing
• Knowledge Graphs
• Machine Learning
• +5 more

Dr. Sirui Li is a Lecturer at Murdoch University's School of Information Technology within the College of Science, Technology, Engineering and Mathematics. Her research focuses on Artificial Intelligence, Natural Language Processing (NLP), Machine Learning, Knowledge Graphs, Data Analysis, Temporal Data, and Multi-modal Models, with applications in medicine, agriculture, and mining. She collaborates with industry partners like BHP and has published in journals such as Food Chemistry and Knowledge and Information Systems , as well as conferences like ICSME and IJCNN. Education: Bachelor of Advanced Computing (Honours) in Computer Science at Australian National University Master of Computing (Specialising in AI) at ANU Ph.D. in Information Technology (AI) at Murdoch University Research interests include interdisciplinary applications of AI, such as clinical coding privacy solutions, disease spread modeling, and drug repurposing for pandemics. Her work emphasizes practical industry integration, demonstrated through awards like the 2024 EMNLP Best Demo Award and the 2023 Iron Ore Circuit Hackathon innovation prize. Professional roles include IEEE Western Australia Section committee membership, conference chair positions, and peer review for top journals. She actively mentors students pursuing Honours, Master's, or PhD projects in her areas of expertise.

Aditya JoshiView profile

Senior Lecturer

• Natural Language Processing (NLP)
• Machine Learning
• Cybersecurity
• +4 more

Dr. Aditya Joshi is a Senior Lecturer in the School of Computer Science & Engineering at the University of New South Wales (UNSW). He specializes in Natural Language Processing (NLP), with a focus on sarcasm detection, dialectal NLP, and ethical AI applications in public health and cybersecurity. He joined UNSW in 2023 following industry roles at SEEK, Notiv, and Fractal Analytics, where he developed NLP systems for recommendation engines and meeting analytics. His research has garnered over 3,000 citations (h-index 26) and secured $3.1M in grants, including Defence Trailblazer and Google exploreCSR awards. Education: Joint PhD (2018) from IIT Bombay (India) and Monash University (Australia); MTech in CSE (2011) from IIT Bombay. Research Interests: Making NLP models robust for non-native English speakers and the LGBTI+ community, algorithmic enhancements to transformers, and applications in public health, cybersecurity, and societal issues. His work spans epidemic intelligence (collaborations with EPIWATCH and IFCYBER), cybersecurity tools like AuditNet, and inclusive AI initiatives such as queer-inclusive workshops funded by Google. He designed UNSW's new NLP course (COMP6713) and co-authored a Wiley textbook on NLP. Notable grants include the A$1.4M 'Comprehensive Defence Data Platform' (Lead CI) and A$92K Google exploreCSR grant for benchmarking dialectal sentiment. His awards include the Best PhD Thesis from IITB-Monash and Best Paper accolades at FAccT 2023 and MoMM 2020. He supervises projects on kernel-based attention reformulation, prompt-based sarcasm detection, and multilingual small-scale LLMs. His service roles include Executive Committee Member at ALTA and arXiv moderator for computational linguistics.

• Audio Signal Processing
• Spatial Audio Technology
• Medical Engineering
• +3 more

Craig Jin is an Associate Professor at the University of Sydney, leading the CARlab (Computing and Audio Research Laboratory) and Spatial Audio Research initiatives within the School of Electrical and Computer Engineering. He holds a BS from Stanford University, an MS from Caltech, and a PhD from the University of Sydney. His work focuses on immersive audio technologies, biomedical signal processing, and assistive technologies for sensory augmentation. Research interests include spatial audio reproduction, binaural processing, acoustic sensing for accessibility, and machine learning applications in signal processing. Key contributions span HRTF interpolation, noise reduction algorithms, and acoustic touch systems for the visually impaired. Recent projects include real-time MRI analysis of vocal tract dynamics and sparse recovery techniques for sound field reconstruction. His publications span over 150 peer-reviewed articles in journals like IEEE Transactions on Audio, Speech, and Language Processing, and conferences such as ICASSP. He advises four current PhD/Master’s students on projects like predictive gesture tracking, voice disorder classification, and magnetic resonance imaging techniques.

• Cybersecurity and Privacy
• Data Management and Data Science
• Distributed Computing and Systems Software

Professor Jun Zhang is a leading academic in cybersecurity at Swinburne University, Australia, where he directs the Cybersecurity Lab. He has been honored as Australia's top cybersecurity researcher and instrumental in establishing Swinburne as a globally recognized cybersecurity research institution. His work includes high-impact papers and multi-million-dollar R&D projects, culminating in awards like the 2021 'Top Cybersecurity Research Institution' accolade. As course director of the Bachelor of Cyber Security, he pioneered an industry-driven teaching model with Deloitte and CSIRO, significantly boosting course enrollment. His collaborations extend to Adobe's Curriculum Innovation Program and the Australian P-TECH initiative, promoting STEM education and cybersecurity awareness. He supervises doctoral candidates and leads grants focused on AI-driven cybersecurity, smart home security, and blockchain-based edge computing. His research spans vulnerability detection, GAN forensics, IoT security, and privacy preservation in OSNs. Research interests include cybersecurity fundamentals, data science applications, and distributed systems. Notable achievements include the PTFix framework for Java vulnerabilities, the IoTFuzz smart home testing system, and CTI mining methodologies. Awards reflect his mid-career research excellence and industry partnerships. His grants with CSIRO and defense organizations emphasize real-world impact, addressing challenges from malware detection to adversarial machine learning. The Cybersecurity Lab and collaborative projects like Artchain demonstrate his commitment to bridging academia and industry. Professional activities include supervising over 20 HDR students and securing grants totaling millions. His work on blockchain-based edge storage (CSEdge) and SDCCP congestion control highlights innovation in networking. Future directions include advancing AI for design collaboration with CSIRO and enhancing privacy in smart energy technologies. His contributions span technical, educational, and community outreach domains, positioning him as a pivotal figure in cybersecurity's evolution.

• Cybersecurity
• Privacy-Preserving Technologies
• Internet of Things (IoT)
• +4 more

Dr. Kanchana Thilakarathna is a Senior Lecturer in Distributed Computing at the University of Sydney's School of Computer Science, and a member of the Centre for Distributed and High Performance Computing. They hold a PhD from the University of New South Wales (UNSW) and a B.Sc. Eng (Hons) from the University of Moratuwa, Sri Lanka. Prior to academia, they worked as a Research Scientist at CSIRO/Data61 and had industry experience as a Mobile Radio Network Engineer. Research Interests : Dr. Thilakarathna focuses on cybersecurity, privacy in mobile and IoT systems, mixed reality privacy, and distributed computing platforms. Their work emphasizes user-centric solutions like the Yalut social media app, which enables decentralized data sharing. Key themes include privacy-preserving techniques, edge computing, and secure federated learning frameworks. Recent Work : Recent articles (2023–2025) explore machine unlearning for large language models, federated learning security, and IoT network slicing using P4 programmability. Their work on synthetic video traffic generation (VideoTrain++) and drone detection (DronePrint) demonstrates cross-disciplinary innovation. Awards : Malcolm Chaikin Prize (2015), Meta Research Awards (2020/2022), and Heidelberg Laureate Fellowship (2019). Grants : ARC Research Hub for Future Digital Manufacturing (2024), NSW Defence Innovation Network Projects (2024/2021), and Facebook Research Awards (2022/2020). Students : Advising 4 current PhD students on topics like wireless trust establishment and machine unlearning. Labs/Teams : Part of the Centre for Distributed and High Performance Computing and Sydney Nano Institute.

Yang SongView profile

Associate Professor

• Computer Vision
• Deep Learning
• Multimodal AI
• +7 more

Yang Song is an ARC Future Fellow and Scientia Associate Professor at the School of Computer Science and Engineering , University of New South Wales (UNSW) . She serves as Associate Head of School (Research) and Co-Director of iCinema , focusing on AI and Computer Vision applications for social good. Education: BEng in Computer Engineering (Nanyang Technological University, Singapore), PhD in Computer Science (UNSW, 2013) Research Areas: Biomedical image analysis, human-centred AI, graph data modeling, neuro-symbolic learning, and AI trustworthiness. Her work develops domain-specific deep learning models for radiological segmentation, histopathology cancer analysis, and 3D reconstruction. Recent projects address explainability in LLMs, fairness in AI, and human-robot interaction frameworks. With over 200 peer-reviewed publications in top venues like CVPR , MICCAI , and NeurIPS , her research spans biomedical imaging, robotics, and general multimodal AI. Scientific Awards include: 2024: ARC Industrial Transformation Research Hub for Human-Robot Teaming 2023: Google Inclusion Research Award 2022: NHMRC Ideas Grant for computational brain imaging 2021: UNSW Engineering Research Excellence Award 2020: Scientia Fellowship (UNSW) 2019: ARC Future Fellowship She supervises 24 current PhD/MPhil students and has graduated 15 advisees, including placements at Harvard University and Siemens Healthineers. Her grants include collaborations with Surf Life Saving Australia and industry partnerships for AI-driven solutions.

Richard GarnerView profile

Lecturer

• Category Theory
• Algebraic Structures
• Homotopy Type Theory
• +3 more

Richard Garner is a lecturer at Macquarie University's School of Mathematical and Physical Sciences, Faculty of Science and Engineering. He specializes in teaching mathematics to engineering and computing students in units like MATH2055 and MATH1007, focusing on problem-solving and real-world applications. His teaching philosophy emphasizes authentic mathematical experiences, blending abstract concepts with practical examples, such as connecting multivariable calculus to AI technologies. School: School of Mathematical and Physical Sciences University: Macquarie University Teaching Areas: Mathematics for engineering and computing, convolution, multivariable calculus Richard won a Student Nominated Award in the 2023 Vice Chancellor’s Learning and Teaching Awards, reflecting his commitment to student-centered education. He prioritizes clarity in course design, using visual tools and accessible materials to enhance learning, and fosters a supportive environment where students feel comfortable asking questions. Key Teaching Strategies Organized iLearn layouts following Macquarie University's standards Multiple formats for lecture materials (diagrams, color-coded slides) Weekly task clarity and real-world problem framing Live worked examples and transparent success criteria His research spans category theory, computational effects, and homotopy theory, with publications on topics like comodels, monoidal bicategories, and enriched categories. Richard's work bridges abstract mathematics with applications in computer science and logic. Scientific Awards 2023 Vice Chancellor’s Learning and Teaching Award (Student Nominated) Students praise his ability to make complex concepts intuitive, his enthusiasm for mathematics, and his dedication to explaining the 'why' behind the subject. His teaching design, including time-sensitive banners and structured weekly content, has been highlighted as exemplary.

Thilina HalloluwaView profile

Lecturer

• Human-Computer Interaction
• Educational Technology
• Artificial Intelligence
• +4 more

Dr. Thilina Halloluwa is a Teaching Focused Lecturer in the Department of Human-Centred Computing at The University of Queensland (UQ). He holds a PhD in Human-Computer Interaction from Queensland University of Technology (2019) and a Computer Science undergraduate degree from the Sri Lanka Institute of Information Technology. With over 15 years of academic and industry experience, his research emphasizes real-world impact in education technology, financial inclusion, smart agriculture, and HCI. Educational Background: PhD in Human-Computer Interaction, Queensland University of Technology (2019) Bachelor of Computer Science, Sri Lanka Institute of Information Technology Research Interests: Education for All: Leveraging technology to enhance collaborative learning and social experiences in education. Human Money Interaction: Designing ethical AI solutions for financial services, particularly for underserved communities. Smart Agro: Developing AI-driven tools for crop disease detection, yield optimization, and precision agriculture. Software Project Estimation: Improving effort estimation accuracy through explainable AI (Metrix project). Key Contributions: Developed UrbanAgro (tomato disease detection) and BellCrop (bell pepper disease datasets). Pioneered Dhana Labha , a financial management tool for rural Sri Lankan communities. Advanced online exam proctoring systems for low-resource settings. Previous Roles: Lecturer at University of Sydney (2023) Senior Lecturer at University of Colombo (2013–2023) Lab/Team Affiliations: Smart Agro Project: AI-driven agricultural solutions Metrix Initiative: Software project estimation frameworks