Find Academic Supervisors

A/Prof Xanthe Spindler serves as Discipline Leader in Forensic Science within the School of Mathematical and Physical Sciences at the University of Technology Sydney (UTS). With over 15 years of dedicated academic service at UTS, she has established herself as a leading researcher in forensic trace evidence analysis and latent fingermark detection. Dr. Spindler's educational journey began with a Bachelor of Science (Forensic)(Honours) from the University of Newcastle (2006), followed by a PhD in Applied Chemistry from the University of Canberra (in collaboration with UTS Centre for Forensic Science). Her academic appointments at UTS include sessional academic (2010-2012), Chancellor's Postdoctoral Research Fellow (2012-2016), and Lecturer (2016-present). Her research program focuses on understanding and improving methods for detecting latent fingermarks on forensic evidence and at crime scenes. She investigates factors affecting detection success rates and develops better methods using new biomolecular and chemical technologies. Dr. Spindler supervises a collaborative research group working on diverse forensic problems spanning fingermark detection, fingerprint identification, criminalistics, and questioned document examination. Her publication record reveals an evolution from fundamental studies of fingermark composition to practical applications of advanced detection techniques. Recent work demonstrates expanding focus to trace evidence beyond fingerprints, including fiber transfer dynamics in assault scenarios, submersion effects on evidence, and bacterial profiling for donor characteristics. She has made significant contributions to forensic science literature, particularly in nanoparticle applications for fingermark detection. Dr. Spindler holds memberships in the International Fingerprint Research Group and the Australian and New Zealand Forensic Science Society. Her research collaborations include Australian Federal Police, NSW Police, Victoria Police, and international universities. She has successfully secured substantial research funding from the Australian Research Council, US National Institute of Justice, and Defence Science and Technology Group for projects including 'Development of next-generation fingermark lifters' and 'Next-generation latent fingermark detection using functional nanomaterials.' Her work bridges academic research with practical forensic applications, directly impacting crime scene investigation methodologies.

• Forensic Science
• Fingermark detection
• Fingerprint identification
• +6 more

Sam Elman is a Postdoctoral Research Fellow at the School of Computer Science, University of Technology Sydney. His research focuses on Quantum mechanics and quantum information theory Mathematical aspects of free-parafermion solvability Topological entanglement entropy in multiple dimensions Optimal scheduling of quantum graph states Recent work includes publications on path decomposition algorithms for quantum computation and integrability of spin systems via frustration graphs. He is available for Masters Research or PhD student supervision. He is affiliated with the Faculty of Engineering and Information Technology Centre for Quantum Software and Information (QSI) UTS Research Centre

• Quantum Mechanics
• Quantum Information Theory
• Classical Mechanics
• +3 more

Professor Zhengfeng Ji is an Adjunct Professor at the Centre for Quantum Software and Information within the Faculty of Engineering and Information Technology at the University of Technology Sydney. His research focuses on quantum computation, quantum information theory, and quantum communication. Quantum complexity theory Quantum algorithms Quantum network optimization Entanglement characterization Recent publications highlight advancements in quantum network routing frameworks and quantum proof systems. He has secured multiple grants including the Sydney Quantum Academy Postdoctoral Fellowship and ARC Discovery Projects. Quantum Exponential Time Hypothesis Quantum PCP Conjecture Post-quantum Cryptographic Protocols Scientific awards include: SQA Scholarship Sydney Quantum Academy Postdoctoral Fellowship SUSTech Scholarship

• Quantum computation
• Quantum information, computation and communication

Dr. Tan Mei Hooi serves as a Senior Lecturer in the School of Engineering, Computing and Built Environment at UOW Malaysia KDU Penang University College, where she has been a dedicated faculty member since December 2007. With over 20 years of combined secondary and tertiary teaching experience, she specializes in Engineering Mathematics and Inventive Problem Solving in Engineering curriculum development. Her academic journey includes a Doctor of Education in Science Education from Universiti Sains Malaysia (2022), a Master of Science in Teaching Mathematics (2006), and a Bachelor of Science with Education in Mathematics from Universiti Teknologi Malaysia (2000). Dr. Tan's research focuses on TRIZ methodology application in engineering education, STEM outreach, and the development of 21st century learning experiences aligned with Industry 4.0 goals. As a MyTRIZ Level 3 Practitioner and certified HRDF trainer, she has pioneered the integration of inventive problem-solving techniques into engineering curricula, examining how these approaches enhance both hard and soft skills development among students. Her work bridges theoretical knowledge with practical application through workshops that foster creativity and innovative thinking. Her scientific contributions have been recognized through multiple awards including UOW Malaysia's 15-Year Long Service Award (2022), the MyTRIZ Competition 2018 Second Runner-up position, and the 2015 Best Lecturer award. Additional recognitions include Tokens of Appreciation from various schools for Science Carnival exhibitions and professional membership in the Association for Science and Mathematics Education Penang (ASMEP). Dr. Tan actively mentors through her role as Advisor to the UOW Malaysia Engineering Society since 2013 and demonstrates exceptional commitment to educational outreach, having conducted over 100 workshops for secondary schools nationwide since 2015. Her grant-funded initiatives include the Creative Thinkers Creativity Bowl Workshop series, STEM showcases at TechDome Penang, and specialized TRIZ training programs for both students and educators, collectively impacting thousands of learners across Malaysia. Her leadership extends to national and international collaborations, including serving as Malaysia's delegate to Friedrich-Alexander University in Germany for the USM-FAU Germany Empower Collaboration project in 2016 and acting as conference reviewer for MyTRIZ events. Dr. Tan's comprehensive approach to engineering education combines rigorous academic scholarship with practical skill development and community engagement.

• TRIZ Methodology
• Engineering Mathematics
• STEM Education
• +4 more

Professor May El Barachi is the Head of School of Computer Science at University of Wollongong in Dubai. An Egyptian Canadian Computer Scientist and Smart Technology Expert, she holds degrees in Telecom Engineering, Computer Engineering, and Computer Science. Professor El Barachi has established herself as a leader in smart technologies research with international standing. Her academic journey includes a B.Sc. in Electronics and Communication Engineering from the Arab Academy for Science and Technology, followed by an M.A.Sc. in Computer Engineering and a Ph.D. in Computer Science from Concordia University, Canada. As Head of School, she has spearheaded the design of an innovative master's program in digital transformation and revamped computer science majors to enhance student learning experiences. Professor El Barachi's research spans multiple domains including Smart Transportation, Smart Cities, BioTech, and Inclusion & Innovation. Her work focuses on developing practical solutions to real-world problems through the integration of legacy and emerging technologies. She has established collaborations with international universities in Canada, the UK, and Lebanon, as well as industry partners including Rogers Canada, Du, Cleveland Clinic Abu Dhabi, UAE Ministry of Health, and Talk5. She has raised more than 4 Million Dirhams in research funding over the past five years and has received the exemplary faculty award twice (2013 and 2015) for excellence in teaching, research, and service. Professor El Barachi is also a certified trainer in emerging technologies including Blockchain, Big Data, Data Science, Internet of Things, and Artificial Intelligence, holding numerous industry certifications from IBM, EC Council, and CISCO. Exemplary faculty award (2013, 2015) EC Council Certified Ethical Hacker CISCO CCNA Certified Instructor Multiple IBM certifications across Big Data, Blockchain, AI, and Cloud Computing As an educator, Professor El Barachi employs a student-centric, pragmatic teaching approach that emphasizes real-world relevance through examples, systems, and case studies. She has developed and taught courses in Distributed Systems, Information Security, Networking, Mobile Computing, and Emerging Technologies, fostering an interactive and engaging learning environment for her students.

• Smart Technologies
• Computer Science
• Information Technology
• +13 more

Irene Yang is a Lecturer in Engineering Mathematics at Swinburne University of Technology, Sarawak's Faculty of Engineering, Computing and Science. Holding dual degrees from the University of Science Malaysia (BScEd Mathematics and MSc Teaching of Mathematics), she brings extensive expertise in technological innovation for STEM education. Education: Master of Science (Teaching of Mathematics), University of Science Malaysia Bachelor of Science in Education (Mathematics), University of Science Malaysia Her research pioneers screencast video applications in engineering mathematics education, examining how student-produced content transforms learning engagement. Current investigations focus on blended learning frameworks where learners create explanatory videos for complex optimization problems, turning abstract concepts into tangible digital artifacts that enhance peer-to-peer knowledge construction. Publication trends reveal consistent focus on technology-mediated pedagogy since 2014, with recent work analyzing perception data from video-based interventions. Her scholarship bridges educational theory and classroom practice, emphasizing measurable learning outcomes through innovative assessment methods in mathematics education. Awards and Recognition: Highly Commended, Swinburne Teaching Excellence Award (2020) Best Paper Award, Taylor's Teaching Conference (2016) Dual Shortlistings, Wharton-QS Reimagine Education Awards (2015) Malaysian Mathematical Science Society membership Funded by Swinburne Sarawak Research Grants, her projects involve collaborative classroom research with engineering students as co-investigators. She mentors future educators through hands-on technology integration projects, maintaining active supervision of undergraduate research while developing scalable video pedagogy models for STEM disciplines. Her international conference presentations demonstrate commitment to global educational advancement. Though not leading a formal laboratory, she cultivates dynamic classroom research environments where teaching innovations become publishable scholarly contributions. Her approach transforms traditional mathematics instruction into interactive digital experiences, positioning students as knowledge creators within engineering education ecosystems.

• Mathematics Education
• Educational Technology
• Teaching and Learning
• +3 more

Mehdi Neshat is a Visiting Scholar at the Data Science Institute within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He holds a PhD in Engineering from the University of Adelaide (2016-2020) and has extensive experience as a research data scientist specializing in computational optimization methods applied to complex engineering and healthcare problems. His educational background includes: PhD in Engineering, University of Adelaide (2016-2020) Neshat's research focuses on developing and applying advanced computational methods to solve real-world challenges. He specializes in evolutionary algorithms, swarm intelligence, and machine learning techniques for optimizing renewable energy systems, particularly wave and wind energy converters. His work extends to healthcare applications including genomic analysis and medical diagnostics, as well as structural engineering optimization and smart building energy management. His interdisciplinary approach bridges theoretical algorithm development with practical implementation across multiple domains, demonstrating exceptional versatility in computational problem-solving. Analysis of his recent publications reveals a strong trend toward sophisticated ensemble methods and hybrid optimization approaches that combine multiple algorithms to overcome limitations of single-method approaches. His research shows increasing sophistication in handling multi-objective optimization problems, particularly in renewable energy systems where trade-offs between power output, system stability, and cost must be balanced. The geographical focus of his energy research centers on Australian coastal regions, with practical applications for wave and wind farm deployment. Neshat has received significant recognition for his research contributions: Back-to-back Best Paper Prizes at the GECCO conference (2019 and 2020), a CORE A-ranked international optimization and machine learning conference His collaborative research spans multiple institutions and disciplines. Previously, he served as a Postdoctoral Research Associate with the Genomic analysis team at the Australian Centre for Precision Health, Cancer Research Institute, University of South Australia, and as a Senior Research Fellow at the Center for Artificial Intelligence Research and Optimization, Torrens University Australia. His work demonstrates consistent engagement with multidisciplinary teams across engineering, computer science, and healthcare domains, with a strong emphasis on practical implementation of theoretical methods. At UTS, Neshat contributes to the Data Science Institute's research agenda, focusing on applying advanced computational methods to complex real-world problems where traditional analytical approaches fall short. His current work continues to expand the boundaries of optimization techniques for renewable energy systems while exploring new applications in healthcare analytics and structural engineering.

• Machine Learning
• Evolutionary Algorithms
• Swarm Intelligence
• +5 more

Dr. Yujie Qi is a Senior Lecturer at the University of Technology Sydney (UTS), specifically within the School of Civil and Environmental Engineering. She serves as Program Co-leader of the UTS Transport Research Center (TRC) within the Faculty of Engineering and Information Technology. Her academic journey began with Bachelor's and Master's degrees in (Civil/Geotechnical) Engineering at Zhengzhou University, China, followed by a PhD from the University of Wollongong (UOW), Australia, which she completed in 2018 with an 'Industry Engagement Award' and 'Special Commendation' for outstanding doctoral research. Dr. Qi's educational background includes: PhD in Geotechnical Engineering from University of Wollongong (2014-2018) Master's degree in Geotechnical Engineering from Zhengzhou University (2011-2014) Bachelor's degree in Engineering from Zhengzhou University (2007-2011) Bachelor of Art from Zhengzhou University (2009-2011) Dr. Qi's primary research focuses on soil dynamics, granular mechanics, geo-mathematical modeling, and large-scale dynamic testing , with particular emphasis on the innovative use of waste and marginal materials in transport infrastructure . Her pioneering work on energy-absorbing materials has revolutionized approaches to railway track design, specifically through the utilization of waste rubber to enhance rail substructure performance. She has conducted extensive investigations on three key waste materials: discarded coal wash from Australian coal mines, steel furnace slag from steel manufacturing plants, and rubber crumbs derived from off-the-road vehicle tires. For the first time, she identified optimal ratios of these waste materials to enhance railway longevity and developed energy-absorbing concepts to analyze and interpret track substructure deformation mechanisms. Dr. Qi has been instrumental in introducing energy-absorbing materials to enhance rail substructure performance, and her research has been recognized by national and international firms including Bridgestone and Sydney Trains. The recent publications by Dr. Qi demonstrate a strong focus on sustainable transportation infrastructure using recycled materials, particularly rubber-based solutions for railway engineering. Her work spans from fundamental material characterization to large-scale field testing, with particular emphasis on energy absorption properties, track performance improvement, and circular economy applications. The research consistently bridges theoretical modeling with practical engineering applications, showing how waste materials can be effectively repurposed to solve infrastructure challenges while reducing environmental impact. A notable trend in her recent work is the integration of machine learning methodologies with traditional geotechnical approaches to predict material behavior and optimize infrastructure design, as evidenced by her publications on hybrid ML methodologies for predicting rail ballast breakage. Dr. Qi's exceptional contributions have been recognized with numerous prestigious awards: John Carter Young Professional Award (2024) Sir M. Visvesvaraya Award from ICE (UK) (2023) Bright Spark Lecture Award from ISSMGE (2018) Best Paper Award at the 9th Asian Young Geotechnical Engineers Conference (2019) Award for Industry Engagement Impact from University of Wollongong (2017) Examiners' Commendation for Outstanding Thesis (2018) Dr. Qi has successfully secured significant research funding, including ARC Discovery Projects (DP220102862) titled 'The Role of Energy Absorbing Rubber Grid on Ballast Track Performance' (2022-2025) and ARC Linkage Projects (LP200200915) titled 'Field Data based Predictive Maintenance and Enhanced Track Design Procedure' (2021-2025). She actively supervises research students across multiple projects focused on energy-absorbing materials, track performance, and waste material applications in transportation infrastructure. Her teaching portfolio includes subjects such as Problematic Soils and Ground Improvement Techniques, Road Engineering Practice, Mechanics of Solids, and Soil Behaviour, where she has served as coordinator, teacher, and tutor. As Program Co-leader of the UTS Transport Research Center, Dr. Qi collaborates with a multidisciplinary team of researchers focused on innovative transportation solutions. Her laboratory work involves advanced testing facilities including large-scale process simulation testing apparatus (PSTA) and prototype cubic triaxial facilities for evaluating material behavior under cyclic loading conditions. The research group has conducted significant field trials, including a fully instrumented track section at Chullora, New South Wales, to validate laboratory findings in real-world conditions. Dr. Qi's team maintains strong industry connections with organizations such as Sydney Trains and Bridgestone, ensuring practical relevance and implementation potential for their research outcomes, while promoting sustainable and cost-effective practices in transportation infrastructure.

• Geotechnical Engineering
• Soil Dynamics
• Granular Mechanics
• +6 more

Soudabeh Shemehsavar serves as a Lecturer in the College of Science, Technology, Engineering and Mathematics at Murdoch University, Australia, where she contributes to statistical and data science education. Her academic foundation includes: Bachelor of Science in Statistics from Shiraz University (1993-1997) Master Degree in Statistics from Amirkabir University of Technology (1998-2000) Doctor of Science in Applied Mathematics from Amirkabir University of Technology (2001-2007) Her research integrates theoretical and applied statistics across diverse domains: Development of stochastic degradation models for mechanical systems to optimize maintenance and reliability Advanced survival analysis techniques including cure models for biomedical lifetime data Random polynomial applications in civil infrastructure design, traffic pattern prediction, and economic modeling Theoretical extensions of Poisson-Dirichlet processes Current focus on ensemble learning methods for high-dimensional classification problems Her work demonstrates strong interdisciplinary connections between statistical theory and practical implementations in engineering, medical research, and economic systems.

• Stochastic Processes
• Survival Analysis
• Reliability Engineering
• +4 more

Yohei Sakurai serves as an Associate Professor in the Department of Mathematics at Saitama University, Japan, with his academic base at the university's campus in Saitama City (255 Shimo-Okubo, Sakura-ku, Saitama 338-8570). His research program centers on differential geometry, with concentrated expertise in Riemannian geometry and geometric analysis. Key investigation areas include Comparison geometry, Spectral geometry, Isoperimetric problems, Metric measure geometry, Optimal transport theory, Geometric flows, Harmonic maps, and Minimal surfaces. These interconnected fields address fundamental questions about geometric structures, curvature constraints, and transformation dynamics in mathematical spaces.

• Differential Geometry
• Riemannian Geometry
• Geometric Analysis
• +8 more

Dr. Matt Felicetti is a dedicated Lecturer in Engineering at La Trobe University's Bendigo campus, specializing in robotics, electronics, and artificial intelligence. He holds a PhD in randomized artificial intelligence for industrial applications and a bachelor's degree in computer systems engineering with top honors. Matt plays a pivotal role in enhancing the engineering capstone program and teaches subjects including Robotic System Design, Advanced Research, and Advanced Engineering Innovation. His educational background includes: PhD in Randomized Artificial Intelligence for Industrial Applications, La Trobe University (2019-2022) Bachelor of Engineering (Computer Systems) with First Class Honors, La Trobe University (2013-2016) Advanced Diploma of Electronics, Swinburne University of Technology (2011-2012) Matt's research primarily focuses on collaborative industry partnerships through the RAMPS R&D group, implementing innovative engineering solutions using electronics, sensors, embedded systems, robotics, AI, and algorithm design. He has a particular interest in field robotics and machine vision in agriculture. Beyond industry applications, Matt specializes in randomized artificial intelligence algorithms, particularly Stochastic Configuration Networks, with a focus on optimizing these algorithms for industrial environments. His research extends to low-level computing aspects including binary operations, data encoding, and hardware implementation on FPGAs or small embedded devices. His recent publications demonstrate a strong progression from theoretical algorithm development to practical implementations in specific industry contexts, with increasing attention to hardware implementation and agricultural robotics applications. The research shows a clear trajectory toward real-world industrial problem solving. Matt has received numerous scientific awards and recognitions: David Myers Medal Nancy Millis Medal SEMS Teaching Award for Industry Relevance and Student Engagement David Myers Research Scholarship D.M. Myers University Medal Fellow of the Higher Education Academy (FHEA) In terms of academic service, Matt serves as an Associate Editor for the journal Industrial Artificial Intelligence and has conducted peer reviews for Neural Computing and Applications, IEEE Transactions on Industrial Informatics, and Information Sciences. He currently leads the funded research project "Robotic Based Sewer Pipe Condition Assessment" through SmartCrete CRC (2024-2027). His teaching portfolio includes coordinating Robotic System Design, Advanced Research, and Ideas for Innovation courses. Matt is an active member of the RAMPS R&D group, which focuses on crafting innovative solutions for industry-specific problems. His work bridges academic research with practical industry applications, particularly in agricultural robotics and industrial AI implementation, demonstrating strong collaboration with researchers like Ross R, Wang D, and Putland S.

• Robotics
• Artificial Intelligence
• Electronics
• +6 more

Christopher Drovandi is a Professor at Queensland University of Technology and a Chief Investigator in the Australian Research Council Centre of Excellence for Mathematical and Statistical Frontiers (ACEMS). His research spans statistical methodology development and applications across diverse fields including ecology, biomedicine, and environmental science. Dr. Drovandi's primary research interests focus on advancing Bayesian statistical methods, particularly in the areas of: Approximate Bayesian Computation (ABC) and likelihood-free inference Synthetic likelihood methods Sequential Monte Carlo algorithms Bayesian experimental design Statistical computing for complex models His recent work demonstrates a strong emphasis on addressing model misspecification in Bayesian inference, developing robust computational methods for intractable likelihoods, and applying advanced statistical techniques to solve real-world problems in ecology, biomedicine, and environmental science. Drovandi has made significant contributions to both the theoretical foundations and practical applications of modern Bayesian statistics. Dr. Drovandi has received research funding through multiple Australian Research Council grants and collaborates extensively with researchers across disciplines. His work has been published in top-tier statistical and interdisciplinary journals including the Journal of the American Statistical Association, Bayesian Analysis, and PLOS Computational Biology.

• Bayesian Statistics
• Approximate Bayesian Computation
• Statistical Modeling
• +6 more

Baki Kocaballi serves as a Senior Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS), a position held since May 2020. He concurrently maintains an Honorary Research Fellow appointment at Macquarie University since the same date. His academic foundation includes a PhD in Interaction Design from the University of Sydney, completed in 2013. His research critically examines the convergence of artificial intelligence, digital health, and interaction design, with particular emphasis on multimodal and conversational systems. This interdisciplinary work manifests through projects like the Activating the Everyday Research Program and investigations into spatial collective intelligence developed with UNSW colleagues through the Smart Cities Research Cluster. His methodological approach integrates topological thinking—a conceptual framework he actively develops through dedicated research blogging—to challenge conventional spatial and relational assumptions in design. Analysis of his recent publications reveals dominant trends in healthcare AI applications, particularly conversational agents for clinical settings, digital scribes for documentation reduction, and context-aware systems for chronic disease management. These works consistently bridge technical AI development with human-centered design principles, reflecting his core commitment to usable and trustworthy digital health solutions. Best DC Paper Award at European Conference of Cognitive Ergonomics Doctoral Consortium (2010) Best Poster Award at Research Expo'09, University of Sydney (2009) His teaching portfolio includes advanced subjects in Design Thinking (2015-2017), Installation Studio (2012), and the Interdisciplinary Design Lab (2011), where he emphasizes human-centered methodologies, sensor-based interactive environments, and real-world problem solving. He actively supervises Masters and PhD research while collaborating across institutions through the Visualisation Institute Research Network and Trustworthy Digital Society initiatives. His artistic practice in photography and video complements his academic work, with exhibitions including the MAMA Art Foundation National Photography Prize and multiple international screenings.

• Artificial Intelligence
• Digital Health
• Interaction Design
• +5 more

Christy Jie Liang is an Associate Professor at the School of Computer Science, University of Technology Sydney (UTS), where she leads the Data Visualisation Research Lab in the Visualisation Institute. With extensive experience in both academic and industry settings, including appointments at IBM and Peking University, she has established herself as a leading researcher in data visualization and visual analytics. Dr. Liang earned her PhD in Data Visual Analytics from UTS, where she was awarded the University Medal with First Class Honours. Her educational background includes a Bachelor of Information Technology (First Class Honours) also from UTS. Professor Liang's research focuses on data visualization and visual analytics, with particular emphasis on information visualization, narrative visualization, and the application of these techniques to real-world problems. Her work spans multiple domains including finance, food safety, biomedical applications, smart cities, and social media. She has developed novel visualization techniques and owns five intellectual properties in this field. Her recent publications demonstrate a clear trajectory toward more sophisticated visualization techniques that integrate machine learning, with increasing focus on narrative visualization, user engagement across demographics, and practical applications in domains such as public health and education. The interdisciplinary nature of her work is evident in collaborations across computer science, behavioral science, and domain-specific applications. Dr. Liang has received significant recognition for her work, including: University Medal with First Class Honours from UTS Capital Markets CRC Honours scholarship Australian Postgraduate Awards As an educator, Professor Liang coordinates core subjects for Bachelor of Information Technology, Bachelor of Computer Science with Honours, Master of Interaction Design, and Master of Business Analytics programs. She has recently developed enterprise learning courses including short courses and micro-credentials in data visualization education. Her leadership extends to service roles as associate editor for JVLC and Journal Visual Informatics, program committee member for numerous conferences, and advisory board member for the Australian Computer Society and Peking University Medical Visualization Centre. Professor Liang leads the Data Visualisation Research Lab, which focuses on developing innovative visualization techniques and applying them to real-world problems. The lab maintains strong industry connections, with collaborations spanning government agencies, academic institutions, and commercial enterprises across multiple continents.

• Data visualisation
• Visual Analytics
• Information Visualisation
• +4 more