Professor Timothy Trudgian is a leading mathematician in the field of analytic number theory , currently affiliated with the School of Physical, Environmental and Mathematical Sciences at UNSW Canberra . He also collaborates closely with the Number Theory Group at UNSW Sydney. With a BSc (Hons) from Australian National University (2005) and a DPhil from the University of Oxford (2010) , his work focuses on the Riemann zeta-function , distribution of primes , and primitive roots . He actively supervises PhD students and offers scholarships for high-achieving applicants. Education : BSc (Hons) - Australian National University (2005), DPhil - University of Oxford (2010) Research Areas : Analytic number theory Distribution of primes Primitive roots Riemann zeta-function Finite field arithmetic Computational number theory His recent publications and grants, including an Australian Research Council Future Fellowship (2016-2020) , emphasize explicit bounds and zero-free regions for zeta and L-functions. He supervises students such as Matteo Bordignon and Valeriia Starichkova , with a focus on collaborative research and international conferences. Professor Trudgian's work spans both theoretical and computational aspects of number theory.
Dr. Mark Watkins is an academic staff member at the University of Sydney's School of Mathematics and Statistics, affiliated with the Computational Algebra Research Group. His research focuses on Number Theory , Algebraic Geometry , and Computational Algebra , with a particular emphasis on elliptic curves, modular forms, and lattice theory. He has contributed to topics ranging from Selmer group distributions to combinatorial game theory in chess. PhD in Mathematics Member of the Computational Algebra Research Group Email: mark.watkins@sydney.edu.au Watkins' recent publications (2010–2022) explore advanced mathematical concepts such as quadratic twists of elliptic curves, spectral proofs of class numbers, and algorithmic chess strategies. His work bridges theoretical mathematics with computational tools, including contributions to the Magma software system for algebraic computations. He has no listed scientific awards but maintains active research collaborations with institutions globally. His research addresses fundamental questions in number theory and algebraic geometry, with applications to cryptography and computational methods.
Associate Professor Braden Phillips is the Deputy Dean of Learning and Teaching in the Faculty of Sciences, Engineering and Technology at the University of Adelaide. He holds a joint appointment in the School of Electrical and Electronic Engineering. His academic career includes roles as a lecturer at Cardiff University (2000–2002) and founding partner of Current Dynamics, an electronic hardware design venture. Phillips specializes in digital microelectronics and computer engineering, with research focusing on computer arithmetic, cognitive computing architectures, and high-level digital logic synthesis. He has contributed to educational scholarship through work on active learning, scalable assessment, and inclusive pedagogy. His supervisory record includes over a dozen PhD students in areas spanning embedded systems, reconfigurable computing, and low-power design. Phillips’ research has been published in leading journals and conferences, addressing challenges in hardware optimization and algorithmic efficiency. Education: PhD in Computer Engineering (thesis: 'An Optimised Implementation of Public Key Cryptography for Smart Card Processors') Teaching: Coordinated courses in analog/digital electronics, embedded systems, and microelectronics design. Affiliations: Adelaide Education Academy, Diversity and Inclusion in Teaching Community of Practice. Research Themes: His work bridges hardware design and educational innovation, with recent emphasis on cognitive architectures and curriculum design. Key contributions include approximate computing methodologies, decimal floating-point arithmetic implementations, and minimal-weight digit conversion algorithms.
Professor Craig Costello is a leading cryptographer at the Queensland University of Technology (QUT) , affiliated with the Faculty of Science and the School of Computer Science . His work focuses on post-quantum cryptography , particularly isogeny-based and lattice-based cryptographic constructions , contributing to global efforts in securing digital infrastructure against quantum computing threats. Notable research trends include: Advancements in supersingular isogeny key exchange (SIKE/SIDH) Exploration of pairing-friendly abelian varieties for cryptographic cycles Efficient genus 2 isogeny algorithms and Kummer surface optimizations Development of twin smooth integer detection for cryptanalysis His publications emphasize quantum-resistant protocols , mathematical foundations , and practical cryptographic implementations . Contact: craig.costello@qut.edu.au
Jun Yong Park is a Lecturer in Mathematics at the University of Sydney, where he is a member of the pure mathematics research group in the School of Mathematics & Statistics. He received his Ph.D. in mathematics from the University of Minnesota in 2018 under the supervision of Craig Westerland. Prior to joining the University of Sydney, he was a research fellow at the University of Melbourne, Max Planck Institute for Mathematics in Bonn, and IBS Center for Geometry and Physics in Pohang. Dr. Park's research interests lie at the intersection of algebraic geometry and number theory, with a particular focus on the arithmetic theory of algebraic varieties, moduli spaces, and rational points on stacks. His work often explores counting families of varieties over function fields and connections to number theory. His research aligns with the Faculty of Science Research Strengths in "Understanding the Universe" and "Fundamental Laws of Nature." Dr. Park has published numerous papers in prestigious journals including Mathematische Zeitschrift, Mathematische Annalen, and Research in the Mathematical Sciences, with research focusing on moduli stacks of elliptic surfaces, arithmetic geometry, and the distribution of rational points. His publication record shows a strong focus on using motivic techniques to study arithmetic distributions and cohomological stability of moduli spaces. Dr. Park has received support from notable institutions including the Simons Foundation. He is actively involved in the academic community, organizing seminars such as the "Spaces, Functions and Numbers Seminar" at the University of Sydney. He teaches various mathematics courses at the University of Sydney, including Geometry & Topology (MATH3061), Multivariable Calculus and Modelling (MATH1062/1023), and Discrete Mathematics (MATH1004). He has also taught specialized topics courses on elliptic surfaces and number theory, demonstrating his commitment to both teaching and advancing mathematical knowledge.
Professor Philip Leong is a faculty member at the University of Sydney's School of Electrical & Information Engineering, serving as Director of the Computer Engineering Laboratory. He holds a B.Sc., B.E., and Ph.D. from the University of Sydney. His academic career spans roles at institutions like the Chinese University of Hong Kong and industry collaborations with companies like ST Microelectronics and CruxML Pty Ltd. Research Interests: Leong specializes in FPGA-based solutions for high-performance computing, financial systems, medical monitoring (e.g., Parkinson's disease), and environmental forecasting. He pioneers applications in edge-based machine learning, low-latency systems, and hardware-software co-design. Key Projects : Radio Frequency Machine Learning Edge-based Training of Deep Neural Networks using FPGAs Awards : 2005 FPT Best Paper Award 2007 & 2008 FPL Outstanding Paper Awards Teaching: He instructs courses in embedded systems, computer architecture, and digital logic (e.g., ELEC3607, ELEC5741). His lab focuses on custom hardware and parallel software to address real-world challenges like financial risk modeling and climate prediction. Labs/Teams: Leads the Computer Engineering Lab, affiliated with The Net Zero Institute, Sydney Nano Institute, and the Charles Perkins Centre.
Dr. Michael Norrish is an Associate Professor at The Australian National University , affiliated with the School of Computing and the Mathematical Sciences Institute . He specializes in formal methods, interactive theorem proving (particularly with the HOL4 system), and formal semantics for complex systems. His work emphasizes rigorous software verification and foundational computer science research. Dr. Norrish holds a PhD in Computer Science from the University of Cambridge (1999) , along with a BA and BSc(Hons) from Victoria University of Wellington. His research has led to significant contributions in verified compilers (e.g., CakeML), formalized algorithms (e.g., AKS primality test), and reproducibility challenges in scientific software. Research Interests include: - Formal verification of theorem provers - Verified systems programming - Machine learning integration in automated reasoning - Reproducibility in computational research His recent articles focus on advancing theorem proving tools (e.g., HOL4), reproducibility debt in scientific software, and verified compiler frameworks like PureCake and Pancake. He co-organized the 15th International Conference on Interactive Theorem Proving (ITP 2024) and leads projects on AI trustworthiness in Health, Safety & Environment (Tech4HSE). Current projects include: - Tech4HSE: AI Trust & Trustworthiness (ANU, 2024–2029) - Formalization of mathematical algorithms in HOL4
Dr. Yuval Rishu Sanders is a Senior Lecturer at the School of Computer Science within the Faculty of Engineering and Information Technology at the University of Technology Sydney (UTS). He is affiliated with the Centre for Quantum Software and Information (QSI), where he conducts cutting-edge research in quantum computing and quantum information theory. Dr. Sanders holds a PhD from the University of Waterloo, Canada, and has established himself as a prominent researcher in the quantum computing field with numerous high-impact publications. Dr. Sanders' academic journey began with a Bachelor of Science (First Class Honours) from the University of Calgary in 2008, followed by a Master of Science from the same institution in 2011. He completed his Doctor of Philosophy at the University of Waterloo in 2016. His career progression includes positions as a Research Associate at Macquarie University (2016-2021) and at UTS (2021-2022), before becoming a Permanent Faculty member at UTS in September 2022. Dr. Sanders' research focuses on the theoretical foundations of quantum computing, with particular expertise in quantum algorithms, quantum simulation, and quantum error correction. His work addresses fundamental questions about the computational advantages of quantum computers over classical systems, with a special emphasis on developing practical quantum algorithms for real-world applications. He is particularly interested in improving the efficiency and accuracy of quantum simulations, developing better methods for quantum state preparation, and establishing rigorous computational cost models for quantum algorithms. Dr. Sanders has made significant contributions to the field of quantum linear systems solvers, quantum measurement theory, and quantum resource theories. His research statement emphasizes the need for reliable computational cost analysis to determine when quantum computers will outperform classical computers for useful tasks. Analysis of Dr. Sanders' publication record reveals a consistent focus on advancing the theoretical underpinnings of quantum computing. His most recent work (2024-2025) centers on improving quantum simulation techniques through better product formulae, while his earlier work (2018-2022) demonstrates expertise across multiple quantum computing subfields including quantum algorithms for fermionic systems, quantum error characterization, and quantum measurement theory. A notable trend in his research is the development of more efficient quantum algorithms that reduce resource requirements while maintaining accuracy, which is crucial for near-term quantum applications. Dr. Sanders serves as an Associate Editor for the IEEE Transactions on Quantum Engineering since February 2023, demonstrating his standing in the quantum computing research community. His publication record includes articles in prestigious journals such as PRX Quantum, Physical Review Letters, and New Journal of Physics, with significant citation counts indicating the impact of his research. Dr. Sanders is actively involved in funded research projects that advance quantum computing theory and applications. His current projects include 'Building the Theoretical Foundation of Refinement Techniques for Quantum Programming' (2025-2027), 'The QB-suite: a framework for quantum algorithm design and benchmarking' (2024-2027), and 'Quarkov Decision Processes' (2023-2027). He has also contributed to significant projects such as 'Tools for fault-tolerant resource estimation' (2022-2025) and 'Defence acquisition optimisation using quantum algorithms' (2021-2024). His research vision includes developing software tools that can automate the analysis of quantum computations, potentially enabling a 100,000-fold speedup in the design iteration process for quantum applications. As a member of the Centre for Quantum Software and Information at UTS, Dr. Sanders collaborates with a multidisciplinary team of researchers working on various aspects of quantum computing. His research group focuses on developing theoretical frameworks and practical tools for quantum algorithm design, with particular emphasis on making quantum computing more accessible and efficient. Dr. Sanders' work bridges theoretical quantum computing with practical applications, contributing to the broader goal of realizing useful quantum advantage.
Dr Paul Vrbik is a Senior Lecturer at the School of Electrical Engineering and Computer Science, Faculty of Engineering, Architecture and Information Technology at The University of Queensland. He holds a Bachelor (Honours) in Mathematics from McMaster University, a Master of Science in Mathematics and Computer Science from Simon Fraser University, and a PhD in Computer Science from the University of Western Ontario. His research focuses on Cyber Security, Software Engineering, and broader areas including Mathematics (algorithms, stochastic processes) and Education in Computing (academic dishonesty, assessment methods). Notable contributions include novel proofs in linear algebra, studies on student perceptions of academic integrity, and algorithmic advancements in algebraic geometry. Recent work highlights include examining student understanding of academic dishonesty in computer science (2024), a systematic review of testing modalities in engineering education (2022), and contributions to determinant identities in mathematics (2023). His publications span journals like Mathematics Magazine and conferences such as SIGCSE and IEEE EDUCON. Dr Vrbik collaborates on interdisciplinary projects, blending theoretical computer science with practical educational research. His academic profile includes contributions to computational tools like the RegularChains library and advancements in symbolic computation algorithms.
Dr. Matt Skerritt is a Lecturer in Applied Mathematics at the School of Science, RMIT University, located at City Campus, Australia. His research focuses on Applied Mathematics, Pure Mathematics, and Numerical and Computational Mathematics. He specializes in optimization algorithms, number theory, and computational methods, with notable contributions to the Douglas-Rachford method, Giuga’s primality conjecture, and algorithm extensions like the PSLQ algorithm. He also explores educational tools using software such as Mathematica and Maple, emphasizing computational learning and pedagogy. His work bridges theoretical mathematics with practical applications in computational science and education. Research interests include the dynamics of iterative methods, integer relations, and primality testing, alongside the development of educational resources for computational mathematics. His publications span topics like geometric algorithms, numerical analysis, and symbolic computation, reflecting a commitment to advancing both theoretical and applied mathematical research.
Nicole Sutherland is a researcher affiliated with the University of Sydney, focusing on computational algebra, number theory, and Galois theory. Her work spans global function fields, finite field extensions, and symbolic computation. Key Research Areas: Galois theory, finite fields, computational algebra, algebraic number theory Notable Collaborations: Co-authored publications with researchers like Claus Fieker, David Krumm, and Stephen Cohen. Recent Publications: 2023: Computing splitting fields using Galois theory ; 2021: Galois groups over rational function fields ; 2019: Primitive elements in finite fields . Contact: nicole.sutherland@sydney.edu.au | Phone: 93513049 | Office: Carslaw Building, University of Sydney.
Dr. Denis Kleyko is a Visiting Research Fellow at La Trobe University's Business Analytics department. His work focuses on neuromorphic computing, hyperdimensional computing, and neuro-inspired algorithms. He has contributed to frameworks like NeuroBench for benchmarking neuromorphic systems and authored comprehensive surveys on vector symbolic architectures. Key research interests include neuromorphic reservoir computing, sparse randomized embeddings, and cognitive mapping through neural circuits. His publications span journals such as Nature Communications and IEEE Transactions, emphasizing interdisciplinary approaches in machine learning and neuroscience. Collaborations include international teams addressing challenges in seizure prediction, compositional learning, and high-dimensional data processing. Notable outputs include foundational work on HyperSEED and efficient optimization using Ising machines.
Endre Szemerédi is the State of New Jersey Professor of Computer Science at Rutgers University and Professor Emeritus at the Alfréd Rényi Institute of Mathematics. A pioneer in discrete mathematics, he proved Szemerédi's theorem on arithmetic progressions and developed the Szemerédi regularity lemma. His work bridges combinatorics, computer science, and number theory. Education includes studies at Eötvös Loránd University and a Ph.D. from Moscow State University under Israel Gelfand. Szemerédi has held visiting positions at Stanford University, McGill University, University of South Carolina, and University of Chicago. Research fundamentally advanced extremal graph theory, combinatorial number theory, and randomized algorithms. Contributions include the Szemerédi-Trotter theorem and the Hajnal-Szemerédi theorem. Abel Prize (2012) for discrete mathematics contributions Rolf Schock Prize (2008) for work on arithmetic progressions Leroy P. Steele Prize (2008) for seminal research
Professor Mariano Kulish is a Professor of Macroeconomics at the School of Economics, University of Sydney. He holds a PhD in Economics from Boston College (2005) and previously worked at the Reserve Bank of Australia's Economic Research Department. His research focuses on macroeconomics, monetary policy, structural changes, and applied econometrics, with notable contributions to DSGE modeling, zero interest rate policies, and commodity price impacts. Key research themes include analyzing economies undergoing structural shifts, fiscal policy in open economies, and the implications of unconventional monetary policies like yield curve control. His work frequently addresses policy-relevant issues such as disinflation strategies, terms of trade volatility, and the stability of inflation-unemployment relationships. He has secured grants including the Australian Research Council's 2019 Discovery Project on fiscal policy in open economies. His publications span top journals like the Journal of Monetary Economics , Journal of Applied Econometrics , and European Economic Review . Recent work explores fiscal arithmetic in growth slowdowns, international spillovers of monetary policy, and the Dutch Disease hypothesis in commodity-rich economies. Professor Kulish’s research combines theoretical modeling with empirical analysis, emphasizing policy relevance. He maintains an active presence in academic collaborations and policy discussions, reflecting his dual role as a researcher and former central bank economist.
Ryan Wu is a researcher specializing in machine learning and its applications in environmental modeling and energy systems. His work bridges artificial intelligence with practical challenges in coastal oceans, sediment transport, wind-speed forecasting, and electrical load prediction. Research Focus : Machine learning for environmental and energy applications Key Contributions : Physics-informed models, robust regression techniques, temporal LASSO regression, and novel optimization algorithms Recent Publications : Focused on computational intelligence for sediment concentration forecasting, wind-speed prediction, and electric load optimization. Subfields span predictive analytics, time series analysis, and environmental engineering.
