Research Output per year
Personal profile
Roles and responsibilities
Head of School
Biography
Mark Reynolds obtained his Bachelors degree at The University of Western Australia (UWA) with honours in Pure Mathematics and Statistics, his PhD at Imperial College London (IC) in Computing and a Diploma in Education from UWA. After lecturing at Kings College London, he moved back to Perth and then to UWA as an Associate Professor. He is currently the head of the School of Physics, Mathematics and Computing. His research interests include AI, Machine Learning, Logic, Formal methods in Software Engineering and Optimisation.
Funding overview
Over $10M in funding over the last ten years for a variety of industry, ARC and CRC funded projects. Projects include the ARC Training Centre for Transforming Maintenance through Data Science, investigations of the use of AI and Video Analytics in Intelligent Transport Systems, Projects in the use of logic and formal methods for ensuring correctness of complex systems.
Teaching overview
Developed and taught courses in Software Engineering, Computational Modelling, Machine Learning, Human-Computer Interaction, Logic and Computation, Computational Geometry and Introductory Programming.
24 PhD Students (12 completed), 30 Masters students and 10 honour students
Keywords
- Artificial intelligence
- Automated reasoning
- Formal methods in software
- Formal specification and verification of concurrent systems
- Logic
- Logical foundations of computer science
- Modal/temporal logic
- Reasoning about agent interaction and collaboration
- Software engineering
- Verification of systems
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Temporal logic
Engineering & Materials Science
Logic
Mathematics
Model checking
Engineering & Materials Science
Electric power distribution
Engineering & Materials Science
Temporal Logic
Mathematics
Trajectories
Engineering & Materials Science
Genetic algorithms
Engineering & Materials Science
Theorem proving
Engineering & Materials Science
Network
Recent external collaboration on country level. Dive into details by clicking on the dots.
Research Output 1985 2019
Aleatoric dynamic epistemic logic for learning agents
French, T., Gozzard, A. & Reynolds, M., 1 Jan 2019, PRICAI 2019: Trends in Artificial Intelligence - 16th Pacific Rim International Conference on Artificial Intelligence, Proceedings. Nayak, A. C. & Sharma, A. (eds.). Springer-Verlag Berlin, p. 433-445 13 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11670 LNAI).Research output: Chapter in Book/Conference paper › Conference paper
Epistemic Logic
Dynamic Logic
Proposition
Probability distributions
Probability Distribution
A modal aleatoric calculus for probabilistic reasoning
Gozzard, A., French, T. & Reynolds, M., 2019, Logic and its applications : 8th Indian Conference, ICLA 2019, Delhi, India, March 1-5, 2019, Proceedings. Aquil Khan, M. & Manuel, A. (eds.). Berlin: Springer, p. 52-63Research output: Chapter in Book/Conference paper › Conference paper
Probabilistic Reasoning
Calculi
Possible Worlds
Sound
Multi-agent Systems
An Adaptive-Phasor Approach to PMU Measurement Rectification for LFOD Enhancement
Chau, T. K., Yu, S., Fernando, T. L., Iu, H. H. C., Small, M. & Reynolds, M., 1 Sep 2019, In : IEEE Transactions on Power Systems. 34, 5, p. 3941-3950 10 p., 8674594.Research output: Contribution to journal › Article
Phasor measurement units
Damping
Recovery
Voltage regulators
Synchronous generators
A Novel Decentralized LTL Monitoring Framework Using Formula Progression Table
Bataineh, O., Rosenblum, D. S. & Reynolds, M., 1 Jan 2019, Model Checking Software - 26th International Symposium, SPIN 2019, Proceedings. Biondi, F., Given-Wilson, T. & Legay, A. (eds.). Springer, p. 38-55 18 p. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); vol. 11636 LNCS).Research output: Chapter in Book/Conference paper › Conference paper
Linear Temporal Logic
Temporal logic
Progression
Decentralized
Table
1
Citation
(Scopus)
A quest for a one-size-fits-all neural network: Early prediction of students at risk in online courses
Monllao Olive, D., Huynh, D., Reynolds, M., Dougiamas, M. & Wiese, D., 15 Apr 2019, In : IEEE Transactions on Learning Technologies. p. 171-183 13 p.Research output: Contribution to journal › Article
neural network
Students
Neural networks
student
learning
Projects 2004 2023
Multi-Objective Genetic Algorithm Optimisation For Network Widening and Maintenance Scheduling
Sun, C., French, T., Hu, Y., While, R., Reynolds, M. & Biermann, S.
1/11/19 → 31/08/20
Project: Research
Drone Video Analytics for Short-Term Traffic Prediction
Sun, C., Reynolds, M., Huynh, D., Hassan, G. M. & Biermann, S.
1/05/19 → 31/03/20
Project: Research
ARC Training Centre for Transforming Maintenance through Data Science
Small, M., Hodkiewicz, M., Reynolds, M., Liu, W., While, R., French, T., Cripps, E., Cardell-Oliver, R., Rohl, A., Loxton, R., O'Halloran, K., Calo, V. & Tan, T.
1/01/19 → 31/12/23
Project: Research
Enhanced short and longer term network performance prediction capabilities through data-driven analytics and simulation
Biermann, S., Sun, C., Liu, W., Reynolds, M. & Olaru, D.
1/07/17 → 30/06/20
Project: Research