Mingyu Guo

Research Interests

Artificial Intelligence Autonomous agents and multiagent systems Market-based mechanisms Mathematical Economics Optimisation Machine learning Reinforcement learning Emerging defence technologies Analysis of Algorithms and Complexity Operations Research Microeconomic Theory

Dr Mingyu Guo

Program Director

School of Computer Science and Information Technology

College of Engineering and Information Technology

Eligible to supervise Masters and PhD as Principal Supervisor - email supervisor to discuss availability.

Available For Media Comment.

I am a Senior Lecturer, Program Director of the Master of Computer Science, and Graduate Research Coordinator in the School of Computer Science and Information Technology, Adelaide University. Before joining Adelaide, I was a Lecturer at the University of Liverpool. I hold a Ph.D. in Computer Science from Duke University, where my dissertation was runner-up for the IFAAMAS Victor Lesser Distinguished Dissertation Award. I also hold an M.S. in Applied Mathematics from the University of Florida and a B.S. in Mathematics from Zhejiang University.

I use algorithmic game theory and optimisation to study how AI systems make decisions when participants have competing interests. My research spans two themes:

Agentic Commerce and the Economics of Generative AI. I study how AI agents buy, sell and negotiate on behalf of people and organisations. Current work includes advertising in language-model responses, the procurement and pricing of AI services, and auction design for AI tokens.

AI for Drone and Cyber Defence. I develop algorithms for coordinating drone teams and protecting enterprise networks against cyber attacks. This work addresses how to choose effective strategies at scale while accounting for an adversary's responses, with support from defence and industry partners.

Highlights

  • AAAI 2027 Workshop Program Co-Chair, co-leading the conference-wide workshop program.
  • AAMAS 2026 Best Student Paper nomination (one of five nominated papers), for drone defence work through an industry PhD with Saab Australia.
  • Executive Dean's Award, 2025, International Engagement – International Partnership.
  • Invited research talks, 2024, at Google DeepMind, Carnegie Mellon, Harvard and Oxford.
  • 90+ publications, including 50+ at CORE A*/A venues; A$1.5M+ in project funding as lead or co-investigator since 2022; 10 HDR completions.

Selected Publications

  • NeurIPS 2026 (CORE A*): Extending Myerson's Optimal Auctions to Correlated Bidders via Neural Network Interpolation. With Carnegie Mellon.
  • COLM 2026: Incentive-Aware Multi-Fidelity Optimization for Generative Advertising in Large Language Models. With Carnegie Mellon, Oxford and Harvard, co-authored with my Honours student.
  • IJCAI 2026 (CORE A*): Taming Treewidth DP with Modulators: A General Booster for Graph Heuristics.
  • ICML 2026 (CORE A*): Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality Inputs with Confidence-Guided Gate.
  • AAMAS 2026 (CORE A, Best Student Paper nomination, oral): Building Large-Scale Drone Defenses from Small-Team Strategies.
  • AAAI 2026 (CORE A*, oral): On the Edge of Core (Non-)emptiness: An Automated Reasoning Approach to Approval-based Multi-winner Voting.
  • NeurIPS 2025 (CORE A*): Can Dependencies Induced by LLM-Agent Workflows Be Trusted?
  • IJCAI 2025 (CORE A*): Adaptive Wizard for Removing Cross-Tier Misconfigurations in Active Directory.
  • ACM Transactions on Evolutionary Learning and Optimization 2025: Hardening Active Directory Graphs via Evolutionary Diversity Optimization-based Policies.
  • AAAI 2024 (CORE A*): Worst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis. Sole author.
  • AAAI 2024 (CORE A*): Limited Query Graph Connectivity Test.
  • INFOCOM 2024 (CORE A*): Catch Me if You Can: Effective Honeypot Placement in Dynamic AD Attack Graphs.
  • AAAI 2023 (CORE A*): Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs.
  • IJCAI 2023 (CORE A*): Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint.
  • AAAI 2022 (CORE A*): Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs.

Industry Collaboration

I collaborate with industry and government on decision problems that combine AI, optimisation, and strategic interaction, including Saab Australia and the Defence Science and Technology Group (defence and autonomous systems), the Cyber Security Cooperative Research Centre (cybersecurity), SA Pathology (health operations), and DDDI Group (PropTech). I welcome conversations about collaborative research, industry PhDs and grant partnerships.

Prospective Students

I welcome enquiries from prospective PhD/MPhil students in multi-agent AI, generative AI, optimisation, and related areas.

Current Adelaide University students are welcome to contact me about Honours and research projects. A project may target a research publication, an industry-facing prototype, or both. For example, my Honours student co-authored our COLM 2026 paper on generative advertising in large language models with researchers at Carnegie Mellon, Oxford and Harvard. Other student projects have involved industry partners.

My academic homepage: 

https://mingyuguo.github.io/

Date Position Institution name
2026 - ongoing Program Director, Master of Computer Science University of Adelaide
2026 - ongoing Graduate Research Coordinator University of Adelaide
2025 - 2025 Acting Associate Dean International (North and East Asia), Jul–Sep 2025 University of Adelaide
2024 - 2024 Visiting Researcher, Jun–Jul 2024 University of Oxford
2024 - ongoing Senior Lecturer University of Adelaide
2024 - 2024 Faculty Visitor, Jan–Dec 2024 Carnegie Mellon University
2023 - 2025 Associate Head International (interrupted in 2024 due to SSP) University of Adelaide
2013 - 2023 Lecturer University of Adelaide
2010 - 2013 Lecturer University of Liverpool

Date Institution name Country Title
2010 Duke University United States PhD in Computer Science
2006 University of Florida United States MS in Applied Mathematics
2004 Zhejiang University China BS in Mathematics

Year Citation
2026 Otani, R., Namikoshi, K., Sakurai, Y., Guo, M., & Oyama, S. (2026). Static-Graph Estimation in Hybrid GNNs Using Behavioral-Similarity and Clustering-Based Methods. IEEE Access, 14, 42734-42750.
DOI
2025 Goel, D., Ward, M., Neumann, A., Neumann, F., Nguyen, H., & Guo, M. (2025). Hardening Active Directory Graphs via Evolutionary Diversity Optimization based Policies. ACM Transactions on Evolutionary Learning and Optimization, 5(3), 36 pages.
DOI Scopus2 WoS2
2025 Dinh, N. T., Karimi-Arpanahi, S., Pourmousavi, S. A., Guo, M., Lemos-Vinasco, J., & Liisberg, J. A. R. (2025). On the financial consequences of simplified battery sizing models without considering operational details. Journal of Energy Storage, 129, 117104-1-117104-9.
DOI
2025 Goel, D., Shen, H., Tian, H., & Guo, M. (2025). Tracking top-k structural hole spanners in dynamic networks. Cluster Computing, 28(6), 21 pages.
DOI Scopus1 WoS1
2025 Goel, D., Ward, M., Neumann, A., Neumann, F., Nguyen, H. X., & Guo, M. (2025). Hardening Active Directory Graphs via Evolutionary Diversity Optimization-based Policies.. ACM Trans. Evol. Learn. Optim., 5, 19:1.
DOI
2024 Dinh, N. T., Karimi-Arpanahi, S., Pourmousavi, S. A., Yuan, R., Guo, M., Liisberg, J. A. R., & Lemos-Vinascco, J. (2024). Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes. IEEE Transactions on Smart Grid, 15(5), 4636-4648.
DOI Scopus9 WoS6
2024 Goel, D., Shen, H., Tian, H., & Guo, M. (2024). Effective graph-neural-network based models for discovering Structural Hole Spanners in large-scale and diverse networks. Expert Systems with Applications, 249, 123636.
DOI Scopus5 WoS5
2024 Guo, M., Goel, D., Wang, G., Guo, R., Sakurai, Y., & Babar, M. A. (2024). Mechanism design for public projects via three machine learning based approaches. Autonomous Agents and Multi-Agent Systems, 38(1), 30 pages.
DOI Scopus1
2023 Ma, C., Zhang, W. E., Guo, M., Wang, H., & Sheng, Q. Z. (2023). Multi-document Summarization via Deep Learning Techniques: A Survey. ACM COMPUTING SURVEYS, 55(5), 37 pages.
DOI Scopus120 WoS84
2023 Goel, D., Shen, H., Tian, H., & Guo, M. (2023). Effective Graph-Neural-Network based Models for Discovering Structural Hole Spanners in Large-Scale and Diverse Networks. CoRR, abs/2302.12442.
DOI
2023 Goel, D., Shen, H., Tian, H., & Guo, M. (2023). Discovering Top-k Structural Hole Spanners in Dynamic Networks. CoRR, abs/2302.13292.
DOI
2023 Guo, M. (2023). Worst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis. CoRR, abs/2305.11011.
DOI
2023 Guo, M., Goel, D., Wang, G., Yang, Y., & Babar, M. A. (2023). Cost Sharing Public Project with Minimum Release Delay. CoRR, abs/2305.11657.
DOI
2023 Dinh, N. T., Karimi-Arpanahi, S., Pourmousavi, S. A., Guo, M., & Liisberg, J. A. R. (2023). Cost-Effective Community Battery Sizing and Operation Within a Local Market Framework. IEEE Transactions on Energy Markets, Policy, and Regulation, 1(4), 536-548.
DOI Scopus12 WoS9
2022 Guo, M., Wang, Z., & Sakurai, Y. (2022). Gini index based initial coin offering mechanism. Autonomous Agents and Multi-Agent Systems, 36(1), 1-20.
DOI Scopus2 WoS3
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Analysis of Evolutionary Diversity Optimisation for Permutation Problems. ACM Transactions on Evolutionary Learning and Optimization, 2(3), 11-1-11-27.
DOI Scopus23
2022 Wang, G., Zuo, W., & Guo, M. (2022). Redistribution in Public Project Problems via Neural Networks. CoRR, abs/2203.05778.
DOI
2022 Goel, D., Shen, H., Tian, H., & Guo, M. (2022). Discovering Structural Hole Spanners in Dynamic Networks via Graph Neural Networks. CoRR, abs/2212.08239.
DOI
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Analysis of Evolutionary Diversity Optimization for Permutation Problems.. ACM Trans. Evol. Learn. Optim., 2, 11:1.
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Niching-based Evolutionary Diversity Optimization for the Traveling Salesperson Problem.. CoRR, abs/2201.10316.
2021 Guo, M., Wang, G., Hata, H., & Babar, M. A. (2021). Revenue maximizing markets for zero-day exploits. AUTONOMOUS AGENTS AND MULTI-AGENT SYSTEMS, 35(2), 15 pages.
DOI Scopus9 WoS5
2021 Guo, M. (2021). An asymptotically optimal VCG redistribution mechanism for the public project problem. Autonomous Agents and Multi-Agent Systems, 35(2), 1-27.
DOI Scopus1 WoS1
2021 Sun, R., Xue, M., Tyson, G., Dong, T., Li, S., Wang, S., . . . Nepal, S. (2021). Mate! Are You Really Aware? An Explainability-Guided Testing Framework
for Robustness of Malware Detectors.
2021 Ma, C., Zhang, W. E., Wang, H., Gupta, S., & Guo, M. (2021). Dependency Structure for News Document Summarization. CoRR, abs/2109.11199.
2021 Guo, M., Li, J., Neumann, A., Neumann, F., & Nguyen, H. (2021). Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs.. CoRR, abs/2112.13175.
2020 Guo, M., Wang, Z., & Sakurai, Y. (2020). Gini Index based Initial Coin Offering Mechanism. CoRR, abs/2002.11387.
2020 Ma, C., Zhang, W. E., Guo, M., Wang, H., & Sheng, Q. Z. (2020). Multi-document Summarization via Deep Learning Techniques: A Survey. CoRR, abs/2011.04843.
2019 Iqbal, A., Gunn, L. J., Guo, M., Babar, M. A., & Abbott, D. (2019). Game theoretical modelling of network/cybersecurity. IEEE Access, 7, 154167-154179.
DOI Scopus23 WoS18
2014 Guo, M., & Conitzer, V. (2014). Better redistribution with inefficient allocation in multi-unit auctions. Artificial Intelligence, 216, 287-308.
DOI Scopus12
2013 Guo, M., Markakis, E., Apt, K., & Conitzer, V. (2013). Undominated Groves mechanisms. Journal of Artificial Intelligence Research, 46, 129-163.
DOI Scopus21
2013 Guo, M., & Deligkas, A. (2013). Revenue Maximization via Hiding Item Attributes. CoRR, abs/1302.5332.
2012 Guo, M., Markakis, E., Apt, K. R., & Conitzer, V. (2012). Undominated Groves Mechanisms. CoRR, abs/1203.1809.
2010 Guo, M., & Conitzer, V. (2010). Optimal-in-expectation redistribution mechanisms. Artificial Intelligence, 174(5-6), 363-381.
DOI Scopus28
2009 Guo, M., & Conitzer, V. (2009). Worst-case optimal redistribution of VCG payments in multi-unit auctions. Games and Economic Behavior, 67(1), 69-98.
DOI Scopus81
2008 Apt, K. R., Conitzer, V., Guo, M., & Markakis, E. (2008). Welfare Undominated Groves Mechanisms. CoRR, abs/0810.2865.

Year Citation
2026 Berker, R. E., Tewolde, E., Conitzer, V., Guo, M., Heule, M., & Xia, L. (2026). On the Edge of Core (Non-)Emptiness: An Automated Reasoning Approach to Approval-Based Multi-Winner Voting. In Proceedings of the Aaai Conference on Artificial Intelligence Vol. 40 (pp. 16673-16681). Association for the Advancement of Artificial Intelligence (AAAI).
DOI
2026 Otani, R., Namikoshi, K., Sakurai, Y., Guo, M., & Oyama, S. (2026). Behavioral-Similarity and Clustering-Based Methods for Static Graph Estimation in Hybrid GNNs (Student Abstract). In Proceedings of the Aaai Conference on Artificial Intelligence Vol. 40 (pp. 41340-41342). Association for the Advancement of Artificial Intelligence (AAAI).
DOI Scopus1
2026 Douglas, G., Franklin, S., Szabo, C., & Guo, M. (2026). Building Large-Scale Drone Defenses from Small-Team Strategies. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 3 (pp. 1655-1663). CYPRUS, University of Cyprus, Paphos: ASSOC COMPUTING MACHINERY.
DOI
2026 Purnananda, V., Wruck, B. J., & Guo, M. (2026). Evolving LLM-Derived Control Policies for Residential EV Charging and Vehicle-to-Grid Energy Optimization. In Gecco 2026 Companion Proceedings of the 2026 Genetic and Evolutionary Computation Conference (pp. 661-664). ACM.
DOI
2025 Ngo, H. Q., Guo, M., & Nguyen, H. X. (2025). Adaptive Wizard for Removing Cross-Tier Misconfigurations in Active Directory. In Ijcai International Joint Conference on Artificial Intelligence (pp. 7661-7669). International Joint Conferences on Artificial Intelligence Organization.
DOI
2025 Yao, Y., Song, Y., Xie, Y., Fan, M., Guo, M., & Liu, T. (2025). Can Dependencies Induced by LLM-Agent Workflows Be Trusted?. In Advances in Neural Information Processing Systems Vol. 38 (pp. 19776-19814).
Scopus1
2024 Ngo, H. Q., Guo, M., & Nguyen, H. (2024). Catch Me if You Can: Effective Honeypot Placement in Dynamic AD Attack Graphs. In Proceedings - IEEE INFOCOM (pp. 451-460). Vancouver, BC, Canada: IEEE.
DOI Scopus14 WoS9
2024 Guo, M., Li, J., Neumann, A., Neumann, F., & Nguyen, H. (2024). Limited Query Graph Connectivity Test. In M. J. Wooldridge, J. G. Dy, & S. Natarajan (Eds.), Proceedings of the 38th AAAI Conference on Artificial Intelligence (AAAI-24) Vol. 38 (pp. 20718-20725). Online: Association for the Advancement of Artificial Intelligence (AAAI).
DOI Scopus6 WoS5
2024 Guo, M. (2024). Worst-Case VCG Redistribution Mechanism Design Based on the Lottery Ticket Hypothesis. In Proceedings of the AAAI Conference on Artificial Intelligence Vol. 38 (pp. 9740-9748). Online: Association for the Advancement of Artificial Intelligence (AAAI).
DOI
2024 Ngo, H., Guo, M., & Nguyen, H. (2024). Optimizing Cyber Response Time on Temporal Active Directory Networks Using Decoys. In Proceedings of the Genetic and Evolutionary Computation Conference (pp. 1309-1317). Online: ACM.
DOI Scopus2 WoS1
2024 Hyun, S., Guo, M., & Babar, M. A. (2024). METAL: Metamorphic Testing Framework for Analyzing Large-Language Model Qualities. In Proceedings - 2024 IEEE Conference on Software Testing, Verification and Validation, ICST 2024 (pp. 117-128). Toronto, ON, Canada: IEEE.
DOI Scopus33 WoS18
2024 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2024). Evolutionary Multi-objective Diversity Optimization. In M. Affenzeller, S. M. Winkler, A. V. Kononova, H. Trautmann, T. Tusar, P. Machado, & T. Bäck (Eds.), Proeedings of the 18th International Conference on Parallel Problem Solving from Nature, Part IV (PPSN 2024), as published in Lecture Notes in Computer Science Vol. 15151 (pp. 117-134). Cham, Switzerland: Springer Nature.
DOI Scopus5 WoS3
2024 Ma, C., Zhang, W. E., Wang, H., Zhuang, H., & Guo, M. (2024). Disentangling Specificity for Abstractive Multi-document Summarization. In Proceedings of the International Joint Conference on Neural Networks (IJCNN 2024) (pp. 1-8). Yokohama, Japan: IEEE.
DOI Scopus3
2024 Goel, D., Moore, K., Guo, M., Wang, D., Kim, M., & Camtepe, S. (2024). Optimizing Cyber Defense in Dynamic Active Directories Through Reinforcement Learning. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 14982 LNCS (pp. 332-352). Bydgoszcz: Springer Nature Switzerland.
DOI Scopus12
2023 Goel, D., Shen, H., Tian, H., & Guo, M. (2023). Discovering Structural Hole Spanners in Dynamic Networks via Graph Neural Networks. In Proceedings - 2022 IEEE/WIC/ACM International Joint Conference on Web Intelligence and Intelligent Agent Technology, WI-IAT 2022 (pp. 64-71). Online: IEEE.
DOI Scopus2 WoS2
2023 Goel, D., Neumann, A., Neumann, F., Nguyen, H., & Guo, M. (2023). Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23) (pp. 1348-1356). New York, NY: Association for Computing Machinery.
DOI Scopus9 WoS6
2023 Neumann, A., Gounder, S., Yan, X., Sherman, G., Campbell, B., Guo, M., & Neumann, F. (2023). Diversity Optimization for the Detection and Concealment of Spatially Defined Communication Networks. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO '23) (pp. 1436-1444). New York, NY: Association for Computing Machinery.
DOI Scopus17 WoS14
2023 Zhang, Y., Ward, M., Guo, M., & Nguyen, H. (2023). A Scalable Double Oracle Algorithm for Hardening Large Active Directory Systems. In Proceedings of the 18th ACM Asia Conference on Computer and Communications Security (ACM ASIACCS, 2023) (pp. 993-1003). Melbourne Victoria, Australia: Association for Computing Machinery.
DOI Scopus14 WoS10
2023 Ngo, H. Q., Guo, M., & Nguyen, H. (2023). Near Optimal Strategies for Honeypots Placement in Dynamic and Large Active Directory Networks. In N. Agmon, B. An, A. Ricci, & W. Yeoh (Eds.), Proceedings of the 22nd International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2023) Vol. 2023-May (pp. 2517-2519). Richland, SC: International Foundation for Autonomous Agents and Multiagent Systems.
DOI Scopus9
2023 Guo, M., Ward, M., Neumann, A., Neumann, F., & Nguyen, H. (2023). Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs. In Proceedings of the 37th AAAI Conference on Artificial Intelligence (AAAI, 2023) Vol. 37 (pp. 5649-5656). Washington, D.C., USA: Association for the Advancement of Artificial Intelligence (AAAI).
DOI Scopus15 WoS9
2023 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2023). Diverse approximations for monotone submodular maximization problems with a matroid constraint. In E. Elkind (Ed.), Proceedings of the 32nd International Joint Conference on Artificial Intelligence (IJCAI 2023) Vol. 2023-August (pp. 5558-5566). Macao, S.A.R: IJCAI.
DOI Scopus8 WoS8
2022 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2022). Niching-based evolutionary diversity optimization for the traveling salesperson problem. In J. E. Fieldsend, & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22) (pp. 684-693). Online: Association for Computing Machinery.
DOI Scopus6 WoS5
2022 Guo, M., Li, J., Neumann, A., Neumann, F., & Nguyen, H. (2022). Practical Fixed-Parameter Algorithms for Defending Active Directory Style Attack Graphs. In Proceedings of the 36th AAAI Conference on Artificial Intelligence (AAAI-2022) Vol. 36 (pp. 9360-9367). virtual online: AAAI Press.
DOI Scopus23 WoS20
2022 Goel, D., Ward-Graham, M. H., Neumann, A., Neumann, F., Nguyen, H., & Guo, M. (2022). Defending active directory by combining neural network based dynamic program and evolutionary diversity optimisation. In J. E. Fieldsend, & M. Wagner (Eds.), Proceedings of the Genetic and Evolutionary Computation Conference (GECCO'22) Vol. abs/2204.03397 (pp. 1191-1199). New York, NY: Association for Computing Machinery.
DOI Scopus18 WoS17
2022 Dinh, N. T., Pourmousavi, S. A., Karimi-Arpanahi, S., Kumar, Y. P. S., Guo, M., Abbott, D., & Liisberg, J. A. R. (2022). Optimal sizing and scheduling of community battery storage within a local market. In e-Energy 2022 - Proceedings of the 2022 13th ACM International Conference on Future Energy Systems (pp. 34-46). Online: ACM.
DOI Scopus8 WoS9
2022 Ota, M., Sakurai, Y., Guo, M., & Noda, I. (2022). Mitigating Fairness and Efficiency Tradeoff in Vehicle-Dispatch Problems. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 13616 LNAI (pp. 307-319). Online: Springer International Publishing.
DOI Scopus3
2022 Ma, C., Zhang, W. E., Wang, H., Gupta, S., & Guo, M. (2022). Incorporating Linguistic Knowledge for Abstractive Multi-document Summarization. In S. Dita, A. O. Trillanes, & R. I. Lucas (Eds.), Proceedings of the 36th Pacific Asia Conference on Language, Information and Computation, PACLIC 2022 (pp. 147-156). Online: De La Salle University.
Scopus2
2021 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2021). Analysis of Evolutionary Diversity Optimization for Permutation Problems. In Proceedings of the Genetic and Evolutionary Computation Conference (GECCO 2021) (pp. 574-582). New York, NY, United States: Association for Computing Machinery.
DOI Scopus18 WoS6
2021 Wang, G., Guo, R., Sakurai, Y., Babar, M. A., & Guo, M. (2021). Mechanism design for public projects via neural networks. In Proceedings of the 20th International Joint Conference on Autonomous Agents and Multiagent Systems (AAMAS 2021) Vol. 3 (pp. 1380-1388). Richland, SC, United States: International Foundation for Autonomous Agents and Multiagent Systems.
DOI Scopus8
2021 Goel, D., Shen, H., Tian, H., & Guo, M. (2021). Maintenance of Structural Hole Spanners in Dynamic Networks. In 2021 IEEE 46th Conference on Local Computer Networks (LCN) Vol. 2021-October (pp. 339-342). online: IEEE.
DOI Scopus3 WoS3
2021 Wang, G., & Guo, M. (2021). Public Project with Minimum Expected Release Delay. In PRICAI 2021: Trends in Artificial Intelligence: 18th Pacific Rim International Conference on Artificial Intelligence, PRICAI 2021 Hanoi, Vietnam, November 8–12, 2021 Proceedings, Part I Vol. 13031 LNAI (pp. 101-112). Switzerland: Springer International Publishing.
DOI Scopus1
2021 Wang, G., Zuo, W., & Guo, M. (2021). Redistribution in Public Project Problems via Neural Networks. In WI-IAT '21: IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology (pp. 406-413). New York, NY, United States: Association for Computing Machinery.
DOI Scopus2
2019 Guo, M. (2019). An asymptotically optimal VCG redistribution mechanism for the public project problem. In IJCAI International Joint Conference on Artificial Intelligence Vol. 2019-August (pp. 315-321). online: IJCAI Organization.
DOI Scopus3
2019 Sakurai, Y., Oyama, S., Guo, M., & Yokoo, M. (2019). Deep False-Name-Proof Auction Mechanisms. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11873 LNAI (pp. 594-601). Switzerland: Springer.
DOI Scopus3
2018 Kitagawa, K., Guo, M., Kogiso, K., & Hata, H. (2018). Utility design for two-player normal-form games. In 2017 Asian Control Conference, ASCC 2017 Vol. 2018-January (pp. 2077-2082). online: IEEE.
DOI
2018 Guo, M., Yang, Y., & Babar, M. A. (2018). Cost sharing security information with minimal release delay. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 11224 LNAI (pp. 177-193). Switzerland: Springer Nature.
DOI Scopus3 WoS3
2017 Kanda, T., Guo, M., Hata, H., & Matsumoto, K. (2017). Towards understanding an open-source bounty: Analysis of Bountysource. In SANER 2017 - 24th IEEE International Conference on Software Analysis, Evolution, and Reengineering (pp. 577-578). Klagenfurt, Austria: Institute of Electrical and Electronics Engineers (IEEE).
DOI Scopus10
2017 Guo, M., & Shen, H. (2017). Speed up Automated Mechanism Design by Sampling Worst-Case Profiles: An Application to Competitive VCG Redistribution Mechanism for Public Project Problem. In Proceedings of the 20th International Conference on Principles and Practice of Multi-Agent Systems (PRIMA 2017) as published in Lecture Notes in Computer Science Vol. 10621 (pp. 127-142). Switzerland: Springer.
DOI Scopus4 WoS4
2017 Guo, M., Hata, H., & Babar, A. (2017). Optimizing Affine Maximizer Auctions via Linear Programming: An Application to Revenue Maximizing Mechanism Design for Zero-Day Exploits Markets. In Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) Vol. 10621 LNAI (pp. 280-292). Switzerland: Springer Nature.
DOI Scopus5
2017 Hata, H., Guo, M., & Babar, M. (2017). Understanding the Heterogeneity of Contributors in Bug Bounty Programs. In International Symposium on Empirical Software Engineering and Measurement Vol. 2017-November (pp. 223-228). Toronto, CANADA: IEEE.
DOI Scopus36 WoS29
2016 Guo, M. (2016). Competitive VCG redistribution mechanism for public project problem. In Proceedings of the 19th International Conference on Principles and Practice of Multi-Agent Systems: PRIMA 2016 Vol. 9862 LNCS (pp. 279-294). Phuket, Thailand: Springer International Publishing.
DOI Scopus6
2016 Guo, M., Sakurai, Y., Todo, T., & Yokoo, M. (2016). Individually rational strategy-proof social choice with exogenous indifference sets. In Proceeding of the 19th International Conference International Conference on Principles and Practice of Multi-Agent Systems: PRIMA 2016 Vol. 9862 LNCS (pp. 181-196). Phuket, Thailand: Springer International Publishing.
DOI
2016 Guo, M., Hata, H., & Babar, A. (2016). Revenue maximizing markets for zero-day exploits. In Proceedings of the 19th International Conference on Principles and Practice of Multi-Agent Systems: PRIMA 2016 Vol. 9862 LNCS (pp. 247-260). Phuket, Thailand: Springer International Publishing.
DOI Scopus5
2015 Guo, M., Shen, H., Todo, T., Sakurai, Y., & Yokoo, M. (2015). Social decision with minimal efficiency loss: An automated mechanism design approach. In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems Vol. 1 (pp. 347-355). Istanbul, Turkey: ACM.
Scopus5 WoS4
2015 Iwasaki, A., Fujita, E., Todo, T., Iwane, H., Anai, H., Guo, M., & Yokoo, M. (2015). Parametric mechanism design via quantifier elimination. In Proceedings of the 14th International Conference on Autonomous Agents and Multiagent Systems Vol. 3 (pp. 1885-1886). Istanbul, Turkey: IFAAMS.
2014 Guo, M., Deligkas, A., & Savani, R. (2014). Increasing VCG revenue by decreasing the quality of items. In Proceedings of the Twenty-Eighth AAAI Conference on Artificial Intelligence Vol. 1 (pp. 705-711). online: AAAI.
Scopus3
2014 Tsuruta, S., Oka, M., Todo, T., Kawasaki, Y., Guo, M., Sakurai, Y., & Yokoo, M. (2014). Optimal false-name-proof single-item redistribution mechanisms. In Proceedings of the 13th International Conference on Autonomous Agents and Multiagent Systems Vol. 1 (pp. 221-228). Paris, France: International Foundation for Autonomous Agents and Multiagent Systems.
Scopus4
2013 Guo, M., & Deligkas, A. (2013). Revenue maximization via hiding item attributes. In Proceedings of the Twenty-Third International Joint Conference on Artificial Intelligence (IJCAI 13) (pp. 157-163). California; USA: AAAI Press.
Scopus20
2012 Guo, M. (2012). Worst-case optimal redistribution of VCG payments in heterogeneous-item auctions with unit demand. In Proceedings of the 11th International Conference on Autonomous Agents and Multiagent Systems - Volume 2 (pp. 745-752). UK: International Foundation for Autonomous Agents and Multiagent Systems.
2012 Naroditskiy, V., Guo, M., Dufton, L., Polukarov, M., & Jennings, N. (2012). Redistribution of VCG payments in public project problems. In P. Goldberg (Ed.), Internet and Network Economics: Proceedings of 8th International Workshop, WINE 2012, Liverpool, UK, December 10-12, 2012. Vol. 7695 LNCS (pp. 323-336). UK: Springer.
DOI Scopus11
2012 Guo, M. (2012). Worst-case optimal redistribution of VCG payments in heterogeneous-item auctions with unit demand. In 11th International Conference on Autonomous Agents and Multiagent Systems 2012 Aamas 2012 Innovative Applications Track Vol. 2 (pp. 1016-1023).
Scopus10
2011 Guo, M., Naroditskiy, V., Conitzer, V., Greenwald, A., & Jennings, N. (2011). Budget-balanced and nearly efficient randomized mechanisms: public goods and beyond. In N. Chen, E. Elkind, & E. Koutsoupias (Eds.), Internet and Network Economics: Proceedings of 7th International Workshop, WINE 2011, Singapore, December 11-14, 2011 Vol. 7090 LNCS (pp. 158-169). UK: Springer.
DOI Scopus16
2011 Guo, M. (2011). VCG redistribution with gross substitutes. In Proceedings of the Twenty-Fifth AAAI Conference on Artificial Intelligence Vol. 1 (pp. 675-680). Menlo Park, California: AAAI Press.
DOI Scopus10
2010 Guo, M., & Conitzer, V. (2010). False-name proofness with Bid Withdrawal. In W. van der Hoek, G. Kaminka, Y. Lesperance, M. Luck, & S. Sen (Eds.), AAMAS2010 Toronto - The 9th International Conference on Autonomous Agents and Multiagent Systems May 10-14, 2010, Toronto Canada Conference Proceedings Volume1 (pp. 1475-1476). USA: IFAAMAS.
2010 Iwasaki, A., Conitzer, V., Omori, Y., Sakurai, Y., Todo, T., Guo, M., & Yokoo, M. (2010). Worst-case efficiency ratio in false-name-proof combinatorial auction mechanisms. In W. van der Hoek, G. Kaminka, Y. Lesperance, M. Luck, & S. Sen (Eds.), AAMAS 2010 - The 9th International Conference on Autonomous Agents and Multiagent Systems May 10-14, 2010, Toronto Canada, Conference Proceedings, vol. 1 Vol. 2 (pp. 633-640). USA: International Foundation for Autonomous Agents and Multiagent Systems.
Scopus31
2010 Guo, M., & Conitzer, V. (2010). Strategy-proof allocation of multiple items between two agents without payments or priors. In W. van der Hoek, G. Kaminka, Y. Lesperance, M. Luck, & S. Sen (Eds.), AAMAS 2010 - The 9th International Conference on Autonomous Agents and Multiagent Systems May 10-14, 2010, Toronto Canada, Conference Proceedings, vol. 1 Vol. 2 (pp. 881-888). USA: International Foundation for Autonomous Agents and Multiagent Systems.
Scopus50
2010 Guo, M., & Conitzer, V. (2010). Computationally feasible automated mechanism design: general approach and case studies. In Proceedings of the Twenty-Fourth AAAI Conference on Artificial Intelligence (AAAI-10) Vol. 3 (pp. 1676-1679). California; USA: AAAI.
Scopus31
2010 Guo, M., & Conitzer, V. (2010). False-name-proofness with bid withdrawal. In W. V. D. Hoek, G. A. Kaminka, Y. Lespérance, M. Luck, & S. Sen (Eds.), 9th International Conference on Autonomous Agents and Multiagent Systems (AAMAS 2010), Toronto, Canada, May 10-14, 2010, Volume 1-3 (pp. 1475-1476). IFAAMAS.
2009 Guo, M., & Pennock, D. (2009). Combinatorial prediction markets for event hierarchies. In AAMAS '09 8th International Conference on Autonomous Agents and Multi Agent Systems
Budapest, Hungary, May 10 - 15, 2009 (pp. 201-208). South Carolina, USA: International Foundation for Autonomous Agents and Multiagent Systems.
2009 Shi, P., Conitzer, V., & Guo, M. (2009). Prediction mechanisms that do not incentivize undesirable actions. In Stefano Leonardi (Ed.), Internet and Network Economics: 5th International Workshop, WINE 2009, Rome, Italy, December 14-18, 2009, Proceedings Vol. 5929 LNCS (pp. 89-100). Berlin, Germany: Springer.
DOI Scopus23
2009 Guo, M., Conitzer, V., & Reeves, D. (2009). Competitive repeated allocation without payments. In Stefano Leonardi (Ed.), Internet and Network EconomicsL: 5th International Workshop, WINE 2009, Rome, Italy, December 14-18, 2009, Proceedings (pp. 244-255). Berlin, Germany: Springer.
DOI
2009 Guo, M., & Pennock, D. M. (2009). Markets for event hierarchies combinatorial prediction. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 1 (pp. 173-180).
2009 Guo, M., Conitzer, V., & Reeves, D. M. (2009). Competitive repeated allocation without payments. In Lecture Notes in Computer Science Including Subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics Vol. 5929 LNCS (pp. 244-255). Springer Berlin Heidelberg.
DOI Scopus29
2008 Guo, M., & Conitzer, V. (2008). Optical-in-Expectation redistribution mechanisms. In L. Padgham, D. Parkes, J. Muller, & S. Parsons (Eds.), The Seventh International Conference on Autonomous Agents and Multiagent Systems (AAMAS-08), Estoril, Portugal - Proceedings Volume 1 (pp. 1047-1054). Online: Autonomous Agents and Multiagent Systems.
2008 Guo, M., & Conitzer, V. (2008). Undominated VCG redistribution mechanisms. In L. Padgham, D. Parkes, J. Muller, & S. Parsons (Eds.), The Seventh International Conference on Autonomous Agents and Multiagent Systems (AAMAS-08), Estoril, Portugal - Proceedings Volume 1 (pp. 1039-1046). Online: Autonomous Agents and Multiagent Systems.
2008 Guo, M., & Conitzer, V. (2008). Better redistribution with inefficient allocation in multi-unit auctions with unit demand. In EC '08 Proceedings of the 9th ACM Conference on Electronic Commerce (pp. 210-219). USA: Association for Computing Machinery.
DOI Scopus27
2008 Apt, K., Conitzer, V., Guo, M., & Markakis, E. (2008). Welfare undominated Groves mechanisms. In C. Papadimitriou, & S. Zhang (Eds.), Internet and Network Economics: 4th InternationalWorkshop, WINE 2008, Shanghai, China, December 17-20, 2008. Proceedings Vol. 5385 LNCS (pp. 426-437). Berlin, Germany: Springer.
DOI Scopus21
2008 Guo, M., & Conitzer, V. (2008). Undominated VCG redistribution mechanisms. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 2 (pp. 1021-1028).
Scopus25
2008 Guo, M., & Conitzer, V. (2008). Optimal-in-expectation redistribution mechanisms. In Proceedings of the International Joint Conference on Autonomous Agents and Multiagent Systems Aamas Vol. 2 (pp. 1029-1036).
Scopus19
2007 Guo, M., & Conitzer, V. (2007). Worst-case optimal redistribution of VCG payments. In EC 07 Proceedings of the Eighth Annual Conference on Electronic Commerce (pp. 30-39). ACM.
DOI Scopus35

Year Citation
2022 Goel, D., Ward-Graham, M. H., Neumann, A., Neumann, F., Nguyen, H., & Guo, M. (2022). Defending active directory by combining neural network based dynamic program and evolutionary diversity optimisation.. Poster session presented at the meeting of GECCO. ACM.

Year Citation
2010 Guo, M. (2010). Computationally Feasible Approaches to Automated Mechanism Design. (PhD Thesis).

Year Citation
2019 Iqbal, A., Guo, M., Gunn, L., Babar, M., & Abbott, D. (2019). Game theoretical modelling of network/cyber security [Review paper]. ArXiv.org.

Year Citation
2025 Ngo, H. Q., Guo, M., & Nguyen, H. (2025). Adaptive Wizard for Removing Cross-Tier Misconfigurations in Active
Directory.
2025 Zheng, L. N., Zhang, W. E., Guo, M., Xu, M., Maennel, O., & Chen, W. (2025). Rethinking Gating Mechanism in Sparse MoE: Handling Arbitrary Modality
Inputs with Confidence-Guided Gate.
2024 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2024). Evolutionary Multi-Objective Diversity Optimization..
2024 Ngo, H. Q., Guo, M., & Nguyen, H. (2024). Optimizing Cyber Response Time on Temporal Active Directory Networks
Using Decoys.
2023 Goel, D., Neumann, A., Neumann, F., Nguyen, H., & Guo, M. (2023). Evolving Reinforcement Learning Environment to Minimize Learner's Achievable Reward: An Application on Hardening Active Directory Systems..
2023 Guo, M., Li, J., Neumann, A., Neumann, F., & Nguyen, H. (2023). Limited Query Graph Connectivity Test..
2023 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2023). Diverse Approximations for Monotone Submodular Maximization Problems with a Matroid Constraint..
2023 Dinh, N. T., Karimi-Arpanahi, S., Pourmousavi, S. A., Yuan, R., Guo, M., Liisberg, J. A. R., & Lemos-Vinascco, J. (2023). Modelling Irrational Behaviour of Residential End Users using Non-Stationary Gaussian Processes..
2023 Guo, M., Li, J., Neumann, A., Neumann, F., & Nguyen, H. (2023). Limited Query Graph Connectivity Test.
2023 Dinh, N. T., Karimi-Arpanahi, S., Pourmousavi, S. A., Guo, M., Lemos-Vinasco, J., & Liisberg, J. A. R. (2023). On the Financial Consequences of Simplified Battery Sizing Models without Considering Operational Details..
2023 Goel, D., Shen, H., Tian, H., & Guo, M. (2023). Discovering Top-k Structural Hole Spanners in Dynamic Networks.
2022 Guo, M., Ward, M., Neumann, A., Neumann, F., & Nguyen, H. (2022). Scalable Edge Blocking Algorithms for Defending Active Directory Style Attack Graphs..
2021 Do, A. V., Guo, M., Neumann, A., & Neumann, F. (2021). Analysis of Evolutionary Diversity Optimisation for Permutation Problems..

Date Role Research Topic Program Degree Type Student Load Student Name
2026 Principal Supervisor Optimizing Token Economies: An Economic Perspective on Language Model Applications Doctor of Philosophy Doctorate Full Time Miss Shiman Sheng
2026 Principal Supervisor Agentic Commerce: Optimisation and Mechanism Design for Autonomous Economic Agents in Game-Theoretic Markets Doctor of Philosophy Doctorate Full Time Mr Huy Trung Truong
2026 Principal Supervisor Evaluating AI Agents as Autonomous Decision-Makers Master of Philosophy Master Full Time Mr Zhuyi Peng
2026 Principal Supervisor Optimizing AI Compilers for Robust Model Conversion and Cross-Framework Compatibility Doctor of Philosophy Doctorate Full Time Mr Yixin Chen
2025 Principal Supervisor Intelligent Decision Support System based on Game Theory and Reinforcement Learning Doctor of Philosophy Doctorate Full Time Mr Zhenghui Wang
2025 Co-Supervisor A Categorical Approach to Explainable Artificial Intelligence in Machine Learning Master of Philosophy Master Full Time Mr Quoc Dat Ngo
2025 Principal Supervisor Designing Revenue-Optimal Ad Frameworks and Auctions for User-Friendly Large-Language-Models Chats Doctor of Philosophy Doctorate Full Time Mr Felix Lysander Buecheler
2025 Principal Supervisor Redesigning Anatomical Pathology Workflow via Machine Learning and Optimisation Doctor of Philosophy Doctorate Full Time Mr Jialiang Li
2025 Co-Supervisor Soft Prompt Protection Against Adversrial Attack For Large Language Model Application Doctor of Philosophy Doctorate Full Time Mr Jinyang Li
2024 Principal Supervisor Defence Trailblazer project - Modelling malicious drone swarm behaviours Doctor of Philosophy Doctorate Full Time Mr Grant Cameron Douglas
2024 Co-Supervisor Multitasking evolutionary algorithms for combinatorial optimisation Doctor of Philosophy Doctorate Full Time Mr Liam Joshua Daly Wigney
2022 Co-Supervisor Scalable Hypergame models and solutions for Autonomous Cyber Operations. Doctor of Philosophy Doctorate Full Time Mr Quang Huy Ngo
2022 Co-Supervisor Scalable Hypergame models and solutions for Autonomous Cyber Operations. Doctor of Philosophy Doctorate Full Time Mr Quang Huy Ngo

Date Role Research Topic Program Degree Type Student Load Student Name
2022 - 2025 Co-Supervisor Theoretical and Experimental Analysis of Search Heuristics for Problems with Chance Constraints Doctor of Philosophy Doctorate Full Time Mr Xiankun Yan
2022 - 2026 Co-Supervisor Bio-inspired Computing for Problems with Chance Constraints Doctor of Philosophy Doctorate Full Time Mrs Kokila Perera
2021 - 2023 Principal Supervisor Enhancing Network Resilience through Machine Learning-powered Graph Combinatorial Optimization: Applications in Cyber Defense and Information Diffusion Doctor of Philosophy Doctorate Full Time Miss Diksha Goel
2021 - 2025 Co-Supervisor Modelling Consumer Behaviour and Optimising Community Battery for
Improved Efficiency in Local Energy Community		 Doctor of Philosophy Doctorate Full Time Mr Trong Nam Dinh
2021 - 2025 Principal Supervisor Redesigning Anatomical Pathology Workflow via Machine Learning and Optimisation Doctor of Philosophy Master Full Time Mr Jialiang Li
2020 - 2024 Co-Supervisor Deep Learning Based Multi-document Summarization Doctor of Philosophy Doctorate Full Time Ms Congbo Ma
2020 - 2024 Co-Supervisor Analysis of Search Heuristics for Diverse Solutions to Combinatorial Problems Doctor of Philosophy Doctorate Full Time Mr Viet Anh Do
2019 - 2022 Principal Supervisor Machine Learning Approaches to Automated Mechanism Design for Public Project Problem Doctor of Philosophy Doctorate Full Time Dr Guanhua Wang
2016 - 2020 Co-Supervisor Towards Quality-centric Design and Evaluation of Big Data Cyber Security Analytics Systems Doctor of Philosophy Doctorate Full Time Mr Faheem Ullah

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