I am currently a Research Fellow at Monash University, working on
neurosymbolic AI and its application to
intelligent autonomous systems
as a member of the
Vision & Language for Autonomous AI (VL4AI) Lab.
I am also an Honorary Research Fellow at the University of Melbourne, where I supervise
student research projects and am a member of the
Complex Human Data Hub
and the AI and Autonomy Lab.
I received my PhD in Artificial Intelligence
from the University of Melbourne, with a thesis titled
Planning and Goal Recognition in Humans and Machines.
My research combines learning with symbolic reasoning
to build agents that perceive, reason, and plan under uncertainty, and to develop
computational models that help AI systems better understand human
mental states, such as intentions, beliefs, and
goals, to enable more effective interaction with people. By drawing
on methods from automated planning, robotics, human-AI interaction, cognitive science,
and statistics,
I aim to build AI systems that are better aligned with human values and needs,
while also being more interpretable and explainable. My work has been
published in leading venues including
ICAPS, AAMAS, KR, EMNLP, CHI, CogSci, and
IEEE Robotics and Automation Letters, and I actively contribute to
the research community through conference organisation and reviewing, including service
for ICAPS, CogSci, IJCAI, ICRA, KR, and Robotics and Autonomous Systems.
I am advertising a paid summer research project, Active Verification of Action Theories from Informal Specifications, on using active learning to choose the most informative test cases for verifying and refining LLM-generated PDDL domains. Applications close 28 August 2026 — see Opportunities for the full description and how to apply.
Aug. 2026
Invited to give guest lectures on goal recognition in two postgraduate AI planning units: COMP90054 AI Planning for Autonomy at the University of Melbourne and FIT5222 Planning and Automated Reasoning at Monash University. The lectures introduce goal recognition as a problem setting and connect it to the planning techniques covered in both units.
Aug. 2026
Our paper Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents has been accepted at EMNLP 2026 — the Conference on Empirical Methods in Natural Language Processing. We introduce a benchmark for controlled studies of instruction granularity and show a non-monotonic U-shaped relationship between how finely a task is described and how well language-guided embodied agents perform.
Jul. 2026
Visited RWTH Aachen University and TU Darmstadt, giving a talk titled Towards Practical Human-Centred Goal Recognition at both. Thanks to my hosts for the hospitality and for many useful discussions on goal recognition and human-centred AI.
Jul. 2026
Presented our paper A Probabilistic Framework for Hierarchical Goal Recognition at KR 2026 — the 23rd International Conference on Principles of Knowledge Representation and Reasoning, held in Lisbon, Portugal (July 20–23).
May 2026
Honoured to be selected as a DAAD AINeT Fellow on AI in Neuroscience. The fellowship supports international networking and research collaboration between postdoctoral researchers in AI and neuroscience, with opportunities for visiting research stays at German institutions.
Apr. 2026
Our paper A Probabilistic Framework for Hierarchical Goal Recognition has been accepted at KR 2026 — the 23rd International Conference on Principles of Knowledge Representation and Reasoning. We propose a probabilistic framework that extends goal recognition to hierarchical goal structures.
Apr. 2026
Excited to be co-organising two workshops at ICAPS 2026 (Dublin, Ireland, June 27–July 2)! We are running HAXP (Human-Aware and Explainable Planning) and PR-BGI (Planning and Reasoning about Beliefs, Goals and Intentions). Both workshops bring together cutting-edge research on human-AI interaction and autonomous agent reasoning — submissions are welcome!
Feb. 2026
Our paper Proactive Assistance Agent with Online Goal Recognition has been accepted at ICAPS 2026 — the 36th International Conference on Automated Planning and Scheduling. This work addresses proactive assistance by combining online goal recognition with planning in goal-directed environments.
Jan. 2026
Our paper Neurosymbolic Active Goal Recognition in Partially Observable Environments has been accepted at AAMAS 2026 — the 25th International Conference on Autonomous Agents and Multiagent Systems. This work extends active goal recognition into partially observable settings using a neuro-symbolic approach.
Jul. 2025
Our paper Probabilistic Active Goal Recognition has been accepted at KR 2025 — the 22nd International Conference on Principles of Knowledge Representation and Reasoning. We present a probabilistic framework combining joint belief updates with Monte Carlo Tree Search for efficient active goal inference in multi-agent settings.
Jun. 2025
Our paper NEUSIS has been accepted in IEEE Robotics and Automation Letters. NEUSIS is a compositional neuro-symbolic framework that integrates visual perception, probabilistic world modelling, and hierarchical planning for autonomous UAV search missions in complex real-world environments.
Jun. 2025
Started a new position as Research Fellow (Level B) at Monash University, joining the HARNESS project (Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems). I am working on UAV algorithm design, developing hierarchical symbolic algorithms for autonomous navigation and search in unknown environments.
Apr. 2025
Our paper on modeling human sequential decision-making in the Tower of London has been accepted at CogSci 2025 — the 47th Annual Meeting of the Cognitive Science Society. We introduce a profile-based framework and a replanning detection component leveraging thinking time to model individual differences in planning behaviour.
Feb. 2025
Our paper Modeling Higher-Order Human Beliefs Using the Justified Perspective Model has been accepted at CHI 2025 Extended Abstracts — the ACM CHI Conference on Human Factors in Computing Systems, Yokohama, Japan. This work explores the feasibility of anticipating higher-order human beliefs using the Justified Perspective model.
Jun. 2024
Started as Research Fellow (Level B) at Monash University on the ARC discovery project Human Models for Accelerated Robot Learning and Human-Robot Interaction, and concurrently as Honorary Research Fellow at the University of Melbourne, where I supervise student research projects.
May 2024
Honoured to receive the Pragnesh Jay Modi Best Paper Award at AAMAS 2024 (Auckland, NZ) for our paper Human Goal Recognition as Bayesian Inference. This award recognises the best paper at the premier conference on autonomous agents and multi-agent systems.
May 2024
Successfully defended my PhD thesis, Planning and Goal Recognition in Humans and Machines, at the University of Melbourne. Grateful to my supervisors Associate Professor Nir Lipovetzky and Professor Charles Kemp for their guidance throughout my doctoral journey.
Dec. 2023
Our paper Human Goal Recognition as Bayesian Inference has been accepted at AAMAS 2024 — the 23rd International Conference on Autonomous Agents and Multiagent Systems. We use a Bayesian framework to show that actions, timing, and goal solvability all play a role in how humans recognise others' goals.
Apr. 2023
Our paper Comparing AI Planning Algorithms With Humans on the Tower of London Task has been accepted at CogSci 2023 — the 45th Annual Meeting of the Cognitive Science Society. We show that an adaptive lookahead planner provides the best account of human actions and planning times on this classic task.
Feb. 2023
Our paper Goal Recognition With Timing Information has been accepted at ICAPS 2023 — the 33rd International Conference on Automated Planning and Scheduling. We show that incorporating action timing substantially improves goal recognition, especially when few actions have been observed.
Publications
2026
Neurosymbolic Active Goal Recognition in Partially Observable Environments
25th International Conference on Autonomous Agents and Multiagent Systems AAMAS 2026CORE A*
Chenyuan Zhang, Sukai Huang, Hamid Rezatofighi, Mor Vered, Buser Say 1st author
Proactive Assistance Agent with Online Goal Recognition
36th International Conference on Automated Planning and Scheduling ICAPS 2026CORE A*
Effective human–AI collaboration requires agents to do more than simply follow instructions; they must also infer and anticipate human intentions. This ability to understand and predict others' goals is also important in decentralized multi-agent settings, where communication is limited. Although goal recognition has been widely studied in the planning community, its integration with downstream collaborative behaviors, such as determining when and how an agent should assist, remains underexplored. This paper presents a unified model-based framework that combines online goal recognition with a proactive assistance module, enabling an agent to provide efficient support without prior knowledge of another agent's true objective. Experiments in a grid domain show our proposed approach significantly improves collaborative efficiency over existing algorithms and demonstrates strong robustness across diverse configurations. By integrating goal recognition with other decision-making components, this work establishes a solid algorithmic foundation and an extensible framework for the capabilities required in next-generation human–AI collaboration.
@article{shen2026proactive,
title = {Proactive Assistance Agent with Online Goal Recognition},
author = {Shen, Qihao and Hu, Guang and Zhang, Chenyuan},
journal = {Proceedings of the International Conference on Automated Planning and Scheduling},
volume = {36},
number = {1},
pages = {275--283},
year = {2026},
doi = {10.1609/icaps.v36i1.42837}
}
A Probabilistic Framework for Hierarchical Goal Recognition
23rd International Conference on Principles of Knowledge Representation and Reasoning KR 2026CORE A*
Goal recognition aims to infer an agent's goal from observations of its behaviour. In realistic settings, recognition can benefit from exploiting hierarchical task structure and reasoning under uncertainty. Planning-based goal recognition has made substantial progress over the past decade, but to the best of our knowledge no existing approach jointly integrates hierarchical task structure with probabilistic inference. In this paper, we introduce the first planning-based probabilistic framework for hierarchical goal recognition over Hierarchical Task Networks (HTNs). We instantiate the framework by exploiting an HTN planner with a three-stage generative model for likelihood estimation, yielding posterior distributions over goal hypotheses. Empirical results show improved recognition performance over the existing HTN-based recognizer on HTN benchmarks. Overall, the framework lays a foundation for probabilistic goal recognition grounded in hierarchical planning structure, moving goal recognition toward more practical settings.
@inproceedings{zhang2026probabilistic,
title = {A Probabilistic Framework for Hierarchical Goal Recognition},
author = {Zhang, Chenyuan and Ip, Katherine and Rezatofighi, Hamid and Say, Buser and Vered, Mor},
booktitle = {Proceedings of the 23rd International Conference on Principles of Knowledge Representation and Reasoning},
pages = {688--698},
year = {2026},
doi = {10.24963/kr.2026/65}
}
Mini-BEHAVIOR-Gran: Revealing U-Shaped Effects of Instruction Granularity on Language-Guided Embodied Agents
Conference on Empirical Methods in Natural Language Processing EMNLP 2026CORE A*
Instruction granularity is an important yet poorly controlled variable in language-guided embodied AI. Existing benchmarks typically pair each task with a single static instruction, making it difficult to study how agent behavior changes when the same task is described at different levels of detail. We introduce Mini-BEHAVIOR-Gran, a new benchmark for controlled studies of instruction granularity that extends Mini-BEHAVIOR with multiple instruction variants per task, ranging from high-level goal descriptions to step-by-step guidance. Using this benchmark, we compare four candidate metrics for cross-task granularity quantification: token count, entity count, action-verb count, and planning-width, and find that width correlates most consistently with agent performance. Using width to organize training and evaluation further reveals a non-monotonic U-shaped relationship between instruction granularity and performance, with peaks at both fine and coarse extremes. Further analysis suggests that the coarse-granularity performance rebound is associated with shallow grounding, where agents learn vision-dominant policies.
What We Talk About When We Talk About LLM Planning: Evidence for Two Distinct Planning Abilities
When LLMs exhibit uneven performance across planning tasks, these gaps are often attributed to task difficulty. We argue that this explanation is incomplete, as task-level variation may reflect distinct latent planning competencies rather than differences along a single ability spectrum. We study this question on ACPBench-Hard by evaluating multiple LLM families under varying test-time reasoning budgets and applying a multidimensional item response theory model to uncover the latent competency structure underlying LLM planning. The analysis reveals two principal dimensions that shape planning performance: operational reasoning, the ability to evaluate local action applicability and immediate state transitions, and structural enumeration, the ability to reason about goal reachability and landmark structure. Operational reasoning improving under model scaling and longer reasoning traces, while structural enumeration remains comparatively insensitive. Our findings motivate competency-level evaluation of LLM planning, shifting the focus from whether models improve overall to which planning competencies improve, under what conditions, and why.
2025
Probabilistic Active Goal Recognition
22nd International Conference on Principles of Knowledge Representation and Reasoning KR 2025CORE A*
Chenyuan Zhang, Cristian Rojas Cardenas, Hamid Rezatofighi, Mor Vered, Buser Say 1st author
In multi-agent environments, effective interaction hinges on understanding the beliefs and intentions of other agents. While prior work on goal recognition has largely treated the observer as a passive reasoner, Active Goal Recognition (AGR) focuses on strategically gathering information to reduce uncertainty. We adopt a probabilistic framework for AGR and propose an integrated solution that combines a joint belief update mechanism with a Monte Carlo Tree Search (MCTS) algorithm, allowing the observer to plan efficiently and infer the actor's hidden goal without requiring domain-specific knowledge. Through comprehensive empirical evaluation in a grid-based domain, we show that our joint belief update significantly outperforms passive goal recognition, and that our domain-independent MCTS performs comparably to our strong domain-specific greedy baseline. These results establish our solution as a practical and robust framework for goal inference, advancing the field toward more interactive and adaptive multi-agent systems.
@inproceedings{zhang2025probabilistic,
title = {Probabilistic Active Goal Recognition},
author = {Zhang, Chenyuan and Rojas Cardenas, Cristian and Rezatofighi, Hamid and Vered, Mor and Say, Buser},
booktitle = {Proceedings of the 22nd International Conference on Principles of Knowledge Representation and Reasoning},
pages = {880--890},
year = {2025},
doi = {10.24963/kr.2025/85}
}
Modeling Human Sequential Decision-Making in the Tower of London: Incorporating Individual Differences and Timing-Based Replanning Inference
47th Annual Meeting of the Cognitive Science Society CogSci 2025CORE B
Chenyuan Zhang, Yuansan Liu, Dana Kulić, Pamela Carreno-Medrano, Michael Burke 1st author
Modeling human sequential decision-making behavior presents a significant challenge for researchers in artificial intelligence, robotics, and cognitive science. In this paper, we introduce a human behavior model designed to predict actions in the Tower of London task, addressing two critical aspects that have been largely overlooked in existing methodologies. First, we propose a profile-based action prediction framework that extracts user and task profiles from historical data, enhancing action prediction in novel scenarios. Second, we introduce a replanning detection component that leverages thinking time as an indicator of planning processes in the human mind, enabling a more precise representation of cognitive dynamics. Our evaluations demonstrate the effectiveness of the proposed model, achieving superior performance in behavior prediction within the Tower of London task. This work lays the foundation for more robust human behavior modeling in sequential decision-making environments.
@inproceedings{zhang2025modeling,
title = {Modeling Human Sequential Decision-Making in the Tower of London: Incorporating Individual Differences and Timing-Based Replanning Inference},
author = {Zhang, Chenyuan and Liu, Yuansan and Kuli\'{c}, Dana and Carreno-Medrano, Pamela and Burke, Michael},
booktitle = {Proceedings of the 47th Annual Meeting of the Cognitive Science Society},
year = {2025}
}
Modeling Higher-Order Human Beliefs Using the Justified Perspective Model
CHI Conference on Human Factors in Computing Systems – Extended Abstracts CHI 2025CORE A*
This work presents a pilot study involving five participants to explore the feasibility of anticipating higher-order human beliefs through a generalizable formalization using the Justified Perspective (JP) model, and introduces a novel processing algorithm inspired by Item Response Theory (IRT) to effectively estimate human belief reasoning ability from their responses across different scenarios. The preliminary results suggest that human reasoning ability is consistent within the JP model framework, and is positively correlated with nesting ability. The findings highlight the potential of bridging theoretical epistemic frameworks with practical human-centered applications, providing valuable insights for the design of intelligent human-computer interaction systems that more effectively comprehend human cognition.
@inproceedings{li2025modeling,
title = {Modeling Higher-Order Human Beliefs Using the Justified Perspective Model},
author = {Li, Wanchun and Zhang, Chenyuan and Li, Weijia and Hu, Guang and Xu, Yangmengfei},
booktitle = {Extended Abstracts of the CHI Conference on Human Factors in Computing Systems},
year = {2025},
doi = {10.1145/3706599.3720223}
}
NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions
IEEE Robotics and Automation Letters RA-L 2025Q1 Journal
Zhixi Cai, Cristian Rojas Cardenas, Kevin Leo, Chenyuan Zhang, et al. co-1st author
This paper addresses the problem of autonomous UAV search missions, where a UAV must locate specific Entities of Interest (EOIs) within a time limit, based on brief descriptions in large, hazard-prone environments with keep-out zones. The UAV must perceive, reason, and make decisions with limited and uncertain information. We propose NEUSIS, a compositional neuro-symbolic system designed for interpretable UAV search and navigation in realistic scenarios. NEUSIS integrates neuro-symbolic visual perception, reasoning, and grounding (GRiD) to process raw sensory inputs, maintains a probabilistic world model for environment representation, and uses a hierarchical planning component (SNaC) for efficient path planning. Experimental results from simulated urban search missions using AirSim and Unreal Engine show that NEUSIS outperforms a state-of-the-art vision-language model and a state-of-the-art search planning model in success rate, search efficiency, and 3D localization. These results demonstrate the effectiveness of our compositional neuro-symbolic approach in handling complex, real-world scenarios, making it a promising solution for autonomous UAV systems in search missions.
@article{cai2025neusis,
title = {NEUSIS: A Compositional Neuro-Symbolic Framework for Autonomous Perception, Reasoning, and Planning in Complex UAV Search Missions},
author = {Cai, Zhixi and Rojas Cardenas, Cristian and Leo, Kevin and Zhang, Chenyuan and Backman, Kal and Li, Hanbing and Li, Boying and Ghorbanali, Mahsa and Datta, Stavya and Qu, Lizhen and Gutierrez Santiago, Julian and Ignatiev, Alexey and Li, Yuan-Fang and Vered, Mor and Stuckey, Peter J. and Garcia de la Banda, Maria and Rezatofighi, Hamid},
journal = {IEEE Robotics and Automation Letters},
volume = {10},
number = {9},
pages = {9502--9509},
year = {2025}
}
2024
Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability
23rd International Conference on Autonomous Agents and Multiagent Systems AAMAS 2024CORE A*
Chenyuan Zhang, Charles Kemp, Nir Lipovetzky 1st author
Goal recognition is a fundamental cognitive process that enables individuals to infer intentions based on available cues. Current goal recognition algorithms often take only observed actions as input, but here we use a Bayesian framework to explore the role of actions, timing, and goal solvability in goal recognition. We analyze human responses to goal-recognition problems in the Sokoban domain, and find that actions are assigned most importance, but that timing and solvability also influence goal recognition in some cases, especially when actions are uninformative. We leverage these findings to develop a goal recognition model that matches human inferences more closely than do existing algorithms. Our work provides new insight into human goal recognition and takes a step towards more human-like AI models.
@inproceedings{zhang2024human,
title = {Human Goal Recognition as Bayesian Inference: Investigating the Impact of Actions, Timing, and Goal Solvability},
author = {Zhang, Chenyuan and Kemp, Charles and Lipovetzky, Nir},
booktitle = {Proceedings of the 23rd International Conference on Autonomous Agents and Multiagent Systems},
year = {2024},
doi = {10.5555/3635637.3663071}
}
2023
Goal Recognition With Timing Information
33rd International Conference on Automated Planning and Scheduling ICAPS 2023CORE A*
Chenyuan Zhang, Charles Kemp, Nir Lipovetzky 1st author
Goal recognition has been extensively studied by AI researchers, but most algorithms take only observed actions as input. Here we argue that the time taken to carry out these actions provides an additional signal that supports goal recognition. We present a behavioral experiment confirming that people use timing information in this way, and develop and evaluate a goal recognition algorithm that is sensitive to both actions and timing information. Our results suggest that existing goal recognition algorithms can be improved by incorporating a model of planning time on both synthetic data and human data, and that these improvements can be substantial in scenarios in which relatively few actions have been observed.
@article{zhang2023goal,
title = {Goal Recognition with Timing Information},
author = {Zhang, Chenyuan and Kemp, Charles and Lipovetzky, Nir},
journal = {Proceedings of the International Conference on Automated Planning and Scheduling},
volume = {33},
number = {1},
pages = {443--451},
year = {2023},
doi = {10.1609/icaps.v33i1.27224}
}
Comparing AI Planning Algorithms With Humans on the Tower of London Task
45th Annual Meeting of the Cognitive Science Society CogSci 2023CORE A
Chenyuan Zhang, Nir Lipovetzky, Charles Kemp 1st author
This paper explores the extent to which modern AI planners can account for human performance on the Tower of London (TOL) task, a sequential planning puzzle extensively studied in cognitive psychology. We characterize the task using the Planning Domain Definition Language (PDDL) and evaluate an adaptive online planner alongside a family of optimal and satisficing planners. Each planner is assessed on its ability to predict the actions and planning times of participants in a new behavioral experiment. The results suggest that participants use a range of strategies but that an adaptive lookahead planner provides the best overall account of both human actions and planning times.
@inproceedings{zhang2023comparing,
title = {Comparing AI Planning Algorithms With Humans on the Tower of London Task},
author = {Zhang, Chenyuan and Lipovetzky, Nir and Kemp, Charles},
booktitle = {Proceedings of the 45th Annual Meeting of the Cognitive Science Society},
year = {2023}
}
Planning and Goal Recognition in Humans and Machines
Doctoral thesis, School of Computing and Information Systems, University of Melbourne PhD Thesis
Chenyuan Zhang
2018
Improving Motivation Through Real-Time fMRI-Based Self-Regulation of the Nucleus Accumbens
Neuropsychology, 32(6), 764–776 Journal
Zhi Li, Chen-Yuan Zhang, Jia Huang, Yi Wang, Chao Yan, Ke Li, Ya-Wei Zeng, Zhen Jin, Eric F. C. Cheung, Li Su, Raymond C. K. Chan 2nd author
Objective: Impaired nucleus accumbens (NAcc) activation is associated with amotivation and anhedonia, which are resistant to treatment with antipsychotics and antidepressants in schizophrenia. This study trained healthy participants to self-regulate NAcc activation using real-time fMRI neurofeedback. Method: An experimental group (N = 19) receiving NAcc feedback was compared with a control group (N = 5) receiving sham feedback from the posterior parahippocampal gyrus; participants used mental strategies to regulate NAcc activation in a 3T MRI scanner. Results: Seventy-four percent of the experimental group successfully learned to self-regulate the NAcc. These participants showed improved behavioral performance in motivation and decreased functional connectivity between the NAcc and the ventral medial prefrontal cortex, together with increased small-world properties in the reward circuit, suggesting enhanced information integration. The sham control group and participants who failed to self-regulate showed no such improvements. Conclusions: The NAcc could be self-regulated using real-time fMRI neurofeedback and can result in improved motivation in cognitive tasks, with baseline motivation influencing success in self-regulation training.
@article{li2018improving,
title = {Improving Motivation Through Real-Time fMRI-Based Self-Regulation of the Nucleus Accumbens},
author = {Li, Zhi and Zhang, Chen-Yuan and Huang, Jia and Wang, Yi and Yan, Chao and Li, Ke and Zeng, Ya-Wei and Jin, Zhen and Cheung, Eric F. C. and Su, Li and Chan, Raymond C. K.},
journal = {Neuropsychology},
volume = {32},
number = {6},
pages = {764--776},
year = {2018},
doi = {10.1037/neu0000425}
}
2016
Structural Neural Correlates of Multitasking: A Voxel-Based Morphometry Study
PsyCh Journal, 5(4), 219–227 Journal
Rui-Ting Zhang, Tian-Xiao Yang, Yi Wang, Yuxiu Sui, Jingjing Yao, Chen-Yuan Zhang, Eric F. C. Cheung, Raymond C. K. Chan co-author
Multitasking refers to the ability to organize assorted tasks efficiently in a short period of time, which plays an important role in daily life. However, the structural neural correlates of multitasking performance remain unclear. The present study aimed at exploring the brain regions associated with multitasking performance using global correlation analysis. Twenty-six healthy participants first underwent structural brain scans and then performed the modified Six Element Test, which required participants to attempt six subtasks in 10 min while obeying a specific rule. Voxel-based morphometry of the whole brain was used to detect the structural correlates of multitasking ability. Grey matter volume of the anterior cingulate cortex (ACC) was positively correlated with the overall performance and time monitoring in multitasking. In addition, white matter volume of the anterior thalamic radiation (ATR) was also positively correlated with time monitoring during multitasking. Other related brain regions associated with multitasking included the superior frontal gyrus, the inferior occipital gyrus, the lingual gyrus, and the inferior longitudinal fasciculus. No significant correlation was found between grey matter volume of the prefrontal cortex (Brodmann Area 10) and multitasking performance. Using a global correlation analysis to examine various aspects of multitasking performance, this study provided new insights into the structural neural correlates of multitasking ability. In particular, the ACC was identified as an important brain region that played both a general and a specific time-monitoring role in multitasking, extending the role of the ACC from lesioned populations to healthy populations. The present findings also support the view that the ATR may influence multitasking performance by affecting time-monitoring abilities.
@article{zhang2016structural,
title = {Structural Neural Correlates of Multitasking: A Voxel-Based Morphometry Study},
author = {Zhang, Rui-Ting and Yang, Tian-Xiao and Wang, Yi and Sui, Yuxiu and Yao, Jingjing and Zhang, Chen-Yuan and Cheung, Eric F. C. and Chan, Raymond C. K.},
journal = {PsyCh Journal},
volume = {5},
number = {4},
pages = {219--227},
year = {2016},
doi = {10.1002/pchj.137}
}
A Trend Toward Smaller Optical Angles and Medial-Ocular Distance in Schizophrenia Spectrum, but Not in Bipolar and Major Depressive Disorders
PsyCh Journal, 5(4), 228–237 Journal
Yi Wang, Yi Deng, Zhi Li, Xu Li, Chen-Yuan Zhang, Zhen Jin, Ming-Xia Fan, Michael T. Compton, Eric F. C. Cheung, Kelvin O. Lim, Raymond C. K. Chan co-author
Minor physical anomalies (MPAs) are subtle signs of fetal developmental abnormalities that have been considered to be among the most replicated risk markers for schizophrenia-spectrum disorders. However, quantitative approaches are needed to measure craniofacial MPAs. The present study adopted an imaging-based quantitative approach to examine craniofacial MPAs across the spectrum of schizophrenia and affective disorders, to address their sensitivity and specificity. We sampled 31 patients with schizophrenia, 30 of their unaffected relatives, and 30 individuals with schizotypal personality traits, as well as 37 non-schizotypal controls. We also examined 17 patients with bipolar disorder and 19 patients with major depressive disorder. Five craniofacial MPAs were measured on anterior-posterior commissure-aligned T1-weighted images of an individual's native brain space: medial-ocular distance, lateral-ocular distance, optical angle, maximum skull length, and skull-base width. Compared to non-schizotypal controls, patients with schizophrenia and their relatives showed a trend toward having smaller optical angles and medial-ocular distance, while no difference was found in patients with bipolar or major depressive disorders, suggesting some degree of specificity to schizophrenia. Our approach may benefit future research on craniofacial MPAs as risk markers for schizophrenia-spectrum disorders, and may eventually be useful in strategies to enhance risk stratification using multiple risk markers.
@article{wang2016trend,
title = {A Trend Toward Smaller Optical Angles and Medial-Ocular Distance in Schizophrenia Spectrum, but Not in Bipolar and Major Depressive Disorders},
author = {Wang, Yi and Deng, Yi and Li, Zhi and Li, Xu and Zhang, Chen-Yuan and Jin, Zhen and Fan, Ming-Xia and Compton, Michael T. and Cheung, Eric F. C. and Lim, Kelvin O. and Chan, Raymond C. K.},
journal = {PsyCh Journal},
volume = {5},
number = {4},
pages = {228--237},
year = {2016},
doi = {10.1002/pchj.138}
}
Teaching
2026 Guest Lecturer
COMP90054 AI Planning for AutonomyUniversity of Melbourne
FIT5222 Planning and Automated ReasoningMonash University
2026 Teaching Associate
FIT5047 Fundamentals of Artificial IntelligenceMonash University
2020–2024 Teaching Associate
COMP90054 AI Planning for AutonomyUniversity of Melbourne
COMP30027 Machine LearningUniversity of Melbourne
COMP90038 Algorithms and ComplexityUniversity of Melbourne
COMP30024 Artificial IntelligenceUniversity of Melbourne
COMP30026 Models of ComputationUniversity of Melbourne
Opportunities
Open — applications close 28 August 2026, 5pm
Paid Summer Research Project: PDDL Domain Generation with LLMs and SAT-Based Verification
Large Language Models (LLMs) can generate PDDL action theories from informal specifications, but the generated domains may contain missing preconditions, incorrect effects, or unnecessary actions.
The goal of this project is to introduce an active-learning approach to test-case selection. We will investigate how to automatically select the most informative planning tasks for testing and refining an LLM-generated PDDL domain, which could make the verification and refinement process more efficient and effective.
The project will involve PDDL, Large Language Models, active learning, and SAT-based verification. It is particularly suitable for students who have taken COMP90054 AI Planning for Autonomy and have experience with LLMs.
Project
4033 — Active Verification of Action Theories from Informal Specifications
Opportunity
Paid summer research project
Start date
23 November 2026 (negotiable), for 12 weeks
Value
$2,400 ($200 × 12 weeks)
Prerequisite
COMP90054 AI Planning for Autonomy (University of Melbourne) or FIT5222 Planning and Automated Reasoning (Monash). You may be taking either unit this semester, but this must not be your final semester — you need to still be enrolled while the project runs. Experience developing or deploying LLM-based systems is preferred.
Interview
Shortlisted applicants will be interviewed 15–19 September 2026.
