Zhongtian Sun

Dr Zhongtian Sun is a Lecturer in Artificial Intelligence and Machine Learning in the School of Computing at the University of Kent. He is also a Visiting Fellow in the Department of Computer Science and Technology at the University of Cambridge and a Mila AI Policy Fellow.

His research programme focuses on trustworthy artificial intelligence and representation learning, with particular interests in geometric and topological machine learning, graph and hypergraph learning, causal inference, AI safety, and reliable learning and reasoning. His work develops new methods for understanding and improving the reliability, robustness and interpretability of modern AI systems.

His research outputs span major international AI and machine-learning conferences and journals, including NeurIPS, ICML, ACL, The Web Conference, RecSys, ICAIF, AIED, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), AI Open and IJCNN. Applications of this research span healthcare, financial systems, education and other settings in which reliability, interpretability and accountability are important.

Dr Sun holds a PhD in Computer Science from Durham University. Before joining Kent, he conducted research at the University of Cambridge on machine learning for neuroscience and at the University of Oxford on large language models, knowledge graphs and reasoning. He previously worked in asset management, with experience in financial data analysis, quantitative research and investment-related applications of machine learning.

Alongside his academic research, Dr Sun contributes to international discussions on trustworthy and frontier AI. In 2026, he was selected as an expert to participate in the European Commission AI Office Expert Forum on Frontier AI and contributed expert input to the report Enhancing Competitiveness, Sovereignty and Security of the European Union in Frontier AI. He is also a Mila AI Policy Fellow and works on AI safety and system-level risks in agentic AI.  

Dr Sun's research focuses on trustworthy AI and representation learning, with an emphasis on developing principled methods for understanding, improving and evaluating modern learning systems.

• Trustworthy AI and AI Safety

Research on trustworthy and reliable AI systems, including robustness, interpretability, uncertainty, AI safety, reliable reasoning, and the evaluation of emerging AI systems.

• Geometric, Topological and Structured Representation Learning

Developing new approaches to representation learning using geometry, topology, sheaf theory, causal structure, graphs and hypergraphs. Current interests include geometric deep learning, topological machine learning, sheaf-based learning, graph and hypergraph neural networks, causal representation learning, and the structure and dynamics of learned representations.

• Reliable AI in High-Stakes and Human-Facing Systems

Developing and applying trustworthy AI methods in domains where reliability and accountability are particularly important, including healthcare, finance and education. Related interests include large language models, world models, agentic and multi-agent AI, neuro-symbolic reasoning, and AI-assisted decision making.  

Module Co-Lead responsibilities include module and curriculum design, coordination of teaching, and assessment design and delivery.

• COMP5009 — Software Development
• COMP6685 — Deep Learning
• COMP6028 — Natural Language Processing
• COMP7019 and COMP6004 — Machine Learning Algorithms

Dr Sun supervises doctoral researchers at the University of Kent and co-supervises doctoral researchers through research collaborations with the University of Cambridge and the University of Oxford. He also contributes to external PhD supervision and welcomes opportunities for external co-supervision where projects align closely with his research programme.

He welcomes prospective PhD researchers interested in trustworthy AI and AI safety, geometric, topological and sheaf-based machine learning, graph and hypergraph learning, causal representation learning, large language models and agentic AI, and reliable AI applications in healthcare and finance.

Prospective students are welcome to send a CV together with a research proposal identifying a specific research question, relevant existing literature, the limitations of current approaches and the proposed research contribution.  

Research leadership and academic service

• Competition Track Chair, ACM International Conference on AI in Finance (ICAIF 2026)
• General Chair, WIVACE 2026
• Area Chair, ICASSP 2026
• Area Chair, ICLR XAI4Science
• Area Chair, AAAI XAI4Science
• TKDE Special Track Organiser
• Senior PC Member and Regular Reviewer for major AI and machine-learning conferences including ICLR, ICML, NeurIPS, AAAI, ACL, ECAI, IJCNN, AIED, The Web Conference (WWW) and RecSys
• Reviewer for international journals including PNAS, Expert Systems with Applications, and IEEE Transactions on Knowledge and Data Engineering (TKDE)
• Association for Computing Machinery (ACM) Member
• Member, IEEE Task Force on Learning for Graphs

International appointments and policy engagement

• Visiting Fellow, Department of Computer Science and Technology, University of Cambridge
• Mila — Quebec AI Institute, AI Policy Fellow
• Selected Expert, European Commission AI Office Expert Forum on Frontier AI, 2026
• Contributor to the European Commission AI Office report Enhancing Competitiveness, Sovereignty and Security of the European Union in Frontier AI

European Commission AI Office report:
https://digital-strategy.ec.europa.eu/en/library/ai-office-publishes-frontier-ai-expert-findings-eu-competitiveness-sovereignty-and-security

Research translation and industry collaboration

• Academic Lead on an Innovate UK Knowledge Transfer Partnership (KTP)
• Research collaborations with major financial institutions on trustworthy AI, responsible AI and AI in high-stakes financial environments
• Collaborative projects with healthcare and industry partners on AI safety, trustworthy AI and reliable AI systems

Invited talks and public engagement

Invited Speaker — Mila AI Policy Conference, Montreal, Canada

Talk: Auditing System-Level Risks in Agentic AI

YouTube:
https://www.youtube.com/watch?si=r522laJ8PU3hPZNz&v=QmGHjuouRbQ&feature=youtu.be

Podcast Guest — Integrated Cancer Medicine: Research in Focus, University of Cambridge

Invited to discuss explainable multimodal AI for early cancer detection, including Hereditary Diffuse Gastric Cancer (HDGC) and Signet Ring Cell Carcinoma (SRCC), as well as multi-agent decision support grounded in clinical reasoning.

Spotify:
https://open.spotify.com/episode/2rpGkAoTPuJWgwtsYUQlgo

Podcast Guest — TIRIgogy ConnectED, 26th International Conference on Artificial Intelligence in Education

Invited to discuss Teaching, Tracing, and the AI-native Learner, drawing on SPAR-GNN research and teaching practice, including identifying and supporting at-risk learners, selective AI feedback, and teaching in the generative AI era.

Spotify:
https://open.spotify.com/episode/5TCm3wg2JWVe4lhU16JDWx?si=lg6CXPCETdCa9JKMWir3aw

Publications

Dr Sun's research outputs span trustworthy AI, representation learning, geometric and topological machine learning, causal learning, graph and hypergraph learning, large language models and reliable AI systems.

His work appears across major international AI and machine-learning conferences and journals, including NeurIPS, ICML, ACL, The Web Conference (WWW), RecSys, ICAIF, AIED, IEEE Transactions on Neural Networks and Learning Systems (TNNLS), AI Open and IJCNN.  

Additional Links

• University of Cambridge Profile
• Mila AI Policy Fellowship