CIRES Research Postcards: Hechuan Wen

Reflections on research, travel, and collaboration

In 2025 as part of his PhD with CIRES, Dr Hechuan Wen undertook a visiting research placement at the University of Melbourne and presented his work at two major international conferences in Canada. These experiences provided opportunities to share his research, engage with researchers from different disciplines, and gain insight into current developments in the field.

Itinerary

• May-July 2025: Visiting Researcher, Machine Learning and Reasoning (MLR) Group, University of Melbourne. Hosted by Associate Professor Mingming Gong.
• July 2025: Presenter and Delegate, International Conference on Machine Learning (ICML) 2025, Vancouver, Canada.
• August 2025: Presenter and Delegate, ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025, Toronto, Canada.

Research Visit to the University of Melbourne

From May to July 2025, Hechuan completed a three-month visiting research placement with the Machine Learning and Reasoning (MLR) Group at the University of Melbourne, hosted by Associate Professor Mingming Gong. The visit provided an opportunity to work in a different research environment and engage closely with researchers specialising in machine learning.

Reflecting on the experience, Hechuan described the transition from his research group at The University of Queensland to the MLR Group as an opportunity to experience different approaches to research and collaboration. Working alongside researchers from diverse backgrounds broadened his understanding of the field and provided valuable professional connections beyond his immediate area of expertise.

The visit also provided an opportunity to develop new research ideas, collaborate with fellow researchers, and experience a different research environment.

Pictured above: Hechuan with colleagues from the MLR Group at The University of Melbourne.

Presenting at ICML 2025

In July 2025, Hechuan attended the International Conference on Machine Learning (ICML) 2025 in Vancouver, Canada, one of the leading conferences in machine learning research.

The conference provided insights into current developments across the field, including growing interest in large language models, foundation models, post-training techniques, and agentic systems. Through keynote presentations, technical sessions, workshops, and discussions with researchers, Hechuan gained insight into current research priorities and developments.

Alongside attending conference sessions, Hechuan presented his research during poster sessions, engaging with researchers and students from around the world. The experience provided feedback on his work and opportunities to discuss related research with international colleagues.

Publication: Wen, H., Chen, T., Gong, M., Chai, L. K., Sadiq, S., & Yin, H. (2025). Enhancing treatment effect estimation via active learning: A counterfactual covering perspective. In A. Singh, M. Fazel, D. Hsu, S. Lacoste-Julien, F. Berkenkamp, T. Maharaj, K. Wagstaff, & J. Zhu (Eds.), Proceedings of the 42nd International Conference on Machine Learning (Vol. 267, pp. 66437-66466). ML Research Press. https://proceedings.mlr.press/v267/wen25b.html

Pictured above: Hechuan and CIRES Chief Investigator Professor Fred Roosta during ICML 2025.

Sharing Research at KDD 2025

Following ICML, Hechuan attended the ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) 2025 in Toronto, Canada, where he presented his research through both oral and poster sessions.

The conference brought together researchers and practitioners from machine learning, artificial intelligence, and data science. Through keynote presentations, tutorials, workshops, and technical sessions, Hechuan observed many of the same themes discussed at ICML, including increasing interest in foundation models and agentic systems.

He found particular value in KDD’s combination of methodological research and real-world applications. Presenting his work also created opportunities for discussion with researchers working across a range of application areas.

Publication: Wen, H., Chen, T., Ye, G., Chai, L. K., Sadiq, S., & Yin, H. (2025). Progressive generalization risk reduction for data-efficient causal effect estimation. In Proceedings of the 31st ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD ’25). Association for Computing Machinery. https://doi.org/10.1145/3690624.3709305

Pictured above: Poster presentation at KDD 2025 in Toronto.

The Value of Global Research Engagement

Together, these experiences provided opportunities for Hechuan to work in different research environments, present his research internationally, and engage with researchers from a range of disciplines and institutions.

Reflecting on these opportunities, Hechuan said:

Connect with Hechuan