Erdun Gao

Research Fellow

KIPS Group
RAIR; AIML
Adelaide University

Address: 206, AIML Building, Lot Fourteen, Adelaide
Email: erdun.gao AT outlook.com

About Me

I am a Research Fellow at the Responsible AI Research Centre (RAIR), Australian Institute for Machine Learning (AIML), where I am a member of the Kernels and Information Processing Systems (KIPS) Group led by Prof. Dino Sejdinovic. I received my Ph.D. in Applied Statistics (2025) from The University of Melbourne, where I was part of the Machine Learning and Reasoning (MLR) Group, advised by Prof. Mingming Gong and Prof. Howard Bondell. I also hold an M.S. in Signal and Information Processing (2020), supervised by Prof. Zhibin Pan, and a B.E. in Information Engineering (2017), both from Xi’an Jiaotong University.

Research Interests

My long-term research goal is to build reliable decision-making systems, with a focus on causal inference and uncertainty quantification. I believe that uncovering cause-and-effect relationships and properly accounting for uncertainty are key to making AI systems both robust and interpretable in high-stakes scenarios. I also occasionally work on broader machine learning problems related to data and models.

Core Causality: Causal Discovery, Causal Effect Estimation, Causal Experimental Design.
Causality & Machine Learning: Causal Foundation Models, Causal Generative Models.
Machine Learning: Data Valuation, Context-Informed Learning, Uncertainty in LLMs.

Main Papers

[Full List] [Google Scholar]

Observationally Informed Adaptive Causal Experimental Design, Preprint.
Erdun Gao, Liang Zhang, Jake Fawkes, Aoqi Zuo, Wenqin Liu, Haoxuan Li, Mingming Gong, Dino Sejdinovic
Causal-EPIG: A Prediction-Oriented Active Learning Framework for CATE Estimation, Preprint.
Erdun Gao, Jake Fawkes, Dino Sejdinovic
Concept Component Analysis: A Principled Approach for Concept Extraction in LLMs, Preprint.
Yuhang Liu, Erdun Gao, Dong Gong, Anton van den Hengel, Javen Qinfeng Shi
ActiveCQ: Active Estimation of Causal Quantities, ICLR, 2026.
Erdun Gao, Dino Sejdinovic
Domain Generalization via Content Factors Isolation: A Two-level Latent Variable Modeling Approach, MLJ, 2025.
Erdun Gao, Howard Bondell, Shaoli Huang, Mingming Gong
MissScore: High-Order Score Estimation in the Presence of Missing Data, ICML, 2025.
Wenqin Liu, Haoze Hou, Erdun Gao, Biwei Huang, Qiuhong Ke, Howard Bondell, Mingming Gong
A Variational Framework for Estimating Continuous Treatment Effects with Measurement Error, ICLR, 2024.
Erdun Gao, Howard Bondell, Wei Huang, Mingming Gong
Causal Discovery with Mixed Linear and Nonlinear Additive Noise Models: A Scalable Approach, CLeaR, 2024.
Wenqin Liu, Biwei Huang, Erdun Gao, Qiuhong Ke, Howard Bondell, Mingming Gong
FedDAG: Federated DAG Structure Learning, TMLR, 2023.
Erdun Gao, Junjia Chen, Li Shen, Tongliang Liu, Mingming Gong, Howard Bondell
MissDAG: Causal Discovery in the Presence of Missing Data with Continuous Additive Noise Models, NeurIPS, 2022.
Erdun Gao, Ignavier Ng, Mingming Gong, Li Shen, Wei Huang, Tongliang Liu, Kun Zhang, Howard Bondell

Experience

Research Fellow
Jan 2026 – Present

Responsible AI Research Centre, AIML&CSIRO
Researcher
July 2024 – Dec 2025

Centre for Augmented Reasoning, AIML
Postgraduate Studentship
Aug 2023 – Apr 2024

CSIRO
Research Intern
Mar 2021 – Mar 2022

JD Explore Academy
Research Intern
Oct 2020 – Mar 2021

Huawei Noah's Ark Lab

Honors & Awards

Science Abroad Travelling Scholarships (Unimelb)
2022, 2024
NeurIPS Scholar Award
2022
Australian Government RTP Scholarship
2021–2024
Outstanding Master Thesis Award (XJTU)
2020
Outstanding Graduate (XJTU)
2017, 2020
China National Scholarship
2019
First-class Academic Scholarship (XJTU)
2018, 2019
Outstanding Student (XJTU)
2014–2019
Tangzhongying Scholarship (XJTU)
2013–2017

Academic Service

Journal Reviewer: TMLR, TNNLS, TCSVT, MLJ, NN, JSCS
Conference Reviewer: ICML (2022–2026), ICLR (2023–2026), NeurIPS (2023, 2025), UAI (2022–2025)
Organising Committee: Next Generation Responsible AI Symposium (2025)