معرفی
Hadi M. Dolatabadi is a Research Fellow in machine learning at the University of Melbourne node of the ARC Centre of Excellence for Automated Decision-Making & Society (ADM+S). He is affiliated with the School of Computing and Information Systems at the University of Melbourne, where he has nearly completed his Ph.D. focusing on robustness in deep learning. His research contributes to the ADM+S Centre's Machines Research Program, specifically developing algorithms for systematic treatment of bias and unfairness in AI systems.
Hadi earned his Ph.D. from the School of Computing and Information Systems at the University of Melbourne, with his thesis examining current notions of robustness in neural networks and challenging them from novel perspectives. His doctoral research bridges theoretical foundations with practical applications in machine learning.
Hadi's research interests center on machine learning with a strong emphasis on robustness and fairness. He specializes in adversarial robustness, coreset selection, and generative modeling techniques including normalizing flows, GANs, and diffusion models. His approach uniquely combines statistical perspectives with generative modeling frameworks to address fundamental challenges in AI. His work has significant implications for developing more reliable and equitable automated decision-making systems, particularly in contexts where bias and unfairness could have serious societal consequences.
Analysis of Hadi's publication record reveals a strong focus on the intersection of theoretical machine learning and practical AI ethics. His work consistently addresses the tension between model performance and robustness, while increasingly incorporating fairness considerations. The progression of his research shows a movement from foundational robustness questions toward more applied problems related to bias detection and mitigation in real-world AI systems. His publications span top-tier AI conferences including NeurIPS, ECCV, and AISTATS, demonstrating both technical depth and relevance to contemporary AI challenges.
Hadi completed a six-month research internship at Amazon Science during his Ph.D. studies, gaining valuable industry experience while maintaining strong academic research output. His technical expertise spans both theoretical aspects of machine learning and practical implementation of complex algorithms. While specific grants aren't mentioned in the available information, his position at the ARC Centre of Excellence indicates involvement in significant collaborative research funding.
As part of the ADM+S Centre's Machines Research Program, Hadi contributes to a multidisciplinary team addressing challenges in automated decision-making. The Centre brings together researchers from humanities, social sciences, and technological fields to create knowledge and strategies for responsible, ethical, and inclusive automated decision-making systems. Hadi's technical expertise in machine learning provides crucial foundation for the Centre's work on bias and fairness in AI systems.
