Milad Ramezankhani
Applied Data Scientist, Mechanical Engineer, University of British Columbia
My research is focused on data-efficient and uncertainty-aware machine learning for advanced manufacturing. I develop reliable and transparent AI-assisted decision-making tools for high-risk manufacturing applications under data paucity. In particular, I focused on improving the generalization performance of machine learning in real-world applications with limited data, via transfer learning, active learning, physics-informed neural networks (PINNs) and meta-learning.
I am currently a postdoctoral research fellow at Materials and Manufacturing Research Institute (MMRI). I completed my PhD and Master’s degrees at The University of British Columbia in 2023 and 2017, respectively. I also worked as a data analyst at QHR Technologies.
news
Apr 27, 2023 | I successfully defended my PhD thesis entitled “Data-Efficient and Uncertainty-Aware Hybrid Machine Learning in Advanced Composites Manufacturing”. |
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Apr 14, 2023 | On April 17th, I will deliver a tutorial titled “When Data-efficient Machine Learning Comes to the Rescue: An AI-based Optimization Framework for Advanced Manufacturing” at SAMPE 2023 conference. Tutorial materials will be available here. |
May 1, 2022 | I will teach the graduate-level course Multicriteria optimization and design of experiment at UBC this summer. 👨🏻🏫 |
Apr 10, 2022 | Our paper on data-driven multi-fidelity physics-informed learning is accepted at ICPS 2022. |
Jun 17, 2021 | Our conference paper won the best presentation in session award at ICPS 2021. |