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Currently, I am working at Purdue University advised by Professor Bruno Ribeiro, I work on GNN models incorporating randomness to boost efficiency and performance, as well as addressing out-of-distribution challenges. I completed my bachelor's degree with Honors in Computer Science and Mathematics at Trinity College in 2021 under the supervision of Professor Ryan Pellico (Mathematics) and Professor Ewa Syta (Computer Science). Email / CV / Bio / Github / Google Scholar |
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- 10/2024: One paper accepted at Unifying Representations in Neural Models Workshop (UniReps) 2024.
- 10/2024: One paper published at Manufacturing Letters (MFGLET) 41.
- 05/2024: One paper accepted at Uncertainty in Artificial Intelligence (UAI) 2024.
- 03/2024: One paper accepted at North American Manufacturing Research Conference (NAMRC) 52.
- 08/2021: I started my journey with Purdue University
- 05/2021: I graduated Magna Cum Laude from Trinity College.
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My research focuses on graph learning and out-of-distribution problems. I work on improving efficiency and performance for GNN models by incorporating randomness as an alternative to end-to-end training. I’m also interested in enhancing the out-of-distribution robustness of deep neural networks using transformation invariances. |
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Thu Bui, Anugunj Naman, Carola-Bibiane Schönlieb, Bruno Ribeiro, Beatrice Bevilacqua, Moshe Eliasof, UniReps Workshop, 2024 | Under review, 2024 Extended Abstract Developed RAP-GNN, an efficient and performant alternative to the end-to-end training in GNNs, reducing runtime by up to 6 times and memory usage by up to 3 times while maintaining or even surpassing performanceby using random weights for message-passing. |
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Under review, 2024 Developed a test-time adaptation method for pretrained models accessed via any API, specifically targeting transformation-based out-of-distribution challenges, with a particular focus on color changes, achieving up to a 10% improvement over baseline models. |
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Mai Elkady, Thu Bui, Bruno Ribeiro, David I.Inouye, UAI, 2024 paper Developed a metric and data splitting method for evaluating Generative Graph Models, enhancing assessment by distinguishing meaningful and novel models from memorization of the training set or production of non-meaningful graphs. |
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Eunseob Kim, Thu Bui, Junyi Yuan, S Chandra Mouli, Bruno Ribeiro, Raymond A. Yeh, Michael P. Fassnacht, Martin B.G. Jun, NAMRC, 52 and Manufacturing Letters, 42 paper Introduced a novel approach to detect machining chatter by blending expert knowledge with CNNs. The framework digitizes machine tool and sound data, enhancing chatter event labeling accuracy. With an attention block, the model surpasses baselines in both in-distribution and out-of-distribution testing, achieving impressive accuracies of 96% on known and 94.51% on unknown machine tools. |
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Teaching assistant (2021-2022 and 2023 - Now)
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