Research
At GLOW.AI, we are interested in learning and optimization on data-centric AI, collaborative AI, automated AI, and AI for science problems, their applications to LLMs & MLLMs, among others. See below for the research themes we work on.
Data selection, data valuation, knowledge distillation, data attribution, machine unlearning, data ownership, domain generalization
Federated learning, collaborative ML, incentives, reinforcement learning
Active learning, Bayesian & zeroth-order optimization, neural architecture search, meta-learning
Experimental design, physics-informed ML, quantum ML, precision agriculture
Recent News
See the list of our publications here.
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Aug 2024
Congrats! to Rachael Hwee Ling Sim and Xinyi Xu for receiving the prestigious Dean's Graduate Research Excellence Award and also to Jingtan Wang, Zhiliang Chen, Zhenfeng He, Gregory Lau, Xiaoqiang Lin, Zhuanghua Liu, Rui Qiao, and Zhaoxuan Wu for receiving the Research Achievement Award.
Read more here.
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Jul 2024
PINNACLE 🏔️ won the best paper award (out of 225 submissions) at the ICML 2024 Workshop on AI for Science!
Read more here.
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Jul 2024
Our AI Visiting Professorship Proposal (~S$4M for 3 years) on Data-Centric Machine Learning at Scale with Pang Wei Koh is accepted! It is featured on pages 29-30 in Issue 1 (Aug 2024) of Ignite, a new biannual magazine from the Office of the Deputy President (Research and Technology) of NUS.
Read more here.
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Jun 2024
Presented AutoAI and PINNACLE in Adaptive Experimentation Workshop @ Meta NYC.
Read more here.
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May 2024
Presented our works on Data-Centric AI in the Age of LLMs (FreeShap, WASA, and INSTINCT) in the Data-Centric AI workshop at The Web Conference 2024.
Read more here.
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Jul 2024
Bryan served as a Senior Area Chair of ICLR 2025, an Area Chair of AISTATS 2025, ICML 2024, NeurIPS 2024 & AAAI 2025, and a SPC of AAMAS 2025 & IJCAI 2024.