Bryan Kian Hsiang Low
@bryanklow
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Associate Vice President (AI) @NUSingapore, Assoc. Prof. @NUSComputing, Director @AISingapore. #AutoML #BayesianOptimization #FederatedLearning #DataCentricAI
Singapore
Joined August 2020
Inspired by the success of inverse problems in uncovering fundamental scientific laws, our position paper, which is accepted to @emnlpmeeting #EMNLP2025 (findings), argues that inverse problems can be used to efficiently uncover underlying scaling laws for #LLMs that help the
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Joint work with @_Hu_Wenyang @greglau et al. on Dipper (Diversity in Prompts for Producing #LLM Ensembles in Reasoning tasks) is accepted to @emnlpmeeting #EMNLP2025 (main)!.
Given a single model, how do we improve an #LLM’s reasoning performance with limited resources 💻 and inference time⌛️? Can a smaller 1.5B model outperform a 7B model without incurring long inference time from sequential queries? (1/n). @NeurIPSConf #NeurIPS2024 #LLMs
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RT @NUSingapore: Discover how AI education and research initiatives at NUS are nurturing the next generation of skilled AI leaders and prof….
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🔍 Ever wondered who contributed to an #LLM's generated text?.🎯 We introduce WASA (est. since Oct 2023) — a powerful framework for Data Source Attribution via #Watermarking!. 🧠 Our method embeds information on the data source directly into synthetic text — reliable, robust &
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Take a pause and watch sth not related to AI: . Then, scroll to the like button on that youtube video and click it please 🤣😂😅. A brief moment of our children's lives in an elementary/primary school in Singapore.
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🚀 Is slow #LLM inference wasting your compute? .🤔 Ever wondered how compute resources for inference should be better utilized?. We've got a new game plan! 🎮. Presenting TETRIS, our work on speeding up batch #SpeculativeDecoding under limited compute resources! We dynamically
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Join us at the “Singapore AI Frontier Week 2025”, an exciting week of events bringing together researchers, industry experts and companies to discuss and explore the future of AI!. Co-organised by IMDA, National University of Singapore (NUS), Microsoft Research Asia (MSRA),.
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When a company claims that your personal data has been removed from their model, have you ever wondered whether they've indeed done so? 🤔 . If a new paper on arXiv claims that its proposed #MachineUnlearning algorithm can unlearn your personal data from an #LLM, how do we know
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🤔How can we reliably use #Multimodal Large Language Models (#MLLMs) in practical settings? With multiple modalities come more challenges in managing uncertainty and mitigating errors where responses may seem plausible but are incorrect 😱. Introducing ⚖️UMPIRE, an
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Obtaining 🔎 interpretable symbolic equations is critical for scientific understanding and decision making in high-stakes environment. In fact, many such real-world applications (e.g., interactive/adaptive experiments, real-time decision support) demand rapid results within
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Come meet @JingtanW @ray_qiaorui at @icmlconf #ICML2025 E-2312 poster to know more about NICE #DataSelection in #LLMs.
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