Awards

 

 

  • Date: 27 Sep 2024


    Awarded to Assistant Professor Li Qianxiao from the Department of Mathematics for his innovative work on the mathematical foundations of deep learning, a crucial technology that underpins the success and scalability of modern AI systems.

    Qian Xiao, who is also a Presidential Young Professor, has provided through his research the rigorous mathematical frameworks necessary for understanding and improving deep learning methodologies, laying the groundwork for future innovations in the field. His contributions have not only improved our understanding of how and when deep learning works, but also how to systematically improve its performance in new application settings.

     

 

  • Date: July 22, 2024


    Pinnacle: PINN Adaptive Collocation and Experimental Point Selection

    Authors: Gregory Kang Ruey Lau, Apivich Hemachandra, See-Kiong Ng, Bryan Low Kian Hsiang

    Awarded to Gregory Kang Ruey Lau at the 41st International Conference on Machine Learning (ICML 2024)

    Download the paper

     

 

  • Date: 8 Mar 2024


    Our director Professor Wynne Hsu has been awarded as one of the winners for the Asia Women Tech Leaders 2024 award.

    Professor Wynne Hsu, leading the Institute of Data Science at the National University of Singapore, is a key figure in data science and AI in healthcare, focusing on developing new machine learning techniques for real-world applications. Her significant contributions include SELENA+, a deep learning-based solution for the screening of eye disease (diabetic retinopathy, glaucoma, cataract) from retinal images and has co-founded Eyris Pte Ltd. She is the lead-PI in the AISG Grand Challenge in Healthcare to combat diabetes, hypertension, and dyslipidaemia in Singapore, with ongoing clinical trials at SingHealth.

    Her approach focuses on improving the management of chronic disease, through technologies like risk stratification and personalised medication algorithms, aimed for practical use in clinical settings. She fosters collaborations with healthcare institutions and brings together clinicians and data scientists for innovative healthcare solutions. One of her ongoing projects is to develop a digital health companion using large language models to meet the healthcare needs of the Asia-Pacific.


     

 

  • Date: 10 Dev 2023


    Awarded to Bryan Hooi by the 37th Annual Conference on Neural Information Processing System (NeurIPS-23).

    Download the paper

     

 

  • Date: 30 Jul 2023


    Awarded to Zou Yingtian’s paper “MixupE: Understanding and Improving Mixup from Directional Derivative Perspective“ published in Uncertainty in Artificial Intelligence 2023 (UAI 2023).

     

 

  • Date: Jan 2023


    The Singapore Data Science Consortium (SDSC) Dissertation Research Fellowship aims to promote and recognise innovative and impactful data science thesis work of PhD students. This annual programme will provide up to ten (10) cash awards for doctoral students at Singapore universities. Among the recipients of this programme is Wu Zhaoxuan, our PhD student at the Institute of Data Science.

     

 

  • Date: Dec 2022


    The South Asia & Southeast Asia Research Awards are focused on supporting exceptional computer science research in the region. This programme supports faculty at universities or degree-granting research institutions in the region engaged in world-class research in all areas relevant to Google. The program also includes a sub-track to support early-career professors (faculty who have begun their faculty careers within five years of submission of application). Among the recipients of this programme is Sujatha Das Gollapali, our Senior Research Fellow at the Institute of Data Science

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  • Date: Dec 2022


    The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields. Fellowships support promising PhD candidates of all backgrounds who seek to influence the future of technology. This programme currently offer Fellowships in Africa, Australia, Canada, East Asia, Europe, India, Latin America, New Zealand, Southeast Asia and the United States. Among the recipients of this programme is Thong Nguyen, our PhD student at the Institute of Data Science.

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  • Date: 1 Dec 2022


    Swapnil Mishra awarded the Singapore NRF Fellowship by the National Research Foundation

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  • Date: Jun 2022


    Listed in SIGMOD Research Highlight Award for 2022
    “Bipartite Matching: What to do in the Real World When Computing Assignment Costs Dominates Finding the Optimal Assignment”
    ACM SIGMOD Record 51 (1), 51-58,
    Tenindra Abeywickrama, Victor Liang, Kian-Lee Tan,

    SIGMOD Research Highlights are to showcase a set of research projects that exemplify core database research, and aim to make the selected works widely known in the database community, to industry partners, and the broader ACM community.

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  • Date: Jan 2022


    Innovators Under 35 is the most prestigious recognition worldwide from MIT Technology Review. Assistant Professor Jonathan Scarlett has been named in this year’s ‘Innovators Under 35’ Asia Pacific List by MIT Technology Review. The list honours young innovators in five categories: inventors, entrepreneurs, visionaries, humanitarians, and pioneers; it recognises the development of new technology or the creative application of existing technologies to solve global problems in industries such as biotechnology, materials, computer hardware, energy, transportation, communications, and the Internet.

    Dr Scarlett is recognised for being a visionary – defined by MIT Technology Review as someone who finds powerful new uses of technology by looking at things a bit differently from everyone else. He has spent years in studying the mathematical algorithms and theory behind group testing. Before joining the National University of Singapore, he worked as a postdoctoral fellow at Ecole Polytechnique Fédérale de Lausanne (EPFL) and finished his Ph.D. at the University of Cambridge. His research mainly focused on machine learning, signal processing, and information theory.

    In general, the group testing problem consists of determining a small set of defective items (e.g., abstractly representing infected individuals in medical testing) from a larger set of items based on tests on groups of items. It can be thought of as a combinatorial search problem with a flavor of sparse inference. Jonathan’s work provided new precise characterizations of the performance bounds for algorithms and impossibility results, which are the fundamental mathematical limits of the problem.

    He has also broadened the scope of the problem by making fundamental contributions on issues such as partial recovery (e.g. tolerating a small number of false positives or false negatives), proving phase transition behavior on how many tests are required, proving achievability and impossibility results on the performance of group testing algorithms under noise, and several other variations on the problem.

    Besides his group testing work, Jonathan adapted his mathematical studies of group testing to other seemingly distinct signal acquisition problems, which are relevant in applications such as MRI.

    In addition, his work on Bayesian optimization and bandits has also led to publications in machine learning conferences such as NeurIPS, ICML, and COLT.

    He is also the co-author of several survey articles and monographs, two of which were published by Foundations and Trends in Communications and Information Theory.

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  • Date: Nov 2021


    "Multimodal Fusion of Satellite Images and Crowdsourced GPS Traces for Robust Road Attribute Detection”
    Accepted as full paper at 29th ACM SigSpatial International Conference on Advances in Geographic Information Systems (ACM SIGSPATIAL 2021). (2 Nov - 5 Nov, 2021),
    Yifang Yin, An Tran, Ying Zhang, Wenmiao Hu, Guanfeng Wang, Jagannadan Varadarajan, Roger Zimmermann, See-Kiong Ng

    This paper was published in: GeoAI 2019: Proceedings of the 3rd ACM SIGSPATIAL International Workshop on AI for Geographic Knowledge Discovery November 2019 and won the GeoAI 2019 Best Paper Award

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  • Date: Oct 2021


    The Google PhD Fellowship Program was created to recognize outstanding graduate students doing exceptional and innovative research in areas relevant to computer science and related fields. Fellowships support promising PhD candidates of all backgrounds who seek to influence the future of technology. This programme currently offer Fellowships in Africa, Australia, Canada, East Asia, Europe, India, Latin America, New Zealand, Southeast Asia and the United States. Among the recipients of this programme is Shen Li, our PhD student at the Institute of Data Science.

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  • Date: Aug 2021


    Our Grab-NUS AI Lab has continued to generate award-winning research papers. For this year, the Transport AI team’s research on “Optimizing Bipartite Matching in Real-World Applications by Incremental Cost Computation", co-authored by Tenindra Abeywickrama and Professor Kian-Lee Tan (IDS’ founding Director), has won the Best Scalable Data Science Paper Award at the 47th International Conference on Very Large Data Bases (VBLD-2021). Tenindra was a postdoc at Grab-NUS AI Lab who is now working in Grab

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  • Date: Sem 1 AY2021/2022


    Johan Kok Zhi Kang received the Research Achievement Awards from School of Computing for Sem 1 AY2021/2022. This award is presented to PhD students who have achieved outstanding research performance over the past academic year. Johan is one of the IPP (Industry PhD Program) PhD students (2019 intake) funded under our Grab-NUS AI Lab

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  • Date: Mar 2021


    Nicholas Lim Xiang Hui, one of our first batch of IPP (Industry PhD Program) PhD students (2019 intake) funded under our Grab-NUS AI Lab, was selected as a recipient of the SDSC Dissertation Research Fellowship 2020 by the Singapore Data Science Consortium. The award promotes and recognises innovative and impactful data science thesis work of PhD students, Nicholas was recognized for his research in user preference modelling for recommendation.

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