Awards
Date: 22 Jul 2024
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
Date: 30 Jul 2023
Date: Jan 2023
Date: Dec 2022
Date: Dec 2022
Date: 1 Dec 2022
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.
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.
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
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.
Date: Aug 2021
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
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.
Date: Nov 2019