LEE Mong Li

Professor
Ph.D. (Computer Science, National University of Singapore)
M.Sc. (Computer Science, National University of Singapore)
B.Sc. (Computer Science, 1st Class Honours, National University of Singapore)
COM2-03-06
651 62905

Research Areas

  • Computational Biology
  • Database

Research Interests

  • Social Media Analytics
  • Spatio-temporal Databases
  • Retina Image Analysis
  • Cleaning and Integration of Heterogeneous and Semi-structured Data
  • Database Performance Issues in Dynamic Environments
  • Biomedical Informatics
  • Data Management, Including Data Cleaning, Data Fusion, Analysis of Semistructured and Spatio-temporal Data

Profile

Dr Lee Mong Li is a Professor of Computer Science at the National University of Singapore. She has extensive experience in data management, including data cleaning and data fusion (integrating data from disparate sources), and in analysis of semistructured and spatio-temporal data. She has published more than 150 papers in major database conferences and journals, and is the co-author of two books on ``Designing Semi-structured Database'' and ``Temporal and Spatio-Temporal Data Mining''. She plays a key role in several multi-disciplinary government funded research projects to build systems that are practical and deployable. She is actively involved in the research community, holding various key roles and serving on the Program Committees of prestigious database and data mining conferences. Mong Li received her Ph.D., M.Sc. and B.Sc. (Hons 1) degrees in Computer Science from the National University of Singapore. She was awarded the IEEE Singapore Information Technology Gold Medal for being the top student in the Computer Science program in 1989. She was a visiting Fellow at the University of Wisconsin-Madison, USA 1999, and Consultant at QUIQ USA in 2000. She was the co-recipient of Singapore's President Technology Award in 2014. She is also a co-inventor of an AI system that uses deep learning to screen for major eye conditions (diabetic retinopathy, glaucoma suspect and age-related macular degeneration) from retinal images.

Current Projects

  • Retinal Image Analysis for Disease Prediction
  • Information Credibility in New Media
  • Interactive Keyword Search on Databases over Time.

Selected Publications

  • Furong Li, Mong Li Lee, Wynne Hsu. Profiling Entities over Time in the Presence of Unreliable Sources, in IEEE Transactions on Knowledge and Data Engineering (TKDE), 2017.

  • Wee Yong Lim, Mong Li Lee, Wynne Hsu. iFact: An Interactive Framework to Assess Claims from Tweets, in International Conference on Information and Knowledge Management (CIKM), 2017.

  • Chonggang Song, Wynne Hsu, Mong Li Lee. Temporal Influence Blocking: Minimizing the Effect of Misinformation in Social Networks, in 33rd IEEE International Conference on Data Engineering (ICDE), 2017.

  • Lahari Poddar, Wynne Hsu, Mong Li Lee. Quantifying Aspect Bias in Ordinal Ratings using a Bayesian Approach, in 26th International Joint Conference on Artificial Intelligence (IJCAI), 2017.

  • Zhong Zeng, Mong Li Lee, Tok Wang Ling. Answering Keyword Queries involving Aggregates and GROUPBY on Relational Databases, in 19th International Conference on Extending Database Technology (EDBT), 2016. 

  • Furong Li, Mong Li Lee, Wynne Hsu, Wang-Chiew Tan. Linking Temporal Records for Profiling Entities, in ACM SIGMOD International Conference on Management of Data, 2015.

  • Gilbert Lim, Mong Li Lee, Wynne Hsu, Tien Yin Wong. Transformed Representations for Convolutional Neural Networks in Diabetic Retinopathy Screening, in AAAI Workshop on Modern Artificial Intelligence for Health Analytics (MAIHA), 2014.

  • Qiangfeng Peter Lau, Mong Li Lee, Wynne Hsu, Tien Yin Wong. The Singapore Eye Vessel Assessment System, book chapter in Image Analysis and Modeling in Opthalmology, Eddie Y. K. Ng, U. Rajendra Acharya, Jasjit S. Suri, Aurelio Campilho (Eds.), ISBN: 1466559306, CRC Press, 2014.

Awards & Honours

  • Co-recipient of Singapore's President Technology Award, 2014.

Teaching (2019/2020)

  • CS5344: Big-Data Analytics Technology