Guansong Pang is a tenure-track Assistant Professor of Computer Science at the School of Computing and Information Systems, Singapore Management University (SMU). He was a Research Fellow with the Australian Institute for Machine Learning (AIML), University of Adelaide, Australia. Before joining AIML, he received his Ph.D. at University of Technology Sydney, Australia.
He leads the Machine Learning & Applications (MaLA) Lab at SMU. His research investigates novel machine learning techniques, with a research theme focused on abnormal/unknown data instance detection and generalized learning algorithms for creating trustworthy continual AI systems. Some research areas of particular interest include:
Open-world learning (out-of-distribution detection, open-set recognition, long-tailed classification, continual learning, open-vocabulary learning, etc.)
Graph representation learning
Deep reinforcement learning for knowledge discovery
He also explores safety-critical and commercially/scientifically-significant real-world applications, including network intrusion detection, fraud detection, person re-identification, early detection of diseases, learning from biomedicine data, defect detection, biometric anti-spoofing, hate/toxic speech detection, etc.
Actively looking for candidates of the following positions; feel free to get in touch if you are interested (shortlisted candidates will be contacted):
Highly motivated PhD students with strong background in computer science or statistics (scholarships).
Visiting professor/student positions (self-funded: CSC-sponsored, etc).
SMU is ranked No. 40 globally (No. 16 in Asia) in the broad AI category and No.84 globally (No. 15 in Asia) in the overall CS subject according to recent 10 years of top-conference publications(CSRankings, May 2023).
Call for Papers
2024 IEEE Conference on Artificial Intelligence (IEEE CAI 2024), submission deadline: 20 Dec 2023
Frontiers in Big Data on the research topic "Trustworthy Machine Learning on Graphs: Algorithms and Applications", abstract deadline: 11 October 2023
[09/2023] One paper on graph anomaly detection is accepted to NeurIPS 2023
[07/2023] One paper on heterogeneous graph-level anomaly detection is accepted to DSAA 2023
[07/2023] Three papers on anomaly detection and OOD detection are accepted to ICCV 2023
[07/2023] One paper on semi-supervised anomaly detection is accepted to Information Processing & Management (IP&M).
[06/2023] Invited to be Publication Co-chair of IEEE CAI 2024 (IEEE Conference on AI)
[06/2023] One paper on medical image anomaly detection and segmentation is accepted to Medical Image Analysis (MedIA)
[06/2023] One paper on graph-level anomaly detection is accepted to ECML/PKDD 2023
[05/2023] One paper on weakly-supervised anomaly detection is accepted to KDD 2023
[05/2023] One tutorial on anomaly detection is accepted to IJCAI 2023
[04/2023] One paper on deep isolation forest is accepted to TKDE.
[04/2023] Our tutorial on "Revolutionizing Anomaly Detection: Approaches and Guidelines" is accepted to ECML/PKDD 2023
[02/2023] One paper on few-shot open-set recognition is accepted to CVPR 2023
[02/2023] Our tutorial on "Recent Advances in Anomaly Detection" is accepted to CVPR 2023
[12/2022] One paper on graph anomaly detection is accepted to SDM 2023
[11/2022] Honored to be identified as The World's Top 2% Scientists (single recent year) released by Stanford University
[11/2022] Honored to give an invited talk in the A*STAR Centre for Frontier AI Research (CFAR) Rising Star Lecture Series
[11/2022] One paper on cross-domain graph anomaly detection is accepted to AAAI 2023
[09/2022] One paper on implicit relation prediction on graph data is accepted to TKDD
[07/2022] One paper on joint geometry contrastive graph neural networks is accepted for publication at Information Sciences
[07/2022] One paper on out-of-distribution semantic segmentation is accepted for oral presentation at ECCV 2022
[06/2022] One paper on latent factor analysis is accepted to Journal of Big Data
[06/2022] One paper on polyp detection in medical images is accepted to MICCAI 2022 (early accept)
[06/2022] Our TNNLS Special Issue on "Deep Learning for Anomaly Detection" is formally published now
[05/2022] Invited to be part of the local organization team of WSDM 2023
[03/2022] One paper on open-set anomaly detection is accepted to CVPR 2022
[01/2022] Invited to join the editorial board of IEEE Intelligent Systems and International Journal of Data Science and Analytics
[01/2022] One paper on depression prediction is accepted to PAKDD 2022
[01/2022] Join School of Computing and Information Systems @ SMU as assistant professor