Guansong Pang

Assistant Professor 


School of Computing and Information Systems

Singapore Management University

Room 5035, 80 Stamford Rd, Singapore 178902

Email: gspang[at]smu.edu.sg; pangguansong[at]gmail.com

Phone: +65-68264864

[LinkedIn] [GitHub] [Google Scholar] [ORCID] [Twitter

Short Bio

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 interests include machine learning, data mining and computer vision, with a research theme focused on recognizing and generalizing to abnormal/unknown/unseen  data for creating trustworthy AI systems. Some research areas of particular interest include:

He also explores some pivotal real-world applications of these areas, such as network intrusion detection, fraud detection, early detection of diseases/faults, learning from biomedicine data, industrial defect detection, biometric anti-spoofing, hate speech detection, etc.

He actively engages in various professional activities, including regularly serving as PC member/Senior PC member/area chair of top ML, DM, and CV conferences and reviewers of leading journals in these fields. He is also an associate editor of IEEE Transactions on Neural Networks and Learning Systems and an editorial board member of IEEE Intelligent Systems and International Journal of Data Science and Analytics.

Positions

Actively looking for candidates of the following positions; feel free to get in touch if you are interested (only shortlisted candidates will be contacted):

SMU CS ranking: No.51-75 (tied) in the CS&E subject (ShanghaiRanking'23); No.39 in the broad AI category(CSRankings'14-24)

Call for Papers

CVPR 2024 Workshop on Visual Anomaly and Novelty Detection (VAND 2.0),  submission deadline: March 7th, 2024

Recent News

[01/2024] One paper on time series anomaly detection is accepted to Web Conference 2024

[01/2024] One paper on foundation model-enabled zero-shot anomaly detection is accepted to ICLR 2024

[01/2024] One paper on zero-shot recognition in open worlds is accepted to Pattern Recognition.

[01/2024] Our workshop "Visual Anomaly and Novelty Detection (VAND)" is accepted to CVPR 2024. Stay tuned!

[12/2023] Three papers on video anomaly detection, OOD detection in long-tailed recognition, and open-vocabulary object detection are accepted to AAAI 2024

[11/2023] One paper on graph self-supervised learning is accepted to TNNLS

[10/2023] Our DSAA 2023 paper received the Best Paper Award of the Applications Track

[10/2023] Honored to be named in the list of The World's Top 2% Scientists 2023 (a climb from a rank of ~270K in 2022 to ~84K) 

[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