Benjamin Fung

Associate Academic Member

Associate Professor, McGill University, School of Information Studies

McGill University University

Research Topics

AI for Software Engineering

Applied Machine Learning

Cybersecurity

Data Mining

Deep Learning

Information Retrieval

Misinformation

Privacy

Representation Learning

Website

Google Scholar

Biography

Benjamin Fung is a Canada Research Chair in Data Mining for Cybersecurity, as well as a full professor at the School of Information Studies and associate member of the School of Computer Science, McGill University.

Fung serves as an associate editor of IEEE Transactions of Knowledge and Data Engineering and Sustainable Cities and Society. He received his PhD in computing science from Simon Fraser University in 2007.

Dr. Fung has over 150 refereed publications to his credit and and more than 14,000 citations (h-index 57) spanning the fields of data mining, machine learning, privacy, cybersecurity and building engineering. His findings in the fields of data mining for crime investigations and authorship analysis have been reported by the media worldwide.

Publications

Trade-off Between Accuracy and Fairness of Data-driven Building and Indoor Environment Models: A Comparative Study of Pre-processing Methods

Ying Sun

Fariborz Haghighat

Benjamin Fung

2021-10-01

Energy (published)

doi.org

A Data Mining Analysis of Cross-Regional Study of Apparel Consumption

Osmud Rahman

Benjamin Fung

2021-08-28

Comprehensible Science (published)

doi.org

A Data Mining Analysis of Cross-Regional Study of Apparel Consumption

Osmud Rahman

Benjamin Fung

2021-06-19

(published)

doi.org

A Novel Neural Network-Based Malware Severity Classification System

Miles Q. Li

Benjamin Fung

2021-01-01

International Conference on Software and Data Technologies (published)

doi.org

A Novel Neural Network-Based Malware Severity Classification System

Miles Q. Li

Benjamin Fung

2021-01-01

International Conference on Software and Data Technologies (published)

doi.org

The Topic Confusion Task: A Novel Scenario for Authorship Attribution

Malik H. Altakrori

Jackie Cheung

Benjamin Fung

Authorship attribution is the problem of identifying the most plausible author of an anonymous text from a set of candidate authors. Researc… (see more)hers have investigated same-topic and cross-topic scenarios of authorship attribution, which differ according to whether unseen topics are used in the testing phase. However, neither scenario allows us to explain whether errors are caused by failure to capture authorship style, by the topic shift or by other factors. Motivated by this, we propose the topic confusion task, where we switch the author-topic conﬁg-uration between training and testing set. This setup allows us to probe errors in the attribution process. We investigate the accuracy and two error measures: one caused by the models’ confusion by the switch because the features capture the topics, and one caused by the features’ inability to capture the writing styles, leading to weaker models. By evaluating different features, we show that stylometric features with part-of-speech tags are less susceptible to topic variations and can increase the accuracy of the attribution process. We further show that combining them with word-level n - grams can outperform the state-of-the-art technique in the cross-topic scenario. Finally, we show that pretrained language models such as BERT and RoBERTa perform poorly on this task, and are outperformed by simple n -gram features.

2021-01-01

arXiv.org (preprint)

dblp.uni-trier.de

Learning Inter-Modal Correspondence and Phenotypes From Multi-Modal Electronic Health Records

Kejing Yin

William K. Cheung

Benjamin Fung

Jonathan Poon

Non-negative tensor factorization has been shown a practical solution to automatically discover phenotypes from the electronic health record… (see more)s (EHR) with minimal human supervision. Such methods generally require an input tensor describing the inter-modal interactions to be pre-established; however, the correspondence between different modalities (e.g., correspondence between medications and diagnoses) can often be missing in practice. Although heuristic methods can be applied to estimate them, they inevitably introduce errors, and leads to sub-optimal phenotype quality. This is particularly important for patients with complex health conditions (e.g., in critical care) as multiple diagnoses and medications are simultaneously present in the records. To alleviate this problem and discover phenotypes from EHR with unobserved inter-modal correspondence, we propose the collective hidden interaction tensor factorization (cHITF) to infer the correspondence between multiple modalities jointly with the phenotype discovery. We assume that the observed matrix for each modality is marginalization of the unobserved inter-modal correspondence, which are reconstructed by maximizing the likelihood of the observed matrices. Extensive experiments conducted on the real-world MIMIC-III dataset demonstrate that cHITF effectively infers clinically meaningful inter-modal correspondence, discovers phenotypes that are more clinically relevant and diverse, and achieves better predictive performance compared with a number of state-of-the-art computational phenotyping models.

2020-11-12

ArXiv (preprint)

doi.org

arxiv.org

Trends and Applications in Knowledge Discovery and Data Mining

Lida Rashidi

Benjamin Fung

Can Wang

2018-01-01

Lecture Notes in Computer Science (published)

doi.org

Trends and Applications in Knowledge Discovery and Data Mining