Muhammad Abdul-Mageed: Associate Professor at Department of Linguistics, UBC Faculty of Arts

Associate Professor

Faculty of Arts

Research Classification

Artificial intelligence (AI)

Research Interests

Deep Learning

Natural Language Processing

Machine Learning

Computational Linguistics

Social Media Mining

Arabic

Relevant Thesis-Based Degree Programs

Library, Archival and Information Studies, Doctor of Philosophy

Archival Studies and Library Information Studies, Master of Archival Studies and Master of Library and Information Studies

Library and Information Studies, Master of Library and Information Studies

View all programs

Affiliations to Research Centres, Institutes & Clusters

CAIDA: UBC ICICS Centre for Artificial Intelligence Decision-making and Action

Institute for Computing, Information and Cognitive Systems (ICICS)

Language Sciences Institute

Open All

Research Methodology

Deep Learning

Natural Language Processing

machine learning

Social Media

Arabic

Recruitment

Looking to recruit:

Master's students

Doctoral students

Postdoctoral Fellows

Desired start dates: Any time / year round

Potential research project areas:

Deep Learning. Deep learning of natural language. Natural Language Processing. Computational Linguistics. Natural Language Inference. Machine Translation. Misinformation. Detection of Negative and Abusive Content Online. Applications of deep learning in health and well-being.

Other options:

I support public scholarship, e.g. through the Public Scholars Initiative, and am available to supervise students and Postdocs interested in collaborating with external partners as part of their research.

I support experiential learning experiences, such as internships and work placements, for my graduate students and Postdocs.

I am open to hosting Visiting International Research Students (non-degree, up to 12 months).

Complete these steps before you reach out to a faculty member!

Check requirements

Familiarize yourself with program requirements. You want to learn as much as possible from the information available to you before you reach out to a faculty member. Be sure to visit the graduate degree program listing and program-specific websites.
Check whether the program requires you to seek commitment from a supervisor prior to submitting an application. For some programs this is an essential step while others match successful applicants with faculty members within the first year of study. This is either indicated in the program profile under "Admission Information & Requirements" - "Prepare Application" - "Supervision" or on the program website.

Focus your search

Identify specific faculty members who are conducting research in your specific area of interest.
Establish that your research interests align with the faculty member’s research interests.
- Read up on the faculty members in the program and the research being conducted in the department.
- Familiarize yourself with their work, read their recent publications and past theses/dissertations that they supervised. Be certain that their research is indeed what you are hoping to study.

Make a good impression

Compose an error-free and grammatically correct email addressed to your specifically targeted faculty member, and remember to use their correct titles.
- Do not send non-specific, mass emails to everyone in the department hoping for a match.
- Address the faculty members by name. Your contact should be genuine rather than generic.
Include a brief outline of your academic background, why you are interested in working with the faculty member, and what experience you could bring to the department. The supervision enquiry form guides you with targeted questions. Ensure to craft compelling answers to these questions.
Highlight your achievements and why you are a top student. Faculty members receive dozens of requests from prospective students and you may have less than 30 seconds to pique someone’s interest.
Demonstrate that you are familiar with their research:
- Convey the specific ways you are a good fit for the program.
- Convey the specific ways the program/lab/faculty member is a good fit for the research you are interested in/already conducting.
Be enthusiastic, but don’t overdo it.

Attend an information session

G+PS regularly provides virtual sessions that focus on admission requirements and procedures and tips how to improve your application.

ADVICE AND INSIGHTS FROM UBC FACULTY ON REACHING OUT TO SUPERVISORS

These videos contain some general advice from faculty across UBC on finding and reaching out to a potential thesis supervisor.

Supervision Enquiry

If you have reviewed some of this faculty member's publications, understand their research interests and have reviewed the admission requirements, you may .

Graduate Student Supervision

Master's Student Supervision

Theses completed in 2010 or later are listed below. Please note that there is a 6-12 month delay to add the latest theses.

Representation learning for Arabic dialect identification (2022)

Arabic dialect identification (ADI) is an important aspect of the Arabic speech processing pipeline, and in particular dialectal Arabic automatic speech recognition (ASR) models. In this work, we present an overview of corpora and methods applicable to both ADI and dialectal Arabic ASR, then we benchmark two approaches to using pre-trained speech representation models for ADI. Namely, we first employ direct fine-tuning, and then use fixed-representations extracted from pre-trained models as an intermediate step in the ADI process. We train and evaluate our models on the granular ADI-17 Arabic dialect corpus (92% F1 for our fine-tuned HuBERT model), and further probe generalization by evaluating our trained models on coarse-grained ADI-5, (80% F1 for fine-tuned HuBERT).

View record

Investigating the impact of normalizing flows on latent variable machine translation (2020)

Natural language processing (NLP) has pervasive applications in everyday life, and has recently witnessed rapid progress. Incorporating latent variables in NLP systems can allow for explicit representations of certain types of information. In neural machine translation systems, for example, latent variables have the potential of enhancing semantic representations. This could help improve general translation quality. Previous work has focused on using variational inference with diagonal covariance Gaussian distributions, which we hypothesize cannot sufficiently encode latent factors of language which could exhibit multi-modal distributive behavior. Normalizing flows are an approach that enables more flexible posterior distribution estimates by introducing a change of variables with invertible functions. They have previously been successfully used in computer vision to enable more flexible posterior distributions of image data. In this work, we investigate the impact of normalizing flows in autoregressive neural machine translation systems. We do so in the context of two currently successful approaches, attention mechanisms, and language models. Our results suggest that normalizing flows can improve translation quality in some scenarios, and require certain modelling assumptions to achieve such improvements.

View record