Fatemeh Hendijani Fard

Assistant Professor

Computer Science, Data Science
Office: FIP 305
Phone: 250.807.9607
Email: fatemeh.fard@ubc.ca

Graduate student supervisor

Research Summary

Empirical Software Engineering; Code Intelligence; AI4SE; Code Representation Learning; Mining Software Repositories.

Courses & Teaching

Computer Science (Algorithm Design and Analysis, Computer Networks); Data Science (Data Wrangling, Data Visualization)


Dr. Fard is an Assistant Professor at the University of British Columbia (Okanagan Campus). Her research interest lies at the intersection of Natural Language Processing and Software Engineering. Dr. Fard and her team develop code intelligence models focusing on low-resource languages with less computational costs. Few-shot learning, adapters, and (large) language models are at the heart of her works. Her research is an initiative for Diversity and Inclusion to make the benefits of the automated tools and advancements of deep neural networks accessible to the communities of understudied programming languages and those with restricted GPU access.

Dr. Fard teaches at the Master of Data Science Program, is a member of CITECH program and MMRI, is part of the Killam family of scholars and is an IEEE and ACM member. She strongly advocates Diversity and Inclusion, specifically for underrepresented females in STEM.


PhD University of Calgary
MSc Amirkabir University
BSc Sharif University of Technology

Research Interests & Projects

  • AI4SE
  • Code Intelligence
  • Empirical Software Engineering
  • Mining Software Repositories

Code Intelligence

In this theme, we mainly focus on the computational efficiency of code-language models and adapting them to low-resource languages. We conduct empirical studies and develop new techniques to boost the performance of the (L)LMs for low-resource languages such as R, the popular but understudied programming language. We are conducting several research that ranges from developing models for R programming language to building new techniques for adaptation, knowledge distillation, and new architectures. The main applications of our interest are code summarization, method name prediction, code search, code clone detection, and code generation.

Social Aspects of LLMs

Since the release of ChatGPT, there has been a fast trend in introducing new Large language models and their usage for code. Though Copilot and other models aim to help software developers, there are uncertainties about their usage, their effect on developers’ well-being, and developers’ awareness while using them. This research contains a number of studies to identify these effects on the well-being of software developers.

Software Analytics

This stream covers a variety of research, including studying technical debt and mining software repositories.

Selected Publications & Presentations

Google Scholar

Selected Grants & Awards

  • Mitacs Accelerate
  • NSERC Discovery
  • UBC Start-up Fund
  • Izaak Walton Killam Memorial Scholarship
  • Alberta Innovates Technology Futures (AITF)

Professional Services/Affiliations/Committees

I have been PC member of several conferences including FSE’24, MSR’24, FSE’23, ASE’23, ASE’23 (Tutorial Track), SANER’23 (ERA), CANAI’23, ICSME’22, ASE’22 (Tutorial Track), ICSME’21, CANAI’21, SANER ’21 (ERA)


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