Aline Talhouk

Assistant Professor

Research Interests

Computer Science and Statistics
Cancer of the Reproductive System
diagnostic models
Digital health
Machine Learning
personalized medicine

Relevant Thesis-Based Degree Programs



Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round
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).
I am interested in hiring Co-op students for research placements.

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.



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

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.

HGSOC subtypes and treatment interaction : implications for clinical implementation of the PrOTYPE classifier (2023)

High-grade serous ovarian cancer (HGSOC) represents approximately 70% of cases of ovarian carcinoma. Investigation into its molecular features led to the discovery of four subtypes of HGSOC, which differ from each other in their gene expression, cellular composition and survival outcomes. However, methods of whole-genome expression clustering have limited the reproducibility of existing subtype-specific research findings as well as being unsuitable for use in individual cases, such as in a clinical setting. PrOTYPE has been developed as a clinical-grade tool for determining the subtype of HGSOC tumours according to expression of a select panel of genes. To investigate the potential utility of determining HGSOC subtype using PrOTYPE in the clinical decision-making process, this thesis has two aims: 1) explore subtype-specific differences in survival outcomes of HGSOC patients treated with the anti-angiogenic drug bevacizumab, to determine whether subtype could be used to inform treatment decisions, and 2) evaluate the subtypes of paired samples collected before and after treatment to platinum and taxane chemotherapy to determine whether exposure to chemotherapy induces changes in subtype. In Aim 1, I found that cases belonging to the immunoreactive C2.IMM subtype generally had improved overall and progression-free survival when treated with bevacizumab. Results also suggested that the C5.PRO subtype may be associated with poorer survival when treated with bevacizumab compared to if treated with platinum and taxane chemotherapy alone. However, these findings could not be validated in independent cohorts; stratification of patients by subtype-specific treatment response in a clinical setting would require further investigation before being implemented. Additionally, a search for novel genes associated with differential treatment outcomes with bevacizumab identified several genes that may be useful in future research on targeted approaches to bevacizumab treatment. In Aim 2, I found that change in subtype from before to after exposure to neoadjuvant chemotherapy occurred in the majority (11/16) of the cases studied; this indicates that pre-chemotherapy subtype cannot be determined from post-chemotherapy samples. Clinical and prognostic significance of HGSOC subtype after chemotherapy remains an area for future investigation. Subtyping using PrOTYPE could be used in the clinical context to guide treatment decisions and evaluate patient prognosis.

View record

Causal machine learning to optimize treatment decisions for patients with endometrial cancer (2022)

The full abstract for this thesis is available in the body of the thesis, and will be available when the embargo expires.

View record

Large scale federated analytics and differential privacy budget preservation (2021)

This thesis presents two contributions. The first contribution deals with the problem of siloed data collection and prohibitive data acquisition costs. These costs limit the size and diversity of datasets used in health research. Access to larger and more diverse datasets improves the understanding of disease heterogeneity and facilitates inference of relationships between surgical and pathological findings with symptomatic indicators and outcomes. Unfortunately, freely enabling access to these datasets has the potential of leaking private information, such as medical records, even when these datasets have been stripped of personally identifiable information.In the first part of this thesis, we present LEAP, a data analytics platform with support for federated learning. LEAP allows users to analyze data distributed across multiple institutions in a private and secure manner, without leaking sensitive patient information. LEAP achieves this through an infrastructure that maintains privacy by design and brings the computation to the data, instead of bringing the data to the computation. LEAP adds an overhead of up to 2.5X, training Resnet-18 with 15 participating sites, when compared to a centralized model. Despite this overhead, LEAP achieves convergence of the model’s accuracy within 20% of the time taken for the centralized model to converge.One of the techniques used by LEAP to preserve the privacy of sensitive queries is differential privacy. Successive DP queries to a dataset depletes the privacy budget. When the privacy budget is depleted, data curators must block access to the underlying dataset to prevent private information from leaking. In the second part of this thesis, we present a system called the SmartCache. The SmartCache optimizes the use of the privacy budget by interpolating old query results to help answer new queries using a synthetic dataset. Queries answered from the synthetic dataset have a smaller privacy cost, so more queries can be answered before the budget runs out. For statistical queries, the SmartCache saved 30%-50% of the budget for threshold values of 0.99 and 0.999, and for gradient queries it consumed 70% less of the privacy budget when training a fully connected model.

View record

Current Students & Alumni

This is a small sample of students and/or alumni that have been supervised by this researcher. It is not meant as a comprehensive list.

If this is your researcher profile you can log in to the Faculty & Staff portal to update your details and provide recruitment preferences.


Read tips on applying, reference letters, statement of interest, reaching out to prospective supervisors, interviews and more in our Application Guide!