Keegan Korthauer

Assistant Professor

Research Classification

Research Interests

Single-cell analysis
Statistical genomics

Relevant Thesis-Based Degree Programs

Affiliations to Research Centres, Institutes & Clusters



Master's students
Doctoral students
Postdoctoral Fellows
Any time / year round

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.
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  • 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.

A new data driven framework for simulating mendelian randomization data (2023)

Mendelian randomization (MR) is a causal inference method that allows biostatisticians to leverage DNA measurements to study causal effects with only observed data. Recent advancements including two-sample summary-level mendelian randomization (TSSLMR) and the data source IEU OpenGWAS database have lowered the barrier for conducting MR studies and opened the opportunity to mine causal effects. In the first part of the thesis, I show that there is a mismatch between the characteristics of modern TSSLMR data and how articles that propose popular TSSLMR models conduct their simulations. Next, I propose my solution: a data driven simulation framework for MR data that aims to be realistic, interpretable and easy to use thanks to a complementary R package implementation. As for the results, I show that models perform far better in literature-based simulations compared to more realistic simulations based on my proposed framework. Lastly, I warn that the mismatch between simulated and real data along with the obtained results may lead researchers to have over optimistic expectations about models performance in real applications.

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Evaluating omics-based tests with Bayesian Decision Curve Analysis (2023)

Omics-based tests (OBTs) combine high-dimensional omics features into clinical prediction modelsthat predict diagnosis, prognosis, or treatment effects. Past incidences of premature implementa-tion of OBTs into clinical trials have demonstrated the need for increased rigour in their clinicalevaluation. However, their performance assessment is often limited to classification metrics such assensitivity and specificity, with little regard for formal analysis of clinical decision-making. Decisioncurve analysis (DCA) complements classification metrics by combining classical assessment of pre-dictive performance with the consequences of using a test or model to guide clinical decisions. InDCA, the best clinical decision strategy, such as diagnosing or treating based on an OBT, is the onethat maximizes the concept of net benefit: the net number of true positives (or negatives) providedby a given clinical decision strategy. Before reaching real patients, we must be sufficiently confi-dent that new OBTs actually provide superior clinical decision strategies, as compared to default,standard-of-care strategies. Trained on hundreds to thousands of features, OBTs are particularlyprone to chance results. In this context, the present work develops parametric Bayesian approachesto DCA that allow uncertainty quantification around four fundamental concerns when evaluatingOBT-guided clinical decision strategies: (i) which strategies are clinically useful, (ii) what is thebest available decision strategy, (iii) direct pairwise comparisons between strategies, and (iv) whatis the consequence of the current level of uncertainty. We evaluate the methods using simulationstudies and present a comprehensive case study. We also provide an application to a recently-developed OBT for multi-cancer early detection. Software implementation of the method is freelyavailable in the bayesDCA R package. Ultimately, the Bayesian DCA workflow may help cliniciansand health policymakers make better-informed decisions when choosing and implementing clinicaldecision strategies based on OBTs.

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