Ali Bashashati Saghezchi

Assistant Professor

Research Interests

Artificial Intelligence
Computational Pathology
Cancer Genomics
Computational Biology
Digital Pathology
Image Processing
Machine Learning
Ovarian Cancer
Signal Processing
Multi-modal Learning

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).

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.

A review of variant calling methods and biological markers in single-cell sequencing of a mouse model of epithelioid sarcoma (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

Generalizable deep learning models for epithelial ovarian carcinoma classification (2022)

Ovarian carcinoma is the deadliest cancer of the female reproductive system in North America. There are five major histological subtypes which require different treatments. Pathologists diagnose these histotypes by examining hematoxylin and eosin (H&E)-stained whole slide images (WSIs) of tissue. However, histotype diagnosis is not simple, with poor interobserver agreement between general pathologists (Cohen’s kappa 0.54-0.67). We hypothesize that latest machine learning (ML)-based image classification models may be able to recognize ovarian carcinoma histotype sufficiently well that they could aid pathologists in diagnosis.However, the color variation of H&E-stained tissues, especially those from different centers/hospitals, is a longstanding challenge for applications of AI in digital pathology. First, we investigate eight color normalization algorithms as a preprocessing step for artificial intelligence (AI)-based classification. Using multiple datasets of different cancer types, reference images, and cross-validation splits, we show that color normalization significantly improves the classification accuracy of WSIs when the train and test data are from separate institutions (ovarian cancer: 0.25 AUC increase, p = 1.6 e-05, pleural cancer: 0.21 AUC increase, p = 1.4 e-10). Furthermore, we introduce a novel augmentation strategy by mixing color-normalized images using three easily accessible algorithms that consistently improves the diagnosis of test images from external institutions.Secondly, we train four different deep convolutional neural networks to automatically classify H&E-stained images of epithelial ovarian carcinoma using the largest training dataset to date (948slides corresponding to 485 patients). Performance is assessed on an independent test set of 60 patients from another institution. The best performing model achieves a mean diagnostic concordance of 80.97% (Cohen’s kappa 0.7547). As well, in 4 of 8 cases misclassified by ML from the external dataset, two expert subspecialty pathologists rendered diagnoses, based on blind review of the WSIs, that agree with AI rather than the integrated reference diagnosis.Our results indicate that color normalization can reliably improve AI-based diagnosis of WSIs sourced from multiple centers, and specifically that an ML-based ovarian carcinoma classifier is ready for clinical validation studies as an adjunct for informing histotype diagnosis, thereby supporting histotype-specific ovarian cancer treatment and accordingly reduce the deadliness of this disease.

View record

Identification of a novel subtype of endometrial cancer with unfavorable outcome using artificial intelligence-based histopathology image analysis (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


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


Explore our wide range of course-based and research-based program options!