data science phd mcgill

Danilo Bzdok’s research team is focused on data-guided analysis techniques for large datasets from a systems neuroscience perspective. We believe that a strong interdisciplinary approach, with an equal footing in research object and research method, is a prerequisite for forward progress in quantitative neuroscience and personalized medicine. We collaborate with institutions across the globe, identifying pressing questions in medical imaging and health, reframing them as machine learning problems, and translating new insight into biomedicine.

Danilo Bzdok

Principal investigator, mcgill university, mila institute.

Danilo Bzdok is a medical doctor and computer scientist with a dual background in systems neuroscience and machine learning algorithms. After medical training at RWTH Aachen University (Germany), Université de Lausanne (Switzerland), and Harvard Medical School (USA), he completed one Ph.D. in brain-imaging neuroscience (Research Center Juelich, Germany, 2012) and one Ph.D. in computer science in machine learning statistics at INRIA Saclay and Neurospin (France, 2016). Danilo currently serves as Associate Professor at McGill’s Faculty of Medicine and as Canada CIFAR AI Chair at Mila - Quebec Artificial Intelligence Institute, Montreal, Canada, including cross-appointments at the McConnell Brain Imaging Center, Ludmer Centre for Neuroinformatics and Mental Health, Montreal Neurological Institute, and the School of Computer Science at McGill University. In his free time, he enjoys French and Italian culture, language and languages, playing chess or Go, and consuming excessive amounts of music.

Meet The Team

Jakub kopal, postdoctoral fellow, university of montreal.

Jakub Kopal completed his Ph.D. in neuroscience under joint supervision from the University of Chemistry and Technology in Prague and Université Toulouse III – Paul Sabatier in 2021. Currently, he is working as a postdoctoral fellow at the intersection of neuroscience and genetics in the group of Danilo Bzdok at McGill University and Sébastien Jacquemont at CHU Saint-Justine. His research focuses on the influence of genetic mutations on brain structure and function. Otherwise, he enjoys data visualization, pickles and random facts.

Andrea Luppi

Andrea obtained undergraduate and master’s degrees in philosophy, psychology, and neuroscience at Oxford before completing his PhD in neuroscience at Cambridge and the Alan Turing Institute (UK). His doctoral work combined multimodal, multi-species neuroimaging to investigate the neuroscience of altered states of consciousness (coma, anaesthesia, psychedelics) through the lens of complex systems and information dynamics. He is now conducting postdoctoral work with Dr. Bratislav Misic (MNI) and Dr. Danilo Bzdok (MILA), at the interface of AI and neuroscience. Combining whole-brain computational modelling and neuromorphic computing, he investigates how the network architecture and neuromodulatory landscape of the brain jointly shape information processing and cognition. A native of Italy, Andrea enjoys hiking in the mountains, cooking, indoor skydiving, and reading and writing fiction.

Badr Ait Hammou

Badr Ait Hammou is a postdoctoral researcher at the intersection of Artificial intelligence (AI) and neuroscience. His current research focuses on machine learning, data-driven analysis techniques for large-scale biomedical datasets. Before joining the lab, he worked as a postdoctoral researcher at the University of Montreal, where he conducted research on deep learning techniques to solve a variety of medical computer vision problems applied to Ophthalmology, including video action recognition, image generation, and image classification. He completed a Ph.D. in Computer Science at Mohammed V University in Rabat. His doctoral work lies in the area of AI for Big Data analytics. It covers a set of intelligent systems based on distributed machine learning, distributed deep learning techniques, and natural language processing (NLP) to solve several challenges related to real-world problems in the context of big data, such as personalized recommendations, group recommendations, and real-time social media analysis.

Liam Hodgson

Doctoral student.

Prior to joining the Bzdok Lab at McGill, Liam completed his bachelor’s degree in Engineering Physics at UBC and subsequently worked on the R&D team at an additive manufacturing startup. As a doctoral student in the Computer Science department, he is investigating the function and disfunction of the human brain through the lens of single-cell omics, as well as developing computational methods to better analyze and interpret this high-dimensional biological data.

Karin Saltoun

Karin Saltoun is a PhD student in Neuroscience at McGill University. She studied biophysics at York University (Toronto) where she developed a love for using math and physics to understand biological problems on a more fundamental level. During her undergraduate career, first through work in an experimental lab focusing on precision measurements in atomic physics, before moving to a computational neuroscience lab developing algorithms for distinguishing sleep states from brain behaviour. She is currently working in Dr. Bzdok’s lab to understand brain asymmetry at a population level using a variety of machine learning techniques. When she’s not glued to her computer, she enjoys baking desserts or reading fiction.

Jack Stanley

Jack is a PhD student in the Quantitative Life Sciences program at McGill. Prior to joining the lab, Jack completed an Honours BSc in Statistics and Biochemistry at the University of Toronto, where he was a Schulich Leader and National Scholar. His current research is focused on untangling the complexity of human disease and biology using increasingly powerful deep learning approaches. An avid distance runner, Jack is also captain of McGill’s varsity cross country team, and a member of the varsity track team.

Anwesha Bhattacharya

Anwesha is starting her PhD in the Biological and Biomedical Engineering department at McGill university. After completing her Masters in Aersopace Engineering, she went on to apply her engineering skills as a Quantitative Researcher at JP Morgan Chase, Mumbai. She developed her interest in machine learning techniques while working on her Masters thesis for object detection and 6D pose estimation. She also did a summer internship at CERN where she worked on High Granularity Calorimeter data and applied machine learning techniques to classify fundamental particles. She now hopes to use ML techniques to further the domain of neuroscience and better understand the brain. In her leisure time, Anwesha loves reading fantasy, hiking, and yoga. She also enjoys dancing and is trained in Bharatnatyam.

Le is pursuing his PhD in the Integrated Program of Neuroscience at McGill University. He finished his bachelor’s degree in computer science and master’s degree in psychology at the University of Electronic Science and Technology of China. He is interested in the relationship between human behavior and the brain and is currently studying the effect of chronotype (circadian rhythms) on the brain pattern by applying machine learning methods on population datasets. In his free time, Zhou reads fiction, watches movies, and plays games.

Chloé Savignac

Chloé Savignac is a PhD student in the Integrated Program in Neuroscience at McGill University, interested in the applications of machine learning to dementia research. She joined the lab in January 2021 after graduating with First Class Honours from a B.A. & Sc. in Cognitive Science at McGill University. Her PhD work combines Big Data analytics tools with state-of-the-art machine learning techniques to derive factors of Alzheimer’s disease (AD) susceptibility in population datasets of up to half a million participants. Her master’s thesis leveraged the power of structural brain scans from ~40,000 participants of the UK Biobank imaging cohort to find population signatures of familial AD risk as a function of APOE haplotypes. She has since shifted focus to single-cell genomics, aiming to identify patterns of transcriptomic markers uniquely linked to AD disease in male and female patients. Outside the lab, Chloé enjoys learning new languages and exploring cuisines from around the world.

Gregory Bell

Greg completed his bachelor’s degree in Physics, summa cum laude, at Temple University. He then did a M.Sc. at Mcgill in Physics. After working and gaining interest in data science and engineering, Greg joined the Bzdok Lab and currently works with LLM’s and medical data.

Kimia Shafighi

Master’s student.

Kimia is starting her Master’s in the Integrated Program of Neuroscience at McGill University after receiving her bachelor’s in biomedical engineering. She dedicated her undergraduate studies to create the McGill Biodesign team and worked on transformational applications in biotechnology. Now, she is using machine learning techniques to investigate a variety of neuroscience questions at the population scales and discover statistical patterns in large datasets.

Karam Ghanem

Karam Ghanem is a Master’s student in Biomedical Engineering at McGill University. He finished his Bachelor’s degree in Engineering Physics at McMaster University where he immersed himself in the application of physics in a number of engineering and scientific fields. Karam worked as a Firmware Engineer on the R&D team at a telecommunications company where he developed his engineering skillset. He is currently investigating Diffusion Models in a theoretical context.

Justin Marotta

Justin is a student in the Biological & Biomedical Engineering Master’s program at McGill. Prior to joining the lab, he completed his undergraduate degree in Electrical Engineering at Queen’s University and worked as a sales engineer for Cisco Systems focused on cybersecurity solutions. Currently, he is developing a data-driven machine learning approach to perform a phenome-wide investigation of population-level drivers behind adolescent brain and cognitive development. Factors of equity, diversity, and inclusion are a focal point of this investigation. In his free time he enjoys music, reading, and getting outside to ski and hike.

Zilong Wang

Zilong is pursuing an MSc in Neuroscience and Computer Science at McGill University and Mila, after graduating from Honors Cognitive Science also at McGill. He is interested in neuroscience - how humans achieve complex cognitive and motor functions; AI - how AI can helps us advance neuroscience and healthcare. Currently he is investigating the relationship between different cerebellum subregions and cortical areas to understand how the two interact longitudinally. Zilong has several ideas for start-ups centred around AI, Data and Healthcare, please feel free to reach out!

Nicole Osayande

Nicole is a Master’s student in the Biological & Biomedical Engineering program, and she was selected for the inaugural cohort of McCall MacBain Scholars at McGill. She graduated from Queen’s University in spring of 2021 with a bachelor’s degree in Computer Science, specializing in Biomedical Computing. Prior to joining the lab, Nicole worked at IBM as a software developer for the Watson Orchestrate AI team, where she gained hands-on experience with machine learning algorithms and automation tools. Nicole has a strong connection to EDI initiatives as she founded the Queen’s Student Diversity Project at her alma mater and collaborated with the undergraduate admissions and recruitments office to encourage students of diverse backgrounds to pursue their post-secondary studies at Queen’s. She now hopes to bridge her interest in machine learning with her passion for EDI initiatives to introduce the Neuroscience community to diversity-aware population modeling of large-scale datasets via Bayesian hierarchical regression. In her free time, she likes dancing, boxing, content creation, and watching Korean dramas!

Shambhavi Aggarwal

Shambhavi Aggarwal is a student in the Biological & Biomedical Engineering Master’s program at McGill University. She obtained her undergraduate degree in Information Technology, during which she cultivated her interest in Machine Learning and Computer Vision. Her passion led her to pursue research internships in Computer Vision and Quantum Machine Learning at IISc Bangalore and Purdue University, respectively. After graduation, she applied her skills by working as a Computer Vision Engineer at Claritas HealthTech, where she used deep learning techniques to analyze medical data. Her ambition is to expand her knowledge of machine learning algorithms and explore how they can be applied to medical data. During her free time, she relishes activities such as music, singing, and cooking, while also engaging in interesting conversations with people.

Position 20xx-20xx

Incoming master's student, incoming phd student, former lab members, chris zajner, research assistant 2020-2021, nadejda zaharieva, lab manager & ra 2021, nahiyan malik, master's student 2019-2021, julius kernbach, doctoral student 2017-2019, bachelor student 2018-2022, hannah kiesow, doctoral student 2018-2022, hasnain mamdani, enning yang, master's student 2021-2023, devin kreuzer, research assistant 2020-2022.

• Publications

A complete list of our publications can be found on ResearchGate .

Selected Publications

Related teaching on machine learning/data science:

Course outline from 2020 for BMDE 520 Course outline from 2021 for BMDE 520 / COMP 598 Course outline for 2022 for BMDE 520 / COMP 598

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Ph.D. Program Regulations

The School of Computer Science offers a world-class Ph.D. program. The program typically takes 3–4 years, and prepares students for doing advanced research in a wide range of areas relevant to Computer Science. Students conduct research under close supervision of our highly regarded research faculty, work with cutting-edge technology, attend international conferences and workshops, and build important, life-long contacts and relationships with colleagues and faculty. Graduates of our program are highly sought after, going on to work as university faculty, industrial or government researchers, or as leaders in business and development in the technology sector.

Have a look at our many exciting research areas and labs as well as our faculty .

Further, detailed information can found in the sections below. If you have any unanswered questions, feel free to contact the Graduate Coordinator .

Program Details

Successful completion of the Ph.D. program requires a minimum residency, some amount of coursework, and includes various stages of evaluation to ensure good research progress.

Ph.D. studies requires several years of study. Students may be admitted to either year 1 ("PhD1"), or directly to year 2 ("PhD2") if they already hold a completed M.Sc. degree in Computer Science. The main difference is in how many years of full-time residency is required. Students admitted to "PhD1" must complete four years of residency (eight terms), plus one more year as a full-time student, while students admitted to "PhD2" must complete only three years (six terms) of residency, plus one more year as a full-time student. Once these requirements are met, any further time in the degree program is considered additional-session.

Letter of Understanding

This form , which is required by Graduate and Postdoctoral Studies (GPS) for all graduate students admitted to thesis programs, is the starting point for a discussion between the Supervisor and the Student regarding expectations for the duration of the supervisory period. This is to be completed by the student after the discussion with the supervisor by the end of the first semester. Once signed by both parties, the letter should be uploaded directly to myProgress.

Progress Committee and Report

A student's progress through the Ph.D. program is monitored and evaluated on a yearly basis by a Progress Committee. Upon arrival at McGill a new Ph.D. student must, in consultation with his or her supervisor(s), form a Progress Committee. This committee will consist of least three professors---two members of the faculty of School of Computer Science, as well as the student's supervisor(s).

For the first year or two after entry into the program, progress is mainly evaluated when the student takes the comprehensive and proposal (area) exams. If either of these was taken in the last 12 months, there is no need for a detailed progress evaluation meeting, and the student just needs to submit a Progress Report Form (see below) directly to the graduate secretary.

At the beginning of September starting in the third year, the student is required to complete a Progress Report Form and submit it to their Progress Committee. At that time, an evaluation meeting is conducted by the Progress Committee, and the committee assigns a grade of either satisfactory or unsatisfactory with comments. If the mark is unsatisfactory, the Progress Committee offers specific comments to guide the student towards improving his or her performance. Note that earning an unsatisfactory mark twice may be cited as grounds for requiring that a student withdraw from the Ph.D. program.

Here is the progress report form: pdf format . Note that this annual progress report is different from the progress report used in the PhD comprehensive exam.

Comprehensive Exam

By the end of their first year in the program, Ph.D. students must complete a comprehensive examination. Exams are only conducted twice a year, in late August/early September, and again in early January. In order to take the exam, a student must register for COMP 700 in either the winter (January exams) or fall (August/September exams) semester.

The comprehensive exam consists of a Progress Report and a subsequent oral exam. First, several months before the exam is conducted, the supervisor (or co-supervisors), in consultation with other Progress Committee members and with approval from the Ph.D. Program Committee, gives the student a syllabus in an appropriate research area for the student to review. This syllabus is meant to cover significant contributions to a particular research topic, and consists of an organized and motivated list of approximately 15–20 publications, including conference proceedings, journal articles, and theses.

Based on the approved syllabus, the student writes a literature review. The review should demonstrate detailed understanding of some of the seminal developments in addition to familiarity with the broader chronological development of research in the area. The review report should be concise, but clear, and is typically between 12 pages and 15 pages in a single-spaced, 12 point font. This review, along with the rest of the formal Progress Report Form, must be submitted to the Evaluation Committee (via the graduate secretary) at least two weeks before the evaluation meeting takes place.

Here are progress report templates for the comprehensive exam, in latex format , or word format .

The actual Evaluation Committee is formed by the Ph.D. Program Committee and the supervisor (or co-supervisors). This committee evaluates the review document, and conducts the oral examination. The exam itself consists of two parts. During the first part (approx. 40–45 min), the student meets with the Evaluation Committee to verbally discuss the content of the progress report, and in particular answer questions from the committee pertaining to the literature review. A student's supervisor(s) also participates in this examination. Note that while questions are mainly based on the review content, students are also expected to know relevant computer science fundamentals.

During the second part, the committee meets (without the student) to discuss and vote on the student's performance. The committee considers the oral examination, the review itself, the student's performance in courses, and any other relevant academic or research accomplishments. Four Ph.D. Program Committee members (decided by the Chair of the Ph.D. Program Committee) and the student's supervisor are voters (in the case of co-supervision, a single vote is divided among the co-supervisors). A student must have a majority vote of pass in order to pass the exam.

In the event of a failure, the student is given one opportunity to retake the examination in the coming January or September, whichever is closer. After a second failure a student is required to withdraw from the program. Note that under special circumstances, and with approval of their supervisor(s) and the Ph.D. Program Committee, a student may delay the comprehensive exam, but under all circumstances the exam must be successfully completed within two years of initial registration in the Ph.D. program.

Ph.D. Proposal Exam

The proposal, or area exam is designed to test the research ability of the student in the area of the thesis as well as depth of knowledge in those areas of computer science closely related to the thesis topic. It is also used to evaluate a student's research progress, and suitability of their intended research plan.

Most students will take the proposal exam at some point late in their second year of registration. The proposal exam is a public, oral exam, and like the comprehensive exam the student must register for a special course, in this case COMP 701, in the semester in which he or she intends to take the exam. Unlike the comprehensive exam, however, proposals may be conducted at various times during the year, and are scheduled to fit availability of the proposal committee members.

The proposal committee consists of the student's supervisor(s), at least two faculty members from the School of Computer Science, and a representative of the Ph.D. Program Committee. At least two weeks prior to the exam date, the student must submit a 20-page (maximum) written report, single spaced in 12 point font, to the graduate secretary. This is distributed to the committee members, and is followed by the scheduled oral examination. The oral exam begins with an oral presentation by the candidate, summarizing the report, and lasting no more than twenty minutes. This is followed by a question/answer period with the members of the proposal committee, with each member given approximately 20-30 min of questioning (co-supervisor time is divided proportionally).

After questions, the exam moves to a closed session consisting of just the committee members, who, based on the student's progress, report, and performance in the exam vote on pass or failure. In the case of a first failure, the student will be given a single chance to retake the examination within six months. If the student does not schedule the exam within this time period, or fails a second time, the student will be required to withdraw from the program.

Note that proposal exams must be completed within three years of initial registration in the Ph.D. program, and after the successful completion of the PhD comprehensive exam; non-compliance with this rule will result in a failure.

The Ph.D. defense is a public, oral exam, and constitutes the final major stage in the Ph.D. program. This step requires that the completed thesis document has been transmitted to the thesis office, and that both the internal and external examiners have agreed to pass the thesis.

At this point a Ph.D. Defense Committee is selected. Like the proposal exam, a thesis defense may be scheduled for any time that the entire committee is available. The actual defense consists of a brief, pre-meeting of just the Ph.D. Defense Committee members, followed by the public part of the defense. The public part includes an initial, twenty minute presentation by the student, summarizing their thesis work, which is then followed by one or more rounds of questioning by the Ph.D. Defense Committee members. Questions may also be asked by the rest of the defense audience.

After questions, the exam again moves to a closed session consisting of just the committee members. Committee members consider the student's performance in the defense, as well as the written thesis reports, and vote on pass or fail, with a majority vote required to pass. After the meeting the thesis candidate is informed of the results.

Assuming a successful result, the supervisor verifies that the student makes all changes requested by the examiners and the defense committee. Once all changes have been completed, the final version of the thesis is transmitted to the thesis office and validated by the supervisor. This last step signifies that all necessary requirements of the Ph.D. program have been successfully completed.

The actual granting of degrees is done only a few of times per year, and thus while the final version of the thesis can be deposited at any time, convocation ceremonies only occur in the summer (May/June) and fall (October).

There are many specific regulations, forms, and deadlines to be observed in the thesis submission and evaluation process. Students and supervisors should consult the Graduate and Postdoctoral Studies' thesis section for full details and to find detailed regulations on the process.

A detailed description of the admission process and requirements can be found on this page .

Note that as acceptance into the program requires a willing supervisor, Ph.D. applicants are strongly encouraged to contact potential supervisors ahead of time, or shortly after submitting their application.

Funding Opportunities and Fees

A detailed description of funding opportunities and required tuition and other fees can be found this page .

Contacts and Further Questions

If you have questions, concerns, or want to clarify anything, please contact the Ann Jack .

For general admission information, please contact Service Point . Other contact information can be found on our contacts page .

For PhD Graduate Program Director, please contact Prof Luc Devroye .

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Ph.D. Courses

• Nominally, students in the Ph.D. program must successfully complete eight graduate courses, of which at least five are computer science courses.
• Graduate-level courses taken in the past, however, may count towards this requirement. Course reduction requests are considered only in the first few weeks of the beginning of the fall and winter terms and require the student to submit a course reduction request form well in advance. The forms must be sent to the graduate secretary.
• Regardless of the result of the course reduction request, every Ph.D. student must take at least two courses from the School of Computer Science at McGill.
• According to a GPS rule, no more than one-third of the McGill program formal coursework can be credited with courses from another university.
• While the students can use the course reduction request to reduce their course requirement to only two courses, we strongly recommend the students view the possibility of taking several courses during their Ph.D. as an opportunity. The students should take advantage of the many graduate courses that are offered in the department and build a solid and broad foundation for their knowledge of computer science.
• Courses are divided into two broad categories. The students who do not have an undergraduate degree in computer science or computer engineering must have course credit for at least two courses from each category. Note that this is not an exhaustive (or well maintained) list, and students should consult their supervisor or the graduate program director if in doubt.

Category A: Theory and applications

COMP 523 Language-based Security (3 credits) COMP 524 Theoretical Foundations of Programming Languages (3 credits) COMP 525 Formal Verification (3 credits) COMP 527 Logic and Computation COMP 531 Advanced Theory of Computation (3 credits) COMP 540 Matrix Computations (4 credits) COMP 547 Cryptography and Data Security (4 credits) COMP 552 Combinatorial Optimization (4 credits) COMP 553 Algorithmic Game Theory (4 credits) COMP 554 Approximation Algorithms (4 credits) COMP 560 Graph Algorithms and Applications (3 credits) COMP 566 Discrete Optimization 1 (3 credits) COMP 567 Discrete Optimization 2 (3 credits) COMP 610 Information Structures 1 (4 credits) COMP 627 Theoretical Programming Languages (4 credits) COMP 642 Numerical Estimation Methods (4 credits) COMP 647 Advanced Cryptography (4 credits) COMP 649 Quantum Cryptography (4 credits) COMP 690 Probabilistic Analysis of Algorithms (4 credits) COMP 760 Advanced Topics Theory 1 (4 credits) COMP 761 Advanced Topics Theory 2 (4 credits) COMP 526 Probabilistic Reasoning and AI (3 credits) COMP 550 Natural Language Processing (3 credits) COMP 561 Computational Biology Methods and Research (4 credits) COMP 564 Advanced Computational Biology Methods and Research (3 credits) COMP 579 Reinforcement Learning (4 credits) COMP 618 Bioinformatics: Functional Genomics (3 credits) COMP 680 Mining Biological Sequences (4 credits) COMP 652 Machine Learning (4 credits) COMP 611 Mathematical Tools for Computer Science (4 credits) COMP 588 Probabilistic Graphical Models (4 credits)

Category B: Systems and applications

COMP 512 Distributed Systems (4 credits) COMP 520 Compiler Design (4 credits) COMP 529 Software Architecture (4 credits) COMP 533 Model-Driven Software Development (3 credits) COMP 535 Computer Networks 1 (4 credits) COMP 575 Fundamentals of Distributed Algorithms (3 credits) COMP 612 Database Programming Principles (4 credits) COMP 614 Distributed Data Management (4 credits) COMP 621 Program Analysis and Transformations (4 credits) COMP 655 Distributed Simulation (4 credits) COMP 667 Software Fault Tolerance (4 credits) COMP 762 Advanced Topics Programming 1 (4 credits) COMP 763 Advanced Topics Programming 2 (4 credits) COMP 764 Advanced Topics Systems 1 (4 credits) COMP 765 Advanced Topics Systems 2 (4 credits) COMP 521 Modern Computer Games (4 credits) COMP 522 Modelling and Simulation (4 credits) COMP 546 Computational Perception (4 credits) COMP 551 Applied Machine Learning (4 credits) COMP 557 Fundamentals of Computer Graphics (4 credits) COMP 558 Fundamentals of Computer Vision (4 credits) COMP 559 Fundamentals of Computer Animation (4 credits) COMP 514 Applied Robotics (4 credits)

Category A or B depending on the topic:A

COMP 766 Advanced Topics Applications 1 (4 credits) COMP 767 Advanced Topics: Applications 2 (4 credits) COMP 597 Topics in Computer Science 4 (4 credits)

Detailed course descriptions may be found elsewhere on the website.

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Suite 1200 - 2001 McGill College Avenue, Montréal, Québec H3A 1G1

[email protected]

W. Alton Russell, PhD

Assistant professor.

• [email protected]
• twitter.com/altonrus
• 2001 McGill College Avenue, Montreal, QC H3A 1G1
• Our lab sits within the McGill Clinical and Health Informatics Research Group on the 11th floor of 2001 McGill College Avenue. Visitors must arrange for someone to let them into the lab or visit to the reception area at the 12th floor.

Alton joined the McGill School of Population and Global Health as an Assistant Professor in 2022. As a researcher, Alton has developed decision analytic models and data-driven analyses for multiple areas of health policy and clinical practice, including blood donation and transfusion, managing pediatric kidney disease, opioid use disorder and overdose, and gastroenterology. Alton is also:

• Associated investigator, Research Institute of the McGill University Health Centre (RI-MUHC)
• Researcher, McGill Quantitative Life Sciences program
• Scientific advisor, COVID-19 Immunity Task Force
• Member, Group for Research in Decision Analysis (GERAD)

You can view his CV here .

• Data-driven decision analysis
• Health policy modeling
• Health technology assessment

Postdoc, Mass General Hospital Institute for Technology Assessment, 2021

Harvard Medical School

PhD, Management Science and Engineering, 2021

Stanford University

MSc, Management Science and Engineering, 2018

BSc, Industrial Engineering (Health Systems Engineering concentration), 2014

North Carolina State University

BSc, Interdisciplinary Studies (Global Health and Sustainability concentration), 2014

Title description, Dec 7, 2020

The Data-Driven Decision Modeling Lab–or D3Mod Lab–aims to enable the efficient, effective, and equitable use of finite healthcare resources. We use do so by developing, assessing, and applying traditional decision modeling methods (mathematical modeling, simulation, optimization) together with data-driven methods (machine learning, Bayesian statistics). Our work informs challenging decisions in health policy and medicine. We are part of the McGill Clinical and Health Informatics Research Group and the McGill School of Population and Global Health in Montreal, Quebec, Canada.

Lab members

Yuan Yu Postdoc Small area estimation, Bayesian hierarchical modeling, Sampling methods, survey studies, Bayesian applications, statistical and machine learning

Jiacheng Chen Research Associate Public health data science, methods, infectious disease and nutritional epidemiology

Wanjin (Jennifer) Li PhD student Health technology assessment, Economic evaluation, Public health data science

Matthew Knight MSc student Clinical decision-making, Public health data science, Disease surveillance

Melina Thibault PhD student Health informatics/digital health, Health policy modeling, Non-communicable disease epidemiology

Nkasiobi Hossanna Nwobi MSc student Health policy modeling, Health surveillance, Impact evaluation

Ethan McNally BA student Health economic policy evaluation, Decision Modelling

William Daunais BA student Health policy modelling, Health technology assessment

Alton teaches the following courses at McGill:

The D3Mod lab’s research informs health policy and clinical decisions through data-driven modeling and analysis. We use methods from decision science, optimization, epidemiology, health economics, and machine learning to enable the efficient, effective, and equitable utilization of resources. We collaborate with stakeholders in medicine and health policy to maximize our impact on policy and practice while extending the state of the art in data-driven decision modeling.

A major area of focus is data-driven decision analytic modeling , which integrates individual-level data into models that compare health intervention or policy options. Traditionally, decision analyses either model an ‘average’ patient or a relatively homogeneous cohort of synthetic individuals, extracting values from the literature or expert opinion to characterize the impacted population and estimate the impact of policy alternatives. This assumes risks and costs are not distributed across the population and interventions' treatment effects are homogeneous. Our lab is developing methdos to directly integrate individual-level data to reflect the true heterogeneity in patient populations and capture differences in expected outcomes under different policy alternatives. This enables more accurate estimation of the trade-offs involved with an intervention and allows us to look at the distributional impact of interventions to reveal potential inequities.

Current projects

Current research themes include:

Enabling individualized approaches to managing risks of iron deficiency for blood donors using machine learning and decision-analytic simulation modeling

Using simulation modeling to optimize the use of predictive models to prevent service gaps in emergency medical services (paramedics and ambulances)

Advancing the state-of-the-art in integrating data-driven methods (machine learning, Bayesian statistics) into decision-analytic models

Developing and assessing methods for population health surveillance, including adjusting for assay and population differences to allow harmonization of data across cohortss

Model-based economic assessments of health interventions, including cancer screening and surveillance.

Prospective lab members

Affiliated degree programs: I can serve as the thesis supervisor for students in Epidemiology (MSc and PhD), Biostatistics (MSc and PhD), Quantitative Life Sciences (PhD), and Experimental Medicine (MSc and PhD), which includes the MSc in Digital Health Innovation. For students in other degree programs, I may be able to serve as a practicum supervisor, co-supervisor, or committee member.

Current McGill students: If you are interested in working with the lab, please email me (Alton) your CV and a brief note about your interests.

Prospective McGill students: For prospective PhD students please send me (Alton) your CV, your intended degree program, and a brief note about your interests, ideally two to three months before the application deadline. Feel free to reach out earlier if you would like to discuss applying for a specific fellowship or scholarship that is due before the PhD program application. For prospective or admitted MSc students you are welcome to send your CV and a note about your interests. I typically meet with new MSc thesis students in their first semester and have them join the lab in the beginning of their second semester. In some cases, I am able to discuss potential projects and fellowship or studentship opportunities earlier. Applying for graduate funding (fellowships, studentships) cab increase the likelihood of admission to a graduate program and being able to complete your thesis with the D3Mod lab.

Prospective postdoctoral researchers or research staff: Any open research staff opportunities will be advertised on McGill’s Workday platform. Prospective postdoctoral researchers may reach out at any time. Prospective postdoctoral researchers who have external fellowship funding or are willing to apply for funding are more likely to receive a position.

Open science research philospohy

Our group works hard to produce research that is informative, rigorous, transparent, and reproducible. The Decision Modeling Lab Manual describes our approach to open research and dissemination.

Thank you to our funders

Research in the decision modeling lab is supported by grants from:

Canadian Blood Services

The AABB Foundation

Fonds de recherche Québec – Santé

Natural Sciences and Engineering Research Councel of Canada

COVID-19 Immunity Task Force

Major publications

Former Members

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Making data science accessible to all

If you follow science news, you will almost certainly have encountered the term ‘modelling’. From understanding climate change, to predicting the course of a pandemic, to developing the pharmaceuticals to fight one, scientists seem to have a ‘model’ for everything. But have you ever wondered just what the term means and how scientists go about creating models?

The answer almost invariably involves using computers to work with enormous amounts of data. Data can be anything you can imagine – weather records, public health statistics, genetic sequences. In the digital age, our capacity to capture and store data has grown exponentially and with it has grown the potential for researchers to extract meaningful insights from these vast datasets.

“More and more disciplines, not just in [natural] science but social science as well, are becoming more data-intense. There are more datasets, the datasets are bigger, and they are changing more rapidly over time,” says Tim Elrick , Ph.D., who has been teaching data science to McGill students for the past five years.

A new initiative to serve a growing need

Drawing on his experience as director of McGill’s Geographic Information Centre , Elrick is now delivering workshops in data science and statistics through the Computational and Data Systems Initiative (CDSI) , a new facility to help McGill researchers access tools, techniques and expertise for working with large quantities of data. The CDSI is a product of the Faculty of Science’s strategic research plan , a plan which contains several initiatives broadly aimed at bringing communities of researchers with diverse disciplinary expertise together to work on complex problems.

The CDSI works by drawing on the expertise in working with large datasets that traditionally resides in statistics and computer science departments and making it available to researchers in other disciplines through cross-disciplinary research and training. And, as noted in a white paper on the CDSI, there is opportunity for knowledge to flow in the other direction, too: “Computer science and statistics core researchers can benefit from contact with new applications as this exposes them to the many challenges that arise from working on problems at the forefront of research.”

Training workshops well received

Since the CDSI began delivering data science training workshops in fall 2021, there has been strong interest from across the McGill campus. So far, more than half the workshop participants have come from faculties other than Science, including the faculties of Arts, Medicine & Health Sciences, Agricultural & Environmental Sciences, and Dental Medicine & Oral Health Sciences.

Isha Gandhi, a master’s student in dental sciences who attended every single one of the CDSI workshops offered in fall 2021, says she was able to build on her existing knowledge of the statistical programming language, R, as well as acquire new skills in Python. Gandhi has since been able to apply this knowledge to her master’s project on chronic pain.

“I have learned new codes and commands for analyzing and visualizing data, which I have found super interesting and helpful for my project,” she says. “I am applying code script which I learned in the workshop to make my graphs more detailed.”

Learn more about upcoming CDSI workshops

Tailored consulting service coming soon

The CDSI has continued to offer workshops throughout the winter 2022 semester, expanding the lineup to include training streams in both data science and statistics. Meanwhile, the CDSI team has also been moving forward with the development of a consulting service to provide McGill researchers with customized advice on applied statistics, computing methodologies and data analytics, both for ongoing research and at the grant proposal stage. Co-directed by Elrick and José Correa, Ph.D., this new service, named the Consulting Core Facility, will build on the existing strengths of McGill’s Statistical Consulting Service , currently located in the Department of Mathematics & Statistics.

To learn more about what the Computational and Data Systems Initiative can do for you, head to the CDSI website .

It would be extremely helpful if we can see the recording of the workshops to share with my graduate students who could not attend. Professor Ismail

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7 Best Universities in Canada to Study Ph.D. in Data Science

A Ph.D. degree is the highest academic qualification anyone can earn. And with Harvard Business Review declaring data scientist to be the sexiest job of the 21 st century, a Ph.D. in Data Science should probably be called the sexiest university degree of the century.

Universities in Canada, like the ones in other highly advanced countries, are offering such degrees in abundance. Here, we have listed the seven best Canadian universities that offer a Ph.D. degree in Data Science.

Top Schools Offering Ph.D. in Data Science Programs in Canada

1. university of british columbia.

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Beginning its journey over a hundred years ago, the University of British Columbia (UBC) offers higher education to nearly 65,000 students, including over 17,000 international students.

It has two main campuses sited in Vancouver and Okanagan of the province of British Columbia. It is recognized as one of the top 40 universities in the world and top three universities in Canada by a number of major university rankings.

Nobel laureates like economist Robert Mundell and physicist Bertram Brockhouse are a couple of notable alumni.

UBC’s Department of Computer Science administers a Ph.D. in Computer Science program. Its research areas include a number of concepts relevant to Data Science like Data Integration, Text Mining, Web Databases, and Optimization.

It is an on-campus program that is only available in a full-time format. It takes place at the Vancouver campus and can be completed within three to five years.

2. HEC Montréal

HEC Montréal is actually not a university; rather, it is a graduate school of the Université de Montréal. Founded in 1907, the business school currently has over 14,000 students.

All of its classes take place in its two buildings in Montreal, Quebec: the Côte-Sainte-Catherine building and the Decelles building.

Despite being a French-language institution, it also offers programs that have English as the medium of instruction.

Louis R. Chênevert, the former CEO of United Technologies Corporation and president of Pratt & Whitney, graduated from this institution.

HEC Montréal offers a Ph.D. in Administration – Data Science program in collaboration with three other universities: McGill University , Concordia University , and Université du Québec à Montréal (UQAM). The partnership provides students with access to resources from all four Montreal-based institutions.

The English-language program requires 90 credits to be completed in full-time study mode. The duration of the program is typically four to five years.

3. University of Waterloo

Next on our list of universities in Canada with a Ph.D. in Data Science is the University of Waterloo . Founded in 1957, the University of Waterloo (UWaterloo) has a student population north of 40,000.

Around 20 percent of all undergraduate students and 40 percent of all graduate students have come from outside Canada. According to U.S. News and World Report Best Global Universities 2019, the university located in Waterloo is Canada’s best for Computer Science and second-best for Engineering.

It is the alma mater of Rasmus Lerdorf, the programmer who authored the first two versions of the PHP scripting language.

UWaterloo’s Ph.D. in Computer Science includes research areas like Databases, Information Retrieval, and Machine Learning that are relevant to the field of Data Science.

Students may take on this on-campus program in either full-time or part-time study mode. Admission takes place thrice a year at the start of the academic terms Fall, Winter, and Spring.

4. University of Alberta

The University of Alberta is a top-tier Canadian research university based primarily in Edmonton, the capital of Alberta. It was established in 1908, the same year that UBC came into being. The total student enrolment stands at over 40,000.

Nobel-winning physicist Richard E. Taylor and former Canadian prime minister Joe Clark graduated from this university.

Alberta’s Department of Computing Science runs a Ph.D. in Computing Science that has a wide range of research areas. A few of them, including Database Systems and Machine Learning, are related to the field of Data Science.

There’s also a Ph.D. in Statistical Machine Learning offered by the department in collaboration with the Department of Mathematical and Statistical Sciences. These are full-time programs that are ideally completed within five years.

5. Dalhousie University

Founded over two hundred years ago, Dalhousie University (Dal) is one of the oldest universities in the Great White North. Its student population is around 19,000, which includes nearly 4,000 international students representing over 115 nationalities.

Each of its three campuses is located in Halifax, the capital of Nova Scotia. The university is the alma mater of Nobel-winning astrophysicist Arthur B. McDonald, Canadian PMs R.B. Bennett, and Brian Mulroney, as well as former Xerox Corporation CEO and chairman Charles Peter McColough.

Dal offers a Ph.D. in Computer Science which allows students to specialize and develop deep expertise in Data Analytics. The Faculty of Computer Science’s Big Data Analytics & Machine Learning research cluster, which is based in the Institute for Big Data Analytics, helps students to conduct independent and original research. The Ph.D. program takes around three to four years to complete.

6. University of New Brunswick

The University of New Brunswick (UNB) started off in 1785 as Canada’s first English-language university. It has two campuses: one situated in New Brunswick’s capital Fredericton, and the other in the port city of Saint John in the same province. The university has more than 10,000 students hailing from over 100 countries. It was recognized as the country’s most entrepreneurial university in 2014 by Startup Canada.

UNB’s Faculty of Computer Science offers a Ph.D. in Computer Science one of the many research areas is Data Management, Analytics, and Mining. The program can be completed in three years. While it is ideally a full-time program, students could be permitted to take on the program on a part-time basis under certain conditions.

7. Ryerson University

Beginning its journey in 1948 as a postsecondary institute with merely 250 students, Ryerson University has grown into a full-fledged university with over 45,000 students.

It is located in Toronto, Ontario, and is the province’s most applied-to university relative to available accommodation.

Four Seasons Hotels and Resorts founder and chairman Isadore Sharp and former Maple Leaf Sports & Entertainment president and COO Tom Anselmi are a couple of its notable alumni.

Ryerson’s Faculty of Science administers a Ph.D. in Computer Science which provides students with the option of specializing in Data Science and related subjects like Machine Learning and Data Mining. The nominal duration of the thesis-based program is three years. The format for this program is full-time.

FAQS About Studying Ph.D. in Data Science

Which university in canada is best for a ph.d. in data science.

When it comes to Canada, the University of British Columbia would probably be your best pick for getting a Ph.D. in data science. Requiring nearly 5-6 years of your time, this professional degree program will challenge its students to analyze data from a wide range of domains while allowing each student to solve real-world problems created by partnered businesses.

How Long Does It Take to Earn a Ph.D. in Data Science?

If you want to graduate with a Ph.D. in data science, the time required ranges from 4 years to 9 years, depending on the person. To go down this track, you would probably apply for a Doctor of Philosophy in Computer Science and take the track of data analysis, which is a major part of data science.

If you just want to complete a Master’s degree in data science, you only need to spend 10 months of your time in this accelerated professional degree program. Analyzing data from a real-world environment, the experience that you would get from these types of programs would be second to none!

We hope that this article on universities in Canada with Ph.D. in Data Science was helpful. Make sure to also check out our  Data Science Programs for International Students  for some of the currently open data science courses around the world!

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Masters in Data Science in Canada: Top Universities, Eligibility & Job Prospects

Jasmine Grover

Senior Content Specialist | Updated On - Apr 30, 2024

To qualify for the program, you need a  3.0 GPA  in your bachelor's degree in math, computer science, or statistics. You should also demonstrate English language proficiency with a  6.5 IELTS score  and an  average GRE score of 315 . The field of data science offers an average  starting salary of CAD 79,000  with a growing career path. You can find multiple job opportunities after a Master's in Data Science in Canada in top companies like  Google, Shopify, and TD Bank.

A student pursuing a Master's in Data Science in Canada said " The MS in DS program provided me with invaluable skills and knowledge in areas like computer science, mathematics, and big data analytics. I studied at the University of Toronto which offered a supportive environment to thrive academically and professionally. After graduation, I pursued my career in TD Bank with a starting salary of CAD 80,000 ."

Masters of Data Science in Canada: Program Highlights

What are the Top Universities for Masters in Data Science in Canada?

The top universities offering Masters in Data Science in Canada to international students are:

Why Study Masters in Data Science in Canada?

Canada offers a strong combination of affordability, world-class education, and a thriving job market, making it a top destination for a Master's in Data Science. Master's in Data Science (MSc DS) programs in Canada are offered by renowned universities like McGill University (QS 2024 Rank: 30) and the University of Toronto (QS 2024 Rank: 21).

With over 4 million data science job opportunities expected in the next 5 years, Canada is becoming an increasingly appealing study-abroad destination.

What are the admission requirements for a Master's in Data Science (MSc DS) in Canada?

To pursue a Master's in Data Science (MSc DS) in Canada, you need a bachelor's degree with a minimum GPA of 3.0, a GRE, a minimum IELTS score of 6.5, a letter of recommendation, a resume/CV, and admission essays.

Masters in Data Science in Canada: University-wise Admission Requirement

The average admission requirements for MS in Data Science for international students are -

* GRE scores are not required for admission to these universities for 2024 only.

You need to apply for a Student Visa for Canada that is issued by Canadian immigration authorities to grant permission to pursue studies in the country. Without a valid study permit, international students are ineligible to study in Canada. The process for study visa application is as follows – 

How much does a Master's in Data Science cost in Canada

For an international applicant planning to  study in Canada , the costs for studying are distributed into 3 categories: pre-arrival costs, cost of study, and cost of living.

MS in Data Science in Canada Tuition Fees

The annual tuition fee for pursuing a master's in data science in Canada ranges from CAD 10,000 (INR 6,00,000) to CAD 23,000 (INR 14,00,000) for Indian students.

Masters in Data Science in Canada University Fees

Cost of Living in Canada

International students in Canada spend a lot on books, materials and transportation in addition to their tuition fees. The average monthly cost of living in Canada for an international student is around 2,500 CAD (~1.5 lakhs).

What are the job prospects in Canada after Masters in Data Science?

Data science is one of the rapidly growing job sectors producing high-paying jobs in Canada . Some of the leading data science careers and median salaries for a graduate of Masters in Data Science in Canada are:

Some top recruiters hiring graduates of masters in data science in Canada along with their average packages are-

Masters in Data Science in Canada offers diverse career opportunities with costs ranging from 20,000 CAD to 40,000 CAD. Post-graduate work visa options and employment prospects at global companies like Deloitte, Shopify, National Bank of Canada, and IBM Canada make it a popular career choice.

Masters in Data Science in Canada FAQs

Ques. What is the curriculum of a Master's in Data Science program in Canada?

Ans . The curriculum of a Master's in Data Science program in Canada includes topics such as:

• Data mining and machine learning
• Statistical inference
• Data visualization
• Data management and data warehousing
• Big data analytics
• Natural language processing
• Deep learning

Ques. What are the requirements to apply for a student visa in Canada?

Ans . Students are eligible to apply for a study permit in Canada by fulfilling the following requirements:

• Must be enrolled in a designated learning institution
• Must be financially able to pay for the tuition fees, living expenses, and return transportation
• Must have no criminal record
• Must be in good health and be willing to take a medical exam (if required)
• Must prove to an officer that they will leave Canada when the permit expires.

Ques. Which are the cheapest universities offering MS in Data Science in Canada?

Ans . Some cheap and affordable universities in Canada offering MS in Data Science for international students are:

Ques. Which are the top universities for a Master's in Data Science program in Canada?

Ans . Some of the top universities for a Master's in Data Science program in Canada are:

• University of British Columbia
• University of Toronto
• University of Waterloo
• McGill University
• Simon Fraser University

Ques. Can international students work while studying for a Master's in Data Science program in Canada?

Ans . Yes, international students can work while studying for a Master's in Data Science program in Canada, subject to certain restrictions. International students are allowed to work for up to 20 hours per week during the academic session and full-time during breaks.

Ques. What are the placement rates for MS in Data Science graduates from top universities in Canada?

Ans . Top universities in Canada have an average rate of placement of around 85%.

Ques. Which are the most affordable cities for international students in Canada?

Ans . Living in Canada can be expensive for international students. While some major cities can be heavily expensive, there are many cities in Canada which are affordable and have a good environment as well. Here are some such affordable cities in Canada:

• Victoria, British Columbia
• Saskatoon, Saskatchewan
• Halifax, Nova Scotia
• Winnipeg, Manitoba
• Calgary, Alberta

Ques. What are the pre-arrival costs for international students moving to Canada?

Ans . International students are required to take tests like IELTS, GRE, and GMAT before applying to universities in Canada. The pre-arrival costs for international students moving to Canada is -

Ques. Are there other scholarships available for international students pursuing a Master's in Data Science in Canada?

Ans . Some notable scholarships that offer financial assistance to students pursuing Masters in Data Science in Canada are:

Other scholarships for international students are::

• BrokerFish International Scholarship
• Go Clean Scholarship
• Karen McKellin International Leader of Tomorrow Award
• Debesh Kamal Scholarship
• Paul Foundation Scholarships
• National Overseas Scholarship

Ques. What are some of the general requirements needed to apply for a scholarship in Canada?

Ans . Scholarships in Canada are either offered based on merit or based on financial need. A few general requirements you need to fulfil to be eligible for the same are-

• Completion of a Bachelor’s degree with a good score
• A valid Canadian study permit
• Have proof of financial need like proof of income, bank statements, etc. (if applying for a need-based scholarship)
• Have a certificate of merit (if applying for a merit scholarship)
• Fulfill the age-limit criteria (in selected scholarships)

Ques. Can I get a 100% scholarship in Canada for an MS in Data Science?

Ans . Yes, some universities in Canada offering 100%, fully funded scholarships to international students are -

• University of Manitoba Graduate Fellowship
• University of Waterloo Scholarship
• University of Alberta Scholarship
• Pierre Elliott Trudeau Foundation Scholarship

Ques. Is pursuing an MS in Data Science in Canada worth it?

Ans . MS in Data Science in Canada is a good option for international students wishing to study abroad. Once you have graduated with an MS in Data Science, top universities in Canada offer placements in different job profiles like data analyst, functional analyst, analytics manager, and other data science-related positions. The average yearly salary for experienced data science graduates can range from 59,200 CAD to 80,349 CAD (~36.3 Lakhs INR to 49.27 Lakhs INR).

Ques. Which cities in Canada are the best to study MS in Data Science?

Ans . Some of the best universities to study a Master of Science program are situated in cities like

Ques. What specialisations are offered in the MS in Data Science in Canada program?

Ans . Some of the common specializations offered in MS in Data Science in Canada include:

• Artificial Intelligence
• Business Analytics
• Data Engineering
• Data Mining

Trent is ranked as the #1 undergraduate university in Ontario and the #3 undergraduate university in Canada.  Trent is considered to be an affordable university. The cost of attendance at Trent for an international student like you is around 32,306 CAD to 40,402 CAD. This includes an average tuition fee of 19,148 CAD for PG programs and 26,244 CAD for UG programs.

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Selected students will complete the home department’s degree requirements (including the qualifying exam) along with specified statistics requirements including a doctoral seminar, coursework in probability, statistics, computation and statistics, and data analysis, and a dissertation that utilizes statistical methods in a substantial way. 

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Potential drug effective against flesh-eating bacteria

Could combat serious antibiotic-resistant infections, mouse study indicates

by Mark Reynolds • August 2, 2024

Researchers at Washington University School of Medicine in St. Louis have developed a potential drug that is effective against common bacteria that can lead to rare, dangerous illnesses. Shown on the left are untreated Streptococcus pyogenes bacteria. After treatment with the compound, the dish is full of dead bacteria (image on right).

Researchers at Washington University School of Medicine in St. Louis have developed a novel compound that effectively clears bacterial infections in mice, including those that can result in rare but potentially fatal “flesh-eating” illnesses. The potential drug could be the first of an entirely new class of antibiotics, and a gift to clinicians seeking more effective treatments against bacteria that can’t be tamed easily with current antibiotics.

The research is published Aug. 2 in Science Advances.

The compound targets gram-positive bacteria, which can cause drug-resistant staph infections, toxic shock syndrome and other illnesses that can turn deadly. It was developed through a collaboration between the Washington University labs of Scott Hultgren, PhD , the Helen L. Stoever Professor of Molecular Microbiology, and Michael Caparon, PhD , a professor of molecular microbiology, and Fredrik Almqvist, a professor of chemistry at the University of Umeå in Sweden.

A new type of antimicrobial would be very good news for clinicians seeking effective treatments against dangerous pathogens that are becoming more resistant to currently available drugs.

“All of the gram-positive bacteria that we’ve tested have been susceptible to that compound. That includes enterococci, staphylococci, streptococci, C. difficile , which are the major pathogenic bacteria types,” said Caparon, the co-senior author. “The compounds have broad-spectrum activity against numerous bacteria.”

It’s based on a type of molecule called ring-fused 2-pyridone. Initially, Caparon and Hultgren had asked Almqvist to develop a compound that might prevent bacterial films from attaching to the surface of urethral catheters, a common cause of hospital-associated urinary tract infections. Discovering that the resulting compound had infection-fighting properties against multiple types of bacteria was a happy accident.

The team named their new family of compounds GmPcides (for gram-positive-icide). In past work, the authors showed that GmPcides can wipe out bacteria strains in petri dish experiments. In this latest study, they decided to test it on necrotizing soft-tissue infections, which are fast-spreading infections usually involving multiple types of gram-positive bacteria, for which Caparon already had a working mouse model. The best known of these, necrotizing fasciitis or “flesh-eating disease,” can quickly damage tissue severely enough to require limb amputation to control its spread. About 20% of patients with flesh-eating disease die.

This study focused on one pathogen, Streptococcus pyogenes , which is responsible for 500,000 deaths every year globally, including flesh-eating disease. Mice infected with S. pyogenes and treated with a GmPcide fared better than did untreated animals in almost every metric. They had less weight loss, the ulcers characteristic of the infection were smaller, and they fought off the infection faster.

The compound appeared to reduce the virulence of the bacteria and, remarkably, speed up postinfection healing of the damaged areas of the skin.

It is not clear how GmPcides accomplish all of this, but microscopic examination revealed that the treatment appears to have a significant effect on bacterial cell membranes, which are the outer wrapping of the microbes.

“One of the jobs of a membrane is to exclude material from the outside,” Caparon said. “We know that within five to ten minutes of treatment with GmPcide, the membranes start to become permeable and allow things that normally should be excluded to enter into the bacteria, which suggests that those membranes have been damaged.”

This can disrupt the bacteria’s own functions, including those that cause damage to their host, and make the bacteria less effective at combating the host’s immune response to infections.

In addition to their antibacterial effectiveness, GmPcides appear to be less likely to lead to drug-resistant strains. Experiments designed to create resistant bacteria found very few cells able to withstand treatment and thus pass on their advantages to the next generation of bacteria.

Caparon explained that there is a long way to go before GmPcides are likely to find their way into local pharmacies. Caparon, Hultgren and Almqvist have patented the compound used in the study and licensed it to a company, QureTech Bio, in which they have an ownership stake, with the expectation that they will be able to collaborate with a company that has the capacity to manage the pharmaceutical development and clinical trials to potentially bring GmPcides to market.

Hultgren said that the kind of collaborative science that created GmPcides is what is needed to treat intractable problems like antimicrobial resistance.

“Bacterial infections of every type are an important health problem, and they are increasingly becoming multidrug resistant and thus harder to treat,” he said. “Interdisciplinary science facilitates the integration of different fields of study that can lead to synergistic new ideas that have the potential to help patients.”

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McGill SCS Professional Development Certificate in Data Science and Machine Learning

This practical program aims to equip professionals with essential data science and machine learning knowledge and skills needed for a career as a data analyst, machine learning practitioner, or a junior data scientist. Participants have an opportunity to work through all the phases of a complete data science pipeline with structured and unstructured data, formulate a business need or problem into a data science project and select the proper tools and algorithms needed. Focus is placed on interpreting and effectively communicating data insights by using data visualization and storytelling techniques to translate data into business-specific knowledge.

Apply Now Type: Professional Development Certificate Courses:   5 Schedule:   Part-time Time:  Weekday evenings Delivery:   Online Unit:  Technology & Innovation Questions?   info.conted [at] mcgill.ca

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Message from the academic program coordinator.

We are thrilled to introduce to you the Professional Development Certificate in Data Science and Machine Learning. This program is meticulously crafted to equip you with the skills and expertise necessary to thrive in today's data-driven world.

In this program, you will gain an understanding of the fundamental principles and techniques of data analysis, data science, and machine learning. The curriculum covers a wide range of topics, including data analysis, predictive modeling, data visualization, and the utilization of machine learning algorithms. You will also work hands-on with cutting-edge tools and technologies, such as Python, and popular data analytics platforms.

One of the key advantages of this part-time program is the flexibility it offers. We understand the demands of your professional life, which is why the Professional Development Certificate in Data Science and Machine Learning is designed to accommodate your schedule. Whether you are a working professional looking to enhance your skills or an individual with other commitments, the program’s schedule allows you to pursue your education without putting your career on hold.

Join us on this transformative learning journey, where you will receive personalized support from our expert course lecturers and have access to valuable resources and networking opportunities. You will also have the opportunity to learn from and interact with experts in the field. This will allow you to gain a competitive edge and open doors to diverse career paths across industries such as finance, healthcare, marketing, and more.

We look forward to welcoming you to the Professional Development Certificate in Data Science and Machine Learning and witnessing your growth and success!

Key Features

In the program, you will engage in hands-on projects and collaborate with cross-functional teams, applying your newly acquired skills in data science and machine learning to solve real-world problems. This practical experience will enable you to translate your learnings into actionable insights that drive informed business decisions.

This program is focused on the technical aspects and utilizes Python as the primary programming language. Additionally, as a registered student, you will gain complimentary access to industry-leading sites such as DataCamp for the duration of the program; and tools such as Tableau Desktop, and Alteryx Designer for different courses. These tools empower you to work efficiently and effectively, utilizing cutting-edge technologies in data science and machine learning.

In this program, you will not only develop strong technical skills but also hone your ability to interpret and effectively communicate data insights.

Connect with like-minded individuals in the field of data science and machine learning, fostering new networks and collaborations. Engage in discussions, share experiences, and gain valuable insights from peers and industry experts, expanding your professional connections and staying abreast of the latest developments in the field.

Who Should Apply

Designed for those seeking a comprehensive understanding of data science principles, this program covers advanced topics such as predictive modeling, and data-driven decision-making, equipping learners with the technical programming skills to solve complex data problems.

You should attend this program if you are seeking to acquire essential technical data science and machine learning knowledge and skills or if you are pursuing a career as a: data analyst, data engineer, data journalist, machine learning practitioner, or data scientist.

Learning Outcomes

The program is designed to enable you to: 

• Demonstrate solid understanding of relevant statistical, mathematical concepts and computational tools.
• Apply essential data science tools to ingest, clean, process and analyze various large data sets using batch and streaming modes.
• Work through all the phases of a complete data science pipeline with structured and unstructured data.
• Test and evaluate different machine learning techniques and learn how to select the proper one to solve a business problem.
• Formulate a business need or problem into a data science project and select the proper tools and algorithms needed.
• Interpret and effectively communicate data insights by using data visualization and storytelling techniques and translate them into business-specific knowledge.

Program Courses

Delivery Format

This program is offered online.

Online classes offer a flexible learning solution for professionals, allowing you to keep learning while you work on your career.

Admission Requirements

• Bachelor of Engineering (B.Eng.)
• Bachelor of Science (B.Sc.)
• Bachelor of Commerce in MIS (B.Com MIS)
• Bachelor degree in another field and completed the course YCBS 286 – Introduction to Data Analytics with Python

Applicants may also be considered if:

• They do not meet the above criteria but have extensive and relevant experience in software programming or data analytics and have previously completed relevant coursework in calculus, statistics, or computer science will be evaluated on a case by case basis.
• They are 21 years of age or older
• Total course fees: $6,891 CAD
• SCS Career Development Success Package (SCSD) fees: $99 CAD
• Application fees: $101.38 CAD

Total cost of this program: $7091.38 CAD

Please note, the fees above are subject to change. The University reserves the right to change the fee schedule for non-credit courses without notice. The fees above are for the Winter and Summer 2024 terms, and they might change for the Fall 2024 term.

Find more information about the courses in this program here. Find more information about the SCSD fee here.

Career Spotlight

Machine learning brings a new paradigm to how we look at business issues. It is about identifying patterns, making predictions, and providing companies and organizations with the valuable foresight needed to develop strategies and solutions that are grounded in data.

Data science and machine learning are fast becoming recognized as the present and future of computational problem-solving. They provide essential solutions for businesses wanting to stay competitive.

These advancements are happening whether businesses are ready for them or not, so it is important to stay ahead of the curve to give your organization an edge in the market.

Technology is constantly evolving and changing the workplace as we know it. If you are seeking data science knowledge to apply to your profession or to transition into a new career, the Professional Development Certificate in Data Science and Machine Learning gives you comprehensive and practical skills you can start applying immediately to any position and industry.

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Please review the deadlines and guidelines to help make your application process smooth and easy.

PDC Application Guide

Need help applying? Contact us at info.conted [at] mcgill.ca or call 514-398-6200   Questions about your admission? Contact us at admissions.scs [at] mcgill.ca or call 514-398-6200

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