Prospective Trainees /Students
For visiting students: see
this.
Openings
We have openings for projects and/or theses work related to biomedical
imaging, image processing and analysis, 3D shape modeling and related
topics. See Research
overview and Skills needed and
acquired at MIAL. More about MIA topics from
MICCAI
website and from
J.MIA
Funding
- Fully funded positions are available for outstanding students
and trainees at different levels (undergrad, masters,
doctorate, postdoctoral)
- Funding is primarily or completely provided through research
assistantships.
- Funding for conference travel is also available.
- Top-ups for fellowship holders (e.g. NSERC) is available.
- There are other several sources of top-ups and
internal
funding from SFU and external funding form
NSERC,
CIHR,
MSFHR and
others...
Infrastructure
- Student will have access to an exciting research environments
and state-of-the-art infrastructure (software, workstations,
high-performance computers, visualization, etc) at the medical image analysis lab and
through other computing infrastructures e.g.
SCIRF,
WESTGRID, in
addition to access to unique medical image data from national and
international clinical collaborators!
Courses requirements
- PhD students typically need to pass 4 courses (after their
Masters). Masters thesis students must pass 5 courses, whereas masters project
students require 8 courses.
- Take a look at the skills needed and
acquired at MIAL.
- Consider a project-based course
CMPT415 / CMPT894 or my
other courses. There are many other relevant courses in Computing
Science, Engineering, Mathematics, Kinesiology, and even at UBC.
Location
Career opportunities:
- Example companies
- Example research groups/websites
- Vancouver hospitals and medical centres (VGH, LGH, BCCA, BCCW, BRC,
many more...)
Other benefits of carrying out research at MIAL
- Learn about medical imaging and biomedical image
computing. Medical imaging has revolutionized medicine and the role
of computing continues to grow. There is a demand in the industry,
hospitals, and
academia for skilled computer scientists with skills in
interdisciplinary research and development. Also note that Health and
Imaging Science have been identified as strategic research directions
for SFU.
- Learn to apply computing science, engineering, and
mathematical methods to real-life health-related medical
imaging applications.
- Participate in national and
international research collaborations to solve difficult problems related to
health and medical imaging while using computational and mathematical techniques.
- Learn new programming skills or improve your existing
skills (e.g. using Generic programming and
MATLAB) and apply it to medical image processing.
- Learn to use 3D medical imaging software (e.g. slicer, volview) and
medical imaging API (e.g. ITK).
- Learn how to implement medical image processing/analysis algorithms,
analyze the performance and limitations of such algorithms, and invent or design better
algorithms.
- Learn about general health applications of computing,
medical imaging in particular.
- Work and establish links to doctors and hospitals.
Our collaborators appreciate the need for skilled computer scientists
and mathematicians. We work together on solving clinical problems
(diagnosis, therapy, and understanding of diseases), through medical imaging.
- Tap into health funding for graduate and postdoctoral
studies in computer
science, engineering or math (e.g.
CIHR,
MSFHR). Some of my students hold such funding.
- Improve your writing skills and become involved in publishing at national and international
conferences and journals. Travel in Canada or abroad to present your
work Even some of my undergraduate students have
published and participated in international conferences.
- Apply many of the methods learned for analysing medical images
to other visual computing applications.
Interested students?
More info about graduate studies?
Current SFU Trainees/Students
Supervisory committee member
- Andrea Tagliasacchi (PhD student of Richard Zhang)
- Manku Rana (MSc student of James Wakeling)
Former Trainees/Students (at SFU)
(former students, email me to update your current
affiliation)
- Preet Jassi (Undergraduate)
- Yahoo!
- William Ma (MSc
student, co-sup with Greg Mori)
- Rong (Frank) Liu (PhD student, supervisory committee)
- Peyman Rahmati (MSc student)
- Aaron Ward (PhD
student) - IRL at Robarts Research
Institute, School of Medicine
at UWO
- Fil Maj (Undergraduate) -
Nitobi
- Yonas Weldeselassie
(PhD student)
- Miranda Poon (MSc student, co- with Rafeef Abugharbieh) -
MICROSOFT
- Vinay Kotamraju (MSc student, supervisory committee) -
MDA
- Omer Ishaq (MSc
student) - Air University - Pakistan
- Mandana Salajegheh (PhD student)
- Dylan Tisdall (PhD student, supervisory committee) -
Harvard Medical School
- Zhe Fang (MSc student,
co-sup w Torsten Möller) -
EA
- Marc Koppert (Visiting MSc student)
- Weihua (Wallace) Xiong (PhD student, supervisory committee)
- Nariman Hatami (MSc student)
- Judith Hradsky
(MSc student, visiting) - MSMRI
- Cheng Lu (PhD student)
- Vincent Chu
(undergrad, USRA, senior supervisor) - EA
- Peter Plett (undergrad)
- Samuel Liu (undergrad)
- Akiko Campbell (MSc student, senior supervisor)
- Peter Plett (undergrad, USRA, senior supervisor)
- Janis Tong (undergrad, USRA, senior supervisor)
- Andy Rova (RA, co-sup w Anna Celler)
- Tong Liu (MSc student, senior supervisor)
- Roozbeh Ghaffari (MSc student, co-sup. w Brian Funt)
- Behnam Bastani (MSc student, supervisory committee)
- Johnson Yang (undergrad, USRA, senior supervisor)
- Mike McFarland (BEng, supervisory committee)
- Ronnie Chan (BEng, supervisory committee)
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