Meng Zhou
Welcome!
I am a full-time Machine Learning Engineer at SpassMed working on multimodal models for medical data, including physiological time series, medical reports and imaging. If you are interested in AI applications in Medicine/Healthcare, reach out to me and we can have a chat!
Previously, I obtained my Master of Science (MSc.) degree in Computer Science at the University of Toronto in March 2024, where I am very fortunate to be advised by Prof. Farzad Khalvati at the Intelligent Medical Informatics Computing Systems Lab. I was affiliated with The Hospital for Sick Children (SickKids) Research Institute, one of the top three pediatric health-care centres in the world, where I worked in the Department of Neurosciences & Mental Health. I have also spent a wonderful four months at AltaML Government AI lab, working on an Aerial Image Segmentation project for the public sector.
🔥 News
- (May 2024), I will serve as a Reviewer for DGM4MICCAI Workshop @ MICCAI Conference 2024
- (Apr. 2024), Our paper on conditional generation of brain tumor ROIs has been accepted to MIDL 2024, see you in Paris! OpenReview link
- (Feb. 2024), One paper submitted to Medical Imaging with Deep Learning 2024
- (Jan. 2024), I compeleted my Master of Science in Computer Science degree at the University of Toronto
- (Oct. 2023), One paper submitted to Computers in Biology and Medicine, under revision
- (Jul. 2023), I received the Ontario Graduate Scholarship (OGS) at the University of Toronto DCS
- (Jun. 2023), I served as a Reviewer for DGM4MICCAI Workshop @ MICCAI Conference 2023
- (Oct. 2022), I received Mergelas Family Graduate Student Award from the Temerty Faculty of Medicine, University of Toronto
- (Apr. 2022), One paper submitted to Journal of Machine Learnig Research, under revision
- (Mar. 2022), One abstract accepted to Imaging Network Ontario
📖 Research
My research interest lies at the intersection between Deep Learning and Medical Imaging, particularly in developing 3D Medical Image Generation model to improve the diagnostic (segmentation and classification) performance of rare diseases, such as (Pediatric) brain tumors. I am also interested in NLP for healthcare using LLMs. Previously, I have worked on the Sequential Decision research on Multi-Armed Bandits.
My research could be categorized into:
- Computational Methodology in Medical Data (image, text, time series data, etc.)
- Deep Learning applications for Medical Image Analysis
- Medical Image Generation (Image Transformer, Diffusion, Vision-Language, LVMs, etc.)
- NLP for clinical data (LLMs)
- Contextual Multi-armed Bandit Problems
I am funded by the Department of Computer Science, Temerty Faculty of Medicine at the University of Toronto, Mitacs Research Grant, SickKids Hospital, and the Ontario Graduate Scholarship.
🏆 Honors and Awards
- Ontario Graduate Scholarship Recipient, Department of Computer Science, University of Toronto, 2023
- DCS Graduate Program Fellowship, Department of Computer Science, University of Toronto, 2023
- Mergelas Family Graduate Award, Temerty Faculty of Medicine, University of Toronto, 2022
- Dean’s Honor List, Queen’s University, 2019-2022
- John Ursell Tutor Award, Queen’s University, 2020
🚩 Other Milestones
- I obtained my Honours Bachelor’s degree in Computing, Specialized in Computing and Mathematics from Queen’s University. During my undergrad years, I have the privilege to work under the supervision of Prof. Parvin Mousavi at the Medical Informatics Laboratory on my undergraduate honours thesis, “Domain Transfer Through Image-to-Image Translation in Prostate Cancer Detection”; and Prof. Yanglei Song on the Contextual Multi-Armed Bandit problem. You can see more details in the Publicaton section on the top.
- I graduated from Yale Secondary School, a beautiful high school in British Columbia in June 2017.
Navigate the top bar for more details, enjoy!
Who visited my website before? check out this:
