Our mission at McMedHacks is to provide bright minds such as Marcia and Vasileios with the tools to get started with exceptional projects such as theirs. We are delighted to see their accomplishments and wish them all the best with their project.
If you are interested in becoming a part of McMedHacks in the 2022 rendition, you can sign up using this early interest form.
Dr. Christopher Deufel is an assistant professor and medical physicist in the Department of Radiation Oncology at Mayo Clinic, in Rochester, Minnesota. Dr. Deufel received his Ph.D. in experimental physics from Cornell University in 2007 and completed residency training in therapeutic medical physics at the Mayo Clinic from 2007-2010. Dr. Deufel is the chair of research for the Division of Medical Physics at Mayo Clinic and practice leader for Brachytherapy in the Department of Radiation Oncology. He also supervises physics resident education in brachytherapy and conducts research in brachytherapy applicator design, clinical brachytherapy techniques, and radiation treatment planning methods. His work has been published Nature Methods, Radiotherapy and Oncology, the International Journal of Radiotherapy and Oncology Biology Physics, Physics in Medicine and Biology, the Brachytherapy Journal, and the Journal of Applied Clinical Medical Physics. Dr. Deufel is an executive board member for the American Brachytherapy Society, vice-chair of the Working Group for Brachytherapy Clinical Applications for the American Association for Physicist in Medicine, and has been invited to speak at numerous national and international seminars.
I am Bonaventure Dossou, I hold a BSc in Maths from Russia, and currently a MSc at Jacobs University, Germany. I am a research intern at Mila Quebec AI Institute. I am also an intern at Roche Canada and got into the “Scientist in Residence” at Modelis, a drug discovery research company based in Montreal. My areas of interest are computer vision, and NLP (machine translation, speech recognition, etc.) I am also the co-creator of the FFR: Fon-French Neural Machine Translation. I share most of my opinion, and work + research on Twitter: @bonadossou.
My current research is focused on applications of machine learning to healthcare with an emphasis on COVID-19. My broad interests are in deep learning, casual inference, reinforcement learning, statistical genetics, and proteomics.
I am super excited to be a part of the Mentor team and look forward to meeting all of you!
I recently graduated from McGill with a BSc in Stats & Computer Science. I’m interested in machine learning theory and its applications for social good. I’m currently working at Morgan Stanley as a Technology Summer Analyst, focusing on data science in finance.
Hi, my name is Fynn and I’m a 4th Year Undergraduate Comp Sci student at McGill. I’m interested in machine learning and artificial intelligence with a focus on reinforcement learning (RL). This summer I’m pursuing my interest in the field by applying RL techniques to optimization problems in a research lab setting.
We are very excited to announce that Dr. Farhad Maleki will be instructing our McMedHacks workshops on June 27th and July 4th about the evaluation of machine learning models and 3D image augmentaion and its use case in semantic segmentation for medical images.
Dr. Farhad Maleki is a postdoctoral researcher at the McGill University Health Centre. He completed his Ph.D. in Computer Science from the University of Saskatchewan, focusing on computational techniques for biomedical data analysis. Dr. Maleki’s current research is on predictive modeling in the absence of large-scale labeled imaging datasets.
You can learn more and get in touch with Dr. Maleki here:
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Information about upcoming events can be found on our website.
Jon is a Professional Engineer (APEGS) with a background in Electronic System Engineering (B.A.S.c University of Regina). Jon previously worked in the industrial automation and control systems industry before co-founding Limbus AI in 2017. Jon’s role at Limbus AI is overall direction of software development specifically of the Limbus Contour application and mostly focusing on the client side (user interface, integration with clinical workflows, pre and post processing of images prior to AI model application, etc.). Jon also oversees regulatory affairs and works closely with regulatory and quality staff to ensure Limbus AI is complying with all applicable regulations and deploying safe and effective products to market. You can follow Jon on LinkedIn.
Carter leads the machine learning efforts at Limbus AI. His focus is primarily on the data pipeline and machine learning models, various image processing applications, and integration with the user-facing application. Carter studied electrical engineering at the University of Regina followed by graduate studies in computational neuroscience at the University of Waterloo. Before Limbus AI, Carter worked as a data scientist and machine learning engineer with a focus on NLP.
You can get in touch with Carter Kolbeck on LinkedIn.