Contributing to our Health -- A Series About StFX Research Making A Difference In Our Communities
StFX is a leader in health innovation and entrepreneurship in Nova Scotia. In this ongoing series, we proudly shine a spotlight on our health research leaders, research and community health partnerships and their impact. For more on the Contributing to our Health series, click the link below.
Contributing to our Health series
“It (the research) fills a gap in clinical care by avoiding repeated blood draws from at-risk patients.” ~ Dr. Jacob Levman
Dr. Jacob Levman is an associate professor in the StFX Department of Computer Science. He has taught at StFX since 2016. His research focuses on combining computer technologies and medical data to help improve the standard of patient care.
Tell me a bit about your research program?
My research program combines computer technologies (such as machine learning) and medical data (such as MRI exams, and electrocardiogram like data) to answer questions about human anatomy and physiology, and hopefully, to also build the next generation of technologies to improve the standard of patient care.
Could you speak about any results so far?
My lab has developed machine learning technology for monitoring the hearts of patients in intensive care units, and we expect to be able to deploy it clinically for prospective evaluation in clinics of collaborators in Japan in the coming years. This technology provides lactate estimates for Pediatric Intensive Care Unit (PICU) patients non-invasively and continuously. It fills a gap in clinical care by avoiding repeated blood draws from at-risk patients.
What impact do you hope it will have?
I am hopeful my work will lead to an improved standard of patient care, a better understanding of human anatomy and physiology, and improved machine learning technology.
Could you tell me about yourself?
I studied computer engineering for my Bachelor’s, electrical and computer engineering for my Master’s, and then transitioned to medical biophysics school, which was a big change for me (Faculty of Medicine at the University of Toronto, and I was stationed in a hospital). I went on to do postdocs at Sunnybrook Hospital, in biomedical engineering, at the University of Oxford, and in neuroscience at Harvard Medical School. I conducted a large-scale study, which included a manuscript on the abnormal development of the cortex in autism, which was selected by the editor-in-chief as one of the leading papers in the journal over a two-year period. My colleague, Dr. Pascal Tyrrell and I founded an organization to promote and establish standards for the reliable and explainable use of artificial intelligence in medical imaging (https://www.real-mi.org/). Dr. Tyrrell’s lab’s recent paper, on which I am also a co-author, which is on sample size considerations in machine learning applied to medical imaging, just won the editor’s choice for leading paper of the year award from the Canadian Association of Radiologists’ Journal. I’m also visiting faculty at the Martinos Center for Biomedical Imaging (Massachusetts General Hospital, Department of Radiology, Harvard Medical School, Massachusetts Institute of Technology), where I’m doing research largely focused on brain MRI, and the use of advanced computational techniques.
What drew you to health research?
As a young computer engineer, I learned the fun of solving problems, and creating technologies that will continue solving people’s problems after I create the technology, it is a lot of intellectually stimulating fun! But when I looked towards my future, I felt as though I didn’t just want to work on problems, that in the end, are simply making a company more efficient, and thus assisting them to make more money. I identified medical applications as the most desirable domain in which to apply myself and made a big switch in focus from an industrial trajectory in computer technology focused on communications and embedded systems, to an academic one in medical technologies and analysis of medical data. I am excited about the potential to discover new things about human anatomy and/or physiology, particularly in the brain.
How did you become a researcher?
I had been enrolled in an industrially focused undergraduate program, and near the end of that program, I realised I wasn’t going to be happy working for a company doing traditional engineering work for the rest of my life, and so I made a late stage life recalibration towards becoming a teacher, but I knew I would only find it sufficiently intellectually stimulating if I were to become a PhD so that I could teach at university. After being tasked, as a graduate student, with combined research and teaching (as a teaching assistant), I became more and more interested in research, especially the more I was exposed to medical research.
What do you enjoy about being a researcher at StFX?
I like that I have small class sizes and the opportunity to integrate research and teaching in the classroom. This can involve course projects that allow students to pursue designing machine learning systems for problems they have never addressed before, and can also be extended to allow them to address problems that haven’t been addressed before by anyone. I give students tremendous flexibility in choosing something of interest to themselves, and I especially enjoy seeing what the leading students produce. I also like incorporating cutting edge research into lectures, running directed studies courses where a student earns course credit for pursing an in depth research project under my direction, and running in-class competitions that allow students to pursue a technical challenge creatively and competitively.
What’s something surprising about yourself that people wouldn’t know?
I love creativity and encouraging my own creative output. I’ve always been a singer, which is a fun creative outlet for me, and even though I’m not skilled at creative writing and the use of floral language, over the past three months I’ve started writing a book or background material for a show, with a main character who invents a series of machines that change the course of history in a futuristic sense.
