Deepa Jahagirdar, Msc
I am nearing the completion of my PhD in Epidemiology at McGill University where I received training and experience in statistical methods, analysis of large quantities of spatiotemporal data and epidemiologic methods. In all my work, I love to distill complexity into an elegant story. This conviction drives my interest in data visualization, statistical methods, predictive modelling, econometric methods to identify causal effects, and everything from visual and descriptive exploration to machine learning algorithms to make sense of big data. It also drives my strong interest as a writer, in teaching and, ultimately, in finding the beauty in chaos.
Katie Dunkley-Hickin, MSc
In my Master’s in Epidemiology and while working at the Lady David Institute of the Jewish General Hospital I heavily employed econometric methods, in particular for investigating the causal impacts of policy introduction on population health outcomes. I have always worked with many disparate sources of data which I pride myself in cajoling into a coherent picture.
Having spent years in the noble (but slow as molasses) world of academia I have since caught the bug for consultancy, and am thrilled to be working and learning in this fast-paced and (my favourite part) collaborative environment.
Aside from the rush I get when I finally solve that knotty coding problem, I also love cats (dogs are great, but nothing beats a purring kitty), books, crochet, yoga and food. Especially chocolate. Okay, any dessert.
Nancy Zhu, BSc
I am a Masters student in Epidemiology at McGill University. In summer 2017, I did an internship at Health Canada, where I developed several R shiny applications for interactive visualization of Canada Vigilance Adverse Reaction Database. The experience motivated me to explore more in big data, data visualization and data mining. With open-source communities, there are endless learning opportunities in this field and I am thrilled to be part of the journey!
Aman Verma, PhD
I have a PhD in Epidemiology from McGill University, and an undergraduate degree in Computer Science. I have experience in developing machine learning systems with large databases, particularly for scientific data in healthcare. I am comfortable learning any programming language, but I have recently become particularly interested in R. I get excited about applying fancy statistical and machine learning techniques to large datasets, particularly in healthcare.
I am currently involved in a number of projects, including measuring how following opioid prescription guidelines can decrease the risk of opioid overdose, modeling trajectories of chronic obstructive pulmonary disease, and how we can better prioritize ambulance calls using secondary healthcare data.