Co-Operative Education

During my undergraduate studies at the University of British Columbia, I participated in the Science Co-Op program. This means that I stopped my studies to work full-time in partner institutes to gather work experience, and better inform my career goals. Indeed, this experience led me away from pure maths (my initial passion) to computational physics/chemistry.

My work placements in reverse-chronological order:

Machine Learning for Optical Physics

Under the supervision of Prof Kirk Madison at UBC, and with the support of an NSERC USRA, I performed theoretical research on a quantum pressure standard using machine learning/statistical techniques (principally, LASSO and dimensionality reduction with sklean).

Machine Learning for Polymer Sequencing

Under the supervision of Prof Oleg Yazyev, in collaboration with Prof Aleksandra Radenovic at EPFL, I worked on DNA/polymer sequencing using TensorFlow and keras. I improved upon a model that took fixed-length sequences to one that can handle arbitrary-length sequences.

Machine Learning for Experimental Condensed Matter Physics

Under the supervision of Prof Christian Ast, I worked on prescreening scanneling tunneling microscopy (STM) images and signals using MATLAB and convolutional neural networks. This was my first introduction to machine learning and to solid state physics, but I managed to have a working model by the end of the work term.

Data Analyst at the Royal Canadian Mounted Police

My first work term was with the RCMP (the national police service of Canada) at their headquarters in Surrey, BC. I worked on staffing analysis, checking their database to determine performance metrics (principally response times) and produces staffing analyses for the heads of the detachments that I studied. I also started a collaboration between the RCMP and UBC to study this further, though I decided not to stay in the study and pursued physics instead.