I am currently a Data Engineer on the Data & Analytics team at OMERS with a broad range of experiences in machine learning, data, and product development.
In my current role, I am focused on developing data and machine learning products to support functions across OMERS (investment teams, actuarial, corporate functions).
Prior to joining OMERS, I was a Software Engineer at integrate.ai where I developed the infrastructure to operationalize models through the integrate.ai platform for real-time and batch use cases in the
customer intelligence world. I have also worked on projects in responsible AI, data strategy, and machine learning modelling in the past across a number of industries including financial services, energy, infrastructure, and consumer marketing.
I hold a Bachelor of Computing (Hons) and Master of Science in Computer Science (with a thesis in machine learning) from Queen’s University.
Please feel free to continue reading or reach out to me about any interesting opportunities!
As a Data Engineer on the Data & Analytics team, I build data oriented software products to support a variety of functions across OMERS.
As a Data Engineer on the Global Investments team, I help build the underlying infrastructure to support and operationalize quant functions for investment teams in public and private markets (Capital Markets, Ventures). I have worked on projects including analysis of real-time streaming of trading data, the operationalization of NLP for market sentiment, and the transformation of large financial datasets for BI/data science.
As a Software Engineer, I worked on operationalizing AI models into tangible customer treatments through the integrate.ai platform. I developed the data pipelining required to allow seamless integration between the integrate.ai platform and existing infrastructure of our customers. I am also responsible for ensuring that our production pipelines are serving clients, building internal tooling for data scientists, and gathering requirements for new customer use cases. In addition to my technical role, I worked as a team lead for executing on a number of customer specific features, and I have conducted research on the Ethics of AI with collaborators at the University of Toronto.
As a Data & Analytics Consultant, I worked on enabling companies with analytics and AI capabilities through the development of data science/machine learning models, data strategy roadmaps, data architecture, and DW/EDL implementations. Through my work at EY, I have gained exposure with clients in the financial, telecommunications, and technology sectors.
As an IT Consultant at Black & McDonald, I provided insight to management on strategic technology implementations to solve complex business problems. In this role, I led the development, testing, debugging and deployment of Black & McDonald's first mobile application to collect data on facilities management operations and create analytic insights for management and clients. Furthermore, I have improved collaboration and efficiency of teams within the organization by implementing digital solutions. Other projects include project forecasting models using business data and developing ETL processes between Oracle JD Edwards and Bamboo HR Cloud system.
As a research assistant in the Queen's School of Computing under the supervision of Dr. Dorothea Blostein, my focus surrounded the application of machine learning for simulating biomechanical tensegrity (tensional integrity)
structures. I worked on improving the quality of tensegrity structure simulation and the implementation of a machine learning framework for PushMePullMe3D and the NASA Tensegrity Robotics Toolkit. This work was an integral component of my
Undergraduate and Master of Science thesis.
Awards: NSERC Undergraduate Student Research Award (2016)
Courses: Data Mining (CISC/CMPE 333), Neural and Genetic Computing (CISC/CMPE 452/874)
Teaching assistant for third year undergraduate computer science/engineering data mining course, covering topics a broad range of data mining techniques including supervised/unsupervised learning and statistical data mining techniques.
Teaching assistant for a fourth year undergraduate/graduate level computer science/engineering/cognitive science course on fundamental concepts in neural networks and genetic algorithms.
Computing - Specialization in Biomedical Computing
Graduated with distinction
As a component of my Masters thesis, I conducted research in the School of Computing at Queen's University under the supervision of Dr. Dorothea Blostein.
My research focused on implementing a machine learning framework for parameter optimization of tensegrity (tensional integrity) structures into the simulation platforms: PushMePullMe3D and the NASA Tensegrity Robotics Toolkit.
Tensegrity structures can provide an abstract model of the tension and compression within a structure, these models are believed to be insightful for areas of research in soft robotics, biomechanics, cellular biology and structural mechanics.
These structures are often composed of cables under tension interconnected with rods under compression to produce a stable and adaptive structure.
PushMePullMe3D and the NASA Tensegrity Robotics Toolkit are simulation platforms that allow users to interact with and understand tensegrity structures. Constructing tensegrity structures can require a high amount of precision to provide insightful results.
The use of a machine learning framework aims to reduce the difficulty of tensegrity construction by optimization parameters such as cable and strut lengths as well as tensile and compressive forces within the structure.
After work, I can often be found cycling, at the climbing gym, or on a squash court. I also enjoy golfing and playing hockey whenever the opportunity presents itself. I am avid musician playing piano and guitar.
Please feel free to contact me about new opportunities or projects!