Chris Callaghan, M.Sc.
Matthew Spencer, Ph.D.
Principal Scientist

A native Montréaler, Chris studied math, chemistry and physics at Dawson College in the mid-80’s. He then went on to earn a B.Sc. from McGill in natural history and wildlife science, and an M.Sc. from the University of Ottawa, with a focus on how human activities affect natural ecosystems. Chris entered the Canadian federal public service in the early 90s, where he took on various positions related to environmental toxicology, ecosystem management, sustainable development and environmental auditing. It was during his time with the Auditor General of Canada that Chris became interested in the policies and frameworks associated with measuring organizational performance and accountability. In 2006, Chris opened his own consulting firm specializing in program evaluation. As he gained insight into some of the challenges associated with program evaluation theory and practice, he observed what he sees as serious gaps in how interventions are designed and measured. Chris is now concerned with developing better means to understand how interventions work, how to improve design processes, and how to support evidence-informed decision-making. His vision is a world in which decision-making is systematically supported with evidence, including research, expert knowledge, stakeholder narrative, and open/big data. To realize this vision, Chris is focused on the use of cutting-edge concepts and technologies, including those associated with data science and artificial intelligence, to gather, analyze and test the evidence needed to bring policy-making into the modern era.

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Matt is PolicySpark’s Principal Scientist. Matt is an applied scientist with expertise in enterprise-scale machine learning, software development, data science, and computational biology. He is the engine behind PolicySpark’s innovative technology. He obtained a BASc. in computer engineering from the University of Ottawa and went on to earn a Ph.D. in cybernetics from the University of Reading in the United Kingdom. The focus of his thesis was complex network models of brain connectivity. Matt is an effective international and inter-disciplinary collaborator. He has over 10 years of experience working with teams around the world and communicating complex ideas to a wide range of audiences. He has 5 years of experience leading machine learning projects at Amazon. His technical expertise includes machine learning research and development employing a wide variety of technologies, including TensorFlow, Sklearn, Spark, Python and AWS. Other strengths include natural language processing, biosignal/time-series analysis, complex network analysis (Graph Theory), predictive stochastic models, reinforcement learning, programming (Python, Java, C#, C/C++, MATLAB), and enterprise software architecture.