Data Science Advisory Board 2025-2026
Members are listed alphabetically by their last names.
Alex Coman | Google Inc.
As Director of Engineering for Google Ads, Alex is responsible for the infrastructure systems that power the open and free internet across the ad-supported web and apps ecosystems. Alex’s focus is on evolving the ad serving infrastructure with the help of his teams, towards supporting Google’s ad business evolution, delivering high performance at internet scale, and ultimately helping Google Ads to create value for users, publishers, advertisers and Google. Prior to joining Google Ads, Alex has contributed to other Google products such as Google Search, Google’s foray into social networks and Google Shopping. Alex holds a Ph.D. in Computer Science from University of Alberta with specialization in Data Management.
Harper Forbes | Hoffmann-La Roche
Harper Forbes is the Director of Data Sciences at Hoffmann-La Roche with 25 years of experience in the biometric pharmaceutical research industry, specializing in Biostatistics and Statistical Programming. As a people and product leader, Harper guides global projects and develops teams of statisticians and programmers across multiple therapeutic areas. Expertise includes statistical methodologies and strategy for clinical development. Embracing innovation, Harper actively promotes the incorporation of AI to create efficiencies in coding, data interpretation, and communication. Throughout his career, Harper has consistently focused on recruiting, developing, and mentoring talent in the data science field. Harper has a Masters of Science in Statistics from the University of Guelph.
Bethany L. | Canadian Centre for Cyber Security
From a background of Math, Theoretical Linguistics, and Computational Linguistics, Bethany worked in academia then applied Data Science within the Government of Canada. She draws on a deep understanding of language data combined with technical skills to create automated processes and Machine Learning enabled applications to support frontline analysts and wrangle real world data for productive use.
Miguel Lacerda | Balyasny Asset Management
Miguel Lacerda is the Director of AI Enablement at Balyasny Asset Management. Prior to this, he was the Chief Data Scientist at a boutique asset management firm and the Group Head of Advanced Analytics at a large financial services company in South Africa. Before moving into the private sector, he was a Lecturer and Researcher at the University of Cape Town, where he led the development of the first undergraduate and masters programs in Data Science in the country. He holds a PhD in Mathematics from the University of Galway in Ireland.
Paul McNicholas | Professor, Department of Mathematics & Statistics | McMaster University
Paul McNicholas is a Professor and Dorothy Killiam Fellow in the Department of Mathematics and Statistics. He is the (Tier 1) Canada Research Chair in Computational Statistics and a member of The College of the Royal Society of Canada. Paul’s research focuses on computational statistics, particularly mixture model-based clustering and classification. His current research interests include non-Gaussian mixtures, matrix-variate distributions, and real problems in big data analytics.
Eric Morrow | BMO Financial Group
Eric is currently the Managing Director of the Enterprise Data Science & AI group within BMO's Data and Analytics (DnA) organization. As a horizontal function within BMO, his team of data scientists and AI developers engages with groups and services across the organization to develop anything from data-driven insights to production ML/AI-based solutions on topics ranging from price optimization to cybersecurity-threat detection and beyond.
Prior to BMO, Eric worked in the aerospace industry on spacecraft development and robotic operations on the International Space Station. He has also worked in the exploration geophysics field on innovative gravity measurement systems.
Eric holds a PhD in geophysics from Harvard University and Masters degrees in physics and aerospace engineering from the University of Toronto.
Raymond Ng | Professor, University of British Columbia
Raymond Ng is the Canada Research Chair on data science and analytics. He is the founding Director of the UBC Data Science Institute, and also a founding co-director of the UBC AI & Health Network. He is an elected fellow of the Royal Society of Canada. For both 2022 and 2023, he was named one of the world’s top-75 academic data science leaders by the MIT-based CDO magazine. Ng’s main research area for the past three decades is on data mining, with a specific focus on health informatics, text mining, and Natural Language Processing. He has published over 260 peer-reviewed publications on those topics. (H-index 75; total citations 41,000+)
Eric Rancourt | Statistics Canada
Eric Rancourt is Assistant Chief Statistician and Chief Data Officer at Statistics Canada where he has had multiple roles over the last 35 years. He is responsible for strategic data management, methodology and analysis. These include data standards, quality, registers, geographic systems, modelling, data science, AI, ethics, privacy, and legal aspects.
Eric is Chair of the Board of Governors of the Canadian Statistical Sciences Institute (CANSSI). He co-chairs the Assistant Deputy Ministers Committee on Data and Information and the Standards Council of Canada’s AI Data Governance Collaborative. He is also a member of the Canadian Research Data Centre Network, a member of the National Data Advisory Council of Australia and a member of the Abu Dhabi International Statistical Advisory Committee. Eric has been active in the fields of data representativity, administrative and alternative data, data frameworks and data ethics. His academic background includes degrees in statistics, history, and philosophy. He is Chair of the Survey Methods Section of the American Statistical Association; Chair of the Board, Survey Methodology Journal and is an elected member of the International Statistical Institute.
Anindya Sen | Professor, Department of Economics
Anindya Sen is a professor at the Department of Economics where he has taught since 1999 and is the current Acting Executive Director for the University of Waterloo Cybersecurity & Privacy Institute, and the Director for Graduate Diploma in Computational Data Analytics for the Social Sciences & Humanities (CDASH). In 2014 he was recognized for his innovations in teaching and mentoring of students with the University Award for Distinguished Teaching.
He has published research on the relationship between higher cigarette taxes and smoking, the impacts of market power on prices charged to consumers and firm level productivity and innovation, and the effects of higher minimum wages on employment and poverty. His current work has focused on: using advanced Machine Learning models in the analysis of government interventions on the spread of COVID-19 and identifying best Emergency Room protocols to reduce readmissions from cardiac arrests; the economics of data markets and privacy; studying human-computer interaction in the context of cybercrimes; and understanding societal trends in trust, and misinformation.
Stephen Smith | Department of Electrical and Computer Engineering | Director, Waterloo Data & Artificial Intelligence Institute
Stephen L. Smith is a Canada Research Chair in Autonomous Systems and a Professor in Electrical and Computer Engineering at the University of Waterloo where he directs the Autonomous Systems Lab. He is director of the Waterloo Data & Artificial Intelligence Institute. He was a postdoctoral researcher in the Computer Science & Artificial Intelligence Lab at MIT. He completed his PhD at the University of California, Santa Barbara (UCSB), his Master's at the University of Toronto, and his B.Sc. at Queen's University, Kingston. He is a licensed Professional Engineer (PEng) with the Professional Engineers Ontario, a Senior Member of the IEEE, and an associate editor of the IEEE Transactions on Robotics. His research focuses on control, optimization and learning for autonomous systems and robotics.
Stephen Vavasis | Professor, Department of Combinatorics & Optimization
Vavasis received a Bachelors in Mathematics from Princeton in 1984, a Masters (i.e., Part III of the Tripos) in Mathematics from Cambridge in 1985, and PhD in Computer Science from Stanford in 1989. He was an assistant, then associate, then full professor of computer science at Cornell University from 1989 to 2006. Since 2006 he has been a professor in the Department of Combinatorics and Optimization at University of Waterloo. He served as Associate Dean for Computing for the Faculty of Mathematics 2017-2020. He has held summer or sabbatical positions at Argonne, Sandia, Bell Labs, Xerox PARC, NASA Ames and elsewhere. He is a past winner of the Hertz Graduate Fellowship, Churchill Scholarship, Presidential Young Investigator award, and Guggenheim Fellowship.
He is currently the director of graduate Data Science programs.