News

It is with great pleasure that the Department of Statistics and Actuarial Science at the University of Waterloo welcomed Assistant Professor Peijun Sang.

Peijun (Perry) Sang is a tenure-track assistant professor. He received his PhD in Statistics from Simon Fraser University in 2018. His research interests include functional data analysis, high dimensional regression, copula modeling and risk analysis.

Jeroen de Mast is a visiting full professor who will be in Waterloo every Fall term for the next number of years. He received his PhD in Industrial Statistics from the University of Amsterdam in 2002 and until recently was also a professor in the Business School at the University of Amsterdam. His research expertise covers operations management, operations strategy, industrial statistics, quality and reliability engineering, product and process design, project and process management and organizational change.

Not only do Canadians nearing retirement or already retired expect to work longer, but a majority of them believe they’ll have low liquid retirement assets.

PhD candidate Saisai Zhang and professors Mary Hardy and David Saunders conducted the 2016 Ontario Retirement Survey (ORS). The report examines the retirement concerns and risk preferences of 1,000 randomly selected Ontario pre-retirees and retirees aged 50 to 80.

Dr. Matthias Schonlau is the 2018 winner of the NISS Distinguished Alumni Award. The National Institute of Statistical Sciences is the US-based National Institute of Statistical Sciences.

The University of Waterloo won the 2017 Centers of Actuarial Excellence (CAE) grant competition!

The Society of Actuaries has awarded the University of Waterloo a research grant of USD $297,000 on a 3-year project entitled “Maintaining Financial Stability in an Era of Changing Climate and Demographics”. This project is intended to develop models and pricing methods, and to create new risk measures and risk management solutions, pertaining to changes with the climate and demographics. Professor Johnny Li is this project’s principal investigator.

Source: Daily Bulletin.

The University of Waterloo's Laboratory for Knowledge Inference in Medical Image Analysis (Kimia Lab) announced in May 2018 that its AI project for digital pathology has been awarded a grant by the Ontario Research Fund – Research Excellence program (ORF-RE). The project aims to develop an intelligent search engine for digital pathology that can retrieve relevant cases from large archives, auto-caption the images, and facilitate consensus building.

The Ontario government will fund the 5-year project with a grant in amount of $3.2M. Huron Digital Pathology, as the industrial partner of Kimia Lab, will contribute $500k to the project. The company is the only Canadian manufacturer of digital scanners for pathology. Four professors from the University of Waterloo (Mark Crowley, Ali Ghodsi, Oleg Mikhailovich, and Hamid Tizhoosh), together with the machine learning group at the University of Guelph led by professor Graham Taylor (Vector Institute), and professor Shahryar Rahnamayan (UOIT) will collaborate with three hospitals to design and test an advanced search engine for large pathology archives. Grand River Hospital (Kitchener, ON), Southlake Regional Health Centre (Newmarket, ON) and University of Pittsburgh Medical Center (PA, USA) will not only provide data but also validate the results of the project.

Continue reading on the Daily Bulletin. 

Researchers at the University of Waterloo have found that sentiments in the nursing notes of health care providers are good indicators of whether intensive care unit (ICU) patients will survive. 

Hospitals typically use severity of illness scores to predict the 30-day survival of ICU patients. These scores include lab results, vital signs, and physiological and demographic characteristics gathered within 24 hours of admission. 

Professor Ruodu Wang received a $120,000 Discovery Accelerator Supplement (DAS) from the Natural Sciences and Engineering Research Council (NSERC) for a proposal titled “Model Uncertainty and Robustness in Risk Management.”

Samuel Wong (PhD 2013, Harvard University) joins our department from the University of Florida where he was an Assistant Professor.  Samuel's research focuses on developing analytical methods to tackle data-driven problems arising in scientific domains. Currently, his main applications of interest are protein structure prediction, learning dynamic systems in biology, and quality assessment of forest products. Statistical areas featured in his work include Bayesian modeling, Monte Carlo methods, and approximate inference strategies. With his data science focus, Samuel is keen on solving problems arising through collaboration, where both principled methodology and large-scale computation are needed.  To learn more about Samuel's research, please visit his website.

Throughout the 2016 US presidential campaign, Hillary Clinton’s private email server was a hot topic. A University of Waterloo professor and senior undergraduate student have now built a tool to analyze the contents of the server.