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Statistics and Actuarial Science PhD candidate Rui Qiao was one of the six students who won the 2019 Huawei Prize for Best Research Paper by a Mathematics Graduate Student. This award recognizes the impact of his Deep learning enables de novo peptide sequencing from data-independent-acquisition mass spectrometry with a prize of $4,000.

Companies that fail to curb their carbon output may eventually face the consequences of asset devaluation and stock price depreciation, according to a new study out of the University of Waterloo.

The researchers further determined that the failure of companies within the emission-intensive sector to take carbon reduction actions could start negatively impacting the general stock market in as little as 10 years’ time.

Statistics and Actuarial Science professor and director of the Data Analytics Lab Ali Ghodsi has been named a 2018 Vector Institute Faculty Affiliate.

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.

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. 

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.

Photography by Jon White

Over 100 undergraduate and graduate students gathered in Mathematics 3 early Saturday morning to tackle large datasets at The Data Open, a competition that brings together the best minds in mathematics, engineering, science and technology to collaborate and compete using the world’s most important data sets. Students received the data sets at 8:00 a.m. and, in teams of three to four, had until 3:30 p.m. to analyze the data, extract meaningful insights, and propose solutions to a socially impactful problem.

Imagine analyzing 10 trillion data points, from a variety of sources, collected at an extremely fast rate. The data can help address business challenges companies faces every day, and possibly even predict client behavior– but you’re not sure where to start. How do you sift through the data that’s available to you, and draw out just the information you need?