2018 Telfer School Research Excellence Awards
Established Researcher of 2018
Professor Bijan Raahemi
Establishing his research in the areas of data analytics and business intelligence, Professor Raahemi is on the forefront of cutting-edge and industry relevant research. His research excellence is evident through his high number of peer-reviewed publications. He has published in Information Sciences, Expert Systems with Applications, and many other reputable journals.
Having earned an international reputation as an expert in data analytics, Dr. Raahemi has been able to attract external research and partnership grants to continue developing high impact research projects. For instance, in a new research project about high dimensional data, Professor Raahemi and his team are developing solutions that will improve services in the healthcare sectors, prevent fraud in financial transactions, and safeguard businesses from cyberspace attacks.
Professor Raahemi is also building a new technology that will help public transit companies reduce passenger wait times. In partnership with SMAT Transit Solutions, Professor Raahemi and his research team are developing an algorithm that will predict the number of passengers who are on the bus and the number of passengers who are still waiting at the bus stations. Ultimately, this new tool will help public transport companies decide how many additional buses are needed in a particular bus route during peak hours.
Emerging Researcher of 2018
Professor Jonathan Li
Professor Jonathan Li has distinguished himself through exceptionally high-quality research. He has published in Management Science, Operations Research, and two other reputable journals. The caliber of journals in which Professor Li has published his work is not only proof of his rigorous research methods—it also confirms that his research is highly relevant to the fields of operations research, financial engineering, and business analytics.
For instance, in his forthcoming article in Operations Research, he developed advanced mathematical models that complement the current measures of loss. Financial institutions such as banks should not rely solely on historical data (past market behavior) to measure risk. Complementary tools like the one Professor Li proposes will help banks create risk models that provide a more comprehensive picture of what is likely to happen in the market in the future.
Professor Li’s study makes a crucial contribution to research in risk management. Finding solutions that can help banks better measure and control their loss will also benefit society in general. The decisions banks make not simply affect major investors and big corporations, but also anyone who has mutual funds, retirement funds, or an education fund for their children.