Four researchers awarded $480,000 from NSERC DAS program

June 26, 2015

Four Faculty of Applied Sciences projects have been awarded a competitive Discovery Accelerator Supplement as researchers tackle issues that range from improving energy storage in zero-emission vehicles to developing diagnostic tools using everyday graphite pencils.   

In recognition of their innovative and original research programs, the four researchers collectively earned $480,000 from NSERC’s Discovery Accelerator Supplement (DAS) program.  

Individually, each DAS award is valued at $120,000 over three years and this funding enables established researchers to capitalize on an opportunity, such as a recent research breakthrough, a paradigm shift, or a new strategy to tackle a scientific problem or research question.

The DAS program recognizes those with strong potential to become international leaders in their field, and supports them as they compete with the best in the world.

2015 Faculty of Applied Sciences Discovery Accelerator Supplement (DAS) recipients

Engineering science professor Ash Parameswaran develops innovative biomedical sensors using graphite from everyday pencils. His technology will address the need for low-cost diagnostic tools to monitor hormone levels and detect diseases such as some cancers.

Engineering science professor Jie Liang develops novel signal processing algorithms to enhance the quality of photos and videos uploaded to social media. Compression reduces page loading time and bandwidth, but it also produces unwanted effects such as image pixilation and colour changes — issues Liang is trying to address.

Mechatronic systems engineering professor Erik Kjeang develops electrochemical energy systems used in zero-emission vehicles and power generators. Kjeang’s research aims to enhance the longevity of electrochemical energy storage and conversion devices – a common challenge for technologies in the marketplace.

Computing science professor Funda Ergun designs and analyzes streaming algorithms to identify structural trends in big data sets. She hopes her research will help accelerate innovation in science and engineering by capturing scientifically meaningful data in colossal data sets. The algorithms she creates address one of big data’s big challenges — memory limitations.