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Discover 7 Mac Eng research projects from social robots to algorithmic watchdogsJuly 18, 2022

The federal government is investing in McMaster Engineering researchers, who are working on exciting new developments in materials science, civil engineering and computing and software.

McMaster researchers are receiving nearly $11 million in funding for 51 research projects through the Natural Sciences and Engineering Research Council of Canada’s (NSERC) 2022 Discovery Research Program.

Fourteen McMaster researchers, of which half were McMaster Engineering professionals, will also receive the Discovery Launch Supplement, which is awarded to early-career researchers in the first year of their grants to help them start their careers. 

From social robots to improving biomedical materials, learn more about the innovative work from seven McMaster Engineering researchers:

Sonia Hassini

Civil Engineering

Sustainable Stormwater Management Approach: Optimal Implementation of Low Impact Development Systems

While most-used management practices for stormwater runoff have been end-of-pipe infrastructures, which divert runoff to the nearest receiving waterbody, there has been recent uptake in Canadian cities of low-impact development technologies. These technologies, like rain gardens and green roofs, aim to manage runoff close to its source.  

Since reliable observed data is scarce, there are levels of uncertainty to their long-term functionality, performance, maintenance, placement and collective impact on the large-scale watershed stormwater management. 

This research will develop a framework that can assist in implementing sustainable stormwater management in Canadian urban areas under climate change and rapid urban development. The generated knowledge will also ensure the resilience and sustainability of Canadian communities under extreme rainfall events, climate change and urban growth for future generations. 



Shahab Asoodeh

Computing and Software

Algorithmic Watchdog for Differential Privacy: From Theory to Practice 

Powerful advances in machine learning can allow adversaries to gather personal information from an individual’s expanding footprint. Despite the widespread adoption of differential privacy (DP), there’s no standardized approach for evaluating and monitoring these technologies. 

This research will create mathematical methods that precisely characterize the risks of private information leakage and reduced utility in existing DP algorithms. It will create a rigorous blueprint for scalable “algorithmic watchdogs” that monitor DP technologies for misuse and unintended harm.  

In the long term, the research aims to assist data scientists in governments and corporations with the tools to guarantee meaningful and operational privacy. 


 Lingyang Chu

Computing and Software

Interpretations and Actions towards Trustworthy Graph Neural Networks

Graph neural networks (GNNs) have been adopted by large companies – like Google, Amazon, Facebook and Microsoft – to develop and achieve state-of-the-art performance in business applications. But most models are built and used as black boxes, which are not easily interpreted to human. 

This issue makes it difficult to verify and control the behaviours of GNNs, and lays severe security problems, reduces trust from users, and prohibits mass deployment.  

The research will develop trustworthy business intelligence based on reliable and consistent interpretations on GNNs. It will advance the frontier in the fast-growing area of trustworthy big data analytics.



 Denise Geiskkovitch

Computing and Software

Robot Design for Young Children

As social robots emerge into environments with young children, recent research highlights that the interactions can lead to negative and inappropriate interactions, like bullying and over-trust. 

While robots are being designed and marketed for young children, Geiskkovitch says, there isn’t knowledge of the best way to do so. 

By using novel methods for assessing child-robot interaction with preschool-aged children, this research will move the field forward by developing prescriptive frameworks to guide robot design and interaction behaviours to lead to positive outcomes. 

It will be the first research program in Canada (and one of the few in the world) to investigate this area. The research will include user-centred workshops, laboratory experiments and field studies to get a better understanding of young children’s expectations of robots’ social components (e.g. friendliness), as well as logistical requirements (e.g. microphone placement) and outcomes (e.g. trust.) of child-robot interaction. 


Claudio Menghi

Computing and Software

Automated Support for CyberPhysical Systems Design: from Theory to Practice

Most of our industries – including automotive, energy and healthcare – rely on cyber-physical systems for their daily operations. But CPS design is complex, error-prone and resultantly expensive, and failures can be catastrophic.  

Engineers need automated support for CPS design, and despite many successes, there are still many challenges that prevent extensive usage of these techniques in practice.

The research program will support engineers in developing safe CPS by defining novel software engineering solutions while targeting four challenges, including the need for: 

  • Automated techniques that search and detect flaws in CPS design
  • Help in understanding the causes of the problems
  • Procedures that automatically translate human artifacts info machine-processable inputs
  • Comprehensive tools that support engineers in running different analysis on the CPS design.


Yingying Wang

Computing and Software

Modeling Diverse, Personalized and Expressive Animations for Virtual Characters through Motion Capture, Synthesis and Perception 

Character animation plays key role in game development, robotics, virtual reality and augmented reality applications. Previous research on modeling motions to be natural and realistic have focused on generic motion content, meaning a lack of individual styles across characters.   

This research will find solutions to generate stylized motions with variants that match the diversity in the real-world in order to promote Diversity, Equity and Inclusion in the virtual one. 

A major challenge to stylized motions is the lack of large-scale motion style databases. This research will capture, learn and model three stylistic features: 

  • Demographic styles belonging to different groups of people, e.g. age, gender and race
  • Personalized styles resulting from personalities
  • Body build
  • Expressive styles for varied emotional and physical states for the same individual under different scenarios. 


Kyla Sask

Materials Science and Engineering

Engineered surfaces for the control of protein and cell interactions and improved biomedical materials

When biomaterials encounter biological fluids like blood, protein adsorption occurs rapidly and influences subsequent interactions with cells, including platelets, white blood cells and microbes. 

For medical devices like catheters, stents, and more, these responses can lead to blood clotting, inflammation or infection, and ultimately, failure of the device. For the devices to work, anticoagulant and antiplatelet drugs are still required, and their use can lead to complications. 

In order to improve biomaterials, surface modifications can be applied to alter their properties; physical modifications to control nanoscale properties and biofunctionalization can in turn allow control of cell response and reduce adverse reactions.  

This research will move toward an enhanced understanding of protein and cell interactions with multifunctional materials using advanced surface modification methods for improved devices. 

There were 13 other McMaster researchers who received funding through the Discovery stream: 

  • Samir Chidiac
  • Douglas Down
  • Thomas Doyle
  • Shiva Kumar
  • Vladmir Mahalec
  • Prashant Mhaskar
  • Mehdi Moradi
  • Mehdi Narimani
  • André Phillion
  • Ravi Selvaganapathy
  • Sesha Srinivasan
  • Li Xi
  • Gu Xi