Seven graduate students have received fellowships from McMaster’s MacDATA Institute to pursue interdisciplinary projects that apply data analysis, collection and curation methodologies to a number of areas, including water quality monitoring, children’s mental health outcomes and brain activity in PTSD, among others.

“New and innovative data science methodologies are allowing researchers to get more from their data than ever before,” says Paul McNicholas, director of the MacData Institute and Tier 1 Canada Research Chair in Computational Statistics.

“This fellowship brings together students and researchers from all faculties to approach data analysis in novel ways – ways that are helping to reveal new insights from increasingly complex data.”

Each fellow is co-supervised by at least two faculty members who, together, cover data analytics and subject matter expertise. At least one of the supervisors is a member of the MacDATA Institute.

The fellowship program is part of the MacData Institute’s mission to promote innovative research and training programs related to data science, and to work with and support faculties and institutes on data issues and initiatives.

The new MacDATA fellows are:

Robert Gutgesell

Environmental toxicants and reproductive success of river otters (Lontra canadensis) in the Athabasca Oil Sands Region

Robert Gutgesell is an MSc. student in Physiology and Pharmacology in the Faculty of Health Sciences. He will study the impacts of oil sands toxicants on the reproductive health of river otters in the Athabasca Oil Sands Region, which lies within Canada’s boreal forest in the Mackenzie watershed. The region is the home of multiple Indigenous communities that have repeatedly raised concerns regarding the impact of toxicants arising from oil sands activity on local wildlife and human health.

Supervisors: Andrew McArthur and Sigal Balshine.

Yixin (Elliott) Huangfu

Leveraging Thermal Imaging to Improve Autonomous Driving Safety

Yixin Huangfu is a PhD student in mechanical engineering. His research will explore the possibility of using therman imaging to help the perception and sensory systems in self-driving cars. His research will refer to the state-of-the-art image recognition methodologies and adapt them to thermal images.

Supervisors: Saeid Habibi and Martin Von Mohrenshildt.

Yue Wang

The relationship between the nuclear and mitochondrial genomes of an emerging, multidrug resistant fungal pathogen

Yue Wang is a PhD candidate in biology. Her interdisciplinary research will involve genetics, bioinformatics, data handling and analysis, and programming, using data analytics to interpret the relationships between nuclear and mitochondrial genomes, “which could potentially help elucidate the population structure of the emerging human fungal pathogen Candida (C.) auris.”

Supervisors: Jianping Xu and Paul Higgs.

Qianqian Zhang

Real-time Prediction of Chloride Concertation in River Water Based on Continuous Sensors and Machine Learning

Civil engineering PhD candidate Qianqian Zhang’s research will apply machine learning expertise to focus on addressing data limitations in water quality monitoring, exploring the potential connection between the water quality and sensor data.

Supervisors: Zoe Li and Dr. Emil Sekerinski

Anne Fuller

Understanding neighbourhood factors contributing to risk and resilience for child mental health among children with low income

Health Research, Evidence and Methods PhD candidate Anne Fuller aims to determine the extent to which mental health outcomes of children and young people vary across neighbourhoods; and how associations between low family income and mental-health outcomes of children and youth vary across neighbourhoods. She will also work on identifying characteristics and processes operating within neighbourhoods that either exacerbate or attenuate associations between low family income and mental-health outcomes in children and youth.

Supervisors: Paul McNicholas and Kathy Georgiades.

Mohammad Chaposhloo

Investigating Differential Patterns of Hippocampal Connectivity Among Subtypes of Posttraumatic Stress Disorder via fMRI

Mohammad Chaposhloo is a PhD candidate in Psychology, Neuroscience and Behaviour. This project aims to examine the mechanistic role of the hippocampus in PTSD patients as compared to the dissociative subtype of posttraumatic stress disorder (PTSD+DS) patients. The project will use recently collected resting-state fMRI data acquired from over 193 PTSD and PTSD+DS patients and neurotypical healthy controls, as well as data collected from a smaller sample of PTSD patients pre- and post-treatment.

Supervisors: Suzanna Becker and Margaret McKinnon.

Ritchie Ly

Advanced Software Tools for Accelerating Data Processing of Multiplexed CE-MS Metabolomic Workflows Using R and Shiny

Chemisty and biology PhD student Ritchie Ly’s project aims to develop accelerated data workflow tools for faster and more reproducible data processing and visualization of MSI-CE-MS metabolomics data sets. The project will deliver an accelerated data pipeline for metabolomics-based researchers who are interested in comprehensive profiling in large-scale epidemiological studies based on a package of data analysis tools optimal for MSI-CE-MS.

Supervisors: Dr. Philip Britz-McKibbin and Andrew McArthur