Oshawa profs, Ontario Tech-led research team solving crimes through bloodstream analysis
Published May 1, 2023 at 10:14 am
An Ontario Tech-led team has been awarded $250,000 in federal research funding to study crime scene bloodstream pattern analysis.
The team will be led by Principal Investigator Dr. Theresa Stotesbury (Assistant Professor, Forensic Science) and Co-Principal Investigator Dr. Peter Lewis (Associate Professor, Computer Science), who is also Canada Research Chair in Trustworthy Artificial Intelligence.
The interdisciplinary research project also includes:
- Cecilia Hageman, Assistant Professor and Undergraduate Program Director (Forensic Science), Ontario Tech
- Caitlin Pakosh, JD, Assistant Professor (Forensic Science), University of Toronto-Mississauga
- Amanda Lowe, Forensic Research and Training Analyst (Forensic Identification Services), Ontario Provincial Police
The project, dubbed project ‘Reimagining forensic BPA from the bottom up,’ will be funded through the New Frontiers in Research exploration stream.
“I’m really looking forward to working with our team on this research. I think we will have a real impact on improving the rigour, understanding and communication in this exciting field of forensic science,” said Stotesbury.
Crime scene investigations require reliable forensic science tools to make air-tight cases, eliminating any potential for ambiguity or doubt. But current methods used to interpret evidence at crime scenes have been criticized globally for subjectivity and lack of scientific rigour.
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One area of criticism is the existing classification schemes of bloodstain pattern analysis, which sometimes lack clarity concerning the cause and formation of the bloodstain and are not conserved as the environment varies.
Some branches of forensic science have modified existing schemes in response; but more recently, fundamental issues with the assumptions behind these classification schemes have been exposed.
“The importance of building models that are trustworthy cannot be overstated in applications in the criminal justice system. Integrating explainable and interpretable machine learning methods with domain expertise are essential to this research,” Lewis explained. “The potential impact in reducing wrongful convictions, as well as the interdisciplinary nature of this project and the partnerships that have been built to deliver it, make this a particularly exciting area of research to work in.”
The team will take a data-driven approach to produce a bloodstain pattern analysis tool aimed at helping and guiding everyone connected to the criminal justice system in a “transparent and interpretable way.” The tool will be underpinned by a new classification scheme that integrates both data-driven insights and forensic expertise, and captures a more rigorous, accurate, and informed understanding of the causes of bloodstains.
The research team will use unsupervised machine learning, a family of techniques that analyzes untagged data, and facilitates the emergence of natural patterns, which may otherwise remain hidden or obscured by human bias. This approach is completely different from supervised learning, already in use in BPA, where assumptions from historical taxonomies form the basis of classification, rather than emerge from the data.
Unsupervised learning algorithms have contributed to significant advances in a wide range of disciplines from medical diagnosis to marketing, revolutionizing previous manual processes based solely on expertise, experience and intuition. A wide range bloodstain patterns from indoor and outdoor environments will examine if BPA data can enable a similar revolution in data-driven forensic science.
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