James R. Green

James R. Green is a Professor in the Department of Systems and Computer Engineering at Carleton University, where he has been a faculty member since 2005. He holds a PhD from Queen's University and is a licensed Professional Engineer (P.Eng.) and Senior Member of IEEE. His work integrates machine learning, biomedical informatics, and high-performance computing.

His educational background includes:

• B.A.Sc. in Systems Design Engineering, University of Waterloo (1998)
• M.Sc.(Eng.), Queen's University (2000)
• PhD, Queen's University (2005)

Dr. Green's research focuses on machine learning challenges in biomedical informatics, particularly class imbalance and rare event prediction. Key areas include protein structure, function, and interaction prediction; microRNA detection in unique species; non-contact neonatal monitoring; and accelerating scientific computing via parallel architectures like the Cell BE processor. His lab has developed several widely used bioinformatics tools such as PIPE, ProtDCal, and PCI-SUMO.

His recent publications reflect a strong trend in computational biology and machine learning, with applications in proteomics, genomics, and medical diagnostics. He has published over 100 peer-reviewed papers and secured funding from NSERC, CIHR, CFI, ORF, OCE, MITACS, and IBM.

Scientific and teaching recognitions include:

• Three teaching awards
• NSERC Best Project Award (twice: 2006-2007 and 2007-2008)
• Multiple student projects resulting in conference papers (e.g., CMBEC)

He has supervised numerous undergraduate capstone projects in areas such as assistive technologies, robotic systems, and bioinformatics. His teaching portfolio includes courses in Pattern Classification, Machine Learning, Computer Architecture, and Biomedical Engineering. He leads an active research group that bridges computer engineering and life sciences, fostering interdisciplinary collaboration.

Lab and research team initiatives include:

• Development of open-access web servers for protein analysis
• Collaborations with biologists and clinicians
• Integration of hardware and software for medical applications