Ian Renner

Dr. Ian Renner is a Senior Lecturer in the School of Information and Physical Sciences at the University of Newcastle, specializing in Data Science and Statistics. He holds a PhD in Statistics from the University of New South Wales, complemented by a Master of Statistics from the University of Utah and a Bachelor of Science in Mathematics from Valparaiso University. His research focuses on species distribution models (SDMs), particularly leveraging presence-only data and point process models. He developed the PPM-LASSO approach and maintains the R package 'ppmlasso' for model implementation.

Education:

• PhD (Statistics), University of New South Wales
• Master of Statistics, University of Utah
• Bachelor of Science (Mathematics), Valparaiso University

Research Interests: Dr. Renner's work bridges statistics and ecology, emphasizing the development of robust SDMs. Key areas include:

• Unifying MAXENT and Poisson point process models
• Observer bias correction in ecological data
• Integration of regularization techniques (e.g., LASSO) for predictive accuracy
• Application of citizen science data in conservation

His methodologies address challenges like taxonomy changes and sampling biases in species distribution studies.

Publications: His recent work highlights advancements in SDM stability, citizen science applications, and regularization methods. Key themes include improving model reliability through penalized likelihoods and addressing ecological data complexities.

Awards:

• JB Douglas Award (2011)
• Runner-up for Best Student Talk (2011)
• EJG Pitman Prize (2010)

Grants & Supervision: He has secured $12,838 in internal grants, including a visiting fellowship at CNRS (France) and conference funding. He currently co-supervises a PhD on deep learning for speech depression recognition and has guided two other students in statistical ecology and methodology.

Labs/Teams: Leads the development of the 'ppmlasso' R package, collaborating with researchers like Olivier Gimenez and Eric Beh to advance ecological statistics.