Christopher Saunders

Christopher Saunders is a Professor of Statistics and affiliate faculty in Natural Resource Management at South Dakota State University (SDSU). He holds a Ph.D. in Statistics from the University of Kentucky, an M.S. in Statistics from the same institution, and a B.S. in Mathematics from California State University, Chico. His professional experience spans roles at The MITRE Corporation, George Mason University, and as a visiting professor at the University of Salzburg. Saunders specializes in statistical learning theory, forensic identification of source problems, and biometric analysis, with applications in forensic science, pattern recognition, and large-scale simulations. His work emphasizes quantitative methods for evidence interpretation, algorithmic bias mitigation, and forensic data validation.

Research interests include statistical methods for forensic source attribution, hierarchical data modeling, and the application of machine learning to forensic challenges. Saunders has contributed to foundational studies on error rate assessment in forensic evidence, probabilistic evidence evaluation, and the development of automated systems for handwriting and biometric analysis. His interdisciplinary work bridges statistics, computer science, and forensic science, addressing practical problems in criminal investigations and national security.

• Educations:
  • Ph.D. in Statistics, University of Kentucky, 2008
  • M.S. in Statistics, University of Kentucky, 2006
  • B.S. in Mathematics, California State University, Chico, 2002
  • Visiting Scientist, FBI Labs Forensic Science Research, 2013

Notable awards include the 2020 Outstanding Researcher Award and 2016 Young Investigator Award from the Jerome J. Lohr College of Engineering. His grants include leadership on NIH-funded projects and collaborations with MITRE Corporation, National Institute of Justice, and other federal agencies. Saunders has advised graduate students in forensic statistics and led teams developing statistical frameworks for evidence interpretation and forensic validation.

His research has advanced methodologies for explosive material analysis, handwriting examination, and forensic database design. Current projects focus on algorithmic fairness in forensic systems and improving the accuracy of biometric and trace evidence evaluation through advanced statistical modeling.