Description:

Abstract: Forensic scientists usually perform one of three tasks: (1) reconstruction, where they attempt to infer events that might have taken place at a crime scene; (2) investigation, where they attempt to establish a list of the potential donors of a given trace; and (3) evaluation, where they attempt to determine if a particular trace was made by a specific source. 

The determination that a particular trace originates (or not) from a specific source involves considering two mutually exclusive propositions: Hp - the trace originates from the considered source; and Hd - the trace originates from a different source in a population of potential sources. This can be represented as a non-nested model selection problem. In that case, under Hp, the trace is a random sample from the specific source considered by the scientist; under Hd, the trace is a random sample from an unknown source in the considered population. 

Forensic science relies on many different types of evidence to support the detection of crime and the identification of criminals. Most people are aware that forensic scientists are concerned with the analysis of fingerprints, DNA, firearms and drugs. It is less well known that forensic scientists are also analysing evidence material such as ear impressions, paint fragments or dust particles. The evaluation of simple DNA evidence is well understood due to the simplicity of the features considered and our ability to develop probabilistic models based on genetics theory. Unfortunately, likelihood-based inference and model selection for pattern and trace evidence involve considering high-dimension heterogenous random vectors which likelihoods do not exist.

During this talk, I will present two methods that we are currently developing and that enable the quantification of the weight of pattern and trace evidence in forensic science. The first method relies on the well-known Approximate Bayesian Computation (ABC) algorithm. Our implementation improves on the various implementations of this algorithm involves by using the Receiver Operating Characteristics (ROC) curve to remove the need to choose a threshold and to alleviate the curse of dimensionality affecting this family of algorithms. The second method involves using kernel functions to express the similarity between pairs of objects as a score, and modelling the distributions of vectors of scores under Hp and Hd. This method can be seen as a probabilistic multi-class version of the well-known Support-Vector Machines. I will present some examples of the application of these techniques to fingerprint and paint evidence. 

Cedric Neumann was awarded a PhD in Forensic Science from the University of Lausanne, Switzerland. From 2004 to 2010, Cedric worked at the Forensic Science Service (FSS) in the United Kingdom. As head of the R&D Statistics and Interpretation Research Group, he contributed to the development of the first validated fingerprint statistical model. This model was used to support the admissibility of fingerprint evidence in U.S. courts. 

Cedric is currently an Associate Professor of Statistics at the South Dakota State University (SDSU). Cedric's main area of research focuses on the statistical interpretation of forensic evidence, more specifically fingerprint, shoeprint and traces. Cedric has taught multiple workshops for forensic scientists and lawyers alike. Cedric served on the Scientific Working Group for Friction Ridge Analysis, Study and Technology (SWGFAST), was a member of the Board of Directors of the International Association for Identification (IAI) and is the resident statistician of the Chemistry/Instrumental Analysis area committee of the NIST-Organisation of Scientific Area Committees (NIST-OSAC).

Cedric serves on several Editorial Boards, including Forensic Science International and Law, Probability and Risk. Cedric is the 2009 ENFSI Emerging Forensic Scientist and the 2016 SDSU Berg Young Faculty.