Federico Veneri, a Ph.D. student in statistics and a Center for Statistics and Applications in Forensic Evidence (CSAFE) researcher, has received a fall 2024 Research Excellence Award presented by the Graduate College and the Department of Statistics at Iowa State University.
Veneri was nominated by his major professor Danica Ommen, an associate professor of statistics, for his work in developing machine learning methods for pairwise dependent data with applications in forensic evidence interpretation.
“I am deeply honored to receive this award. I started working with Dr. Ommen and CSAFE in 2020 after joining ISU to pursue a Ph.D. in statistics,” said Veneri. “This award marks the culmination of my academic journey at ISU.”
The Research Excellence Award recognizes outstanding graduate students who excel not only in their academic pursuits but also in producing exceptional research, as documented in their theses and dissertations.
Ommen praised Veneri’s capabilities, saying, “It has been an absolute pleasure working with Dr. Veneri. Federico’s blend of attention to detail, creative thinking and perseverance made him the perfect candidate for this award. I can’t wait to see what Fede accomplishes when he leaves ISU.”
Alicia Carriquiry, CSAFE director, highlighted Veneri’s contributions to the center. “Federico is a deep thinker and has an impressive command of both the classical and the latest developments in statistical theory and methodology. I am not surprised that he would be selected to receive the Research Excellence Award, because he has been incredibly productive and has greatly contributed to the research mission of CSAFE. He will be missed!”
Veneri’s dissertation is titled “Resampling methods for score likelihood ratio based inference for source attribution problems.” His research addresses the challenges of determining the origins of items, especially in forensic science, where questions arise, such as whether specific bullets were fired from a suspect’s gun.
To improve inference in these cases, Veneri introduces resampling plans and an ensemble learning approach for score likelihood ratios, enhancing existing machine learning algorithms that measure similarity between items.
“My work has already been applied to handwriting and glass analysis but is not limited to these areas. I am looking forward to seeing if promising results can also be found in other domains,” Veneri said.
Veneri earned his bachelor’s degrees in economics and statistics and a master’s in mathematical engineering from the Universidad de la Republica in Uruguay. In 2018, Veneri was awarded a Fulbright Scholarship by the Fulbright Commission and the Uruguayan National Agency of Research and Innovation. They provided the support for his first two years at Iowa State, where he received his master’s degree in statistics in 2021.
As an active researcher, Veneri has authored and co-authored several papers, including “Ensemble learning for score likelihood ratios under the common source problem” in Statistical Analysis and Data Mining, which recently received over 1,000 views. In addition, he has incorporated many of his machine learning methods into an R package that will be available for forensic practitioners to use in casework.
Veneri is optimistic his research will benefit the forensic science community. He recognizes that while machine learning-derived scores are becoming a popular tool for dealing with complex features typical in forensic evidence, there is still a need to develop the theoretical underpinnings of these approaches to understand their statistical properties better. He hopes his research can contribute to the latter.
