Research focus

The intersection of genetic identity, DNA based forensic science and issues pertaining to national security. We leverage molecular biology and bioinformatics to address applied scientific questions relating to (1) the interpretation of low quality, low quantity DNA samples, (2) laboratory and computational (machine learning) approaches to facets of DNA mixture interpretation and (3) DNA based geolocation – the exploitation of biologicals to aid in tagging, tracking locating targets of interest.

Current Students:

  • Morgan Frank
  • Zachary Herzog
  • Cameron Moore
  • Jenny Nguyen
  • Natalia Pedraza
  • Veronica Poole
  • Amani Rafiq
  • Julia Sousa
  • Megan Voshage
  • Taylor Walther


  • Congratulations!:
    • Recent student achievements
      • Morgan Frank – Internship at the Hennepin County Sheriff’s Office Forensic Science Laboratory (MN) (2023)
      • Cameron Moore – Internships at the Michigan State Police Forensic Laboratory (2023)
      • Veronica Poole –  SOURCE grant 2023
      • Haley Crooks – Internship at the San Diego Police Department Crime Lab (2022)
      • Natalia Pedraza – SOURCE grant 2022-2023
    • Hiring news – recent Bioforensics research alumni!
      • Morgan Frank (2023) – Forensic Scientist (DNA) – Onondaga County Center for Forensic Sciences
      • Cameron Moore (2023) – Forensic Scientist (DNA) – Onondaga County Center for Forensic Sciences
      • Taylor Zekri (2022) – Forensic Scientist (DNA) – Minnesota Bureau of Criminal Apprehension
      • Haley Crooks (2022) – Forensic Scientist (DNA) – City of San Diego Police Department Crime Laboratory
      • Amber Vandepoele (2022) – Forensic Scientist (DNA) –  Oregon State Police
      • Jonathan Hogg (2021) – Forensic Biologist (DNA) at the New York State Police Forensic Investigation Center
      • Bridget Essing (2021) – Scientist in the Department of Defense


PACE Software

In the News

 Recent Publications

  • Marciano, M. A.1, Hall, A., Mozayani, A., Cromp, T., & Maynard, H. P. (2023). Enhancing research and collaboration in forensic science: A primer on human subjects’ research protection. Forensic Science International: Synergy, 7, 100443.
  • Schulte, J, Marciano, MA, Scheurer, E, Schulz, I. (2023) A systematic approach to improve downstream single-cell analysis for the DEPArray™ technology. J Forensic Sci.; 00: 1– 19.
  • Marciano M. and Maynard H. (2023). Enhancing Research and Collaboration in Forensic Science: A Primer on Data Sharing Forensic Science International: Synergy. 100323, ISSN 2589-871X,
  • D’Angelo O., Vandepoele A.C.W., Adelman, J. and Marciano, MA. (2022). Assessing non-LUS stutter in DNA sequence data. Forensic Science International: Genetics. 59,102706.
  • Marciano, M.A. and Crill, J. (2021). Genetics and Biodiversity: Tools to Improve Geolocation and Source Attribution. Research Shorts. National Intelligence Press Publications. November 8, 2021.
  • Watkins, D., Myers, D., Xavier, H., and Marciano, M. (2021). Revisiting Single Cell Analysis in Forensic Science. Nature Scientific Reports 11, 7054.10.1038/s41598-021-86271-6. [OPEN ACCESS]
  • Young, B., Marciano, M., Crenshaw, K., Duncan, G., Armogida, L., and McCord, B. (2021) Match Statistics for Sequence-Based Profiles from Forensic PCR-MPS Kits. Electrophoresis 0, 1-10.
  • Malanio BP, Mehta, P, Kurimsky MT, Marciano, MA. (2020) Quantification of Cellular Material on Fired and Unfired Ammunition. AFTE Journal 52(4), 230-238.
  • Marciano, Michael & Adelman, Jonathan. (2019). Developmental Validation of PACE™: Automated Artifact Identification and Contributor Estimation for use with GlobalFiler™ and PowerPlex® Fusion 6c Generated Data. Forensic Science International: Genetics. 43. 102140. 10.1016/j.fsigen.2019.102140.
  • Adelman, J.D., Zhao A., Eberst, D.S. and Marciano, M.A. (2019). Automated detection and removal of capillary electrophoresis artifacts due to spectral overlap. Electrophoresis, 0, 1-9.
  • Marciano M.A., Williamson V.R. & Adelman J.D. (2018). A Hybrid Approach to Increase the Informedness of CE-based Data Using Locus-Specific Thresholding and Machine Learning. Forensic Science International: Genetics 35, 26-37
  • Williamson, V.R, Laris T.M., Romano R. & Marciano, M.A.  (2018). Enhanced DNA Mixture Deconvolution of Sexual Offense Samples Using the DEPArray™ System.  Forensic Science International: Genetics 34, 265-276). DOI:
  • Marciano M.A.,Panicker S.X., Liddil G.D., Lindgren D. & Sweder K.S. (2018). Development of a Method to Extract Opium Poppy (Papaver somniferum L.) DNA from Heroin.  Nature Scientific Reports 8, 2590. doi:10.1038/s41598-018-20996-9
  • Marciano, M. A. & Adelman, J. D. (2017). PACE: Probabilistic Assessment for Contributor Estimation—A machine learning-based assessment of the number of contributors in DNA mixtures. Forensic Science International: Genetics, 27, 82-91. DOI: 

A Hybrid Machine Learning Approach for DNA Mixture Interpretation

The Bioforensics group has developed a DNA mixture deconvolution software to explore the use of machine learning as a potential method for enhanced deconvolution. The software related to this initial proof of concept study can be downloaded here. Please contact us for any questions or support needs.

This project was supported by Award No. 2014-DN-BX-K029 awarded by the National Institute of Justice, Office of Justice Programs, U.S. Department of Justice. The opinions, findings, and conclusions or recommendations expressed in this publication/program/exhibition are those of the author(s) and do not necessarily reflect those of the Department of Justice.


Inventors: Michael A. Marciano and Jonathan D Adelman

Abstract: Methods and systems for characterizing two or more nucleic acids in a sample. The method can include the steps of providing a hybrid machine learning approach that enables rapid and automated deconvolution of DNA mixtures of multiple contributors. The input is analyzed by an expert system which is implemented in the form of a rule set. The rule set establishes requirements based on expectations on the biology and methods used. The methods and systems also include a machine learning algorithm that is either incorporated into the expert system, or utilizes the output of the expert system for analysis. The machine learning algorithm can be any of a variety of different algorithms or combinations of algorithms used to perform classification in a complex data environment.