Our research focus is at the intersection of genetic identity, DNA based forensic science and issues pertaining to national security. With specific emphasis on the analysis and interpretation of low quality/quantity human and non-human DNA samples using both laboratory techniques and computational tools including machine learning to address problem sets such as DNA mixture interpretation and single cell analyses.

My background as a practicing forensic scientist at the Onondaga County Center for Forensic Sciences has provided a foundation in field based applications of forensic techniques that bridge the gap between practitioners and the research community. I later joined the SRC Inc. Bioforensics group in pursuit of new challenges in applied research and development relating to national security and global threats. I engage the law enforcement, Department of Defense and intelligence communities.

• New York State Commission on Forensic Science (2021-Present)
• Patent: Methods and systems for prediction of a DNA profile mixture ratio (U.S. Pat. No. 10854316; 2020)
• Member of the Organization of Scientific Area Committees (OSAC)– Biological Data Interpretation and Reported Subcommittee (2019-Present)
• STEM Project of the Year 2017, Technology Alliance of Central New York -PACE: Probabilistic Assessment for Contributor Estimation
• Patent - Universal DNA profiling (10329609; 2019)
• American Association of Forensic Sciences, Member
• Council of Forensic Science Educators, Full Member

• Marciano M.A. and Adelman J.D. PACE: Probabilistic Assessment for Contributor Estimation— A machine learning-based assessment of the number of contributors in DNA mixtures. Forensic Science International: Genetics 27 (2017) 82–91.
• Marciano M.A., Panicker S.X., Liddil G.D., Lindgren D.*, Sweder K.S. Development of a Method to Extract Opium Poppy (Papaver somniferum L.) DNA from Heroin. Nature Scientific Reports 8 (2018) 2590.
• Williamson V.R.*, Laris T.M.*, Romano R., Marciano M.A. Enhanced DNA Mixture Deconvolution of Sexual Offense Samples Using the DEPArray™ System. Forensic Science International: Genetics. 34(2018) 265-276.
• Marciano M.A., Williamson V.R.* and Adelman J.D. A Hybrid Approach to Increase the Informedness of CE-based Data Using Locus-Specific Thresholding and Machine Learning. Forensic Science International: Genetics. 35 (2018) 26-37.
• 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. and Adelman J.D. 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(2019) 102140.

(*) mentored student