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Stephanie Morgan

As seen in: Nature, MDPI, BioRxiv, Common Sense Media, The Lancet, Pediatrics, JD Supra, TAPinto Millburn and Short Hills, The Journal Record, TrainingZone and
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Articles

First clinical pregnancy following AI-based microfluidic sperm detection and recovery in non-obstructive azoospermia

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By laura gemmell, Stephanie Morgan, George Koustas, Robert W Prosser
| The Lancet Verified
Columbia University Fertility Center, Department of Obstetrics and Gynecology, Columbia University Medical Center, New York, NY 10019, USA Publication History: Published October 31, 2025 DOI: 10.1016/S0140-6736(25)01623-X External LinkAlso available on ScienceDirect External Link Copyright: © 2025 Elsevier Ltd. All rights are reserved, including those for text and data mining, AI training, and similar technologies.
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The Development of Wet Laboratory Methods for Improved Downstream Analysis of High Throughput Sequencing Data for Virus and Viroid Diagnostics of Vitis vinifera in Australian Post-Entry Quarantine

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By Stephanie Morgan, Naima Tasnim, Emmanuelle C McFarlane, Bhuwaneshwariba Vala
| BioRxiv
Abstract High throughput sequencing (HTS) methods are an essential tool in Australia's Post-Entry Quarantine (PEQ) facility to facilitate the rapid, reliable and sensitive diagnostics of plant viruses and viroids. Small RNA sequencing (sRNAseq) for the detection of viruses and viroids has been implemented for several plant commodities in PEQ, including clonal grasses, Prunus spp., Rubus spp., and Fragaria spp., with the potential to expand to further plant genera.
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Lehigh professors uncover AI racial bias in mortgage approvals

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By Stephanie Morgan, Elliot Munson
| The Brown and White Verified
Three Lehigh professors and a Babson professor conducted a study in April 2024 that found racial bias in mortgage approval ratings produced by artificial intelligence and discovered a solution. By experimenting with leading commercial large language models including OpenAI’s GPT-4 Turbo, the study found that AI consistently generates higher approval ratings for white applicants than African American applicants with identical credit scores.
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