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Audris Mockus

University of Tennessee, Knoxville, Meta Platforms, Inc

34 papers hereh-index 5313.9k citations232 works total

Matching runs newest-first, so older work may not be attached to this profile yet.

author position
  • first author2
  • middle author3
  • last author27

Across the 32 of 34 papers where every author was matched, so the position is known.

fields
  • cs.SE26
  • cs.CV4
  • cs.LG2
  • cs.SI1
  • stat.ME1
affiliations
  • University of Tennessee, Knoxville, Meta Platforms, Inc
Homepage
same name
  • Audris Mockus — 15 papers, h 1

Either other researchers who publish under this name, or the same person where the external sources have not merged their records.

identity via Semantic Scholar / OpenAlex

activity
20182025
most citedDeriving a Usage-Independent Software Quality Metric

24 citations · 108 across the 18 of their papers we have counts for

collaborators
Showing cs.CVShow all

4 papers · 1 filter

cs.CV2024★ 3 cited

Towards Automation of Human Stage of Decay Identification: An Artificial Intelligence Approach

Anna-Maria Nau, Phillip Ditto, Dawnie Wolfe Steadman +1

Determining the stage of decomposition (SOD) is crucial for estimating the postmortem interval and identifying human remains. Currently, labor-intensive manual scoring methods are…

cs.CV2021

Pseudo Pixel-level Labeling for Images with Evolving Content

Sara Mousavi, Zhenning Yang, Kelley Cross +2

Annotating images for semantic segmentation requires intense manual labor and is a time-consuming and expensive task especially for domains with a scarcity of experts, such as Fore…

cs.CV2019★ 2 cited

An Analytical Workflow for Clustering Forensic Images

Sara Mousavi, Dylan Lee, Tatianna Griffin +2

Large collections of images, if curated, drastically contribute to the quality of research in many domains. Unsupervised clustering is an intuitive, yet effective step towards cura…

cs.CV2019

Machine-assisted annotation of forensic imagery

Sara Mousavi, Ramin Nabati, Megan Kleeschulte +1

Image collections, if critical aspects of image content are exposed, can spur research and practical applications in many domains. Supervised machine learning may be the only feasi…

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Not affiliated with arXiv. Researcher data from Semantic Scholar (ODC-BY) and OpenAlex.