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researcher

Emmanuel Sekyi

3 papers hereh-index 11 citations4 works total

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

author position
  • middle author2
  • last author1

Across the 3 of 3 papers where every author was matched, so the position is known.

fields
  • cs.CE1
  • cs.LG1
  • q-bio.QM1

identity via Semantic Scholar / OpenAlex

activity
20222026
most citedLearning to Detect Interesting Anomalies

3 citations · 4 across the 3 of their papers we have counts for

collaborators

3 papers

q-bio.QM2026

Artificial Intelligence Can Match Domain Experts in Evidence Extraction and Critical Appraisal of Microbial Oncogenesis Research Publications

Kaela Kokkas, Hairong Wang, Richard Klein +11

Confirmed oncogenic microbes contribute significantly to cancer burden. Identifying novel microbial oncogenicity could yield strategies that will reduce disease burdens. However, r…

cs.CE2025★ 1 cited

Small Language Models Can Use Nuanced Reasoning For Health Science Research Classification: A Microbial-Oncogenesis Case Study

Muhammed Muaaz Dawood, Mohammad Zaid Moonsamy, Kaela Kokkas +5

Artificially intelligent (AI) co-scientists must be able to sift through research literature cost-efficiently while applying nuanced scientific reasoning. We evaluate Small Languag…

cs.LG2022★ 3 cited

Learning to Detect Interesting Anomalies

Alireza Vafaei Sadr, Bruce A. Bassett, Emmanuel Sekyi

Anomaly detection algorithms are typically applied to static, unchanging, data features hand-crafted by the user. But how does a user systematically craft good features for anomali…

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