Focused representations
& topic discovery
FoTo connects targeted topic modeling with document visualization. My work on fSNE explores how to preserve local structure around selected points of interest.
I develop methods that help us find relevant information in large text collections and summarize it while preserving its meaning.
My research connects focused analysis, topic modeling, and unsupervised multi-document summarization, with applications in public-health communication.
Methods for exploring, organizing, and synthesizing text around the information that matters to a user.
FoTo connects targeted topic modeling with document visualization. My work on fSNE explores how to preserve local structure around selected points of interest.
I investigate unsupervised abstractive summarization across multiple documents and reviews, with an emphasis on preserving meaning, capturing consensus, and retaining query-relevant topics.
With an interdisciplinary team, I develop human-in-the-loop methods to analyze public-health messages and distinguish contradictions, legitimate updates, and contextual differences.
Sociological Methodology · 56(2), 147–176
arXiv:2601.10926 · Preprint
LREC-COLING 2024 · Torino, Italy · pp. 7406–7416
IEEE/ACM BDCAT 2022 · REU Symposium · pp. 259–264
AIMLSystems 2021 · pp. 1–7
NLP and text mining research spanning focused analysis, topic modeling, summarization, and interdisciplinary public-health communication.
Evaluation and analysis of AI model reliability, reasoning, and failure modes.
Review of AI-enhanced computer science learning materials for technical accuracy and effective instruction.
Applied AI and machine learning instruction, hands-on activities, and support for learners building technical skills.
Additional teaching experience includes support for Python, data structures, database systems, and introductory computer science.
Poster presentation of FoTo: Targeted Visual Topic Modeling for Focused Analysis of Short Texts at the Center for Decoding the Universe Quarterly Forum, Stanford Data Science.
Poster presentation of Human-in-the-Loop Agentic Auditing of COVID-19 Public-Health Messaging: Detecting Contradictions, Updates, and Differences at the Mayo Clinic AI Research Summit.
Supported undergraduate researchers with research computing, programming, experimental workflows, and the path from an idea to a research paper.
Served as Vice President of Activities, helping organize research events and opportunities for graduate students to share their work.
Former president and subsequent advisor, supporting cultural programming, student connections, and a welcoming community at NMSU.
Our latest manuscript on human-in-the-loop NLP for longitudinal analysis of evolving public-health communication.
Sanuj Kumar, Sumanth Reddy Nandhikonda, Heather Harper, Jagdish Khubchandani, and Marshall A. Taylor
Our Public Health Message Consistency System (PHMCS) combines NLP and human review to identify potentially inconsistent messages in evolving public-health communication.
Status: Submitted to JMIR Infodemiology; publicly available through JMIR Preprints.
Complete publication history, appointments, teaching, and service.
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For research collaborations, academic opportunities, or conversations about NLP and focused analysis.