Prospective students

I will not be considering applications for Ph.D. students before December 2026. If you are a prospective PhD student hoping to work with me, instead of emailing me, directly apply to the INFO PhD program. A compelling application will include:

• a candidate statement that explains why we would be a good fit for each other, for example, because you have prior research related to my ongoing projects
• a curriculum vitae that demonstrates your research potential through publications in natural language processing workshops, conferences, or journals such as those on the ACL Anthology.

If you are a current University of Arizona undergraduate or masters student, I will be taking on one-semester directed research students in Fall 2026 to work on SemEval 2027 shared tasks. If you are interested in such a directed research, wait until the SemEval 2027 tasks are announced (probably in August 2026), then send me an email with which task you would like to participate in, and a list of your prior experience and/or coursework in natural language processing and machine learning. I will select a small number of directed research students from the best such applications.

I am not taking on any advisees who are not currently students at the University of Arizona.

Graduate assistants

Please do not email me asking for paid teaching assistant or grader positions. Teaching assistant and grader positions are assigned by the department; I have no role in that process.

Please do not email me asking for paid research assistant positions. When I have funding for student research, I allocate it to students who are already working in my lab, typically my doctoral students.

Current students

• Kadir Bulut Özler
• Sarah Stueve
• Jiacheng Zhang
• Yeaeun Kwon
• Ayesha Khatun

Graduated students

• Abhyuday Singh, B.S., Computer Science, University of Arizona, 2025
  Thesis: Comparing Pretrained Llms Across Transformer, State-Space And Hybrid Architectures
• Xin Su, Ph.D., Information, University of Arizona, 2024
  Dissertation: Structured Information Extraction and Applications in Complex Reasoning
• Sarah Hyunju Song, M.S., Computer Science, University of Arizona, 2024
  Thesis: Metadata Enhancement Using Large Language Models: Improving the Quality of Aggregated Records in the iSamples Project
• Tugay Bilgis, B.S., Computer Science, University of Arizona, 2024
  Thesis: Revisiting Medical Concept Normalization: a Comparative Analysis of Transformer-based Models and Search Engine Approaches
• Zeyu Zhang, Ph.D., Information, University of Arizona, 2023
  Dissertation: Improving Geocoding by Incorporating Geographical Hierarchy and Attributes Into Transformer Networks
• Winston Zeng, B.S., Computer Science, University of Arizona, 2023
  Thesis: Fine-tuning Transformer-based Natural Language Generation Algorithms for USDA Grains Reports for Farmers, Producers, and Small Businesses
• Yiyun Zhao, Ph.D., Linguistics, University of Arizona, 2022
  Dissertation: How to probe linguistic knowledge and bias
• Amanda Bertsch, B.S., Computer Science, University of Arizona, 2021
  Thesis: Deep Learning for Bias Detection on the English Wikipedia
• Dongfang Xu, Ph.D., Information, University of Arizona, 2021
  Dissertation: Neural Network Algorithms for Ontology Informed Information Extraction
• Jiacheng Zhang, M.S., Computer Science, University of Arizona, 2021
  Thesis: General Benefits of Mono-Lingual Pre-Training in Transformers
• Vikas Yadav, Ph.D., Information, University of Arizona, 2020
  Dissertation: Evidence Retrieval for Explainable Question Answering
• Farig Sadeque, Ph.D., Information, University of Arizona, 2019
  Dissertation: User behavior in social media: engagement, incivility, and depression
• John Osborne, Ph.D., Computer and Information Sciences, University of Alabama at Birmingham, 2016
  Dissertation: Machine Learning of Composite Concepts and the Alleviation of The Content Completeness Problem in Text Mention Normalization
• Upendra Sapkota, Ph.D., Computer and Information Sciences, University of Alabama at Birmingham, 2015
  Dissertation: Improving the performance of cross-domain authorship attribution