PhD Student Position

There are two openings for new PhD students with Prof. Gondy Leroy in the MIS department at the University of Arizona. The student will work on projects involving natural language processing (NLP) and machine learning (ML) and the research is funded by grants from the National Institutes of Health. Most research will be in the context of the Autism Spectrum Disorder (ASD) project. We focus on machine learning algorithms to label electronic health records (EHR) of children at high risk for autism. Both traditional and deep learning, potentially leveraging each other, will be developed and evaluated. In addition, we also focus on the EHR free text and identify phenotypic behavioral expressions of diagnostic criteria. Rule-based NLP will be combined with machine learning NLP. For all algorithms, we will systematically track transparency, bias, quality, and quantity of information in EHR and effects on performance and human decision making. The final technology is intended to be used as a human-in-the loop decision tool and will be developed using rapid prototyping in interaction with domain experts. It will be evaluated in a user study with representative clinicians.

 Interested students should apply to the MIS doctoral program at the University of Arizona by January 15, 2022. They should mention Prof. Leroy’s name as the person they wish to work with. Highlighting technical skills and prior research will be beneficial to the student’s application. Detailed information on requirements, financial support and benefits, is available here: https://eller.arizona.edu/programs/doctoral/mis/admissions



2021: Awarded $1.55 Million From NIMH for Health Information Technology to Support Autism Spectrum Disorders (ASD) Risk Assessment for Early Diagnosis.

2021: Eller MIS Professor Awarded $1.4 Million From NLM/NIH To Study Audio Information Delivery

Get evidence-based text simplification guidelines based on our studies.

Text Simplification Editor: The first version of our text simplification tool is now available. It includes lexical simplification, negation detection (you'd be surprised how often you use double negation, e.g., not illogical), grammar feedback, and a topic visualization section, ah and a few remaining issues that need fixing. All simplification suggestions are backed by user studies.

How does it work: 

  • Copy and paste your text and click 'simplify'. You will get suggestions that you can use.
  • The Lexical Chains tab shows how topics are distributed throughout the text. Try and get the same topics in the same paragraph.

This is a semi-automated simplification editor because with medical text, we have to ensure information remains correct. Nobody but a human can do this at the moment!


Press: Wildcat: Eller researcher uses skills to hunt autism data

Just useful: AMCIS 2017 Doctoral Consortium in Boston - Panel Presentation: the job talk.


Independent Studies

Looking for an independent study to complete your skills and get some in-depth experience while doing an interesting research project? 

* Participation is limited to students who completed the Web Mining and Computing course, the Data Mining Course or a Computational Linguistics course  and received an A.