CV

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Education

• PhD in Computer Science & Cognitive Science, University of Colorado Boulder, 2022
• MS in Computer Science, University of Colorado Boulder, 2020
• BA in Computer Science & Mathematics, University of Virginia, 2016

Teaching

• Introduction to Computing GPTI (Summer 2021)
• Senior Capstone Project TA (Fall 2020 and Spring 2021)
• Graduate Natural Language Processing TA (Fall 2019)
• Software Development Methods and Tools TA (Fall 2018, Spring 2019, and Spring 2020)
• Introduction to Computing TA (Fall 2017 and Spring 2018)

Research

University of Colorado Boulder

• Postdoctoral Researcher (Summer 2022 - Present)
  • Developed the wordvec.colorado.edu website that supports BERT, word2vec, and LSA-based semantic language comparisons with an understandable and easy to use interface
  • Developing and validating AI-based explainability methods that link language features to neurodegenerative clinical biomarkers to improve clinical interpretability and transparency.
  • Conducting participatory design for the creation of clinician interfaces for AI-generated decisions
• Students at Risk Detection (Summer 2021 - Spring 2022)
  • Created a tool to automatically flag abusive behavior, protected class discrimination, and harm to self and others in CU Boulder Faculty Course Questionnaire responses using state of the art natural language processing and machine learning techniques
  • Defined a human in the loop evaluation pipeline where machine learning predictions and human expertise are harnessed together
• Educational AI (Fall 2020)
  • Extracted BERT sentence embeddings of student speech transcripts for use in an AI tutoring system
  • Fine-tuned a pre-trained BERT model for use in a novel AI tutoring system
• Word Embedding API/Website (Fall 2019)
  • Built an API for generating word embedding similarity measures between pieces of text using Python
  • Input text was preprocessed, converted to LSA or Word2Vec embeddings, and cosine distances between various segments of the text were generated
  • Developed the final user facing website using HTML and JavaScript
• Computational Psychiatry (Summer 2018 & 2019)
  • Collected immediate and delayed verbal recalls to short stories from mentally ill and presumed healthy study participants
  • Extracted surface level and semantic features from verbal recalls to build machine learning models for predicting human ratings of recall content and classifying participants as mentally ill or healthy
  • Modeled speech and language features to predict self-reports of emotion

Marymount University

• Computational Psychiatry (April 2019 - Spring 2022)
  • Developed a pipeline to extract Natural Language Processing features from psychiatric speech data
  • Implemented machine learning models based on language features to characterize Mild Cognitive Impairment and Alzheimer’s Disease

Publications

Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership Permalink

Chandler, C., Foltz, P.W., Cheng, J., Bernstein, J.C., Rosenfeld, E.P., Cohen, A.S, Holmlund, T.B., and Elvevåg, B. (2019). Overcoming the bottleneck in traditional assessments of verbal memory: Modeling human ratings and classifying clinical group membership. In Proceedings of the NAACL-HLT 2019 Workshop on Computational Linguistics and Clinical Psychology. pp. 137-147.

Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness Permalink

Chandler, C., Foltz, P.W., and Elvevåg, B. (2020). Using Machine Learning in Psychiatry: The Need to Establish a Framework That Nurtures Trustworthiness. Schizophrenia Bulletin. Volume 46, Issue 1, pp. 1114.

Predicting Self-Reported Affect from Speech Acoustics and Language Permalink

Chandler, C., Foltz, P.W., Cheng, J., Cohen, A.S., Holmlund, T.B., and Elvevåg, B. (2020). Predicting Self-Reported Affect from Speech Acoustics and Language. In Proceedings of the LREC 2020 Workshop on: Resources and Processing of Linguistic, Para-linguistic and Extra-linguistic Data from People with Various Forms of Cognitive/Psychiatric/Developmental Impairments (RaPID-3). pp. 9-14.

Machine learning for ambulatory applications of neuropsychological testing Permalink

Chandler, C., Foltz, P.W., Cohen, A.S., Holmlund, T.B., Cheng, J., Bernstein, J.C., Rosenfeld, E.P., and Elvevåg, B. (2020). Machine learning for ambulatory applications of neuropsychological testing. Intelligence-Based Medicine, Volumes 1-2, 100006.

Applying speech technologies to assess verbal memory in patients with serious mental illness Permalink

Holmlund, T.B., Chandler, C., Foltz, P.W., Cohen, A.S., D., Cheng, J., Bernstein, J., Rosenfeld, E., and Elvevåg, B. (2020). Applying speech technologies to assess verbal memory in patients with serious mental illness. npj Digital Medicine 3 (1), 1-8.

Acceptability of Collecting Speech Samples from the Elderly via the Telephone Permalink

Diaz-Asper, C., Chandler, C., Turner, R. S., Reynolds, B., and Elvevåg, B. (2021). Acceptability of collecting speech samples from the elderly via the telephone. DIGITAL HEALTH.

Extending the usefulness of the verbal memory test: The promise of machine learning Permalink

Chandler, C., Holmlund, T.B., Foltz, P.W., Cohen, A.S., and Elvevåg, B. (2021). Extending the usefulness of the verbal memory test: The promise of machine learning. Psychiatry Research. Volume 297, 113743. ISSN 0165-1781.

Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment Permalink

Chandler, C., Foltz, P.W., Cohen, A.S., Holmlund, T.B., and Elvevåg, B. (2021). Safeguarding against spurious AI-based predictions: The case of automated verbal memory assessment. NAACL-HLT 2021 Workshop on Computational Linguistics and Clinical Psychology

Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies Permalink

Chandler, C., Foltz, P.W., and Elvevåg, B. (2022). Improving the Applicability of AI for Psychiatric Applications through Human-in-the-loop Methodologies. Schizophrenia Bulletin. Themed Issue: Translating Natural Language Processing (NLP) into mainstream schizophrenia assessment.

Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation Permalink

Cohen, A.S., Rodriguez, Z., Warren, K.K., Cowan, T., Masucci, M.D., Granrud, O.E., Holmlund, T.B., Chandler, C., Foltz, P.W., and Strauss, G.P. (2022) Natural Language Processing and Psychosis: On the Need for Comprehensive Psychometric Evaluation. Schizophrenia Bulletin, Themed Issue: Translating Natural Language Processing (NLP) into mainstream schizophrenia assessment. Volume 48, Issue 5, Pages 939–948

Using automated syllable counting to detect missing information in speech transcripts from clinical settings Permalink

Diaz-Asper, M., Holmlund, T.B., Chandler, C., Diaz-Asper, C., Foltz, P.W., Cohen, A.S., and Elvevåg, B. (2022) Using automated syllable counting to detect missing information in speech transcripts from clinical settings. Psychiatry Research. Volume 315, 114712.

Increasing access to cognitive screening in the elderly: Applying natural language processing methods to speech collected over the telephone Permalink

Diaz-Asper, C., Chandler, C., Turner, R.S., Reynolds, B., and Elvevåg, B. (2022) Increasing access to cognitive screening in the elderly: Applying natural language processing methods to speech collected over the telephone. Cortex

Reflections on the nature of measurement in language-based automated assessments of patients’ mental state and cognitive function Permalink

Foltz, P.W., Chandler, C., Diaz-Asper, C., Cohen, A.S., Rodriguez, Z., Holmlund, T.B., and Elvevåg B. (2022) Reflections on the nature of measurement in language-based automated assessments of patients' mental state and cognitive function. Schizophrenia Research.

Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors Permalink

Holmlund, T.B., Chandler, C., Foltz, P.W., Diaz-Asper, C., Cohen, A.S., Rodriguez, Z., Elvevåg, B. (2022). Towards a temporospatial framework for measurements of disorganization in speech using semantic vectors. Schizophrenia Research.

Work Experience

Lockheed Martin (December 2016 - March 2019)

• Software Engineer
  • Developed an API for running Markov Chain Monte Carlo (MCMC) simulations on financial data
  • Created an AngularJS application for demonstrating the MCMC simulations
  • Produced visualizations in Tableau for analyzing financial data and results of MCMC simulations
  • Developed a program to assist in the discrete embedding of data in PNG files using Python and Perl
  • Researched and reported advanced techniques for detecting malicious JavaScript code

C2 Education (August 2016 - July 2017)

• Tutor
  • Worked with students in a 3:1 setting to improve their math and computer science skills and prepare for standardized tests and college

Center for Open Science (August 2015 - June 2016)

• Developer Intern
  • Conducted research on Elasticsearch database optimizations for the Open Science Framework (OSF)
  • Created widgets using Elasticsearch query results and the JavaScript graphing libraries C3 and D3
  • Harvested metadata from published research to include in the SHARE data set

Research in Industrial Projects for Students (Sponsor: Google LA; June 2015 - August 2015)

• Student Researcher and Project Manager
  • Developed collaborative filtering models based on the Yelp Challenge Dataset for rating predictions which incorporated matrix factorization, natural language processing, topic modeling, and geographic location
  • Presented project findings at the Institute for Pure and Applied Mathematics at UCLA, Google, and the 2016 Joint Mathematics Meetings

Relevant Skills

Programming Languages

Proficiency with Python, C++, HTML, CSS, JavaScript, \LaTeX; Familiarity with MATLAB, R

Libraries

Scikit-learn, Pandas, NumPy, SciPy, Altair, Matplotlib, TensorFlow, PyTorch

Platforms

GitHub, Google Cloud Services, Amazon Web Services

Funding and Awards

• CU Boulder Computer Science Outstanding Research Award (2021)
• Nelson A. Prager Family and James H. Martin Endowed Graduate Fellowship (2020)
• CU Boulder Summer Research Fellowship for an outstanding Ph.D. TA (2019)
• Travel grant to attend the CRA-W Grad Cohort Workshop for Women (2018 and 2019)
• Institute of Pure and Applied Mathematics travel grant to present research at Joint Mathematics Meeting in Seattle, WA (2016)
• University of Virginia travel grant to attend Grace Hopper Celebration of Women in Computing (2016)
• University of Virginia Dean’s List (2014-2016)

Service

• Reviewer for Schizophrenia Bulletin and Psychiatry Research
• PhD Student Faculty Search Committee
• CU Boulder Graduate School Peer Mentor
• CU Boulder Computer Science Peer Mentor