Curriculum Vitae

Alexander L. Hayes
Indiana University Bloomington
Luddy School of Informatics, Computing, and Engineering
ProHealth Lab: Informatics East 255
918 E. 10th Street
Bloomington, IN 47401

Technical Skills

Languages: Python, Shell Scripting, Java, C/C++, JavaScript, Racket, Julia

Libraries: NumPy, SciPy, scikit-learn, Pandas, NetworkX, pytest

Tools: Git, GitHub, GitHub Actions, JIRA, ReadTheDocs, Travis-CI, CircleCI, AppVeyor, CodeCov, PyPi

Development Platforms: Linux/UNIX, Jekyll, Android, Arduino, Google Cloud Platform

Documentation Tools: LaTeX, Sphinx, Javadoc, Doxygen, Markdown, ReStructured Text

Workflows: Continuous Integration (CI), Gitflow


Doctor of Philosophy (Ph.D.) Health Informatics
School of Informatics, Computing, and Engineering
Indiana University, Bloomington, IN

Bachelor of Science (B.S.) Computer Science
Security Informatics Minor, Class of 2017, GPA: 3.5 Cumulative
School of Informatics, Computing, and Engineering
Indiana University, Bloomington, IN


Indiana University, Bloomington
Luddy School of Informatics, Computer Science, and Engineering

  • Research AssistantComputer Vision Lab (January 2022 — Present)

    • Investigated explainability in time series problems
    • Implemented a Bayesian network (BN) explainability technique as a Python package targeting the pomegranate library
    • Extended the technique toward handling time series problems where the sequence is represented by a dynamic Bayesian network (DBN)
  • Mentor - Research Experience for UndergraduatesProHealth Lab (May 2022 — July 2022)

    • Mentored for a project analyzing smartwatch data alongside clinical data
    • Extended prior work for infrastructure development in the Hoosier Moms Cohort
    • Wrote course material for exploratory data analysis, scientific programming, and git
  • Research AssistantProHealth LabPrecision Health Initiative (January 2019 — December 2021)

    • Secondary analysis on incidence of gestational diabetes
      • Developed tools for data cleaning and pre-processing for creating reproducible data partitions:
      • Solved the binary class imbalance problem (imbalance of 1 to 32).
      • Reduced features (original feature space ~7000 variables)
      • Explained predictions for a clinical decision support setting.
    • Infrastructure development for Hoosier Moms Cohort
      • Implemented caching to work with snapshots of the database
      • Decreased analysis time from >72 hours to <5 minutes
      • Prototyped a dashboard for exploratory visualization (hmc-dashboard)

CareBand Inc.
222 West Merchandise Mart Plaza #1230, Chicago, IL

  • Developer and Machine Learning Research Consultant (February 2020 — August 2020)

    • Implemented solutions for indoor location tracking.
    • Developed models to analyze trends in user behavior.

The University of Texas at Dallas
Department of Computer Science, Richardson, TX

  • Teaching Assistant (August 2018 — December 2018)

    • Fall 2018 – Automata Theory – CS 4384.001
      Led two lectures on finite automata minimization. Graded assignments and exams, prepared and verified automata examples prior to lectures, and held four hours of office hours per week to answer questions.
  • Research AssistantStARLinG Lab (May 2018 — August 2018)

    • Extended the lab’s open source tool for converting raw text into relational facts. Rewrote the software so it could be used as a command-line tool or as an imported Python package. Released the software as rnlp.
    • Documented, unit tested, and ensured correctness of a Python port of Relational Functional Gradient Boosting (rfgb).
  • Teaching Assistant (August 2017 — May 2018)

    • Spring 2018 – C Programming in a UNIX Environment – CS 3377.501
      Provided feedback on C++ programming assignments and bash scripts in terms of documentation, style, and functionality of code.
    • Fall 2017 – Automata Theory – CS 4384.001
      Graded assignments and exams, prepared and verified automata examples prior to lectures, and provided additional support to students outside of class.

Indiana University, Bloomington
Department of Informatics and Computer Science

  • Undergraduate Researcher, STARAI Lab (July 2017 — July 2018)
    • Explored methods combining Natural Language Processing and Statistical Relational Learning for information extraction on SEC Form S-1 Documents.
    • Facilitated the public release of the lab’s source code onto GitHub, distributed as BoostSRL. Maintained the BoostSRL wiki and tutorials.
  • Undergraduate Researcher, ProHealth Research Experience for Undergraduates (May 2016 — August 2016)
    • Built on research which previously inferred adverse side-effects of drugs based on text data mined from the web. Our work focused on predicting drug-drug interactions from data mined from OpenFDA, PubMed, and a variety of Blogs.
  • Camp Counselor, SICE Summer Camp (2014, 2015, 2016, 2017)
    • Led sessions on intermediate Python programming, Scratch, Raspberry Pi, information security, and data analytics.
    • Introduced high school students to Indiana University’s campus, navigated them between sessions where they learned about computer science and informatics.


Publications and Poster Presentations

Conference Attendance

  • International Conference on Artificial Intelligence in Medicine (AIME) 2021: Online, Hosted in Porto, Portugal. Spotlight Paper Presentation. (2021-06-15, 2021-06-18) Conference URL
  • Association for the Advancement of Artificial Intelligence (AAAI) 2020: Hilton New York Midtown, New York, New York, USA. Workshop Poster Presentation. (2020-02-06, 2020-02-08)
    • Ninth International Workshop on Statistical Relational AI (StarAI 2020) Workshop URL
  • International Conference of Machine Learning (ICML) 2019: Long Beach Convention Center, Long Beach, California, USA. Attendee. (2019-06-14, 2019-06-15)

Service - Open Source Contributions

scikit-learn-contrib / imbalanced-learn

imbalanced-learn “A Python package to Tackle the Curse of Imbalanced Datasets in Machine Learning”

Changes proposed:

Code review:

Community questions I helped resolve:

SPFlow / SPFlow

SPFlow “An easy and extensible library for sum-product networks.”

Changes proposed:

Community questions I helped resolved:

microsoft / LightGBM

LightGBM “A fast, distributed, high performance gradient boosting (GBT, GBDT, GBRT, GBM or MART) framework based on decision tree algorithms, used for ranking, classification and many other machine learning tasks.”

Changes proposed:

Code review:

Community questions I helped resolve:

google-research / arxiv-latex-cleaner

arxiv-latex-cleaner: “arXiv LaTeX Cleaner: Easily clean the LaTeX code of your paper to submit to arXiv”

Changes proposed: