Nick Kroeger

Computer Science PhD Student

(He/Him)

Advancing Neural Network Interpretability
and Robustness

Learn More About Me
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About Me

I'm a Computer Science PhD Student at the University of Florida studying Machine Learning. I completed my M.S. (2021) and B.S. (2018) in Computer Science and Minor in Music Performance (Saxophone) at the University of Florida.

In February 2022, I established a Slack community around Explainable AI (XAI) to help connect XAI/IML researchers, professionals, and enthusiasts from around the world. If you'd like to discuss interpretability methods, get help on challenging problems, and meet experts in your field, send us a DM at @XAI_Research on Twitter!

Research Interests: Interpretable Machine Learning, Neural Networks, Outlier Detection, and Underwater Acoustics.

Research Lab: Former: Dr. Paul Gader's Computing For Life
Current: Dr. Vincent Bindschaedler's Trustworthy Machine Learning
University of Florida
Computer and Information Science and Engineering
Gainesville, FL 32608
Email: nkroeger.cs {at} gmail.com

My CV GitHub Twitter / X

Publications


  1. Kroeger, N. M., Ley, D., Krishna, S., Agarwal, C., Lakkaraju, H. (2023). In-Context Explainers: Harnessing LLMs for Explaining Black Box Models. arXiv preprint arXiv:2310.05797.
  2. Meerdink, S., Bocinsky, J., Zare, A., Kroeger, N. M., McCurley, C., Shats, D., & Gader, P. (2021). Multitarget Multiple-Instance Learning for Hyperspectral Target Detection. IEEE Transactions on Geoscience and Remote Sensing, 60, 1–14.
  3. Koelmel, J. P., Tan, W. Y., Li, Y., Bowden, J. A., Ahmadireskety, A., Patt, A. C., Kroeger, N. M., et al. (2022). Lipidomics and Redox Lipidomics Indicate Early Stage Alcohol-Induced Liver Damage. Hepatology Communications, 6(3), 513-525.
  4. Koelmel, J. P., Paige, M. K., Aristizabal-Henao, J. J., Robey, N. M., Nason, S. L., Kroeger, N. M., et al. (2020). Toward Comprehensive Per- and Polyfluoroalkyl Substances Annotation Using FluoroMatch Software and Intelligent High-Resolution Tandem Mass Spectrometry Acquisition. Analytical Chemistry, 92(16), 11186–11194.
  5. Koelmel, J. P., Kroeger, N. M., Ulmer, C. Z., Bowden, J. A., Patterson, R. E., et al. (2017). LipidMatch: An Automated Workflow for Rule-Based Lipid Identification Using Untargeted High-Resolution Tandem Mass Spectrometry Data. BMC Bioinformatics, 18, 1–11.
  6. Koelmel, J. P., Kroeger, N. M., Gill, E. L., Ulmer, C. Z., Bowden, J. A., et al. (2017). Expanding Lipidome Coverage Using LC-MS/MS Data-Dependent Acquisition with Automated Exclusion List Generation. Journal of the American Society for Mass Spectrometry, 28(5), 908–917.
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Tutoring and Mentoring

Personalized 1-on-1 learning experiences tailored to your needs.

Audience

  • 6th-12th grade
  • Undergraduates
  • Graduates
  • Post-Graduate

Content

  • Study Skills, College Prep, and Research Mentorship
  • Beginner to Advanced Computer Programming (Scratch, MATLAB, and Python) for Preteens to College Engineers
  • Beginner to Advanced Machine Learning Tutoring
  • Custom Curriculum

Availability & Pricing

Mon-Fri online, weekly or on-demand sessions (Pacific time zone)

Contact for pricing!

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