Alexandra (Ola) Zytek
CV - Github - Google Scholar - Project Website - LinkedIn
Machine learning algorithms are becoming increasingly powerful - but how can we extend their benefits to a diverse set of real-world domains? Models continue to be black-boxes that confuse and concern users, and using them can be a difficult and complicated process.
My research aims to bridge the gap between algorithms and humans through collaborations with end-users and development of software systems and interfaces. Through these methods, ML applications can better support the nuances of real-world domains and users.
I am a PhD student at MIT, working in the Data to AI Lab under the supervision of Kalyan Veeramachaneni.
Projects
Pyreal
tutorial - documentation - github - paper
Python library for low-code generation of ML explanations that are readily understood by users, even those without ML expertise.
Sibyl-API
Generalizable REST API for readily-understandable explainable ML.
Sibylapp
demo - github - paper 1 - paper 2
Customizable front-end UI for bringing explainable ML into real-world domains.
Explingo
Using LLMs to generate and evaluate more natural, usable ML explanations.
Selected Publications
Zytek, A., Wang, W. E., Koukoura, S., & Veeramachaneni, K. (2023). Lessons from Usable ML Deployments Applied to Wind Turbine Monitoring. In NeurIPS XAIA.
Zytek, A., Pido, S., Veeramachaneni, K. (2024). LLMs for XAI: Future Directions for Explaining Explanations. To be presented in ACM CHI HCXAI.
Zytek, A., Arnaldo, I., Liu, D., Berti-Equille, L., & Veeramachaneni, K. (2022). The Need for Interpretable Features: Motivation and Taxonomy. In KDD Explorations.
Zytek, A., Liu, D., Vaithianathan, R., & Veeramachaneni, K. (2021). Sibyl: Understanding and Addressing the Usability Challenges of Machine Learning In High-Stakes Decision Making. In IEEE Transactions on Visualization and Computer Graphics (VIS).
Cheng, F., Liu, D., Du, F., Lin, Y., Zytek, A., Li, H., Qu, H. & Veeramachaneni, K. (2021). VBridge: Connecting the Dots Between Features, Explanations, and Data for Healthcare Models. In IEEE Transactions on Visualization and Computer Graphics (VIS). Honorable Mention.
Thesis
Zytek, A. (2021). Towards Usable Machine Learning (S.M. thesis, MIT).
Contact
zyteka at mit dot edu