Alexandra (Ola) Zytek
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.
Python library for low-code generation of ML explanations that are readily understood by users, even those without ML expertise.
Generalizable REST API for readily-understandable explainable ML.
Customizable front-end UI for bringing explainable ML into real-world domains.
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., 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.
Zytek, A., Liu, D., Vaithianathan, R., & Veeramachaneni, K. (2021, May). Sibyl: Explaining Machine Learning Models for High-Stakes Decision Making. In Extended Abstracts of the 2021 CHI Conference on Human Factors in Computing Systems (pp. 1-6).
Zytek, A. (2021). Towards Usable Machine Learning (S.M. thesis, MIT).
zyteka at mit dot edu