Maria-Florina (Nina) Balcan
Maria-Florina Nina Balcan studies the theoretical foundations of machine learning and algorithms that adapt to data. Balcan’s research connects machine learning with theoretical computer science, optimisation, and algorithmic game theory. Her interests include limited supervision, representation learning, and the analysis of practical algorithms. Balcan received the ACM Grace Murray Hopper Award for 2019. Her work lies at the intersection of learning theory and algorithm design.
- Works
- 323
- Citations
- 5,787
- h-index
- 41
Publication numbers from OpenAlex, as of 14 October 2026.
Timeline
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2019
Grace Murray Hopper Award
Balcan received the ACM Grace Murray Hopper Award for 2019.
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2020
Data-driven algorithm design
Balcan contributed the chapter Data-Driven Algorithm Design to Beyond the Worst-Case Analysis of Algorithms, published by Cambridge University Press in 2020. This work concerns algorithms informed by data about their tasks.
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2024
Interpretable decision trees
Balcan and Dravyansh Sharma co-authored Learning Accurate and Interpretable Decision Trees, presented at UAI in 2024. The shared work studies learning methods that combine accuracy with interpretable representations.
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2025
Research recognition
AAAI elected Maria-Florina (Nina) Balcan as a Fellow in 2025.
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As of October 2026
Foundations of machine learning
Balcan’s research connects machine learning with theoretical computer science, optimisation, and algorithmic game theory. Her interests include limited supervision, representation learning, and the analysis of practical algorithms.
Sources and credits
Photo: www.cs.cmu.edu; Maria-Florina (Nina) Balcan profile photograph; creator not stated. License: Not stated by source. Source: cs.cmu.edu. Cropped to a square and resized. Ask us to remove or replace this photo.