Maria-Florina Balcan, commonly known as Nina, is a renowned computer scientist and the Cadence Design Systems Professor of Computer Science at the School of Computer Science (MLD and CSD) at Carnegie Mellon University. She is a leading expert in machine learning, artificial intelligence, and theoretical computer science, with a research focus on modern machine learning foundations, algorithm design and analysis, and computational and data-driven approaches in game theory and economics.
In her research, Nina is dedicated to developing new analysis models and algorithms for interactive learning, learning from limited supervision, distributed learning, learning representations, and lifelong learning. She also uses machine learning for algorithm design and investigates the computational and data-driven aspects of multi-agent systems. Her research has made significant contributions to the theoretical foundations of modern machine learning and algorithmic game theory.
Nina is a recipient of several prestigious awards, including the 2019 ACM Grace Murray Hopper Award, awarded to the outstanding young computer professional of the year. She is also a Simons Investigator and has received numerous research grants from the National Science Foundation and other agencies. Nina has served as Program Committee Co-chair for several top-tier machine learning conferences, including NeurIPS 2020, ICML 2016, and COLT 2014. She was recently invited as a speaker at the International Congress of Mathematicians in 2022 and the Stony Brook International Conference on Game Theory in 2021. Moreover, she was the Uhlenbeck Lecturer in the Women in Mathematics program at Princeton in 2022.
Nina holds a Ph.D. in Computer Science from Carnegie Mellon University and a Bachelor's in Mathematics and Computer Science from the University of Bucharest. She is an active member of the machine learning community and has published over 100 research papers in top-tier conferences and journals. She is also a sought-after mentor and collaborator, keen to promote diversity and inclusion in computer science.