Maria-Florina Balcan

Maria-Florina (Nina) Balcan is a Romanian-American computer scientist whose research concerns algorithms for machine learning, including active learning and kernel methods, and algorithmic game theory, including random-sampling mechanisms and envy-free pricing. She is an associate professor of computer science at Carnegie Mellon University.[1]

Education and career

Balcan is originally from Romania, and earned a bachelor's degree in 2000 from the University of Bucharest, earning summa cum laude honors with a double major in mathematics and computer science. She continued at the University of Bucharest for a master's degree in computer science in 2002, and then earned a Ph.D. in computer science in 2008 from Carnegie Mellon University.[2] Her dissertation, New Theoretical Frameworks for Machine Learning, was supervised by Avrim Blum.[3]

After working as a postdoctoral researcher at Microsoft Research New England, she became an assistant professor in the Georgia Institute of Technology College of Computing in 2009. She returned to Carnegie Mellon as a tenured faculty member in 2014.[2]

Service

Balcan was program committee co-chair for three major machine learning conferences, COLT 2014, ICML 2016, and NeurIPS 2020. She is the general chair for ICML 2021.[2]

Recognition

Balcan is a Microsoft Faculty Fellow (2011), a Sloan Research Fellow (2014) and a Kavli Frontiers of Science Fellow (2015).[2] She was the 2019 winner of the Grace Murray Hopper Award of the Association for Computing Machinery, for her "foundational and breakthrough contributions to minimally-supervised learning".[4]

References
  1. "Nina Balcan", Faculty profiles, Carnegie Mellon University Computer Science, retrieved 2020-05-23
  2. Curriculum vitae (PDF), retrieved 2020-05-23
  3. Maria-Florina Balcan at the Mathematics Genealogy Project
  4. Maria Balcan Receives 2019 ACM Grace Murray Hopper Award, Association for Computing Machinery, April 8, 2020
This article is issued from Wikipedia. The text is licensed under Creative Commons - Attribution - Sharealike. Additional terms may apply for the media files.