|Nina Balcan Is ACM Young Computer Professional of 2019|
|Written by Sue Gee|
|Thursday, 09 April 2020|
ACM has named Maria-Florina Balcan of Carnegie Mellon University as the recipient of the 2019 ACM Grace Murray Hopper Award, for influential and pioneering work in machine learning which has solved longstanding open problems.
The ACM (Association of Computing Machinery) always makes its awards looking back to the preceding year. Last week we reported that the 2019 ACM Prize in Computing had been awarded to David Silver for contributions to deep reinforcement learning and now the 2019 ACM Grace Murray Hopper Award goes to Maria-Florina Balcan, also known as "Nina", for her work in the related area of minimally-supervised learning.
Established in 1971 this award, worth $35,000 funded by Microsoft, recognizes a single recent major contribution made by an individual aged 35 or younger.
Nina Balcan received Bachelor’s and Master’s degrees from the University of Bucharest, Romania in 2000 and 2002, respectively and earned a PhD in Computer Science from Carnegie Mellon University in 2008. She is now is an Associate Professor of Computer Science at Carnegie Mellon University where her research interests include learning theory, machine learning, theory of computing, artificial intelligence, algorithmic economics and algorithmic game theory, and optimization.
Balcan's technical contributions for receiving this award are in various aspects of machine learning. In particular she introduced the first general theoretical framework in semi-supervised learning where algorithms use large amounts of easily available unlabeled data to augment small amounts of labeled data to improve predictive accuracy. In the related area of active learning, where the algorithm processes large volumes of data and intelligently chooses the datapoints to be labeled, she established performance guarantees for active learning that hold even in challenging cases when noise is present in the data. She also devised novel algorithms in clustering, the unsupervised learning technique in which an algorithm groups datapoints with similar properties.
Commenting on the award, ACM President Cherri M. Pancake said:
“Nina Balcan wonderfully meets the criteria for the ACM Grace Murray Hopper Award, as many of her groundbreaking contributions occurred long before she turned 35. Although she is still in the early stages of her career, she has already established herself as the world leader in the theory of how AI systems can learn with limited supervision. More broadly, her work has realigned the foundations of machine learning, and consequently ushered in many new applications that have brought about leapfrog advances in this exciting area of artificial intelligence.”
Balcan’s publications are among the most cited in the machine learning theory field, and she continues to be a prolific author. Her most recent publications include chapters on “Data-Driven Algorithm Design” and “Noise in Classification,” for the book Beyond the Worst-Case Analysis of Algorithms, which will be published later this year.
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|Last Updated ( Thursday, 09 April 2020 )|