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First-year doctoral student Elizabeth Bennewitz is one of 45 finalists with a chance of receiving up to $250,000 in support from the Fannie and John Hertz Foundation.

The funding will support his work on the generation and use of synthetic datasets to improve the accuracy of machine learning methods.

The two-year fellowships offer an opportunity to work closely with MC2 faculty and graduate students in multiple areas of cybersecurity and privacy.

Machine Learning Researchers’ Algorithm Could Help Increase Global Supply of a Precious Resource

Nicole Yunger Halpern brings a vast intellectual curiosity to physics, from tackling abstract theory to collaborating with experimentalists, all the while drawing distinct connections between diverse disciplines of physics.

The grant will support his efforts to develop a comprehensive and fundamental understanding of robust machine learning algorithms against both natural and adversarial threats.

His research interests were broad and included scientific computing and algorithms related to applied mathematics, fluid mechanics, acoustics, electromagnetism, plasma physics, nanoscience, and more.

He says the journal has sparked his interest on numerous new ideas and concepts related to algorithms.

He was specifically recognized for his contributions to cryptographic protocol design and cryptography education.

The computer science graduate student specializes in affective computing—the study and development of intelligent systems that can understand, interpret and respond to human emotions and behavior.