News & Announcements

Katz Named ACM Fellow

Jan 19, 2022 - He was specifically recognized for his contributions to cryptographic protocol design and cryptography education.

Mittal Named 2022 Adobe Research Fellow

Jan 13, 2022 - 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.

UMD Joins U.S. Cyber Command’s Academic Engagement Network

Jan 13, 2022 - The initiative's goal is to create a robust and accessible pool of qualified cyber professionals that can assist CYBERCOM in its mission of defending critical U.S. information networks.

Cummings Receives BBI Seed Funding to Study Hearing Loss Using Machine Learning

Jan 12, 2022 - The project was one of five that received seed funding from the UMD Brain and Behavior Institute this year.

Postdoctoral Scholar Sanket Receives Drones Ph.D. Thesis Award

Jan 10, 2022 - The annual award recognizes a doctoral thesis that shows great potential and aligns with the scientific mission of Drones, an international open-access journal.

Measuring How Malware Behaves in the Real World

Jan 04, 2022 - MC2 researchers have been recognized for their analysis of malware behavior in the first large-scale study of its kind.

QuICS Researchers Receive 2021 NISQ Computing Paper Award

Dec 20, 2021 - The researchers were recognized for their work involving quantum optimization.

UMD Team Wins Best Paper Award at NeurIPS 2021 Workshop

Dec 17, 2021 - It was recognized as the best paper presented at SafeRL, a workshop that was part of the 35th Conference on Neural Information Processing Systems (NeurIPS), held virtually from December 6–14.

Lin Elected Fellow of National Academy of Inventors

Dec 08, 2021 - She was recognized for her contributions in virtual reality, computer graphics and robotics.

Humans vs. Machines: Examining the Effectiveness of Automated Topic Modeling Evaluations

Dec 06, 2021 - In a paper being presented this week, authors affiliated with the CLIP Lab argue that topic model developers should reassess the increasing use of machine learning to evaluate their work.

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