Two Amazon scientists recently won awards recognizing long-standing contributions to their fields of research.
Mike Hicks, senior principal scientist, won the Special Interest Group on Programming Languages (SIGPLAN) Distinguished Service Award from the Association for Computing Machinery (ACM), recognizing distinguished service contributions to the programming languages community.
The Institute of Electrical and Electronics Engineers (IEEE) awarded the Signal Processing Magazine Best Paper Award to René Vidal, an Amazon Scholar who is the Herschel Seder Professor of Biomedical Engineering and director of the Mathematical Institute for Data Science at Johns Hopkins University.
SIGPLAN Distinguished Service Award
The SIGPLAN Distinguished Service Award recognizes Hicks’s longtime contributions to the organization. From 2015 to 2018, he served as the SIGPLAN chair, expanding SIGPLAN support for the Programming Languages Mentoring Workshop and creating open access to the Proceedings of the ACM on Programming Languages.
Thanks to Hicks’s work, the research papers selected for presentation at major SIGPLAN conferences are free and permanently accessible upon publication.
“Winning this award means a lot to me. Those who have won it before have done so much to grow and strengthen the SIGPLAN community, and I’m honored to have had the chance to do my part and now stand among them,” Hicks said. “As computing continues to expand its influence to more areas of daily life, I am very optimistic that SIGPLAN-aligned computer scientists will have an important and positive impact.
“Many people have helped me in my career, both directly and indirectly, and it feels great to pay that help forward,” Hicks said. “I’m inspired to see the energy and creativity of younger researchers and students, and I’m glad to help them grow their talents. I’m also pleased to see them embrace service and community building, take responsibility for SIGPLAN’s health, and keep the community strong and welcoming.”
Before taking a leave of absence in January 2022 to work full time at Amazon, Hicks was a professor of computer science at the University of Maryland for 20 years. There, he co-directed the Lab for Programming Languages and was the director of the Maryland Cybersecurity Center.
Hicks’s research focuses on using programming languages and analyses to improve the security, reliability, and availability of software. He also has begun to explore programming languages for quantum computation.
At Amazon, Hicks works for the Amazon Web Services (AWS) Identity organization within its Automated Reasoning Group. The automated reasoning tools that Hicks and the group develop help raise the security bar of critical AWS services and help AWS customers achieve the same assurance for their applications.
Best paper award
IEEE Signal Processing Magazine is a scientific journal that publishes advances in signal processing, a branch of electrical engineering that models and analyzes data. The Best Paper Award honors the author of a paper of “exceptional merit and broad interest” that has appeared in the magazine ten or more years ago.
Vidal’s winning paper “Subspace Clustering: Applications in Motion Segmentation and Face Clustering,” published in 2011, explores the strengths and weaknesses of different approaches to subspace clustering in theory and practice.
“I am humbled by this recognition,” says Vidal, who was also honored with the Edward J. McCluskey Technical Achievement Award earlier this year. “My goal was to explain multiple subspace clustering methods in a way that is accessible to the broader community, to provide insights that relate different methods to each other, as well as a standardized way of comparing them.”
Clustering is an area of machine learning that deals with separating data into multiple groups “without necessarily having supervision about what those groups mean,” Vidal explained.
Clustering is useful for high-dimensional datasets, such as in medical imaging, when it’s difficult to assign tags or labels to massive volumes of features. Subspace clustering finds clusters within these datasets by making assumptions about the structure of the different groups.
