Nina's research focuses on theoretical machine learning. Machine learning as a discipline has had an incredibly dramatic effect on computing over the past two decades in areas such as facial identification, voice recognition, spam detection, language translation, recommender systems and self-driving cars with often the same basic algorithmic techniques used for these very different applications. Nina’s research gets at the cores of these algorithms, giving a theoretical basis and justification for many of the tools used in practice. Nina not only has deep technical depth, but also the unique ability and vision to set foundations for her field. In her short career so far, she has provided new conceptual frameworks and algorithms for paradigms of highest activity and importance in both theoretical and practical machine learning today, that have been crucial to the progress of these areas --- this includes her work in semi-supervised learning, interactive learning, similarity based learning, and distributed learning. She also developed truly insightful and novel connections between machine learning and other areas including algorithmic game theory, discrete optimization, and analysis of the algorithms beyond the worst case. Additionally, her work resolved long-standing open questions within classic learning models and also addressed key questions in algorithmic game theory.
Patrick is a leading research in mobile security. As cellular networks converge with other IP based networks, they become targets of attacks that are common in the Internet world. Such networks enable a significant portion of the world’s population to communicate and connect to the Internet. The mobility and always connected nature of phones and their increasing capabilities are enabling new mobile health and ecommerce applications. Finally, as a closed system in the past, telephony was viewed as a trusted service and many applications rely on such trustworthiness. As the sophistication and reach of cyber security threats continues to grow, it is important that we understand the vulnerabilities in cellular and mobile networks and proactively address these threats that could become serious sources of harm in the future. This is exactly the focus of Dr. Traynor’s research program. He did pioneering work in understanding the serious nature of the security vulnerabilities that exist in such networks. He also explored scalability of such systems under severe conditions such as in the event of an emergency.
Nina and Patrick join previous SCS Sloan Fellows Dana Randall, Nick Feamster, Chris Peikert and Santosh Vempala.
