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Microsoft at a glance. Microsoft licensing brief. Microsoft Education plans comparison. Microsoft His group builds efficient and trustworthy computer systems that improve the efficiency of data center applications, provide systems support for heterogeneous platforms, find and fix bugs, and improve hardware security.

Her recent work includes leveraging information theory and active learning to improve the efficiency of brain-computer interfaces, leveraging natural language processing for speech enhancement in cochlear implants, and acoustic surveillance in individuals with left ventricular assist devices.

Her research interests lie at the intersection of computer vision and robotics. David received his PhD in computer science from Stanford University in Her primary research interests span across fields in natural language processing, machine learning, and computational social science. Her research focuses on understanding the social aspects of language and building responsible NLP systems with social intelligence.

Her work has received multiple best paper awards and nominations at top human computer interaction and natural language processing conferences. His research is centered on building fundamental verification and synthesis techniques that help programmers write software that meets their intent.

In particular, his current main focus is on building practical and predictable program synthesis techniques that can be applied to computer networks, program repair, and machine learning. Her research interests are in the areas of cloud computing, computer architecture, and applied machine learning. Her recent work focuses on leveraging ML to improve the performance predictability, resource efficiency, and security of large-scale datacenters.

Her research interests lie in the ability of robots to develop broadly intelligent behavior through learning and interaction. To this end, her work spans machine learning, robotics, and computer vision, including deep learning for end-to-end robotic perception and control, meta-learning algorithms that enable flexible adaptation to new tasks and environments, and methods for self-supervised robot learning at scale. In her research, she develops novel hardware and software systems that advance personal fabrication technologies.

Sloan Fellow as well as a Forbes 30 under 30 in Science. His work focuses on the design of provably efficient algorithm for solving fundamental and pervasive large-scale problems in optimization and data-analysis.

His research interests are in the areas of networked computer systems and applied machine learning. His current research focuses on learning-augmented network systems, programmable networks, and network protocols and algorithms for datacenters. Sloan Research Fellowship , and multiple best paper awards. Stefano Ermon is an assistant professor of computer science in the CS Department at Stanford University, where he is affiliated with the Artificial Intelligence Laboratory, and is a fellow of the Woods Institute for the Environment.

His research is centered on techniques for probabilistic modeling of data, inference, and optimization, and is motivated by applications in the emerging field of computational sustainability. Jessica Hullman is an assistant professor in computer science and journalism at Northwestern. The goal of her research is to develop computational tools that improve how people reason with and make decisions from data.

She is especially interested in challenges that arise in presenting data to non-expert audiences, where the need to convey a clear story often conflicts with goals of transparency and faithful presentation of uncertainty. Her current research focus is on uncertainty representation through interactive visual interfaces that enable users to articulate and reason about their prior beliefs. Prior to joining Northwestern, she was an assistant professor at the University of Washington Information School.

Her PhD is from the University of Michigan, and she spent a year as a postdoctoral scholar in computer science at the University of California, Berkeley. Yin Tat Lee is an assistant professor in the Paul G. His research interests are primarily in algorithms, and they span a wide range of topics such as convex optimization, convex geometry, spectral graph theory, and online algorithms.

His primary research goal is to find algorithms for solving a general class of convex optimization problems. Tucker Prize.

Raluca Ada Popa is an assistant professor of computer science at UC Berkeley working in computer security, systems, and applied cryptography.

His recent contribution includes a new framework to identify pervasively overlapping modules in networks, network-based algorithms to predict viral memes, and a new computational approach to study food culture. Byung-Gon Chun is interested in creating new platforms for operating and distributed systems. He is currently developing a big data platform that makes it easy to implement large-scale, fault-tolerant, heterogeneous data processing applications. He has also built systems that seamlessly integrate cloud computing with mobile devices for improved performance, reliability, and security.

Prior to joining Seoul National University, Chun was a principal scientist at Microsoft, a research scientist at Yahoo! The goal is the development and use of machine-learning techniques to study digital text corpora associated with cognitive processes, aiming at identifying the mental operations underlying behavioral processes, with application to mental health and education. Roxana Geambasu works at the intersection of three computer science fields: distributed systems, operating systems, and security and privacy.

Roxana obtained her Ph. His research interests include i parsing natural language into semantic representations e. David Steurer investigates the power and limitations of efficient algorithms for optimization problems that are at the heart of computer science and its applications.

A focus of his work has been the Unique Games Conjectures whose resolution—no matter in which direction—promises new insights into the capabilities of efficient algorithms. As part of the research effort to resolve this conjecture, he studies provable guarantees of the sum-of-squares method, a compelling meta-algorithm that applies to a wide-range of problems and has the potential to unify the design of efficient algorithms for difficult optimization problems.

Steurer received his PhD from Princeton University and was a postdoctoral researcher at Microsoft Research for two years before joining Cornell University. Sloan Research Fellowship. His main research interest is in the theory and practice of cryptography. He works on lattice-based cryptography, building advanced cryptographic primitives using integer lattices; leakage-resilient cryptography, defining and developing algorithms resilient against adversarial information leakage; and more recently, the theory and practice of computing on encrypted data, constructing powerful cryptographic objects such as fully homomorphic encryption and functional encryption.

Vinod got his Ph. Her theoretical contributions include analysis of high-dimensional estimation of graphical models and developing tensor methods for learning latent variable models. She has applied the developed algorithms to various problems in social networks and computational biology. She has been a visiting researcher at Microsoft Research New England. In her research, she develops theoretical tools to address problems in data privacy and to understand individual incentives in other complex settings.

He builds computational models based on experimental results and theories from the fields of neuroscience and biology and deploys them on robotic systems navigating in challenging real world environments. This novel research methodology has produced state-of-the-art results in robotics and yielded insights into how the brain may map and navigate the world. After spending two post-doctoral years at the Massachusetts Institute of Technology Artificial Intelligence Lab, he joined the University of Toronto as an assistant professor in the departments of Statistics and Computer Science.

His primary interests lie in artificial intelligence, machine learning, deep learning, and large-scale optimization. His main research goal is to understand the computational and statistical principles required for discovering structure in large amounts of data. He is an Alfred P. Schapira also has a broad research interest in the interface of computer science, game theory, and economics.

Her research intersecting human-computer interaction and ubiquitous computing particularly focuses on designing, developing, and evaluating natural user interfaces, self-reflection capture tools, and new interaction models for ubiquitous computing. Her work is being applied to healthcare and urban living to support the needs of urban citizens, hospital workers, elders, and individuals with autism and their caregivers.

Ryan Williams works in algorithm design and complexity theory. He studies how to construct more efficient algorithms for solving computational problems, as well as how to mathematically rule out the possibility of efficient algorithms for other problems.

Such impossibility results are generally perceived as very difficult; algorithms can be very clever, and it is hard to reason about all cleverness one could have. The famous P versus NP question asks about the power of efficient algorithms. His honors include some best paper awards and an Alfred P.

Sloan Fellowship. She is particularly interested in adaptive, individualized tutoring systems that learn and self-optimize. Emma also works on health applications and on using information communication technologies to address challenges in low resource settings and developing regions. His research studies the interface of computer science and economics, with a focus on computational aspects of the Internet, online markets, and social networks.

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