2019微软研究院博士奖学金名单出炉, 斯坦福、MIT、CMU等10人上榜- 专知

2019微软研究院博士奖学金名单出炉, 斯坦福、MIT、CMU等10人上榜

Microsoft Research PhD Fellows

Constantin Dory

Stanford University

Constantin investigates color centers in diamond and silicon carbide to utilize them as quantum bits and single photon sources for quantum information processing, which will enable exponential speed-up in a wide range of computing applications. To integrate color centers on a chip, he’s breaking new grounds in fabrication and design techniques to develop efficiently integrated photonics. Artificial intelligence and machine learning-based algorithms design the circuits of the future by engaging the full parameter space. To realize these designs using diamond and silicon carbide (materials that are new to photonics), Constantin developed fabrication methods in the Stanford Nanofabrication Facilities. He characterizes these devices at cryogenic temperatures in quantum optics labs and utilize them as spin-photon interfaces, or to generate nonclassical light. His current efforts are on developing large-scale quantum optical experiments involving several color centers entangled in a quantum circuit. Ultimately, he hopes to make progress toward the applications of color centers in universal quantum computers, quantum repeaters, and quantum transducers.

Danielle Gonzalez

Rochester Institute of Technology

Danielle Gonzalez is currently a PhD student in the Center for Cybersecurity and Software Engineering Department at Rochester Institute of Technology (RIT). She also received a B.S. in Software Engineering from RIT in 2016. The goal of her research is to make security testing during development easier via unit testing. She uses static code analysis, large-scale data mining, natural language processing, and machine learning techniques to learn which critical paths and conditions in security tactic implementations can and should be unit-tested and automatically generate artifacts such as test case plans and recommendations that will make the testing process easier for developers while preserving their control over what is tested.

Daehyeok Kim

Carnegie Mellon University

Daehyeok Kim is a third year PhD student in the Computer Science Department at Carnegie Mellon University, where he is advised by Professor Srinivasan Seshan and Professor Vyas Sekar. His research interests lie in the intersection of systems and networking with a current focus on making data centers faster and more efficient by designing novel network primitives with advanced networking hardware such as programmable switches and RDMA NICs.

Jayashree Mohan

The University of Texas at Austin

Jayashree’s research aims at building the next generation of file systems: crash-consistent, energy-efficient, and IO-efficient. Crash consistency is the ability of a file system to ensure data and metadata consistency when a system crash occurs. It is important to build crash-consistent file systems, as applications rely on the guarantees provided by the underlying file system. In addition to crash-consistency, it is important for file systems to be energy-efficient because energy consumption is a key issue in both large-scale data centers as well as small hand-held devices. Furthermore, with the advent of storage technologies with limited write cycles, file systems have to be mindful of their IO footprint. Her work identifies the significance of each of these aspects, building tools and infrastructure to evaluate them, and understanding their impact on file system performance as a whole.

Ramakanth Pasunuru

University of North Carolina at Chapel Hill

Ramakanth Pasunuru is a PhD student in Computer Science at University of North Carolina Chapel Hill, advised by Professor Mohit Bansal in the UNC-NLP group. His research focuses on developing knowledgeable language generation models that incorporate generalizable semantic skills via dynamic multi-task learning and multi-reward reinforcement learning methods, as well as multimodal conversational models that condition on video-based context and continually adapt via feedback and interaction. He is the recipient of an ACL 2017 Outstanding Paper Award and a COLING 2018 Area Chair Favorites Paper Award.

Daniel Rakita

University of Wisconsin-Madison

As robot platforms become increasingly common in people’s homes and workplaces over the coming years, a central challenge will be ensuring people have effective ways to specify to a robot what they want it to do, and endowing robots with effective ways to communicate back to people such that the users can confidently interpret the robot’s intent and understanding given the command at hand. Daniel’s research has centered around this robot “specification-interpretability” problem, such as presenting remote telemanipulation paradigms that affords even novice users the ability to intuitively control a robot with minimal training, creating an automatic dynamic camera method that continuously optimizes a viewpoint for a remote user, and formulating a robot bimanual shared-control method inspired by how people naturally perform bimanual manipulations. Daniel is a PhD student at the University of Wisconsin-Madison, advised by Dr. Michael Gleicher and Dr. Bilge Mutlu.

Raghuvansh R. Saxena

Princeton University

Raghuvansh R. Saxena is a graduate student with the Department of Computer Science at Princeton University. His research involves developing and studying models of communication that capture the intricacies of modern communication systems, such as wireless and radio networks. Raghuvansh is also interested in other areas of theoretical computer science, such as algorithmic game theory and complexity theory. He received his Bachelor of Technology degree in Computer Science and Engineering from IIT Delhi in 2016, with Institute Rank 1.

Joana M. F. da Trindade

Massachusetts Institute of Technology

The goal of Joana’s research is to develop systems that enable data scientists and practitioners to analyze their data more efficiently and with less programming and/or cognitive effort. Despite the growing popularity and availability of graph data, existing graph analytics tools are either too slow, or too cumbersome to use; this forces upon biologists, chemists, and otherwise network scientists the choice between high-performance or usability. Existing graph databases provide accessible declarative language interfaces, yet their performance is nowhere near that of state-of-the-art graph processing libraries. Similarly, parallel graph processing libraries offer outstanding performance, but require expertise in parallel programming. So far in her PhD, Joana has identified several key challenges in this space, as well as contributed query optimization techniques that yield significant performance speedups for graph databases. Ultimately, she envisions a world where network data scientists can conduct their research without getting bogged down by cumbersome tools.

Zuxuan Wu

University of Maryland

Zuxuan is a third-year PhD student in the Department of Computer Science at the University of Maryland, advised by Professor Larry S. Davis. His research interests are computer vision and machine learning. In particular, his research focuses on developing efficient frameworks through conditional computation for automated visual understanding and learning robust feature representations with limited supervision. He is also interested in large-scale video recognition, and he has developed a few systems that achieved top-notch performance in several international benchmark competitions such as the YouTube-8M Video Classification Challenge.

Katherine Ye

Carnegie Mellon University

People have always used tools to extend the reach of their bodies and minds. Just as the invention of the lever enabled people to build monuments, the invention of pen and paper—and then the text editor—enabled people to synthesize ideas at a massively new scale. Today, computing power is cheap, networking ubiquitous, and data abundant. Yet today’s personal tools remain much the same as those of a few decades ago. Katherine’s research reimagines personal tools as modern mediums that natively incorporate computational intelligence—that is, techniques for modeling, search, and synthesis—to augment the human ability to think and create. Currently, Katherine leads the team that builds Penrose, a platform that enables people to create beautiful diagrams just by typing mathematical notation in plain text.

来源：

https://www.microsoft.com/en-us/research/academic-program/phd-fellowship/#!fellows

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