Krysta M. Svore

Krysta M.  Svore
RESEARCHER
.

Quantum Architectures and Computation Group (QuArC)
Microsoft Research
One Microsoft Way
Redmond, WA 98052, USA
E-mail: ksvore at microsoft dot com
Tel: (425) 421-6996 Fax: (425) 936-7329

About Me

I am a Researcher and the manager of the Quantum Architectures and Computation Group (QuArC) at Microsoft Research in Redmond, WA. I am passionate about quantum computation and determining what problems can be better solved on a quantum computer.  My research focuses on quantum algorithms and how to implement them on a quantum device, from how to code them in a high-level programming language, to how to optimize the resources they require, to how to implement them on the hardware.  Our team works on designing a scalable, fault-tolerant software architecture for translating a high-level quantum program into a low-level, device-specific quantum implementation, which we call LIQUi|>; (pronounced "liquid", language-integrated quantum operations).  We work in collaboration with Microsoft Station Q, Microsoft Research's center for topological quantum computation. 

Other research interests include machine learning algorithms, both classical and quantum. I am interested in learning to rank algorithms, Web search and information retrieval, including features and training methods, and the dynamics of the Web and its users over time.

I received my Ph.D. with Honors in Computer Science from Columbia University in 2006 under Dr. Alfred Aho and Dr. Joseph Traub.  I spent one year as a visiting researcher at MIT under Dr. Isaac Chuang,  and several months at Caltech under Dr. John Preskill and Dr. Panos Aliferis.  I interned for several years at IBM Research under Dr. David DiVincenzo and Dr. Barbara Terhal.  I received a B.A. in Mathematics, with a minor in Computer Science and French, from Princeton University in 2001.

Publications

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    Videos
    Quantum Computation for Quantum Chemistry: Status, Challenges and Prospects - Session 5
    Quantum Computation for Quantum Chemistry: Status, Challenges and Prospects - Session 5
    Krysta Svore and Dave Wecker
    13 November 2012
    Quantum Computation for Quantum Chemistry: Status, Challenges, and Prospects - Session 1
    Quantum Computation for Quantum Chemistry: Status, Challenges, and Prospects - Session 1
    Michael Freedman, Krysta Svore, Matthias Troyer, and Markus Reiher
    12 November 2012
    Is Scalable, Reliable Quantum Computation Possible?
    Is Scalable, Reliable Quantum Computation Possible?
    Krysta Svore
    19 January 2006

     http://videolectures.net/nipsworkshops2010_svore_lrc/

    Learning to Rank on a Cluster using Boosted Decision Trees

    Krysta Svore

    11 December 2010

    NIPS 2010 Workshop: Learning to Rank on Cores, Clusters and Clouds.

     

    We investigate the problem of learning to rank on a cluster using Web search data composed of 140,000 queries and approximately fourteen million URLs, and a boosted tree ranking algorithm called LambdaMART. We compare to a baseline algorithm that has been carefully engineered to allow training on the full dataset using a single machine, in order to evaluate the loss or gain incurred by the distributed algorithms we consider. Our contributions are two-fold: (1) we implement a method for improving the speed of training when the training data fits in main memory on a single machine; (2) we develop a training method for the case where the training data size exceeds the main memory of a single machine that easily scales to far larger datasets, i.e., billions of examples, and is based on data distribution. Results of our methods on a real-world Web dataset indicate significant improvements in training speed.

     

    http://videolectures.net/wsdm2011_svore_utq/

    Understanding Temporal Query Dynamics

    Krysta Svore

    February 2011

    4th International Conference on Web Search and Data Mining (WSDM '11)

     

    Web search is strongly influenced by time. The queries people issue change over time, with some queries occasionally spiking in popularity (e.g., earthquake) and others remaining relatively constant (e.g., youtube). The documents indexed by the search engine also change, with some documents always being about a particular query (e.g., the Wikipedia page on earthquakes is about the query earthquake) and others being about the query only at a particular point in time (e.g., the New York Times is only about earthquakes following a major seismic activity). The relationship between documents and queries can also change as people's intent changes (e.g., people sought different content for the query earthquake before the Haitian earthquake than they did after). In this paper, we explore how queries, their associated documents, and the query intent change over the course of 10 weeks by analyzing query log data, a daily Web crawl, and periodic human relevance judgments. We identify several interesting features by which changes to query popularity can be classified, and show that presence of these features, when accompanied by changes in result content, can be a good indicator of change in query intent

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