Welcome to the Q4C (QForce) Series

Queens For Computing
Queens College CUNY Computer Science Colloquium

This colloquium is intended to bring together Computer Science and Data Science researchers in the tri-state area (especially in NYC) and to foster collaboration. We welcome talks on any topic of interest to the CS community, including theory, algorithms, machine learning, and data science. If you are interested in attending in-person or online, or would like to give a talk, please contact the organizers.

  1. Monday, 08/28/2023, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Surya Teja Gavva, Queens College CUNY

    Title: Explicit Signings for the Kadison-Singer Problem in Graphs.

    Abstract: The solution to the long-standing Kadison-Singer problem by Marcus, Spielman, and Srivastava demonstrates the existence of unweighted spectral sparsifiers for graphs. However, the solution based on the expected characteristic polynomial only establishes existence, leaving the computation of sparse approximations as an important open problem. In this study, we present algorithms (along with explicit signings) tailored for specific classes of graphs. Of particular interest is the variety of tools from harmonic analysis, discrepancy theory, and random regular graphs that appear while analyzing this problem.

  2. Monday, 09/11/2023, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Boris Aronov, NYU

    Title: Dynamic Approximate Multiplicatively-Weighted Nearest Neighbor

    Abstract: In the nearest-neighbor problem, given a set P of points (in the plane, in 3-space, or higher dimension), we want to preprocess P so that, given another point q, its nearest neighbor (closest point) in P can be found efficiently. The approximate version of the problem does not insist that we return the point that's closest to q: returning a point that is a little further away is acceptable.

    We describe a dynamic data structure for approximate nearest neighbor (ANN) queries with respect to multiplicatively weighted distances with additive offsets. Queries take polylogarithmic time, while the cost of updates is amortized polylogarithmic. The data structure requires near-linear space and construction time. The approach works not only for the Euclidean norm, but for other norms in R^d, for any fixed d. We employ our ANN data structure to construct a faster dynamic structure for approximate SINR queries, ensuring polylogarithmic query and polylogarithmic amortized update for the case of non-uniform power transmitters, thus closing a gap in previous state of the art.

  3. Tuesday, 10/10/2023, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Karthik C.S., Rutgers University

  4. Monday, 10/16/2023, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Chee Yap, NYU

  5. Monday, 10/23/2023, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Janani Sundaresan, Rutgers University

  6. Monday, 11/06/2023, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Yifan Sun, Stony Brook University

  7. Monday, 11/13/2023, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Shubham Jain, Stony Brook University

The seminar is organized by Mayank Goswami
Email Contact: mayank.goswami@qc.cuny.edu