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. Wednesday, 08/31/2022, 12:15PM - 1:30PM
    Science Building, A225
    Speaker: Jonathan Gryak, Department of Computer Science, Queens College CUNY

    Title:
    Intelligent Integration of Multimodal Data for Clinical Decision Support

    Abstract:
    For many diseases and illnesses, the analysis of individual data modalities such as imaging or electronic health records alone is insufficient for accurate modeling - only through the integration and processing of all salient sources of information can a model be created that produces reliable clinical recommendations. This makes clinical decision support a rich area for the development of novel machine learning and data science methodologies.

    In this presentation I will provide an overview of multimodal data analysis along with examples where this approach was used in clinical applications, including postoperative cardiac care and heart failure. Though the developed techniques were motivated by clinical problems, the methodologies are broadly applicable to many machine learning and data science tasks.

  2. Wednesday, 09/14/2022, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Adam Kapelner, Department of Mathematics, Queens College CUNY
    Abstract: We consider the problem of evaluating designs for a two-arm randomized experiment with an incidence (binary) outcome under a nonparametric general response model. Our two main results are that the priori pair matching design of Greevy et al. (2004) is (1) the optimal design as measured by mean squared error among all block designs which includes complete randomization. And (2), this pair-matching design is minimax, i.e. it provides the lowest mean squared error under an adversarial response model. Theoretical results are supported by simulations and clinical trial data.

  3. Wednesday, 09/28/2022, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Mayank Goswami, Department of Computer Science, Queens College CUNY
    Title: On Policemen, Carpenters, and Face Readers
    Abstract: In this talk I will describe three problems from my recent research.
    1. The first concerns computing diverse patrolling routes for a policeman, to minimize the incentive for an attacker to attack a location.
    2. The second problem is a generalization of the so-called nuts-and-bolts problem, where a disorganized carpenter wants to match a collection of nuts and bolts without comparing nuts to nuts or bolts to bolts.
    3. The third problem concerns the computer vision application of computing Teichmuller maps, which are maps that minimize angle distortion. Given images of two faces and landmark correspondences on each, I will show how the theory of complex analysis, differential equations, and computational geometry come together to give an algorithm for this problem.
    Bonus: At the end I will also describe a new open problem that I do not know how to solve, which I call the paranoid-driver problem. This is a problem that lies in the intersection of computational geometry and variational calculus.

  4. Wednesday, 10/19/2022, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Riko Jacob, IT University of Copenhagen, Denmark

  5. Wednesday, 10/26/2022, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Tim Mitchell, Department of Computer Science, Queens College CUNY

    Title:
    Convergence rate analysis and improved iterations for numerical radius computation

    Abstract:
    For the discrete-time dynamical system $x_{k+1} = Ax_k$, the spectrum of $A \in \mathbb{C}^{n \times n}$ tells us about the asymptotic behavior of the system, but it often does not capture information about the transient behavior. To assess this, i.e., how large may $\|A^k\|_2$ become for intermediate values of $k$, we must turn to other quantities. One possibility is the numerical radius, which is the modulus of a globally outermost point in the field of values of a matrix. In this talk, we consider two very different existing approaches to computing the numerical radius, and via new analyses, show that it is actually better to combine them in a new hybrid algorithm compared to using either by itself.

  6. Wednesday, 11/09/2022, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: MD Mahbubur Rahman, Department of Computer Science, Queens College CUNY

  7. Wenesday, 11/23/2022, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Alla Rozovskaya, Department of Computer Science, Queens College CUNY

  8. Wednesday, 12/07/2022, 12:15PM - 1:30PM
    Science Building, C205
    Speaker: Charlene Tsai, Department of Computer Science, Queens College CUNY

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