{"id":13800,"date":"2025-04-09T17:42:20","date_gmt":"2025-04-09T21:42:20","guid":{"rendered":"https:\/\/www.qc.cuny.edu\/academics\/cs\/?page_id=13800"},"modified":"2026-02-07T15:36:08","modified_gmt":"2026-02-07T20:36:08","slug":"fall-2022-seminars","status":"publish","type":"page","link":"https:\/\/www.qc.cuny.edu\/academics\/cs\/fall-2022-seminars\/","title":{"rendered":"Fall 2022 Seminars"},"content":{"rendered":"

[et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” background_image=”https:\/\/www.qc.cuny.edu\/academics\/cs\/wp-content\/uploads\/sites\/137\/2023\/11\/Students-scaled.jpeg” min_height=”500px” custom_padding=”10px|||||” global_colors_info=”{}”][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”31px||0px|||” global_colors_info=”{}”][et_pb_row _builder_version=”4.23.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.23.1″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” border_width_bottom=”1px” border_color_bottom=”#E71939″ global_colors_info=”{}”]<\/p>\n

Queens College Computer Science Colloquium<\/h2>\n

Fall 2022<\/h3>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font=”–et_global_body_font||||||||” text_font_size=”16px” hover_enabled=”0″ global_colors_info=”{}” sticky_enabled=”0″]<\/p>\n

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 Seminar Organizer, Mayank Goswami at mayank.goswami@qc.cuny.edu<\/a>.<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][\/et_pb_section][et_pb_section fb_built=”1″ _builder_version=”4.27.4″ _module_preset=”default” custom_padding=”13px|||||” global_colors_info=”{}”][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” min_height=”367px” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”1. Intelligent Integration of Multimodal Data for Clinical Decision Support” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]Wednesday, 08\/31\/2022, 12:15pm – 1:30pm
\nRoom: Science Building, A225
\nSpeaker: Jonathan Gryak<\/a>, Department of Computer Science, Queens College, CUNY<\/a><\/p>\n

Abstract:<\/strong> 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.<\/p>\n

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.[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”2. Evaluating Designs for a Two-Arm Randomized Experiment ” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]<\/p>\n

Wednesday, 09\/14\/2022, 12:15pm – 1:30pm
Room: Science Building, C205
Speaker: Adam Kapelner<\/a>, Department of Mathematics, Queens College CUNY<\/a><\/p>\n

Abstract:<\/strong> 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.<\/p>\n

[\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”3. On Policemen, Carpenters, and Face Readers” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]<\/p>\n

Wednesday, 09\/28\/2022, 12:15pm – 1:30pm
Room: Science Building, C205
Speaker: Mayank Goswami<\/a>, Department of Computer Science, Queens College, CUNY<\/a><\/p>\n

Abstract:<\/strong> Abstract: In this talk I will describe three problems from my recent research.<\/p>\n

    \n
  1. The first concerns computing diverse patrolling routes for a policeman, to minimize the incentive for an attacker to attack a location.<\/li>\n
  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.<\/li>\n
  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.<\/li>\n<\/ol>\n

    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.<\/p>\n

    [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”4. Convergence Rate Analysis and Improved Iterations for Numerical Radius Computation” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]Wednesday, 10\/26\/2022, 12:15pm – 1:30pm
    \nRoom: Science Building, C205
    \nSpeaker:     Tim Mitchell<\/a>, Queen College, Department of Computer Science<\/a><\/p>\n

    Abstract: <\/strong>For the discrete-time dynamical system \n \n x<\/mi>\n \n k<\/mi>\n +<\/mo>\n 1<\/mn>\n <\/mrow>\n <\/msub>\n =<\/mo>\n A<\/mi>\n \n x<\/mi>\n k<\/mi>\n <\/msub>\n<\/math>, the spectrum of \n A<\/mi>\n ∈<\/mo>\n \n \n C<\/mi>\n <\/mrow>\n \n n<\/mi>\n ×<\/mo>\n n<\/mi>\n <\/mrow>\n <\/msup>\n<\/math> 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 \n ‖<\/mo>\n \n A<\/mi>\n k<\/mi>\n <\/msup>\n \n ‖<\/mo>\n 2<\/mn>\n <\/msub>\n<\/math> become for intermediate values of \n k<\/mi>\n<\/math>, 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. [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”5. Fragile Complexity of Comparison-Based Algorithms” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]<\/p>\n

    Wednesday, 11\/02\/2022, 12:15pm – 1:30pm
    Room: Science Building, C205
    Speaker: Riko Jacob, IT University of Copenhagen, Denmark<\/p>\n

    Abstract: <\/strong> We initiate a study of algorithms with a focus on the computational complexity of individual elements, and introduce the fragile complexity<\/strong> of comparison-based algorithms as the maximal number of comparisons any individual element takes part in. We give a number of upper and lower bounds on the fragile complexity for fundamental problems, including Minimum, Selection, Sorting and Heap Construction<\/em>. The results include both deterministic and randomized upper and lower bounds, and demonstrate a separation between the two settings for a number of problems. The depth of a comparator network is a straight-forward upper bound on the worst case fragile complexity of the corresponding fragile algorithm. We prove that fragile complexity is a different and strictly easier property than the depth of comparator networks, in the sense that for some problems a fragile complexity equal to the best network depth can be achieved with less total work and that with randomization, even a lower fragile complexity is possible.<\/p>\n

    [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”6. Design of a Low-Power Wide-Area Network over White Spaces” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]<\/p>\n

    Wednesday, 11\/09\/2022, 12:15pm – 1:30pm
    Room: Science Building, C205
    Speaker: MD Mahbubur Rahman<\/a>, Department of Computer Science, Queens College, CUNY<\/a><\/p>\n

    Abstract: <\/strong> The Internet of Things (IoT) applications, such as sensing and monitoring, smart agriculture, and smart cities, aim to utilize IoT devices (i.e., sensors) to enhance the quality of life, health, and safety of communities in both urban and rural areas. Due to the growing demand for these applications, the number of IoT sensors is increasing rapidly and is expected to reach approximately 29 billion by 2030. IoT sensors are typically battery-powered and dispersed widely (e.g., in thousands) over long distances. It thus becomes extremely challenging to connect and coordinate them for periodic or sporadic data collection at a BS (base station) and make time-critical data-driven decisions. In this talk, I will discuss the design, implementation, and deployment experiences of a novel low-power wide-area network technology called SNOW (sensor network over white spaces), which can connect and coordinate thousands of sensors and enable energy-efficient and low-latency data collection at a BS.<\/p>\n

    [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”7. Making Progress on Language Learner Educational Application Tasks” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]<\/p>\n

    Wednesday, 11\/23\/2022, 12:15pm – 1:30pm
    Room: Science Building, C205
    Speaker:     Alla Rozovskaya<\/a>, Queens College, Department of Computer Science<\/a><\/p>\n

    Abstract:<\/strong> In this talk, I will describe our recent work on two educational application tasks for language learners: Grammatical Error Correction (GEC) and developing cloze exercises. Standard evaluations of GEC systems make use of a fixed reference text generated relative to the original text. We study the performance of GEC systems relative to closest gold \u2013 a gold reference created relative to the output of a system. Evaluation with closest golds reveals that the real performance is 20-40 points better than standard evaluations show, however, state-of-the-art systems prefer to make local spelling and grammar edits, leaving out more complex word-level changes. We propose a novel method to address this deficiency of GEC models.<\/p>\n

    The second part of the talk will focus on generating cloze exercises through back-translation. In a cloze exercise, a student is presented with a carrier sentence with one word hidden, and a multiple-choice list that includes the correct answer and several inappropriate options, called distractors. We use hundreds of back-translations of the carrier sentence via multiple pivot languages to generate a rich set of challenging distractors. We demonstrate that the proposed method significantly outperforms current state-of-the-art.<\/p>\n

    [\/et_pb_text][\/et_pb_column][\/et_pb_row][et_pb_row _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_column type=”4_4″ _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][et_pb_heading title=”8. Machine Learning for Eye Treatment and Intelligent Tutoring” _builder_version=”4.27.4″ _module_preset=”default” global_colors_info=”{}”][\/et_pb_heading][et_pb_text _builder_version=”4.27.4″ _module_preset=”default” text_font_size=”16px” global_colors_info=”{}”]<\/p>\n

    Wednesday, 12\/07\/2022, 12:15pm – 1:30pm
    Room: Science Building, C205
    Speaker: Charlene Tsai,     Queens College, Department of Computer Science<\/a><\/p>\n

    Abstract:<\/strong> Abstract: In this talk, I will share our recent research progress on two problems:<\/p>\n

      \n
    1. The first problem is early detection of polypoidal choroidal vasculopathy on fluorescein angiographic sequence, using recurrent CNN-Transformer network trained with small data from multiple medical sites. The progress in both classification and segmentation will be discussed.<\/li>\n
    2. The second problem is a recommender system for response-to-intervention for reading comprehension. The system is developed with a collection of fourth grade New York English Language Arts (ELA) assessments. No other prior academic or demographic information of students is available for this study.<\/li>\n<\/ol>\n

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