Check back soon for more information on the computer science seminar series. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv. Unless otherwise noted, the seminars meet on Mondays at 4pm in Stanley Thomas 302. However, due to the current pandemic, all colloquia are being conducted virtually.
Demi Qin and Akshay Mehra | Computer Science PhD Students, Tulane University
These presentations will be delivered online. You may access the presentations on Monday, October 19th, at 4:00 pm CST via the following link: https://tulane.zoom.us/j/95685637204 . Meeting ID: 956 8563 7204. Please be sure to mute your microphone when you log on.
Abstract: Topological data analysis (TDA) is attracting increasing interest among researchers in machine learning due to the power of capturing shapes and structure in data. In this talk, we particularly consider biopsy image classification of prostate cancer with TDA that can utilize the topological summaries of images in machine learning tasks. We begin with the theoretical background of TDA and show our previous work on prostate cancer diagnosis by applying TDA in machine learning applications. Next, we define two aspects to improve the use of TDA: 1) A parallel computation pipeline of our previous work; 2) Comparing distance metric on topological summaries. Our results give new insights on when topological summaries could be more suitable and can be used to design better feature-based learning models with TDA.
Abstract: Bilevel optimization problems are at the center of several important machine learning problems such as hyperparameter tuning, learning with noisy labels, meta-learning, and adversarial attacks. In this presentation, I will talk about our algorithm for solving bilevel problems using the penalty method and discuss its convergence guarantees and show that it has linear time and constant space complexities. Small space and time complexities of our algorithm make it an effective solver for large-scale bilevel problems involving deep neural networks. I will present results of the proposed algorithm on data denoising, few-shot learning, and data poisoning problems in a large-scale setting and show that it outperforms or is comparable to previously proposed algorithms based on automatic differentiation and approximate inversion in terms of accuracy, run-time and convergence speed.
Erfan Hosseini, Pan Fang, and Henger Li | Computer Science PhD Students, Tulane University
These presentations will be delivered online. You may access the presentations on Monday, November 9th, at 4:30 pm CST via the following link: https://tulane.zoom.us/j/93662271094 . Meeting ID: 936 6227 1094. Please be sure to mute your microphone when you log on.
Abstract: Gentrification is well-known among sociologists for its complexity and vast effects on urban life. In fact, gentrification can be so complex that sociologists only study specific instances of it. The study of gentrification is important since changes in gentrified urban areas directly affect surrounding suburban and rural areas hence a huge population is involved. In this project, we aim to simulate an urban environment and observe how gentrification starts and how it can affect the city in different situations. Furthermore, we experiment with the factors of gentrification to find possible bottlenecks and try to prevent it. This study can help us understand gentrification better and manage the city in a proper manner while facing it.
Abstract: Measuring similarity of two objects is an essential step in many applications, particularly in comparing objects that can be modeled as graphs. In this project, we explore and learn the existing distance measures for planar graphs. When comparing their performance regarding different factors such as computability, quality of similarity and robustness, none of them have desired result in all these aspects. Besides these computational parts, we also take account of theoretic parts in mathematics for comparison. Specifically, if a distance measure of planar graphs is a metric, we investigate some topological properties of the metric space (e.g., connectedness, completeness and compactness). We comprehensively summarize the existing work in this area and analyze the strengths and weaknesses of these methods. This project will present a critical assessment and concise review of this field that is directly accessible to most people.
Abstract: The worldwide pandemic coronavirus (COVID-19) has grown exponentially and caused huge life and economic loss. Due to its highly contagious nature, it is vital to have a large scale and rapid testing to screen for the virus's presence to control its spread. The recent RT-PCR based group testing or pooled testing seems like an effective method to vastly reduce the number of tests. However, the current group testing suffers from the dilution in pooled samples, which makes it harder to detect early-stage infection with low viral load. We propose a multi-arm bandit framework to balance the trade-off between the number of tests and false-negative rate through dynamically decide the group size and which group to test according to the historical test result during the group testing.
Sushovan Majhi | Mathematics PhD Student, Tulane University
This presentation will be delivered online. You may access the presentation on Tuesday, November 25th, at 10:00 am CST via the following link: https://tulane.zoom.us/j/99521555848 .
Abstract: Most of the modern technologies at our service rely on "shapes" in some way or the other. Be it the Google Maps showing you the fastest route to your destination or the 3D printer on your desk creating an exact replica of a relic---shapes are being repeatedly sampled, reconstructed, and compared by intelligent machines. With the advent of modern sampling technologies, shape reconstruction and comparison techniques have matured profoundly over the last two decades. In this defense talk, we will catch a glimpse of the provable topological methods we propose to advance the study of Euclidean shape reconstruction and comparison. We investigate how topological concepts and results---like the Vietoris-Rips and Cech complexes, Nerve Lemma, discrete Morse theory, etc---lend themselves well to the reconstruction of geodesic spaces from a noisy sample. Our study also delves into the approximation of Gromov-Hausdorff distance, which is deemed as a robust shape comparison framework. We address some of the pivotal questions and challenges pertaining to its efficient computation---particularly for Euclidean subsets. Finally, we present an approximation algorithm, with a tight approximation factor of (1+1/4), for the Gromov-Hausdorff distance on the real line. .