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Colloquia

Fall 2021 Colloquia

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.

Oct 4

Complex Proteoform Identification by Top-down Mass Spectrometry

Xiaowen Liu | Biomedical Informatics and Genomics Division, Tulane University

Abstract: Mass spectrometry-based proteomics has been rapidly developed in the past decade, but researchers are still in the early stage of exploring the world of complex proteoforms, which are protein products with various primary structure alterations resulting from gene mutations, alternative splicing, post-translational modifications, and other biological processes. Proteoform identification is essential to mapping proteoforms to their functions and discovering novel proteoforms and new protein functions. Top-down mass spectrometry is the method of choice for identifying complex proteoforms because it provides a “bird’s eye view” of intact proteoforms. The combinatorial explosion of various alterations on a protein may result in billions of possible proteoforms, making proteoform identification a challenging computational problem. We propose to use mass graphs to efficiently represent proteoforms and design mass graph alignment algorithms for proteoform identification by top-down mass spectrometry. Experiments on top-down mass spectrometry data sets show that the proposed methods are capable of identifying complex proteoforms with various alterations.

About the Speaker: Dr. Xiaowen Liu is a professor of bioinformatics in the Division of Biomedical Informatics and Genomics, Tulane University School of Medicine. He received his Ph.D. degree in computer science from the City University of Hong Kong in 2008. After 4-year postdoc training at the University of Western Ontario, the University of Waterloo, and the University of California, San Diego, Dr. Liu took positions as an Assistant Professor and Associate Professor at the Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis from 2012 to 2021. Recently, he joined Tulane University School of Medicine. His research focuses on developing computational methods for analyzing mass spectrometry data, especially top-down mass spectrometry data. His lab developed TopPIC suite, a widely used software package for proteoform identification by top-down mass spectrometry.

Summer 2021 Colloquia

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.

July 29

Dissertation Defense Talk

Majid Mirzanezhad | Computer Science PhD Student, Tulane University

Please join us for Majid Mirzanezhad’s PhD dissertation defense as described below. This is our first in-person colloquium in a long time! While there is an option to join remotely, we sincerely hope you will be able to join in person. There will be a reception with snacks afterwards.

This talk will be held on Thursday, July 29th, at 2:00 p.m. CST in Stanley Thomas, Room # 302. Please note the special weekday and time for this event. This presentation will also be delivered online at https://tulane.zoom.us/j/99919478181?pwd=d2wvR0ltbjhWSUF6bVZ6VHIrSHVVZz09.

Abstract: The rapid growth of the need for using Geographic Information Systems (GIS), for a better understanding of the environment, has led many researchers and practitioners of various disciplines to design efficient algorithmic methods for confronting the real-world problems arising in the realm of intelligent transportation systems, urban planning, mobility, surveillance systems, and other disciplines over the past few decades. In this dissertation, we consider several topics in computational geometry that involve applications in maps and networks in GIS. We first propose several algorithms that capture the similarity between linear features, notably curves, whose edges are relatively long. One of the popular metrics to capture the similarity between curves is the Fréchet distance. We give a linear-time greedy algorithm deciding and approximating the Fréchet distance and a near linear-time algorithm computing the exact Fréchet distance between two curves in any constant dimension. We also propose several efficient data structures for the approximate nearest-neighbor problem and distance oracle queries among curves under the Fréchet distance.

We exploit the metric studied above for simplification purposes. We specifically consider the problem of simplifying a feature, e.g., graph/tree/curve with an alternative feature of minimum-complexity such that the distance between the input and simplified features remains at most some threshold. We propose several algorithmic and NP-hardness results based on the distance measure we use and the vertex placement of the simplified feature that can be selected from the input's vertices, or its edges, or any points in the ambient space.

About the Speaker: Majid Mirzanezhad is a Ph.D. candidate in the Department of Computer Science at Tulane University. His research area is on computational geometry with applications in GIS and primarily focused on approximation algorithms and data structures for curves and graphs. Prior to pursuing his Ph.D., Majid received his MSc and BSc in computer science from Amirkabir University of Technology (Tehran Polytechnic), Tehran, Iran.

Spring 2021 Colloquia

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.

Apr 26

Interdisciplinary Project Presentations

Karthik Shivaram and Xintian Li | Computer Science PhD Students, Tulane University

These presentations will be delivered online. You may access the presentations on Monday, April 26th, at 4:00 pm CST via the following link: https://tulane.zoom.us/j/94777604484?pwd=MGNHUGNWb09va05NQ0taNDI1NDduUT09 . Meeting ID: 947 7760 4484. Passcode: 511369. Please be sure to mute your microphone when you log on.

Karthik Shivaram

Combating Partisan Homogenization in Content-Based News Recommendation Systems

Abstract: Content-based news recommendation systems build user profiles to identify important terms and phrases that correlate with the user’s engagement to make accurate recommendations. Prior work by Ping et al. [1] suggests that these recommendation systems tend to have a homogenization effect when a user’s political views are diverse over a set of topics. In this work we propose a novel attention-based neural network architecture in a multitask learning setting to overcome this problem of partisan homogenization.

Xintian Li

Evacuation Diffusion Modeling from Twitter

Abstract: Evacuations have a significant impact on saving human lives during hurricanes. However, as a complex dynamic process, it is typically difficult to know the individual evacuation decisions in real time. Since a large amount of information is continuously posted through social media platforms from all populations, we can use them to predict individual evacuation behavior. In this project, we collect tweets during Hurricane Irma 2017, and train a text classifier in an active-learning way to identify tweets indicating positive evacuation decisions from both negative and irrelevant ones. We predict the demographic information for each identified evacuee, based on which we use time series modeling to predict evacuation rate changes over time. We also use the demographic information to help predict possible evacuees in different time ranges. The results can be used to help inform planning strategies of emergency response agencies. . 

Fall 2020 Colloquia

Oct 19

Interdisciplinary Project Presentations

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.

Demi Qin

Fast Prostate Cancer Diagnosis using Topological Data Analysis

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.

Akshay Mehra

Penalty Method for Inversion-Free Deep Bilevel Optimization

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. 

Nov 9

Interdisciplinary Project Presentations

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.

Erfan Hosseini

The Study of Gentrification on Social Urban Simulation - How Income and Interest Can Shape Neighborhoods

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.

Pan Fang

Distance Measures for Embedded Graphs

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. 

Henger Li

Learning to Pool: Multi-Arm Bandit for COVID-19 Group Testing

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. 

Nov 25

Thesis Defense Talk

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 .

Topological Methods in Shape Reconstruction and Comparison

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. . 

 

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