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Colloquia

Fall 2022 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 in Fall 2022 will meet on Thursdays at 12:30 pm in Stanley Thomas 316. All colloquia this semester, unless otherwise noted, will be available for in-person attendance as well as remote attendance via Zoom. Current Tulane faculty, staff, and students are encouraged to attend in person. Zoom details will be provided via the announcement listserv, or you may email dramil1@tulane.edu to request the corresponding link.

Spring 2022 Colloquia

January 24

AI-Driven Big Data Analytics

Justin Zhan | University of Arkansas

Abstract: Data has become the central driving force to new discoveries in science, informed governance, insight into society, and economic growth in the 21st century. Abundant data is a direct result of innovations including the Internet, faster computer processors, cheap storage, the proliferation of sensors, etc, and has the potential to increase business productivity and enable scientific discovery. However, while data is abundant and everywhere, people do not have a fundamental understanding of data. Traditional approaches to decision making under uncertainty are not adequate to deal with massive amounts of data, especially when such data is dynamically changing or becomes available over time. These challenges require novel techniques in AI-driven data analytics, In this seminar, a number of recent funded AI-driven big data analytics projects will be presented to address various data analytics, mining, modeling and optimization challenges. 

About the Speaker: Dr. Justin Zhan is the Arkansas Research Alliance Scholar and Professor of Data Science at the Department of Computer Science and Computer Engineering, University of Arkansas. He is the Director of Data Science, Arkansas Integrative Metabolic Research Center. He is a joint professor at the Department of Biomedical Informatics, School of Medicine, University of Arkansas for Medical Sciences. He received his PhD degree from University of Ottawa, Master degree from Syracuse University, and Bachelor degree from Liaoning University of Engineering and Technology. His research interests include Data Science, Biomedical Informatics, Deep Learning & Big Data Analytics, Cyber Security & Blockchain, Network Science & Social Computing. He has served as a conference general chair, a program chair, a publicity chair, a workshop chair, or a program committee member for over one-hundred and fifty international conferences and an editor-in-chief, an editor, an associate editor, a guest editor, an editorial advisory board member, or an editorial board member for about thirty journals. He has published 246 articles in peer-reviewed journals and conferences and delivered 30 keynote speeches and invited talks. His research has been extensively funded by National Science Foundation, Department of Defense, and National Institute of Health.

January 28

The Privacy Policy Permission Model: A Methodology for Modeling Privacy Policies

Maryam Majedi |University of Calgary

This event will be held on Friday, January 28th. Please note the special weekday for this event.

Abstract: Organizations use privacy policies to communicate their data collection practices to their clients. A privacy policy is a set of statements that specifies how an organization gathers, uses, discloses, and maintains clients' data. However, most privacy policies lack a clear, complete explanation of how data providers' information is used. In this talk, I will present a modeling methodology, called the Privacy Policy Permission Model (PPPM), that provides a uniform, easy-to-understand representation of privacy policies, which can show how data is used within an organization's practice.

About the Speaker: Dr. Maryam Majedi completed a teaching stream postdoc at the University of Toronto, where she worked with the Embedded Ethics Education Initiative (E3I) team and developed and delivered ethics modules for computer science courses. Dr. Majedi completed her Ph.D. in data privacy at the University of Calgary, where she introduced a new technique to model privacy policies. She holds a M.Sc. in High-Performance Scientific Computing from the University of New Brunswick and a Fellowship in Medical Innovation from Western University.

January 31

Designing the Next Generation of User Interfaces for Children

Julia Woodward | University of Florida

Abstract: Children are increasingly interacting with technology, such as touchscreen devices, at home, in the classroom, and at museums. However, these devices are not designed to take into account that children interact with devices differently than adults. As children’s everyday use of technology increases, these devices need to be tailored towards children. In this talk, I will present research exploring the differences between how children and adults interact and think about different technology. Our findings lead to a better understanding of how to design technology for children. I will also present some recent work examining how to design information in augmented reality (AR) headsets for both children’s and adults’ task performance. I will conclude with some takeaways and plans for future work in designing the next generation of user interfaces for children.

About the Speaker: Julia Woodward is a Doctoral Candidate studying Human-Centered Computing in the Department of Computer and Information Science and Engineering at the University of Florida, as well as a National Science Foundation Graduate Research Fellow. Her main research areas include examining how to design better user interfaces tailored towards children, and understanding how children think about and use technology. Through her research, she has identified specific differences between how adults and children interact with technology and has provided recommendations for designing technology for children. Her current dissertation work focuses on understanding how to design information in augmented reality (AR) headsets to aid in adults’ and children’s task performance and how it differs between the two populations. Julia is graduating this year and plans to continue researching and designing technology tailored for children.

February 4

Personality and Emotion in Strong-Story Narrative Planning

Alireza Shirvani |Saint Louis University

This talk will be held online only on Friday, February 4th, at 4:00 p.m. CST. Please note the special weekday for this event. Zoom details will be provided via the announcement listserv, or you may email dramil1@tulane.edu to request the corresponding link.

Abstract: Interactive virtual worlds provide an immersive and effective environment for training, education, and entertainment purposes. Virtual characters are an essential part of every interactive narrative. I propose models of personality and emotion that are highly domain independent and integrate those models into multi-agent strong-story narrative planning systems. My models of emotion and personality enable the narrative generation system to create more opportunities for players to resolve conflicts using certain behavior types. In doing so, the author can encourage the player to adopt and exhibit those behaviors.

About the Speaker: Dr. Alireza Shirvani is a visiting professor at Saint Louis University in the Department of Computer Science. He received his PhD in Computer Science from the University of Kentucky in 2021. His research focuses on Computational Narrative, with a more general interest in Artificial Intelligence for Games. He is particularly interested in generating believable behavior by integrating emotion and personality into virtual characters. One of his major projects, called Camelot, provides a free easy-to-use 3D tool to visualize interactive stories. This engine acts as a presentation layer to an external program, called the experience manager, which can be written in any programming language.

February 14

Overcoming Heterogeneity in Autonomous Cyber-Physical Systems

Ivan Ruchkin |University of Pennsylvania

Abstract: From autonomous vehicles to smart grids, cyber-physical systems (CPS) play an increasingly important role in today's society. Often, CPS operate autonomously in highly critical settings, and thus it is imperative to engineer these systems to be safe and trustworthy. However, it is particularly difficult to do so due to CPS heterogeneity -- the high diversity of components and models used in these systems. This heterogeneity substantially contributes to fragmented, incoherent assurance as well as to inconsistencies between different models of the system.

This talk will present two complementary techniques for overcoming CPS heterogeneity: confidence composition and model integration. The former technique combines heterogeneous confidence monitors to produce calibrated estimates of the run-time probability of safety in CPS with machine learning components. The latter technique discovers inconsistencies between heterogeneous CPS models using a logic-based specification language and a verification algorithm. The application of these techniques will be demonstrated on an unmanned underwater vehicle and a power-aware service robot. These techniques serve as stepping stones towards the vision of engineering autonomous systems that are aware of their own limitations.

About the Speaker: Ivan Ruchkin is a postdoctoral researcher in the PRECISE center at the University of Pennsylvania. He received his PhD in Software Engineering from Carnegie Mellon University. His research develops integrated high-assurance methods for modeling, analyzing, and monitoring modern cyber-physical systems. His contributions were recognized with multiple Best Paper awards, a Gold Medal in the ACM Student Research Competition, and the Frank Anger Memorial Award for crossover of ideas between software engineering and embedded systems. More information can be found at https://www.seas.upenn.edu/~iruchkin

February 21

Using Kernel-level Data Provenance for Intrusion Detection

Xueyuan (Michael) Han |Harvard University

Abstract: Attacks today are increasingly difficult to detect and their damage continues to skyrocket. For example, it takes an average of over 200 days to identify a data breach and costs about $4 million to rectify. More than 18,000 organizations were affected in the late 2020 SolarWinds supply chain attack. Devastating attacks that make headlines (e.g., Equifax, Target, and Kaseya) are no longer isolated, rare incidents.

In this talk, I will present my work on leveraging kernel-level data provenance to detect system intrusions. Kernel-level data provenance describes system activity as a directed acyclic graph that represents interactions between low-level kernel objects such as processes, files, and sockets. I will describe CamFlow, an OS infrastructure that captures such provenance graphs with negligible performance overhead. I will then describe a host intrusion detection system (IDS), called Unicorn, that uses provenance graphs to detect particularly dangerous attacks called advanced persistent threats (APTs). APTs are the main cause of many of today’s large-scale data breaches. Unicorn applies machine learning to provenance graphs to identify system anomalies caused by APTs in real time without a priori attack knowledge.

I will close the talk by discussing challenges and opportunities in provenance-based intrusion detection, including efforts to develop a robust IDS that not only provides timely anomaly detection, but also explains the manner in which an attack unfolds.

About the Speaker: Xueyuan (Michael) Han is a computer science doctoral candidate advised by Professor James Mickens at Harvard University and Professor Margo Seltzer at the University of British Columbia. His research interests lie at the intersection of systems, security, and privacy. His work focuses on combining practical system design and machine learning to detect host intrusions, and designing language-level frameworks that respect user directives for handling private data. He has previously spent time at the University of Cambridge, Microsoft Research, and NEC Labs America. He is a Siebel Scholar and holds a B.S. in computer science from UCLA.

March 7

Automatic Program Repair and Inconsistency Detection

Thibaud Lutellier |University of Waterloo

Abstract: From bug detection to bug repair, software reliability is involved in all parts of the development cycle. Automation is desirable as it can reduce developers' time on these tasks and discover issues that would be hard to find manually. This job talk will present our recent advancements in automatic program repair and inconsistency detection. In the first part of the talk, I will introduce a new automatic program repair technique that uses ensemble learning and a new neural machine translation (NMT) architecture to automatically fix bugs in multiple programming languages. We then extend this work by introducing a pre-trains programming language model and a new code-aware search strategy. This extended approach outperforms all existing automatic program repair techniques on popular benchmarks. In the second part of the talk, I will explain how we propose a new automated inconsistency detection technique to find bugs in PDF readers and files and how we extended it to find bugs in another domain.

About the Speaker: Thibaud Lutellier is a PostDoc Fellow in the Electrical and Computer Engineering Department at the University of Waterloo. His research interests lie at the crossroad between software engineering and artificial intelligence. His recent work includes proposing new AI-driven program repair techniques and new solutions for detecting bugs in deep learning libraries. He got an ACM SIGSOFT Distinguished Paper Award at ASE'20 for his work on analysing variance in DL training.

April 4

More Benign Point-Based Additive Manufacturing Through Process Planning 

Lee Clemon |University of Technology Sydney

This talk will be held on Monday, April 4th, at 3:00 p.m. in Boggs 239. Please note the special time, and venue for this event.

Abstract: Additive manufacturing is a rapidly growing consumer and commercial fabrication industry that may invert the design and manufacturing paradigm for many products. However, this suite of technologies is currently limited by slow cycle times, and a direct trade-off between throughput and precision. Current deposition methods rely on simple algorithms and sequential fabrication of each layer. Improvements in deposition planning can accelerate throughput and reduce resource use. We establish new approaches that leverage the structure of the intended model and relax unnecessary fabrication constraints to circumvent current speed limitations and maximize value adding operations. These efforts explore multiple algorithms to construct an improved toolpath. In addition, material use and energy consumption in additive manufacturing pose a challenge in production scale-up, particularly when considering climate change and waste generation. We characterize the resource intensity in current machines and by typical users to enable designers to make more informed decisions and identify opportunities for waste reduction. With these advances in deposition planning and enabled by multi-material fabrication we propose, new opportunities for creating circular economies leveraging additive manufacturing to give new live to waste materials. We then evaluate the structural implications of material sequestration to enable a redesign of products and product lifecycles for these circular economies. ----

About the Speaker: Dr. Lee Clemon, P.E. is a research scientist in advanced manufacturing and high consequence design and licensed professional engineer. He focuses on the interplay of materials, design, and manufacturing for a more reliable and environmentally conscious industrial world. His current research interests are in process improvement and material property manipulation in advanced manufacturing processes, with an emphasis on additive and hybrid additive-subtractive manufacturing through particulate, wire, layer, and ensemble fabrication methods. He is a management member of the Centre for Advanced Manufacturing, program co-lead of the ARC Training Centre for Collaborative Robotics in Advanced Manufacturing, and member of the RF and Communications Laboratory. Lee also serves the mechanical engineering profession as an active volunteer for ASME providing professional development and training. Lee M Clemon holds a Ph.D. and a M.S. in Mechanical Engineering from the University of California at Berkeley, and a B.S. in Mechanical Engineering from the University of Kansas. He was previously a staff member at Sandia National Laboratories as a design and Research and Development engineer on hazardous substance processing systems and manufacturing process development. Lee became a Lecturer at the University of Technology Sydney, in the School of Mechanical and Mechatronic Engineering.

May 2

A Life in Logic: Colloquium in Honor of Michael Mislove 

This event will be held in honor of Prof. Michael Mislove, who is retiring from Tulane University after over 50 years of service. Please join us in celebrating his work and his time at Tulane.

This event will be held on Monday, May 2nd, from 4:00 p.m. - 6:30 p.m. (CDT) in Boggs 600. Please note the special venue for this event.  

4:00 p.m - Welcome and short intro, Carola Wenk (Computer Science) and Morris Kalka (Math)

4:10 p.m. - 4:40 p.m.: Mike Mislove and Tulane, Some Reminiscences

Jimmie Lawson |Louisiana State University

Abstract: Mike Mislove's professional career at Tulane has intersected significantly with mine over the years, and I would like to share some (by no means complete) reminiscences, recollections, and reflections from those years of his research, leadership, and service, with some important Tulane personalities forming a backdrop.

4:40 p.m. - 5:10 p.m.: Additional Talks

Peter Bierhost |University of New Orleans

Ellis Fenske |United States Naval Academy

5:10 p.m.: Video

Reception with drinks and snacks to follow.

The event will also be available over Zoom for those that cannot attend in person. As referenced above, Zoom details will be provided via the announcement listserv, or you may email dramil1@tulane.edu to request the corresponding link.

May 11

Title: Security Analysis of Binary Code via Architectural and Compiler Support  

Jiang Ming |University of Texas at Arlington

This talk will be held on Wednesday, May 11, at 4:00 p.m. in Stanley Thomas 302. Please note the special weekday and venue for this event.

Abstract: Software security has become a hugely important consideration in all aspects of our lives. As software vulnerabilities and malware occupy a large portion of cyberattacks, automated security analysis of binary code is booming over the past few years. Binary code is everywhere, from IoT device firmware to malicious programs. However, binary code analysis is highly challenging due to binary code's very low-level and complicated nature. My research explores cross-disciplinary methodologies to effectively address the security problems in binary code. In this talk, I will first present my recent work on the security hardening of embedded systems (ASPLOS '22). We take advantage of architectural support to safely eliminate unused code of shared libraries on embedded systems. Our work can significantly reduce the code-reuse attacking surface with zero runtime overhead. Then, I will look into the most common source leading to binary code differences: compiler optimization. Our PLDI '21 awarded paper takes the first step to systematically studying the effectiveness of compiler optimization on binary code differences. We provide an important new viewpoint on the established binary diffing research area and challenge long-held optimization-resistance claims with compelling evidence.

About the Speaker: Jiang Ming is an Assistant Professor in the Department of Computer Science and Engineering at the University of Texas at Arlington. He received his Ph.D. from Pennsylvania State University in 2016. His research interests span Software and Systems Security, with a focus on binary code analysis, hardware-assisted software security analysis, mobile systems security, and language-based security. His work has been published in prestigious conferences, including IEEE S&P, ASPLOS, PLDI, USENIX Security, ACM CCS, NDSS, ICSE, FSE, ASE, and MobiSys. Jiang has been funded by multiple NSF grants, Cisco research award, and UT System Rising STARs program. He was the recipient of UTA College of Engineering Outstanding Early Career Research Award, ACM SIGPLAN Distinguished Paper Award, and ACM SIGSOFT Distinguished Paper Nomination.

May 26

Title: Puzzles in Life, Work, and Computer Science  

Matthew Toups |University of New Orleans

This talk will be held on Thursday, May 26, at 4:00 p.m. in Stanley Thomas 302. Please note the special weekday and venue for this event.

Abstract: Jigsaw puzzles have been produced for centuries, and printed puzzles have been a daily part of newspapers since the early 20th century. Puzzles can provide lightweight recreation, can serve as a distraction during the recent pandemic, or can even be the subject of rigorous computational complexity analysis. Puzzles and puzzle-solving also inform how I approach teaching Computer Science at the undergraduate level. Not only are puzzles a way to add both fun and challenge to courses, but more broadly I have several observations on CS pedagogy which I use puzzles to illustrate. Puzzles not only teach problem-solving, but they can motivate both through competition and co-operation, and can scale up and down in difficulty as needed. We can also step back from small puzzle pieces to examine the larger picture of what we want students to synthesize. I will also figuratively put together some puzzle pieces from my time as a student as a way to introduce my perspective on our discipline.

About the Speaker: Matthew Toups, born and raised in New Orleans, holds a B.S. in Computer Science from Carnegie Mellon University and an M.S. from the University of New Orleans. Since 2016 he has served as I.T. Director for the University of New Orleans' Computer Science Department, providing a wide range of research and teaching technology needs. Additionally he has taught numerous undergraduate systems courses at UNO, and he sponsors a student cybersecurity competition team. He also enjoys solving puzzles.

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. All colloquia this semester will be available for in-person attendance as well as remote attendance via Zoom. Current Tulane faculty, staff, and students are encouraged to attend in person, but, due to the university's Covid-19 protocols, non-Tulane individuals must rely on the Zoom option for attendance. Zoom details will be provided via the announcement listserv, or you may email dramil1@tulane.edu to request the corresponding link.

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.

Nov 8

Interdisciplinary Project Presentations

Tianyi Xu, Taotao Jing,  and Haifeng Xia | Computer Science PhD Students, Tulane University

Tianyi Xu

Contextual Bandits With Probing

Abstract: In various practical applications from clinical trials to recommendation systems and anomaly detection, the problem of sequential decision-making is often encountered. Usually, each action (for example, the user's profile) has related information or context, and only the reward for the selected action is revealed (i.e., the bandit feedback). Since the reward is often a random variable, a statistical approach (contextual bandits) can be applied to solving these problems. Different from choosing one arm each time, we consider a novel extension of the bandit learning framework to incorporate joint probing and play. We assume that before the decision maker chooses an arm to play in each round, it can probe a subset of arms and observe their rewards (in that round). The decision maker then picks an arm to play according to the observations obtained in the probing stage and historical data. Our bandit learning model and its extensions can potentially be applied to a large body of sequential decision-making problems that involve joint probing and play under uncertainty. We will present an efficient algorithm for these problems and establish the regret bound using tools from online learning and statistics.

Taotao Jing

Augmented Multi-Modality Fusion for Generalized Zero-Shot Sketch-based Visual Retrieval

Abstract: Augmented Multi-Modality Fusion for Generalized Zero-Shot Sketch-based Visual Retrieval Abstract: Zero-shot sketch-based image retrieval (ZS-SBIR) has attracted great attention recently, due to the potential application of sketch-based retrieval under the zero-shot scenario, where the categories of query sketches and gallery photo pool are not observed in the training stage. However, it is still under insufficient exploration for the general and practical scenario when the query sketches and gallery photos contain both seen and unseen categories. Such problem is defined as generalized zero-shot sketch-based image retrieval (GZS-SBIR), which is also the focus of this work. To this end, we propose a novel Augmented Multi-modality Fusion (AMF) framework to generalize seen concepts to unobserved one efficiently. Specifically, a novel knowledge discovery module named cross-domain augmentation is designed in both visual and semantic space to mimic novel knowledge unseen from the training stage, which is the key to handle GZS-SBIR challenge. Moreover, a triplet domain alignment module is proposed to couple the cross-domain distribution across photo and sketch in visual space. To enhance the robustness of our model, we explore embedding propagation to refine both visual and semantic features by removing undesired noise. Eventually, visual-semantic fusion representations are concatenated for further domain discrimination and task-specific recognition, which tends to trigger the cross-domain alignment in both visual and semantic feature space.In addition to popular ZS-SBIR benchmarks, a new evaluation protocol specifically designed for GZS-SBIR problem is constructed from DomainNet dataset with more diverse sub-domains, and the promising results demonstrate the superiority of the proposed solution over other baselines.  

Haifeng Xia

Privacy Protected Multi-Domain Collaborative Learning

Abstract: Unsupervised domain adaptation (UDA) aims to transfer knowledge from one or more well-labeled source domains to improve model performance on the different-yet-related target domain without any annotations. However, existing UDA algorithms fail to bring any benefits to source domains and neglect privacy protection during data sharing. With these considerations, we define Privacy Protected Multi-Domain Collaborative Learning (P2MDCL) and propose a novel Mask-Driven Federated Network (MDFNet) to reach a “win-win” deal for multiple domains with data protected. First, each domain is armed with individual local model via a mask disentangled mechanism to learn domain-invariant semantics. Second, the centralized server refines the global invariant model by integrating and exchanging local knowledge across all domains. Moreover, adaptive self-supervised optimization is deployed to learn discriminative features for unlabeled domains. Finally, theoretical studies and experimental results illustrate rationality and effectiveness of our method on solving P2MDCL. 

Nov 29

Hard Instances From Generalized Error Correcting Codes

Victor Bankston | Department of Computer Science, Tulane University

Abstract: Understanding the tractability of certain instances of NP-hard problems is an important issue in modern complexity theory. For example, when restricted to perfect graphs, Independent Set, $\alpha(G)$ can be solved in polynomial time by computing a semidefinite relaxation, Lovasz's theta function $\vartheta(G)$. To better understand the intractability of Independent Set, we seek families of instances of graphs for which $\vartheta(G)$ and $\alpha(G)$ differ to the largest possible extent. We propose a method for constructing such instances based on the fact that $\vartheta(G) > \alpha(G)$ may be viewed as a Bell Inequality. We present a toy model where measurements may be viewed as codes in $\mathbb{F}_5^n$. We then study the Bell Inequalities using the techniques for analyzing error correcting codes. We then study the more natural Pauli measurements and observe that these also have the structure of a generalized error correcting code, an association scheme. Thus, we argue that we can construct hard instances of Independent Set by constructing 2-designs of Pauli measurements.

Dec 6

Automated Deep Learning for Open & Inclusive AI

Jun (Luke) Huan | StylingAI Inc.

Abstract: Big Data, AI, and cloud computing are transforming our society. In areas such as game playing, image classification, and speech recognition, AI algorithms may have already surpassed human experts’capability. We are observing transformations AI and data science produce to industry sectors such as e-commerce, social-networking, finance, health, and transportation among others. My talk covers two parts: (1) a brief introduction to the Baidu AutoDL project where we use deep learning to design deep learning networks and (2) the applications of automated deep learning in different vertical areas including content generation and digital human.

About the Speaker: Dr. Jun (Luke) Huan is the founder, president, and chief scientist of StylingAI Inc., a start-up aiming to developing and applying AI capabilities for automated content generation. Before that he served as a distinguished scientist and the head of Baidu Big Data Laboratory at Baidu Research. Before joining industry, he was the Charles E. and Mary Jane Spahr Professor in the EECS Department at the University of Kansas. From 2015-2018, Dr. Huan worked as a program director at the US NSF in charge of its big data program.

Dr. Huan works on Data Science, AI, Machine Learning and Data Mining. Dr. Huan's research is recognized internationally. He has published more than 150 peer-reviewed papers in leading conferences and journals and has graduated eleven Ph.D. students. He was a recipient of the NSF Faculty Early Career Development Award in 2009. His group won several best paper awards from leading international conferences. Dr. Huan service record includes Program Co-Chair of IEEE BIBM in 2015 and IEEE Big Data 2019.

Dec 8

Special Joint Talk in Both the Algebraic Geometry and Geometric Topology Seminar of the Department of Mathematics and the Department of Computer Science Colloquium

Erin Wolf Chambers| Saint Louis University

This talk will be held on Wednesday, December 8th, at 3:00 p.m. in Gibson 310. Please note the special weekday, time, and venue for this event.

Reeb Graph Metrics From the Ground Up

Abstract: The Reeb graph has been utilized in various applications including the analysis of scalar fields. Recently, research has been focused on using topological signatures such as the Reeb graph to compare multiple scalar fields by defining distance metrics on the topological signatures themselves. In this talk, we will introduce and study five existing metrics that have been defined on Reeb graphs: the bottleneck distance, the interleaving distance, functional distortion distance, the Reeb graph edit distance, and the universal edit distance. This talk covers material from a recent survey paper, which has multiple contributions: (1) provide definitions and concrete examples of these distances in order to develop the intuition of the reader, (2) visit previously proven results of stability, universality, and discriminativity, (3) identify and complete any remaining properties which have only been proven (or disproven) for a subset of these metrics, (4) expand the taxonomy of the bottleneck distance to better distinguish between variations which have been commonly miscited, and (5) reconcile the various definitions and requirements on the underlying spaces for these metrics to be defined and properties to be proven.

About the Speaker: Dr. Erin Wolf Chambers is a Professor at Saint Louis University in the Department of Computer Science, with a secondary appointment in the Department of Mathematics. Her research focus is on computational topology and geometry, with a more general interest in combinatorics and algorithms. Complementing this work, she is also active in projects to support and improve the culture and climate in computer science and mathematics at all levels. She currently serves as editor for several journals, on the board of trustees for the Society for Computational Geometry, and on the SafeToC organizing committee and as an advocate. She received her PhD in Computer Science from the University of Illinois at Urbana-Champaign in 2008, and was a Visiting Research Professor at Saarland University in Summer 2011.

Dec 13

Architectural Support for Interdisciplinary Data Science Research

Lu Peng | Louisiana State University

Abstract: The ever-increasing amount of global data introduces big challenges to computer systems in the aspect of performance, power consumption, reliability, and security. Deep learning and other advanced algorithms have been proposed to handle the problems in the layers of software, however, they require significant computing resources and memory bandwidth. In consequence, traditional CPU-based platforms are no longer the best choices for deploying these algorithms because they do not provide sufficient parallelism. Graphics Processing Units (GPUs) can provide improved performance but at the cost of higher power consumption. FPGAs and ASICs have garnered attention due to their application-specific nature, ability to achieve high degrees of parallelism, and high energy efficiency. In this talk, I will introduce our recent work in the computer system and architectural support for interdisciplinary data science research including hardware accelerator for deep neural networks, accelerator design for smart contracts processing, and an application of blockchain in contact tracing against COVID-19. Other recent work including adapting the B+ tree on Graphics Processing Units (GPUs) and improving resilience for Big Data kernels will be briefly introduced.

About the Speaker: Lu Peng is the Gerard L. “Jerry” Rispone professor with the Division of Electrical and Computer Engineering, Louisiana State University, Baton Rouge, Louisiana. His research interests include computer systems and architecture focusing on many design issues on CPUs and GPUs, hardware accelerators, and applications for deep learning neural networks and blockchains. As PI or Co-PI, he has led or co-led several interdisciplinary research projects with collaborating researchers from different fields: Computer Science, Electrical Engineering, Statistics, Chemistry, Pathobiological Sciences, and Meteorology. His work has been supported by multiple federal and state agencies including NSF, NIH, NRL, DOE/LLNL, ORAU, NASA/LaSpace, BoR, and LSU RoC, as well as industrial companies including Chevron and Xilinx. He was a recipient of the ORAU Ralph E. Power junior faculty enhancement awards in 2007 and the Best Paper Award from IEEE IGSC in 2019 and IEEE ICCD in 2001.

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.  

 

 

 

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