Unless otherwise noted, the seminars in Fall 2022 will meet on Thursdays at 12:30 pm in Stanley Thomas 302. 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 email@example.com to request the corresponding link. If you would like to receive notices about upcoming seminars, you can subscribe to the announcement listserv.
Seung-Jong Jay Park | Louisiana State University/National Science Foundation
This talk will be held on Tuesday, September 6th, at 1:00 p.m. in Boggs 600. Please note the special weekday, time, and venue for this event.
About the Speaker: Dr. Seung-Jong Jay Park is the Dr. Fred H. Fenn Memorial Professor of Computer Science and Engineering at Louisiana State University, where he has worked in cyberinfrastructure development for large-scale scientific and engineering applications since 2004. He received Ph.D. in the school of Electrical and Computer Engineering from the Georgia Institute of Technology. He has performed interdisciplinary research projects including (1) big data and deep learning research including developing software frameworks for large-scale science applications; and (2) cyberinfrastructure development using cloud computing, high-performance computing, and high-speed networks. Those projects have been supported by federal and state funding programs including NSF, NASA, NIH, ONR, and AFRL. He received IBM faculty research awards between 2015-2017. He also served an associate director for the Center for Computation and Technology of LSU between 2016-2018. Since 2021 he has served at the U.S. National Science Foundation (on leave from LSU) as a program director managing research support programs, such as Cyberinfrastructure for Sustained Scientific Innovation (CSSI), Principles and Practice of Scalable Systems (PPoSS), Computational and Data-Enabled Science and Engineering (CDS&E), and others.
Masashi Sugiyama| RIKEN/The University of Tokyo
This talk will be held on Monday, November 28th, at 11:00 a.m. in Boggs 600. Please note the special weekday, time, and venue for this event.
Abstract: When machine learning systems are trained and deployed in the real world, we face various types of uncertainty. For example, training data at hand may contain insufficient information, label noise, and bias. In this talk, I will give an overview of our recent advances in robust machine learning, including weakly supervised classification (positive-unlabeled classification, positive-confidence classification, complementary-label classification, etc), noisy label learning (noise transition estimation, instance-dependent noise, clean sample selection, etc.), and domain adaptation (joint importance-predictor learning for covariate shift adaptation, dynamic importance-predictor learning for full distribution shift, etc.).
About the Speaker: Masashi Sugiyama received a Ph.D. in Computer Science from Tokyo Institute of Technology in 2001. He has been a Professor at the University of Tokyo since 2014 and concurrently Director of the RIKEN Center for Advanced Intelligence Project (AIP) since 2016. His research interests include theories and algorithms of machine learning. He served as Program Co-chairs for Neural Information Processing Systems (NeurIPS) Conference in 2015, International Conference on Artificial Intelligence and Statistics (AISTATS) in 2019, and Asian Conference on Machine Learning (ACML) in 2010 and 2020. He (co)authored Machine Learning in Non-Stationary Environments (MIT Press, 2012), Density Ratio Estimation in Machine Learning (Cambridge University Press, 2012), and Machine Learning from Weak Supervision (MIT Press, 2022).
Valerio Pasucci| University of Utah
This talk will be held at 3:30 p.m. in Stanley Thomas 302. Please note the special time for this event.
Abstract: Effective use of data management techniques for the analysis and visualization of massive scientific data is a crucial ingredient for the success of any experimental facility, supercomputing center, or cyberinfrastructure that supports data-intensive scientific investigations. Data movements have become a central component that can enable or stifle innovation in the progress towards high-resolution experimental data acquisition (e.g., APS, SLAC, NSLS II). However, universal data delivery remains elusive, limiting the scientific impacts of these facilities. This is particularly true for high-volume/high-velocity datasets and resource-constrained institutions.
This talk will present the National Science Data Fabric (NSDF) testbed, which introduces a novel trans-disciplinary data fabric integrating access to and use of shared storage, networking, computing, and educational resources. The NSDF technology addresses the key data management challenges involved in constructing complex streaming workflows that take advantage of data processing opportunities that may arise while data is in motion. This technology finds practical use in many research and industrial applications, including materials science, precision agriculture, ecology, climate modeling, astronomy, connectomics, and telemedicine.
This NSDF overview will include several techniques that allow building a scalable data movement infrastructure for fast I/O while organizing the data in a way that makes it immediately accessible for processing, analytics, and visualization with resources from Campus Computing Cybeinfrastructures, the Open Storage Network, the Open Science Grid, NSF/DOE leadership computing facilities, the CloudLab, Camelion, and Jetstream, just to name a few. For example, I will present a use case for the real-time data acquisition from an Advanced Photon Source (APS) beamline to allow remote users to monitor the progress of an experiment and direct integration in the Materials Commons community repository. We accomplish this with an ephemeral NSDF installation that can be instantiated via Docker or Singularity at the beginning of the experiment and removed right after. In general, the advanced use of containerized applications with automated deployment and scaling makes the practical use of clients, servers, and data repositories straightforward in practice, even for non-expert users. Full integration with Python scripting facilitates the use of external libraries for data processing. For example, the scan of a 3D metallic foam can be easily distributed with the following Jupyter notebook https://tinyurl.com/bdzhf2nx.
Overall, this leads to building flexible data streaming workflows for massive imaging models without compromising the interactive nature of the exploratory process, the most effective characteristic of discovery activities in science and engineering. The presentation will be combined with a few live demonstrations of the same technology including notebooks which are being used to provide undergraduate students of a minority-serving institution (UTEP) with real-time access to large-scale data normally used only by established scientists in well-funded research groups. About the Speaker: Valerio Pascucci is the Inaugural John R. Parks Endowed Chair, the founding Director of the Center for Extreme Data Management Analysis and Visualization (CEDMAV), a Faculty of the Scientific Computing and Imaging Institute, and a Professor of the School of Computing of the University of Utah. Valerio is also the President of ViSOAR LLC, a University of Utah spin-off, and the founder of Data Intensive Science, a 501(c) nonprofit providing outreach and training to promote the use of advanced technologies for science and engineering. Before joining the University of Utah, Valerio was the Data Analysis Group Leader of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory and an Adjunct Professor of Computer Science at the University of California, Davis. Valerio's research interests include Big Data management and analytics, progressive multi-resolution techniques in scientific visualization, discrete topology, and compression. Valerio is the coauthor of more than two hundred refereed journal and conference papers and was an Associate Editor of the IEEE Transactions on Visualization and Computer Graphics.
About the Speaker: Valerio Pascucci is the Inaugural John R. Parks Endowed Chair, the founding Director of the Center for Extreme Data Management Analysis and Visualization (CEDMAV), a Faculty of the Scientific Computing and Imaging Institute, and a Professor of the School of Computing of the University of Utah. Valerio is also the President of ViSOAR LLC, a University of Utah spin-off, and the founder of Data Intensive Science, a 501(c) nonprofit providing outreach and training to promote the use of advanced technologies for science and engineering. Before joining the University of Utah, Valerio was the Data Analysis Group Leader of the Center for Applied Scientific Computing at Lawrence Livermore National Laboratory and an Adjunct Professor of Computer Science at the University of California, Davis. Valerio's research interests include Big Data management and analytics, progressive multi-resolution techniques in scientific visualization, discrete topology, and compression. Valerio is the coauthor of more than two hundred refereed journal and conference papers and was an Associate Editor of the IEEE Transactions on Visualization and Computer Graphics.
Fang Qi, Linsen Li, and Xiaolin Sun | Computer Science PhD Students, Tulane University
This event will be held at 12:30 p.m. in Stanley Thomas 302. Please note the special weekday for this event.
Abstract: Quantum technology is still in its infancy, but superconducting circuits have made great progress toward pushing forward the computing power of the quantum state of the art. Due to limited error characterization methods and temporally varying error behavior, quantum operations can only be quantified to a rough percentage of successful execution, which fails to provide an accurate description of real quantum execution in the current noisy intermediate-scale quantum (NISQ) era. State-of-the-art success rate estimation methods either suffer from significant prediction errors or unacceptable computation complexity. Therefore, there is an urgent need for a fast and accurate quantum program estimation method that provides stable estimation with the growth of the program size. Inspired by the classical architectural vulnerability factor (AVF) study, we propose and design Quantum Vulnerability Factor (QVF) to locate any manifested error which generates Cumulative Quantum Vulnerability (CQV) to perform SR prediction. By evaluating it with well-known benchmarks on three 27-qubit and one 65-qubit quantum machines, CQV outperforms the state-of-the-art prediction technique ESP by achieving on average 6 times less relative prediction error, with best cases at 20 times, for benchmarks with a real SR rate above 0.1%.
Abstract: School rating websites are increasingly used by parents to assess the quality and fit of U.S. K-12 schools for their children. These online reviews often contain detailed descriptions of a school’s strengths and weaknesses, which both reflect and inform perceptions of a school. Existing work on these text reviews has focused on finding words or themes that underlie these perceptions, but have stopped short of using the textual reviews as leading indicators of school performance. In this paper, we investigate to what extent the language used in online reviews of a school is predictive of changes in the attributes of that school, such as its socio-economic makeup and student test scores. Using over 300K reviews of 70K U.S. schools from a popular ratings website, we apply language processing models to predict whether schools will significantly increase or decrease in an attribute of interest over a future time horizon. We find that using the text improves predictive performance significantly over a baseline model that does not include text but only the historical time-series of the indicators themselves, suggesting that the review text carries predictive power. A qualitative analysis of the most predictive terms and phrases used in the text reviews indicates a number of topics that serve as leading indicators, such as diversity, changes in school leadership, a focus on testing, and school safety.
Abstract: In representative democracies, the election of new representatives in regular election cycles is meant to prevent corruption and other misbehavior by elected officials and to keep them accountable in service of the “will of the people." This democratic ideal can be undermined when candidates are dishonest when campaigning for election over these multiple cycles or rounds of voting. Much of the work on COMSOC to date has investigated strategic actions in only a single round. We introduce a novel formal model of pandering, or strategic preference reporting by candidates seeking to be elected and examine the resilience of two democratic voting systems to pandering within a single round and across multiple rounds. The two voting systems we compare are Representative Democracy (RD) and Flexible Representative Democracy (FRD). For each voting system, our analysis centers on the types of strategies candidates employ and how voters update their views of candidates based on how the candidates have pandered in the past. We provide theoretical results on the complexity of pandering in our setting for a single cycle, formulate our problem for multiple cycles as a Markov Decision Process, and use reinforcement learning to study the effects of pandering by both single candidates and groups of candidates across a number of rounds. .
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.
Maryam Majedi |University of Calgary
This event will be held on Friday, January 28th. Please note the special weekday for this event.
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.
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.
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 firstname.lastname@example.org 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.
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.
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.
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.
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.
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 email@example.com to request the corresponding link.
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.
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.