Title: Data Privacy and Compliance

Date/Time: Monday, December 3 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Jim Feltis

Abstract: Data privacy regulations have received a great deal of press recently. You will likely (reluctantly?) encounter them as a programmer. We’ll discuss some practical ways to approach data privacy compliance issues in your future programming work.

Biosketch: Jim Feltis grew up in Billings and graduated from MSU in 1986 with a B.S. in Computer Science, and then completed a Master’s degree in CS at Purdue University. He went on to work as a programmer at IBM and Novell, Inc. He finished law school at BYU in 1998 and then worked at Microsoft in the Windows software build and source code auditing groups. Currently, Jim does privacy compliance for the Oracle Cloud Infrastructure project in Seattle. He and his wife Colleen have 4 kids (all out of the house now) and live across the water from Seattle in Bremerton WA, from where he takes the ferry boat to work every weekday.


Title: Simpson’s Paradox: Logic, Philosophy, and a Dash of History

Date/Time: Monday, November 26 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Prasanta Bandyopadhyay

Abstract: Simpson’s Paradox (SP) arises if the direction of a comparison reverses when data from several sub-groups are combined to form a whole. At least three questions need to be distinguished: (i) Why SP is a paradox? (ii) What are the conditions for its generation? And (iii) what-to-do when confronted with a conflicting statistics? We propose a logic-based account of the first two questions. We contrast our account with two influential causal accounts.  One is advocated by philosophers and statisticians at the Carnegie Mellon University and the other one is by Judea Pearl. We agree with the causal resolution of the what-do-question, but disagree with their proposal about why SP is paradoxical.  We provide a counter-example to the causal accounts. We also provide some historical background of the paradox during the talk.

Biosketch: Dr. Prasanta Bandyopadhyay teaches at the History & Philosophy Department at MSU. He works on the application of probability and statistics to addressing several philosophical puzzles. He works with several people across numerous disciplines including statistics and ecology. He is the only philosopher involved in the nation for the NASA's 6, 000,000 dollar grant with chemists and biologists. He has edited a volume titled Handbook of Philosophy of Statistics (with Malcolm Forster, University of Wisconsin-Madison, 2011, Elsevier) that spans ten disciplines across fourteen nations. His second book called "Belief, Evidence, and Uncertainty: Problems with Epistemic Inference" written with Gordon Brittan and Mark Taper (Springer, 2016). He has just completed a draft of a book called " Bayes Matters: Science, Objectivity, and Inference" to be submitted to Pal-grave-Macmillan this December.


Title: Predictive coding and visual attention during active vision

Date/Time: Monday, November 19 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker: James Mazer

Abstract: During active vision eye movements and attention act together to support efficient allocation of limited neuronal and computational resources in the brain. Current evidence indicates that attention in the human brain is primarily mediated by retinotopically organized priority or salience maps, where the spatial distribution of activity within each map likely changes each time the eye moves.

So how does the attentional control system compensate for the spatial effects of eye movements? We investigated whether oculomotor motor plans trigger predictive shifts in priority maps in humans and monkeys. Our data show that both humans and monkeys actively compensate for saccadic eye movements to sustain attention in a world-centered or spatiotopic reference frame. This compensation depends on a predictive handoff of attentional state information between neurons that starts ~150ms before each eye movement, effectively stabilizing attentional topography across eye fixations during natural vision. 
 
Biosketch: Dr. James Mazer is an Associate Professor in the Department of Cell Biology and Neuroscience at MSU.  He obtained his Ph.D. in Biology from California Institute of Technology in1996 and his B.A. in Psychology from Yale College in1987. His lab focuses on studying mid- and high-level visual processing, specifically links between visual perception, eye movements and neural circuits. They use neurophysiological, psychophysical and computational approaches, combined to improve understanding of the cortical substrates of natural visually guided behavior (‘natural vision’). Their neurophysiological experiments make extensive use of linear and nonlinear systems identification techniques to characterize single neuron selectivity in striate and extrastriate visual cortex.

 Title:  Algorithms to Handle NP-hard Problems

Date/Time: Monday, October 29 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Binhai Zhu

Abstract: Many problems in practice are NP-hard, but we still need to present solutions for them. Approximation and FPT (fixed-parameter tractable) algorithms are two generic methods with a performance guarantee. I will give a brief introduction to approximation algorithms and FPT algorithms. Also, I will show the applications of these methods on genomic scaffold filling and computing genome similarity. Some of these results are done during my sabbatical year in Spring 2018, hence this talk can also be considered as a report of my sabbatical achievements.

Biosketch: Dr. Binhai Zhu obtained his PhD in computer science at McGill University in 1994. After  a 2-year post-doc at Los Alamos National Lab, he has been teaching in Hong Kong, Canada and then US. He is currently a professor in computer science at MSU-Bozeman, and his research interests are algorithms and their applications (in computational biology, social, wireless and vehicular networks, etc). He has published over 200 papers in these areas. 


Title:Biospatial and Biogeographical interrogation of the rumen Microbiome

Date/Time: Monday, October 15 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Carl Yeoman
 
Abstract:  Applying bioinformatic techniques to molecular data we have been exploring the impact of maternal microbial influences on the early choreography of the neonatal calf microbiome and how variations therein affect health and nutrition. In the first study, luminal content and mucosal scraping samples were collected from ten locations in the calf gastrointestinal tract (GIT) over the first 21 days of life, along with postpartum maternal colostrum, udder skin, and vaginal scrapings. Microbiota were found to vary by anatomical location, between the lumen and mucosa at each GIT location, and measures of beta-diversity indicated differential enrichment for maternal vaginal, skin, and colostral microbiota. Most calf sample sites exhibited a gradual increase in α-diversity over the 21 days beginning the first few days after birth. The relative abundance of majorof the major microbial phyla  (Bacteroidetes and Firmicutes), indicated they were differentially enriched in the proximal and distal GIT, respectively. Another major phyla, Proteobacteria, exhibited greater relative abundances in mucosal scrapings relative to luminal content. Forty-six percent of calf luminal microbes and 41% of mucosal microbes were observed in at-least one maternal source, with the majority being shared with microbes on the skin of the udder. In a second study we show that microbes in each region of the gut affect feed efficiency of the animal. Collectively these studies show that it is important to determine the microbial composition throughout the GIT and not just a single location therein.
 
Biosketch: Dr. Yeoman's research exploits molecular techniques to examine the microbiology and microbial ecology associated with animal systems. His specific foci are on the complex microbial ecosystem of the gut and its role in health, nutrition and performance, and the low alpha-diversity microbial ecosystem of the human vaginal tract and its relationship to maternal and neonatal health. His research interests are in understanding the rules of these systems and finding ways to exploit these to improving their influence on human health, livestock systems, the interactions of each with the environment.

The roles communities play in improving bioinformatics: better software, better algorithms, better data

Date/Time: Monday, October 1 4:10 p.m - 5:00 p.m.
Location: 
Barnard Hall 108
Speaker:Iddo Friedberg
 

Abstract: Bioinformatics is a defined as the collection, classification, storage, and analysis of biological data, with an emphasis on information-rich molecular data. The ultimate goal being a better understanding of life, and, more practically, the ability to generate testable hypotheses for experimental verification. While strong attention is being paid to advances in assays, algorithms, software, and hardware geared towards bioinformatic analyses, less attention is paid to the communities facilitating these discoveries, and to the fact that only through conscious community efforts have some of these advances been possible. Here I will discuss three endeavors I have been involved in, showing the need for concerted efforts to improve software, algorithms and data in this field. Software: I will discuss Biopython, a set of free tools for bioinformatics, written by an international team of volunteer developers. Algorithms: I will talk about the Critical Assessment of Function Annotation, or CAFA, an experiment designed to provide a large-scale assessment of computational methods dedicated to predicting protein function, using a time challenge.  Data: recent work we have done exploring the use of crowdsourcing to improve the quality plant phenomic data for training machine learning algorithms.

  1. https://biopython.org
  2. Community-Wide Evaluation of Computational Function Prediction https://link. springer.com/protocol/10.1007% 2F978-1-4939-3743-1_10
  3. Crowdsourcing image analysis for plant phenomics to generate ground truth data for machine learning https://doi.org/10. 1371/journal.pcbi.1006337

Using crowdsourcing for generating training data for image recognition ML

Biosketch: Iddo Friedberg is an Associate Professor at the department of Veterinary Microbiology & Preventive Medicine at Iowa State University. He is interested in large scale analyses of protein sequence, structure and function connections, genome evolution, metagenome analysis and computational protein function prediction. He is leading CAFA, the Critical Assessment of Function Annotations, a global community effort to improve our ability to predict protein function from sequence. Prior to joining ISU, he was an Assistant Professor at Miami University, Oxford, Ohio from 2009 - 2015 and he was a Research Associate at University of California, San Diego from 2007 - 2009. From 2002-2007 he was a Postdoc at Burnham Institute for Medical Research, La Jolla, California. He earned his PhD in Bioinformatics from Hebrew University, Jerusalem in 2002. In his spare time, he enjoys long distance running, old 70's cop shows, spaghetti westerns, and embarrassing his kids.


Leveraging Structure to Uncover Patterns in Complex Large-Scale Data

Date/Time: Monday, September 17 4:10 p.m - 5:00 p.m.
Location: 
Barnard Hall 108
Speaker: 
Veronika Strnadova-Neeley

Abstract: In this talk, I will introduce several challenges to analyzing modern-day, large-scale data sets, and how these challenges may be addressed by leveraging insights about the underlying structure in the data. I will overview my past work on exploiting structure to cluster large-scale, sparse data in genetic mapping and recommender systems. I will then outline several promising research directions based on this work and recent developments, with a focus on several possible future projects for graduate students. I will summarize with a broad overview of my long-term research vision and goals in the field of data mining.

Biosketch:Veronika Strnadová-Neeley is a new Assistant Professor in the School of Computing at Montana State University. She holds a Ph.D. and a Master’s degree in Computer Science from the University of California, Santa Barbara, and a Bachelor’s degree in Applied Mathematics from the University of New Mexico. Her research is focused on developing unsupervised algorithms for large-scale data analysis, and her work has been applied to the domains of genetic mapping and recommender systems. She has previously been selected as an Outstanding Graduate Student in Computer Science at UCSB, an M.I.T. Rising Star in EECS, and a Google Anita Borg Memorial Scholarship Finalist.


Problems in the Design and Utilization of Network Resources

Date/Time: Monday, September 10 4:10 p.m - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Sean Yaw

Abstract: In this talk, I will provide an overview of some of the computational challenges associated with carbon capture and storage (CCS) network design, and their applicability to generalized networks. I will introduce recent progress on optimization techniques for CCS networks, and will present some future problems that may be of
interest to graduate students. Finally, I will briefly talk about additional opportunities with lidar data processing and smart grid job scheduling.

Biosketch: Sean Yaw is an Assistant Professor in the Gianforte School of Computing at Montana State University. He received a Ph.D. in Computer Science from Montana State University in 2017. From 2017-2018, he was a postdoctoral research associate at Los Alamos National Laboratory. His research focuses on optimizing network design and performance. His work spans cloud computing, the smart grid, LIDAR data management, and carbon sequestration infrastructure design.


Welcome Seminar 

Date/Time: Monday, August 27 4:10 p.m - 5:00 p.m.

Location: Barnard Hall 108

Speaker: John Paxton

Abstract: This seminar will provide new and continuing graduate students with (1) useful information, (2) an opportunity to meet other students, staff and faculty, and (3) an opportunity to ask questions.


Sustainable Wireless Communications and Networking for Internet of Things

Date/Time: Monday, April 16th from 11:00 a.m. - 11:50 a.m.
Location: Barnard Hall 347
Speaker:
Dr. Lina Pu

Abstract: Internet of Things (IoT) is the technology that will transform our world in the coming years. IoT-based devices are now ubiquitous (e.g., smartphone, tablet, and smart TVs, etc) in terrestrial, and will expand to the underwater world. Sustainable power supply and spectrum utilization are challenging issues for IoT systems (e.g., wearable devices, underwater sensor networks). Innovative Radio Frequency (RF) energy harvesting technology coupled with IoT is expected to have a broad impact on multiple sectors including healthcare, public safety, and surveillance. Cognitive acoustic technology combined with IoT is a promising solution to sharing acoustic spectrum with marine animals efficiently, friendly, and intelligently. In this talk, Dr. Pu will report her explorations of sustainable wireless communications and networking in terrestrial and underwater environments. First, she will discuss how to manage the energy harvested from ambient RF environment for efficient information transmission. Afterwards, she will introduce her recent work on the optimal energy request from dedicated energy sources for efficient energy utilization. Dr. Pu will also briefly introduce the cyclostationary-based spectrum sensing approach for marine animal detection in underwater cognitive acoustic networks.

Biosketch: Dr. Lina Pu is currently a visiting scientist in the Electrical and Computer Engineering Department, South Dakota School of Mines and Technology, where she initially joined as Research Engineer in August 2016. She received her Ph.D. degree from the Department of Computer Science and Engineering, University of Connecticut, in December 2015. Dr. Pu’s research interests include, but are not limited to: cyber-physical systems, cybersecurity, data analysis in smart health/grid/city, resilience and sustainable network design, wireless/underwater sensor networks, energy harvesting Internet of Things. Dr. Pu has published over 20 papers in prestigious journals and international conferences. She was the recipient of the Best Paper Award IFIP Networking Conference in 2013. She has been serving as TPC members and technical reviewer for dozens of journals and international conferences as well.

Robots: “You’ll never walk alone”

Date/Time: Monday, April 9th from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 108
Speaker:
Dr. Jeffrey Skidmore

Abstract: Robotics is a fascinating and expanding interdisciplinary field with many potential applications. Robots have already been integrated into many areas of our everyday life and will likely become more prevalent in the coming years. In this seminar I will first provide an overview of robotics and introduce a variety of existing robotic systems. I will then highlight a recent project in which I developed a robotic treadmill system to implement a unique approach to gait rehabilitation after stroke or other neurological injury. I will present the design and operation of the system followed by experimental results and the implications for gait rehabilitation. My goal is to have attendees leave the seminar with a better understanding of the field of robotics and the potential to utilize robots to provide economical and effective solutions to interdisciplinary challenges.

Biosketch: Jeffrey Skidmore graduated with degrees in mechanical engineering from Brigham Young University (B.S.) and Arizona State University (Ph.D.) in 2013 and 2017, respectively. He is currently a postdoctoral researcher in the Department of Biomedical Engineering at The Ohio State University. His research interests include bio-inspired robotics, humanitarian robotics, and robot-assisted neurorehabilitation. He has authored several peer-reviewed publications and has presented his research in China, Sweden, and various locations throughout the United States. Jeffrey has received various awards and honors including being named the Outstanding Graduate for the ASU College of Engineering and twice receiving the Achievement Rewards for College Scientists (ARCS) fellowship.

Computational Pan-Genomics: Algorithms and Applications

Date/Time: Monday, March 26, 2018 from 12:00 p.m. - 2:00 p.m.
Location:
Barnard Hall 347
Speaker: 
Alan Cleary

Abstract: As the cost of sequencing DNA continues to drop the number of sequenced whole genomes rapidly grows. In the recent past, the cost dropped so low that it is no longer prohibitively expensive to sequence multiple whole genomes for the same species. This has led to a shift from the single reference genome per species paradigm to the more comprehensive pan-genomics approach, where populations of genomes from one or more species are analyzed together.

The total genomic content of a population is vast, requiring algorithms for analysis that are more sophisticated and scalable than existing methods. Furthermore, existing algorithms are generally not intended for analyzing populations, let alone without a reference, and so they are inappropriate for pan-genome analysis. The focus of this defense is the exploration of new algorithms and their applications to pan-genome analysis, both at the nucleotide and genic resolutions.


Computing Reaction Fluxes from Real World Metabolomics Data

Date/Time: Tuesday, March 27, 2018 from 3:00 p.m. - 5:00 p.m.
Location:
Barnard Hall 347
Speaker: 
Daniel Salinas 

Abstract: Time series metabolomics data can be used to infer rates for the chemical reactions in metabolism. Real world data presents challenges to the application of the stoichiometric models required. In this work, we explore techniques to infer reaction rates from high performance liquid chromatography-mass spectrometry data that is low-coverage, is derived from multiple patients, or has not been matched to metabolites. These techniques are applied to data derived from experiments on osteoarthritic chondrocytes. The techniques used involve modification of the stoichiometric matrix, decomposition of the variation into components according to an analysis of variance model, and selecting a minimal set of pathways that cover the set of mass-to-charge ratios and retention times in the data.

 The results show that the reaction rates inferred from the incomplete data arebiologicallyrelevant, and that the minimal pathways captured ancillary processes that alternative approaches ignored. We conclude that, although the resulting rates and pathways are not conclusive, they provide useful guidance on experiments to pursue. On the experimental side, ourfindingshave lead us to believe that osteoarthritic chondrocytes respond to compression by initiating protein synthesis, opening the possibility of physical therapy as a stimulus for cartilage regeneration.


Empirical Software Engineering and Technical Debt

Date/Time: Friday, March 2, 2018 from 4:10 p.m. - 5:00 p.m.
Location:
Barnard Hall 108
Speaker:
Graziela Simone Tonin

Abstract: The main topic of the talk will be empirical software engineering with a focus on technical debt. The studies and results conducted in two Brazilian software companies and an XP Course will be discussed and compared with the literature. As a result, a conceptual model for technical debt management was constructed. Additionally, projects developed at a Software Engineering Laboratory will be shown, including details on one in smart cities and, an extension project to teach technology and entrepreneurship in poor communities which aims to empower people in difficult situations. Furthermore, works at a Computer Science Junior Company and at the University Business Incubator encouraging students to create and develop their own business will be explained.

Bio: Graziela graduated in 2009 in Computer Science from URI-Brazil. She then worked as a Systems Analyst, IT Coordinator, Project Manager, and Researcher. She finished her Master of Science degree at UFPE-Brazil in 2011, where she has also worked as a researcher at a Samsung project. She started her Ph.D. at USP in 2012, where she received a prestigious Ph.D. Fellowship award from IBM-Research. She lived in several places in Brazil and in the United States where she did an internship at UMBC/Baltimore with Professor Seaman. She gave several lectures in Brazil (AgileBrazil, AgileTrends, Women in Technology), in the United States (Agile 2014) and Germany (XP2017). She was invited to attend the important meeting of scientists on Technical Debt in Dagstuhl/Germany. Her talks focus on empirical software engineering, technical debt, and female entrepreneurship. She is currently an assistant professor at the Federal University of Fronteira Sul -Brazil. Her research is focused on the technical debt area, empirical software engineering, smart cities, and entrepreneurship.


Machine Learning-Enhanced Visualization

Date/Time: Monday, February 26, 2018 from 4:10 p.m. - 5:00 p.m.
Location:
Barnard Hall 108
Speaker:
Matthew Berger

Abstract: Visualization is indispensable for exploratory data analysis, enabling people to interact with and make sense of data. Interaction is key for effective exploration, and is dependent on two main factors: how data is represented, and how data is visually encoded. For instance, text data may be represented as a 2D spatialization and visually encoded through graphical marks, color, and size. Typically, these factors do not anticipate how a user will interact with the data, however, which limits the set of interactions one may perform in data exploration. In this talk I will focus on how machine learning can be used to improve data representations and visual encodings for user interaction. My research is centered on building machine learning models when visualization, and in particular how a user interacts with data, is the primary objective. I will first discuss how to learn data representations for the purpose of interactive document exploration. I will demonstrate how compositional properties of neural language models, built from large amounts of text data, empowers the user to semantically explore document collections. Secondly, I will show how to learn visual encodings for the purpose of exploring volumetric data. Deep generative models are used to learn the distribution of outputs produced from a volume renderer, providing the user both guidance and intuitive interfaces for volume exploration.

Bio: Matthew Berger is a postdoctoral research associate in the Department of Computer Science at the University of Arizona, advised by Joshua A. Levine. Previously he was a research scientist with the Air Force Research Laboratory. He received his PhD in Computing from the University of Utah in 2013, advised by Claudio T. Silva. His research interests are at the intersection of machine learning and data visualization, focusing on the development of visualization techniques that are driven by machine learning models.


Slaying the White Dragon with Computers

Date/Time: Monday, February 12, 2018 from 4:10 p.m. - 5:00 p.m.
Location:
Barnard Hall 108
Speaker:
Marc Rubin

Abstract: In this seminar, Dr. Rubin will summarize how cyber-physical systems can be used to sense, detect, and ultimately predict snow avalanches.  Specifically, Marc will discuss several open applied computer science research projects within avalanche forecasting, including the use of deep learning to detect avalanches within seismic data, machine learning to predict snowpack stability from snow penetrometer data, and computer vision to classify microscopic images of snow crystals.

Bio: Dr. Marc Rubin received his PhD in Computer Science in 2014 from Colorado School of Mines where he was an NSF IGERT Fellow within the interdisciplinary SmartGeo research program.  Dr. Rubin’s research focuses on the application of wireless sensor technologies and machine learning algorithms towards automatic geohazard monitoring.  Dr. Rubin is currently an Instructor and Program Lead of Computer Science at Oregon State University-Cascades and, when not teaching, enjoys learning about new technologies, mountain biking, and skiing.


Towards a Secure Cyber World: Leveraging Human Factors in Improving Security and Privacy

Date/Time:  Monday, 5 February, 2018 from 4:10 PM - 5:00 PM
Location: 
Barnard Hall 108
Speaker: 
Mahdi Nasrullah Al-Ameen

Abstract:  Users are often considered to be the weakest link in security. For example, while creating an authentication secret, in most cases, users choose a password reflecting common strategies and patterns that ease memorization, but offer weak security. System-assigned passwords provide higher security guarantee, however, suffer from poor memorability.  In the first part of my talk, I discuss how I addressed this usability-security tension in user authentication. In particular, I designed and evaluated a novel cued-recognition authentication scheme, which provides users with memory cues to learn system-assigned keywords. I conducted several studies, including real-life field studies, to quantify the impact of providing different types of memory cues, e.g., graphical, verbal, and spatial cues, where I identified the best combination of memory cues to offer high memorability for a secure authentication scheme. In the second part of my talk, I present the findings from a real-world case study, where I explored the factors facilitating the successful preservation of security goals of Panama Paper Project–a worldwide collaboration among hundreds of investigative journalists. I identified the lessons that can be drawn from this case study to support the development of similarly effective processes for both privacy-preserving collaborations and security systems in general. The findings from my studies reflect the significance of considering human factors in improving Cybersecurity and privacy, and highlight the potentials for future work to develop usable and understandable security tools and strategies.

Bio: Dr. Mahdi Nasrullah Al-Ameen is a Post-doctoral Fellow in the School of Computing at Clemson University. His research interest is broad within the domain of Cyber Security, where he is particularly excited about designing and building systems that address the security and privacy challenges faced by end users of existing and emerging technologies. His research focuses on designing a novel security system to provide resilience against cyberattacks, and evaluating its usability in real-life contexts. He also led multiple projects in improving the security and privacy in peer-to-peer (P2P) networks. The findings from his studies are reported in top-tier venues, like USENIX Security Symposium, CHI, IEEE TPDS Special Issue, and in several prestigious venues, including Journal of Networks, Symposium on Usable Privacy and Security (SOUPS),ACM Symposium on Information, Computer, and Communications Security, and European Symposium on Research in Computer Security(ESORICS).  Dr. Al-Ameen completed his PhD from the University of Texas at Arlington, winning the Outstanding Doctoral Dissertation Award for his research on Cyber Security and Privacy.


 

S^1 and S^3 and S^2, oh fy! A digital Hopf fibration

Date/Time: Monday, 29 January, 2018 from 4:10 PM - 5:00 PM
Location: Barnard Hall 103 (Note the room change)
Speaker: Nick Scoville

Abstract: Digital images surround us. They are found in our computers, iPhones, televisions, and more. Because they are so integrated into our lives, there is a constant need to manipulate and investigate these images. Anything that one might want to do with a digital image will inevitably involve some kind of mathematics, whether it be linear algebra, geometry, or topology. To that end, we will introduce topology in the digital setting, noting some places where it is similar and different than in the smooth setting. In particular, we will work with digital homotopy between digital images by viewing a digital image as a graph. Although there is a notion of digital fibration in this context, there seem to be very few non-trivial examples of digital fibrations. We will construct a digital analogue of the Hopf fibration, the most important single example in the history of algebraic topology. Because the 3-sphere in this setting consists of only 24 pixels, this example is robust yet small enough to be written down and investigated explicitly. This talk will be accessible to undergraduates.

Bio: Nicholas Scoville is a faculty member at Ursinus College.  At Ursinus, he is the Joseph Beardwood III Chair of Mathematics.  His research is in topology.  He hols a BS and MS from Western Michigan University, and MA and PhD from Dartmouth College.


The role of application layer mechanisms in content distribution for mobile Web and augmented reality

Date/Time: Monday, October 2, 2017 from 4:10 p.m. - 5:00 p.m.
Location: Barnard Hall 108
Speaker: Mike Wittie

Abstract: In this talk I will present several methods for speeding up mobile Web content delivery in cellular networks. The common theme in these approaches is to give the application layer a choice in the use of network resources. Instead of using cross layer approaches, we rely on light weight application-layer measurement implemented and validated in the context of Akamai's CDN infrastructure. I will also discuss the implications of these results for the delivery of augmented reality content - traffic that is both similar and quite different in its characteristics from the mobile Web.


 Seminars from 2017.