Abstract:

Damaged artifacts that contain text make up an “invisible library” of written material that is incredibly difficult to read. But progress over the past decade using new computer techniques for the digitization and analysis of text found in cultural objects (inscriptions, manuscripts, scrolls) has led to workable, non-invasive methods for reading this invisible library. This talk shows results over the past two decades from digital restoration projects on Homeric manuscripts, Herculaneum material, and Dead Sea scrolls, culminating in the reading of the text from within a damaged scroll unearthed at En-Gedi, which has been hailed as one of the most significant discoveries in biblical archaeology of the past decade. Premised on “virtual unwrapping” as an engine for discovery, this presentation explains the complete process developed for reading the scroll from En-Gedi, and the broader significance of the discovery. The talk will unveil a new approach for imaging texts with some of the most difficult to see inks - where machine learning becomes a crucial part of the digital restoration pipeline. You will leave this talk considering that new machine learning tools in artificial intelligence may indeed be the pathway for rescuing still-readable text from some of the most stubbornly damaged materials, like the enigmatic Herculaneum scrolls.

Bio:

Brent Seales is Professor and Chairman of the Department of Computer Science and Director of the Center for Visualization and Virtual Environments (Vis Center) at the University of Kentucky. He earned his Ph.D. in Computer Science at the University of Wisconsin-Madison in 1991. His research program centers on computer vision and visualization applied to challenges in the digital restoration of antiquities, surgical technology, and data visualization. In the 2012-13 academic year he was a Google Visiting Scientist in Paris, France. In 2015 Dr. Seales and his research team identified the oldest known copy of the book of Leviticus (other than the Dead Sea Scrolls), carbon dated to the third century C.E. The artifact containing the text, which was excavated at En-Gedi in 1972, was too damaged to be opened and read. Seales and his research team provided a reading of the text from non-invasive micro-CT scans. The digital recovery of the readable text was widely recognized as a significant discovery in biblical archaeology.

Abstract:

The American Linguistic Atlas Project (LAP) began in 1929, when Hans Kurath decided to follow the lead of European dialectologists in Germany and France and to create a Linguistic Atlas of American English. Now, the LAP is the largest single survey of regional and social differences in spoken American English. Regional surveys extend from New England to California; some field work was conducted as early as the 1930s, some continues today.

The two earliest surveys are the Linguistic Atlas of New England (LANE) and the Linguistic Atlas of the Middle and South Atlantic States (LAMSAS), for which data was collected by face-to-face interview and transcribed on the spot in fine phonetic notation. These interviews relied on a questionnaire of 104 pages with seven or eight items per page, designed to reveal regional and social differences in everyday vocabulary, grammar, and pronunciation. Over time, the LAP interviews have changed (the questionnaire has been adjusted and more recent interviews have been digitally recorded), but the goal of capturing the regional language use of American speakers has remained. At present, the LAP is comprised of data from over 5000 individual interviews, resulting in a massive amount of linguistic data, all of it connected to specific social and geographic information.

The LAP is currently processing and curating its historical data in an attempt to get it all into a (somewhat) uniform and usable format, thus creating ‘big data’ from historical sources. This talk will give an overview of where we are in that process and will address specifically the ‘big data’ issues of volume, velocity, and variety.

In addition to outlining the challenges and opportunities that the LAP affords, this talk will also address what conceiving of the LAP data as ‘big data’ has already revealed: that the recurring pattern of variation found in such a large amount of data suggests that language functions as a complex system. Characteristic of a complex system is the emergence of scalable, nonlinear patterns and, indeed, at every level of examination, one finds that nonlinear frequency profiles (A-curves) characterize the LAP data. A-curves emerge for entire datasets and also for every subgroup in the data; the order of elements in the profile is likely to differ between groups, and between any group and the overall dataset, and it is these differences in order that effectively create what we refer to as ‘dialects’.

Bio:

As a linguist who specializes in sociolinguistics, my research centers around language variation and change, a subject that I approach from both a large-scale historical perspective using data from the Linguistic Atlas Project and a smaller-scale perspective that focuses on identity-making within individual interactions. My books Language and Material Culture (2015) and Language and Classification (2018) reflect my ongoing interest in the relationship between variation in language and material culture, while the textbook that I co-authored, Exploring Linguistic Science (2018), advocates a view of language as a complex system, with an emphasis on interaction and emergence.

Abstract:

Every day billions of images are uploaded to the Internet and trillions of pixels are gathered by satellites. Together they provide many perspectives of the world, ranging from what someone had for dinner to where new buildings have been constructed. This talk describes my work in extracting geospatial attributes of places from this imagery using deep convolutional neural networks. I will focus on methods that combine overhead and ground-level imagery in novel ways, with applications including image localization, cross-modal hallucination, and fine-grained classification of buildings in urban areas.

Bio:

Nathan Jacobs earned a Ph.D. in Computer Science from Washington University in St. Louis (2010) and a B.S. in Computer Science from the University of Missouri. He is currently an Associate Professor of Computer Science at the University of Kentucky. Dr. Jacobs' research area is computer vision; his specialty is developing learning-based algorithms and systems for processing large-scale image collections. He is a recipient of an NSF CAREER award, and his research has been funded by DARPA, IARPA, NGA, Army Research Lab, Google, ARMY-SMDC, and NIH. His current focus is on developing techniques for mining information about people and the natural world from geotagged imagery, including images from social networks, publicly available outdoor webcams, and satellites.

Abstract:

In this talk, I cover the current state of standardization of the MPI-4 version of the Message Passing Interface (and beyond). For pre-exascale and exascale systems, MPI is being "upgraded." Several new areas are planned for standardization for MPI-4 (MPI-Next, circa 2020) and MPI-Next-next (circa 2023-24), in time for the nominal arrival of the first exascale computers. Areas of particular interest include Sessions technologies for managing large, complex and changing resources underlying the MPI "job." Persistent collective communication is designed to add performance to applications with temporal locality in their use of collective operations. Bleeding-edge area including endpoints, finepoints (partitioned communication), and fault-tolerance models are discussed. A number of other "proposals" and "concepts" will be discussed as well. MPI+X models are also discussed, such as MPI+OpenMP. Sessions and fault-tolerant models offer the potential for significant new application areas for MPI in large-scale cloud applications as well. Improvements to MPI that enable and enhance algorithmic development such as for machine learning are also covered.

Bio:

Anthony (Tony) Skjellum studied at Caltech (BS, MS, PhD). His PhD work emphasized portable, parallel software for large-scale dynamic simulation, with a specific emphasis on message-passing systems, parallel nonlinear and linear solvers, and massive parallelism. From 1990-93, he was a computer scientist at LLNL focusing on performance-portable message passing and portable parallel math libraries. From 1993-2003, he was on the faculty in Computer Science at Mississippi State University, where his group co-invented the MPICH implementation of the Message Passing Interface (MPI) together with colleagues at Argonne National Laboratory. From 2003-2013, he was professor and chair at the University of Alabama at Birmingham, Dept. of Computer and Information Sciences. In 2014, he joined Auburn University as Lead Cyber Scientist and led R&D in cyber and High-Performance Computing for over three years. In Summer 2017, he joined the University of Tennessee at Chattanooga as Professor of Computer Science, Chair of Excellence, and Director, SimCenter, where he continues work in HPC and Cybersecurity, with strong emphases on IoT and blockchain technologies. He is a senior member of ACM, IEEE, ASEE, and AIChE, and an Associate Member of the American Academy of Forensic Science (AAFS), Digital & Multimedia Sciences Division.

Abstract:

The next generation sequencing (NGS) technology has substantially advanced the understanding of cancer biology, identification of potential therapeutic targets, and development of personalized cancer treatment. To enhance NGS-based research at the Markey Cancer Center, we have developed data processing pipelines as well as customized downstream analysis strategies for whole-exome sequencing, RNAseq, ChIPseq, and bisulfite sequencing. Furthermore, we have developed new bioinformatics methods to better understand the mechanism of tumorigenesis and identify potential biomarkers for cancer therapies. Our methods have been applied to data from both cancer patients in Appalachia Kentucky and The Cancer Genome Atlas (TCGA).

Bio:

Chi Wang is an Associate Professor of Biostatistics in the College of Public Health, and the Associate Director of Bioinformatics in the Biostatistics and Bioinformatics Shared Resource Facility in the Markey Cancer Center at the University of Kentucky (UK). Prior to joining UK, he was an Assistant Professor in the Department of Statistics at the University of California, Riverside.

His primary research interest is to develop new statistical and bioinformatics methods for high-throughput genomic, epigenomic, transcriptomic, and metabolomic data and to apply the methods to cancer-related projects. He is also interested in causal inference, survival analysis, and Bayesian methods. He received a doctoral degree in biostatistics from the Johns Hopkins Bloomberg School of Public Health, and both a master’s and bachelor’s degree in statistics from Peking University. He has published over 60 peer-reviewed papers.

Abstract:

The rising usage of systems biology modeling to probe complex signaling pathways raises the potential for their application in computational drug design. Toward this end, we are developing a workflow that predicts the ability of a computationally-designed calcium handling protein to restore dysregulated calcium signaling in cardiac ventricular tissue. The workflow includes 1) a systems biological model of calcium signaling under disease-like conditions 2) sensitivity analysis to identify calcium-dependent pathways whose modulation could recover normal signaling and 3) machine learning to identify potential proteins and sequence mutations to controllably modulate a given calcium signaling pathway. Preliminary results will be discussed in this presentation.

Bio:

Peter Kekenes-Huskey, Ph.D. (PI) is an assistant professor at the University of Kentucky. He graduated summa cum laude with a B.S. in Chemistry from the University of North Carolina Asheville (2001), which was followed by a Fulbright Fellowship to Germany, whereafter he obtained his Ph.D. at the California Institute of Technology (2009). At UNC Asheville, he developed algorithms for the ab initio prediction of halogenated ethane decomposition rates as a Barry Goldwater Scholar with Bert Holmes and George Heard (UNCA). His Ph.D. research with William A. Goddard, III (Caltech) focused on developing Monte Carlo tools and molecular dynamics simulations. Peter complemented his studies with applied mathematics and computer science coursework from algorithm design to parallel computing, in fulfillment of National Science Foundation and Department of Energy Computional Science Graduate Fellowships. Following his Ph.D. work, he worked as a Staff Scientist at Arete Associates from 2007 to 2010, where he developed near-real time image processing and signal detection algorithms for defense applications. Returning to his passion for the physical sciences, he pursued postdoctoral studies with Professors Andy McCammon (Chemistry) and Andrew McCulloch (Bioengineering) at the University of California San Diego under support of American Heart Association and National Institutes of Health fellowships, to understand cardiac calcium signaling using molecular dynamics, partial differential equations and systems biology models. At UK, he is expanding multi-physics descriptions of molecular-driven events with macro-scale phenomena, with myriad applications in biological and nanoscale systems.

Abstract:

Advances in sensor and computer technology over the past generation have transformed many aspects of the earth sciences. At the global level, some of the world’s fastest multi-petaflop supercomputers are used for geophysical data processing and reservoir simulation in the energy industry and climate simulations by academic and government scientists. Closer to home, the 2018 completion of a statewide digital elevation model (DEM) based on airborne LiDAR (a type of laser scanning) offers exceptionally detailed documentation of Kentucky’s topography and insights into the geological processes that gave rise to it. The statewide LiDAR DEM comprises approximately 46 billion centimeter-accurate bare earth heights interpolated on a uniform 5 ft (1.5 m) grid. The underlying unstructured LiDAR point cloud, which includes non-ground returns and ancillary data such as laser return intensity, from which the DEM was created is estimated to comprise about 460 billion records. Subsequent LiDAR data acquisition campaigns, which will allow for detailed estimation of topographic change as they increase both storage and processing demands, are in the planning stages. The Kentucky Geological Survey has established its Digital Earth Analysis Lab—the KGS DEAL—to support development of LiDAR solutions beneficial to the commonwealth. Ongoing and potential research includes coupling physics-based slope stability models with real-time precipitation data to create a statewide landslide and mudflow hazard warning system; developing deterministic and AI-based approaches to sinkhole mapping in an effort to reduce costly damage associated with sudden ground collapse; and using convolutional neural networks to locate improperly abandoned oil and gas wells that may pose environmental and health risks. KGS also operates the Kentucky Seismic and Strong Motion Network, which streams nearly 2 Gb per day from 28 seismic stations across the commonwealth and is capable of detecting events ranging from imperceptible microtremors to major New Madrid seismic zone earthquakes (as well as noise from sources such as mine blasts, passing trains, and electronic interference). KGS is currently developing AI approaches to support automated event detection, particularly of small earthquakes that would be impossible to identify manually, as well as exploring options for stochastic simulations of earthquake effects that take into account the Kentucky’s complicated bedrock and surficial geology.

Bio:

Bill Haneberg is the state geologist and director of the Kentucky Geological Survey and a research professor in the UK Department of Earth & Environmental Sciences. His expertise includes digital terrain modeling using high resolution topographic and bathymetric data, computational geology, GIS applications, and geohazard and risk assessment. Before moving to Kentucky in 2016, he spent 17 years as a consulting geologist, first as a sole practitioner based mainly in the Seattle area and then with Fugro in Houston, where he led a team of geologists, geophysicists, and engineers working on quantitative geohazard and risk assessments for deep-water oil and gas projects around the world. He previously spent 11 years at New Mexico Tech, where he was a research geologist and assistant director of the New Mexico Bureau of Mines & Mineral Resources. The author or co-author of 160+ published papers and abstracts, co-editor of 3 monographs, and author of the AEG Claire P. Holdredge award winning book Computational Geosciences with Mathematica, Bill was also the 2011 AEG-GSA Richard H. Jahns Distinguished Lecturer in Engineering Geology. He earned a B.S. in geology from Bowling Green State University and his M.S. and Ph.D. in geology from the University of Cincinnati.

Abstract:

CCS, in partnership with the ITS Research Computing Group, has been developing infrastructure required to support new and growing demands of Data Science (aka Big Data) research across the campus. We have made considerable advances, improving UK's computational, data analytics, storage, and network capabilities. We will describe the new systems coming online, the capabilities they will bring to researchers, and procedures for usage and access.

Bio:

James Griffioen is a Professor in the Department of Computer Science at the University of Kentucky. He serves as the Director of the Center for Computational Sciences, and also as the Director of the Laboratory for Advanced Networking. His research interests include future internet architectures, cloud computing, software defined networks, protocol design, network monitoring, and testbed networks

Bio:

Dave Buchholz is a Principal Engineer & Worldwide Director for technical enabling for Intel. His focus is not only corporate solutions but capability and solution strategies around verticals like healthcare, education and hospitality. With over 28 years of experience leading client technologies, Dave specializes in Workplace Transformation, Consumerization, Enterprise Client Technology trends & usage models.

Bio:

Paul Kassebaum works as the global team lead for physics at MathWorks, establishing and maintaining collaborations with large physics research teams in the domains of condensed matter, astrophysics, and particle physics

Abstract:

Unstructured clinical text documents such as pathology reports are a primary source of information for cancer registries which process high volumes of reports annually. Information extraction and coding is a manual, labor-intensive process. In this talk I will discuss the latest deep learning technology, presenting both theoretical and practical perspectives that are relevant to natural language processing of clinical pathology reports for computational cancer phenotyping. Using different deep learning architectures, I will present benchmark studies for various information extraction tasks and discuss their importance in supporting a comprehensive and scalable national cancer surveillance program.

Bio:

Dr. Georgia Tourassi is the founding Director of the Health Data Sciences Institute and Group Leader of Biomedical Sciences, Engineering and Computing at the Oak Ridge National Laboratory (ORNL). Concurrently, she holds appointments as an adjunct Professor of Radiology at Duke University and the University of Tennessee and as a joint UT-ORNL Professor of Mechanical, Aerospace, and Biomedical Engineering at the University of Tennessee at Knoxville. Her research interests include medical imaging, biomedical informatics, clinical decision support systems and data-driven biomedical discovery. She serves on the FDA Advisory Committee and Review Panel on Computer-Aided Diagnosis Devices and she has been appointed Charter member of two NIH Study sections. Her scholarly work has led to 9 US patents and innovation disclosures and more than 230 peer-reviewed journal articles, conference proceedings articles, and book chapters. Her research in medical imaging has been featured in numerous high-profile publications such as the MIT Science and Technology Review, Oncology Times and the Economist. Dr. Tourassi s elected Fellow of the American Institute of Medical and Biological Engineering (AIMBE), the American Association of Medical Physicists (AAPM) and the International Society for Optics and Photonics (SPIE). For her leadership in the Joint Design of Advanced Computing Solutions for Cancer initiative, she recently received the DOE Secretary’s Appreciation Award. Dr. Tourassi holds a B.S. degree in Physics from Aristotle University of Thessaloniki, Greece and a Ph.D. in Biomedical Engineering from Duke University.

Abstract:

NSF's Office of Advanced Cyberinfrastructure (OAC) seeks to foster the advanced cyberinfrastructure (CI) that has become essential to progress of all areas of science and engineering research and education. OAC's investments in computing, data infrastructure, software, networking and cybersecurity, and people have enabled new innovations and discoveries. Yet, dramatic changes are occurring in the nature and requirements of science, in the scale and pervasiveness of research data, and in the landscape of technologies and resources. As will be described in this presentation, NSF has sought and received much community input on these changes - which are informing an evolution of OAC’s programs and investments in research CI and CI research to transform science in the 21st century.

Bio:

William Miller is the Science Advisor for the Office of Advanced Cyberinfrastructure (OAC) at the National Science Foundation. Bill leads strategic activities for OAC designed to catalyze new cyberinfrastructure (CI)-enabled pathways for scientific discovery, promote multi-disciplinary activities, and foster international engagement. He leads the Cyberinfrastructure for Emerging Science and Engineering Research (CESER) Program that supports early-stage science-CI collaborations. Current CESER foci are NSF priority investments including the NSF Big Ideas and NSF’s Major Multi-User Research Facilities. Bill has previously served as Acting Deputy of the NSF Office of Budget, Finance and Award Management, and in policy and oversight roles for large facility planning and construction. He has prior professional careers in space mission systems development and in experimental neuroscience, each conducted in the U.S. and internationally.

Bio:

Matt Koop is an architect in the Office of the CTO in the Server and Infrastructure Systems division of Dell EMC. Matt joined Dell EMC in 2016 after leading the global HPC architecture and engineering group for Schlumberger, a major commercial user of HPC. Prior to Schlumberger, Matt led a team providing technology transfer and application optimization expertise to the DoD HPC Modernization Program.

Matt holds a Ph.D. in Computer Science and Engineering from Ohio State University, an MBA from Georgetown University, and a B.S. in Computer Science from Calvin College.

Speaker Info:

Company: Spectra Logic
Position/title: VP of Product Management and Solutions Engineering
Email: davidfe@spectralogic.com
Phone: 303-449-6444,1436

Bio:

David Feller is an executive leader with a long history of delivering world changing consumer electronics products. As VP of Product Management and Solutions Engineering at Spectra Logic, he has ownership of the company’s strategic roadmap across all product lines. With 20+ years of experience in product management, strategic marketing, business development, operations, engineering and executive management, David has led nearly every aspect of small and fortune 100 business. He is especially passionate about product management and strategy – taking a market or customer requirement, evolving it into a concept, and molding an idea through development, launch, and mass production. Mr. Feller ran the product management/marketing group that invented wireless LAN, the group that pioneered putting mini hard drives in MP3/media players, and the group that made real-time security/surveillance remote access available home/business/worldwide.

Abstract: Considering Future Technologies in the World of Everlasting Data

Organizations across the globe have all faced the challenge of managing their unstructured data, which traditionally consists of 60%-90% of their total data. A major aspect of managing data is the ability for the data management software to tier your data, meaning it has the ability to move your inactive data to a more affordable tier of storage. This is where all solutions are not created equal, and organizations need to determine the best way to not only manage their data, but also protect the data that is being managed. How do organizations implement a tiered storage solution that protects their data for as long as they need to keep it? Many data management software products do a great job of identifying the data that should be offloaded from the user’s primary storage (tier 1 storage) but what happens to the data once its moved, and how protected is the data that is now essentially archived? How do you get the data back when you need it? Complete solutions should not only assist in the data management process but ensure that the inactive data being moved is securely protected and always available when its needed. This new focus on archive is not meant to replace backup solutions, but is necessary to enhance data protection for historically critical data. Join us to find out the newest data management strategies to ensure that your primary storage is free of inactive files, your inactive files are efficiently moved to a more affordable tier of storage and your data is protected so that it is forever available when it’s needed.

Bio:

Will has over 8 years in HPC, Big Data, & Artificial Intelligence workloads as well as how customers tailor these solutions towards their requirements. Will currently is the HPE Business Development & Technical Strategy Manager for Artificial Intelligence in North America. He has worked in previous roles in Hyperscale server product management, Big Data, & HPC Solutions. Will is working to enable edge to cloud to core designs with partners & customers to enable A.I.

Abstract:

Multidimensional streaming signals like audio, videos, and multi-spectral imagery are truly the biggest of the Big Data. Collected by sensors at the network edge, these data often contain highly sensitive information and their misuse can lead to significant invasion of privacy. For example, networks of surveillance cameras have been used by some countries in tracking their citizens, and pervasive use of IoT devices in smart homes opens door to recording private behaviors and interactions. It is highly challenging to apply traditional privacy enhancing technologies to handle these data. Cryptographic techniques like homomorphic encryption and garbled circuits are too computationally intensive for any practical applications. The more efficient differential private schemes, on the other hand, rely on additive noise and may not be able to provide adequate protection on semantic contents.

In this talk, I will discuss a number of techniques developed in my research group to process multidimensional signals while ensuring their privacy. Specifically, I will present our work in using generative adversarial network (GAN) and random neural networks to protect signal privacy in distributed deep learning. We use GAN to learn the statistics of sensitive facial data at local sites and generate privacy-preserving synthetic data for public centralized learning. While GAN at network edge can be computationally intensive, random neural network offers a much lighter weight privacy-enhancing protocol suitable for a broad range of IoT applications.

Bio:

Sen-ching “Samson” Cheung is a Professor of Electrical and Computer Engineering and the director of Multimedia Information Laboratory (Mialab) at University of Kentucky (UKY), Lexington, KY, USA. He is the endowed Blazie Family Professor of Engineering. Before joining UKY in 2004, he was a postdoctoral researcher with the Sapphire Scientific Data Mining Group at Lawrence Livermore National Laboratory that won the R&D 100 Award in 2006. He received his Ph.D. degree from University of California, Berkeley in 2002. He is a senior member of both IEEE and ACM. He has the fortune of working with a team of talented students and collaborators at Mialab in several areas in multimedia including video surveillance, privacy protection, encrypted domain signal processing, 3D data processing, virtual and augmented reality as well as computational multimedia for autism therapy. More details about current and past research projects at Mialab can be found at http://vis.uky.edu/mialab.