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Talk Title: The Data Gap in Machine Learning and AI: Why Much of Machine Learning and AI is Still Data Limited, and Some of the Options Available.

Abstract: Although large amounts of online text, images and audio have provided enough data that deep learning models can be developed that significantly improve language translation, image recognition, speech recognition and related applications, developing and deploying machine learning and AI models that provide value and limit bias is still quite difficult in many application areas due to the lack of suitable data. This is especially the case in biology, medicine and health care. We discuss some of the reasons that many important AI problems are still data-limited and some of the approaches that have been taken to address this challenge. We use case studies from machine learning models in COVID-19 and cancer to illustrate some of the challenges and some of the options available.

Bio: Robert L. Grossman is the Frederick H. Rawson Distinguished Service Professor of Medicine and Computer Science and the Jim and Karen Frank Director of the Center for Translational Data Science (CTDS) at the University of Chicago. CTDS is a research center that focuses on data science and its applications to problems in biology, medicine, health care and the environment. He is also a Partner at Analytic Strategy Partners LLC, which helps companies develop machine learning and AI strategies to advance their mission.

https://computerscience.uchicago.edu/people/robert-grossman/

Title of panel will be: Data Science Challenges and Opportunities in Research and Education across the Commonwealth

Abstract:

Title of panel: Data Science Challenges and Opportunities in Research and Education across the Commonwealth

Panel Moderator:

Panelist:

Maureen Doyle, Professor and Chair of Computer Science, Northern Kentucky University

David Fardo, Professor of Biostatistics, Stephen W. Wyatt Endowed Professor of Public Health, University of Kentucky

Jens Hannemann, Associate Professor of Computer Science, Kentucky State University

Harry Zhang, Associate Professor of Computer Science and Engineering, University of Louisville

Talk Title: OURRStore: A Cost-Effective Storage Archive Alternative

Abstract: The data tsunami is upon us: transformative research is increasingly data-intensive, with collections in Terabytes to Petabytes, and Exabytes coming soon. Projections of research storage growth show that, by 2025, research data will outstrip even YouTube. Yet many institutions are underprepared not only for the "volume, velocity and variety" of data, but especially for stewardship of exponentially growing collections. The University of Oklahoma (OU) has been awarded a National Science Foundation grant that has acquired and deployed, and will maintain for 8+ years, a large scale storage resource -- the OU & Regional Research Store (OURRstore) -- to enable faculty, staff, postdocs, graduate students and undergraduates to pursue data-intensive research, by building large and growing data collections, to share these datasets with collaborators and even the public, and to provide this capability to all institutions in (a) the Great Plains Network and (b) Established Program to Stimulate Competitive Research (EPSCoR) jurisdictions. Via an innovative, low-cost business model, researchers buy their own tape cartridges, good for 8+ years, and pay zero usage charges (just cartridge and shipping costs). OURRstore is expected to have hundreds of users.

Dr. Henry Neeman is the Director of the OU Supercomputing Center for Education & Research, Associate Professor in the Gallogly College of Engineering and Adjunct Associate Professor in the School of Computer Science at the University of Oklahoma, as well as the joint co-manager of the XSEDE Campus Engagement program with Dr. Dana Bruson of Internet2.
He received his BS in computer science and his BA in statistics with a minor in mathematics in 1987 from the University at Buffalo, State University of New York, his MS in CS from the University of Illinois at Urbana-Champaign (UIUC) in 1990 and his PhD in CS from UIUC in 1996.
Prior to coming to OU, Dr. Neeman was a postdoctoral research associate at the National Center for Supercomputing Applications (NCSA) at UIUC, and before that served as a graduate research assistant both at NCSA and at UIUC'c Center for Supercomputing Research & Development.
In addition to his own teaching and research, Dr. Neeman has collaborated with dozens of research groups, applying High Performance Computing techniques in fields such as numerical weather prediction, bioinformatics and genomics, data mining, high energy physics, astronomy, nanotechnology, petroleum reservoir management, river basin modeling and engineering optimization.

Talk Title: Hybrid Machine Learning for Adaptive Robotic Welding Control

Abstract: The first commercial use of robotics in manufacturing was for welding operations. Since then, industrial robots have become more diverse and common for automating manufacturing processes. Even though robotic design and control have rapidly progressed in the past half-century, many welding operations are still done manually. The skills needed for complex welding scenarios possessed by trained professionals have yet to be mimicked by current industrial robots. Existing robotic control is incapable of adaptively adjusting its robotic operation in response to a dynamic welding environment, whereas a skilled human welder can. To enable sophisticated and adaptive robotic control, three elements are needed: perception, prediction, and reaction. Perception can be easily realized through in-situ high-speed cameras, but real-time welding quality prediction (e.g., penetration and back-side bead width) and process control (e.g., adjustment of welding speed and current) to stabilize and maximize the welding quality are more difficult. Accurate prediction and real-time reaction rely on the effective and efficient processing of perception data and characterization of this highly dynamic system. Emerging machine learning and deep learning techniques have the potential to realize adaptive robotic control mirroring human capabilities.

This presentation presents a preliminary study on developing a hybrid Machine Learning (ML) framework for real-time welding quality prediction and adaptive welding speed adjustment for GTAW welding at a constant current. The hybrid ML framework includes three elements: Convolutional Neural Network (CNN)-based welding quality prediction, Multiple Layer Perceptron (MLP)-based process modeling, and Gradient Descent (GD)-based controller. With the CNN, in-situ imaging of top-side weld pool can be analyzed to predict the back-side bead width during active welding control. With the MLP, the effect of welding speed on bead width can be quantitively modeled. Through the trained MLP, a computationally efficient GD algorithm has been developed to adjust the travel speed accordingly to achieve an optimal bead width with full material penetration. Because of the nature of gradient descent, the robot would change faster when the quality is further away and then fine-tune the speed when it was close to the goal. Experimental studies have shown promising results on real-time bead width prediction and adaptive speed adjustment to realize ideal bead width.

Bio: Dr. Peng (Edward) Wang joined the Department of Electrical and Computer Engineering at the University of Kentucky since August, 2019. He his Ph.D. degree in Mechanical and Aerospace Engineering from Case Western Reserve University in 2017, respectively. His research interests are in the areas of stochastic modeling and machine learning for machine condition monitoring and performance prediction, manufacturing process modeling and optimization, and human-robot collaboration. Dr. Wang has published over 30 peer-reviewed papers in journals such as CIRP Annals-Manufacturing Technology, IEEE Transactions of Automation Science and Engineering, SME Journal of Manufacturing Systems, and gets over 3,000 citations according to Google Scholar. He is the recipient of the Outstanding Young Manufacturing Engineer Award from the Society of Manufacturing Engineers in 2022, Best Student Paper Award from the IEEE Conference on Automation Science and Engineering (CASE) in 2015, the Outstanding Technical Paper Award from the SME North American Manufacturing Research Conference in 2017, 2020, and 2021, the Best Paper Award from CIRP Conference on Manufacturing Systems in 2020. He also received the First Prize in the Digital Manufacturing Commons (DMC) Hackathon, organized by DMDII in 2016.

Leslie Woltenberg, PhD

Isaac Joyner, MPH

Talk Title: Qualitative Data Science: Exploration of Preceptor Feedback for Health Professions Learners

Abstract: Purpose: It is widely accepted in Health Professions education that preceptor feedback is valuable as part of the formative learning experience for students. However, there is a paucity of literature around effective utilization of such feedback. Prior studies describe the challenges around using feedback to affect behavior change [1] and achieve mastery learning [3]. Also problematic is the lack of a clear educational strategy and theoretical framework by which to operationalize this feedback [2]. The purpose of this study was to explore and describe qualitative themes in preceptor feedback, investigate signals in the data regarding board exam task areas, and identify opportunities to support student achievement learning outcomes.
Methods: This exploratory study used text analysis software (freeware package AntConc) to evaluate qualitative data from the Preceptor Evaluation of Student Performance tool collected over three academic terms (n=1242). The preceptor evaluation/feedback tool is completed by the preceptor at the end of each clinical rotation for each student providing an overall narrative evaluation with suggestions for improvement. Preceptor evaluations were evaluated using board exam blueprint task areas and commonly identified terms and phrases, e.g., “differential diagnosis.” The software also captured the context in which the key words and phrases were used. Mind mapping diagrams were used to depict relationships among the themes.
Results: The text analysis identified 11 themes including “Confidence, Presentation, History” with context. These themes were identified from qualitative data from preceptors across 12 four-month supervised clinical experiences representing academic terms 2020-2022. The mind mapping analysis revealed two broad areas 1) maturity/professionalism and 2) medical task area competence. This broad view highlighted themes observed across a variety of clinical settings and indicated opportunities to further evaluate our curriculum and provide focused student support around board exam task areas. Discussion: Results of this exploratory study suggest that themes from qualitative preceptor feedback may provide meaningful information around curricular modifications that support student attainment of programmatic learning outcomes. Additionally, results from this study can be used to inform a variety of constructive learning strategies to help students further develop non-cognitive skill sets.

Bio : Shelley Irving is a PA educator, has served rural communities as a practicing PA-C for over a decade, and is engaged in scholarship regarding contemporary health professions education and practice.
Leslie Woltenberg has over ten years of experience as an educator, earned a doctoral degree in educational evaluation, and is actively engaged in scholarship regarding innovation in curriculum, assessment, and health professions education. Both presenters are deeply committed to and actively involved in rural education and research.
Isaac Joyner earned an MPH which focused on health program planning and evaluation and 30 years' experience in data management and reporting across a diverse portfolio of systems.

Talk Title: What Machine and Deep Learning Can Contribute to our Understanding of Ancient Societies

Abstract: Because people used stone tools in nearly all times and places, these artifacts are widely considered one of the most important material classes for understanding ancient societies. In my current research, I combine dynamic image analysis and machine learning to the analysis of soil samples taken from archaeological sites in order to classify the tiniest pieces of stone (measuring < 6.0 mm) that are knocked off when a stone tool is being made. Because these tiny artifacts (known as microdebitage) tend to become embedded in the soils where stone tools were made, archaeologists can map the areas where microdebitage is located in order to identify where ancient people were manufacturing stone tools.
When combined with other artifactual data, this has the potential to tell not only how stone tool production was spatially organized but also the class, gender, and occupation of the primary producers of stone tools. In spite of this, archaeologists have shied away from analyzing microdebitage due to the tedious and time-consuming nature of manually sorting, counting, and measuring microdebitage within soil samples. Dynamic image analysis and machine learning have proven to be a powerful combination of tools for this problem, however, decreasing the time needed to analyze a single sample by 98 percent while maintaining a minimum classification accuracy rate of 96 percent.

Bio: Phyllis Johnson is currently a University Research Postdoctoral Scholar in the Department of Anthropology at the University of Kentucky. Dr. Johnson's research involves the development and application of computational methods to address difficult archaeological questions surrounding ancient economies, site formation processes, and social structure. Dr. Johnson has over 15 years of archaeological experience in the Eastern US and Mesoamerica, and her current research combines experimental archaeology with novel machine and deep learning techniques to the examination of stone tool production to illuminate ancient actors, such as commoners and women, who are often rendered invisible in the archaeological record.

Talk Title: Predicting Stock Splits Using Ensemble Machine Learning and SMOTE Oversampling

Abstract: This study predicts stock splits using two ensemble machine learning techniques: gradient boosting machines (GBMs) and random forests (RFs). The goal is to form implementable portfolios based on positive predictions to generate abnormal returns. Since splits are rare events, we use SMOTE oversampling to synthesize new observations of splits in the sample to improve predictions. When predicting stock splits in the next quarter, GBM and RF achieve area under the receiver operating characteristic curve (AUC) scores of around 0.86 and 0.87, respectively. GBM and RF predictions generate monthly five-factor alphas (Fama and French, 2015) of 0.26% and 0.95% among stocks in the smallest size quintile. Three important features for predicting stock splits in both ensemble ML methods are current price levels, the ratio of current price to the price at last split, and stock returns in the past twelve months. When predicting stock splits in the next year, GBMs generate monthly five-factor alphas of 0.38% among small stocks.

Bio: Dr. Mark H. Liu is an associate professor of finance with tenure and the founding director of the master of science in finance program at the University of Kentucky. He is the author of the book Make Python Talk: Build Apps with Voice Controls and Speech Recognition (2021, No Starch Press). His research interest is in machine learning and corporate finance (IPOs, Mergers & Acquisitions, Corporate Governance, Financial Analysts, Dividend Policy, and Corporate Restructuring). He obtained his Ph.D. in finance from Boston College. Dr. Liu has published his research in top finance journals such as Journal of Financial Economics, Journal of Financial and Quantitative Analysis, Journal of Corporate Finance, and Review of Corporate Finance Studies.

Talk Title: Introduction to The WKU Applied Center for Data Science

Abstract: The WKU Bioinformatics and Information Science Center is being rebranded as The WKU Applied Center for Data Science. Our center will focus on-establishing industrial connections, aiding in research collaborations across campus, and develop educational outreach opportunities. In this talk, we will introduce ourselves and our values that will help WKU grow and further develop data science across the WKU campuses and the surround regions.

Bio: Richard Schugart is an associate professor of mathematics at Western Kentucky University. In the Fall 2021, Richard Schugart became the director of the WKU Bioinformatics and Information Data Science Center. Richard will continue as director of the Center re-branded as The WKU Applied Center for Data Science.

Talk Title: Combating Psyber-security Attacks using Machine Learning Techniques

Abstract: Existing literature shows that cyber-psychological issues among online users are on the rise, with mental health being the new cybersecurity attack surface, and COVID related misinformation, disinformation, and “fake news” being the corresponding attack vector amidst the ongoing pandemic. The threat of an online user being a victim to this is so significant that the World Health Organization calls this a COVID 'infodemic'. Psychological experts have termed this as a form of COVID psyber-security attack (COVID-PSA). Research into this area is new and emerging, with a lot of prospective scope of work. In this talk, we present a R&D project, in which we have attempted to address this COVID-PSA threat by implementing a machine-learning driven knowledge recommender, which is meant to be an adviser for users regarding the credibility of online COVID information.

Bio: Seth Adjei, Ph.D. is an assistant professor in the Computer Science department and a co-director of the Data Science Program at the College of Informatics, NKU. His research is in the application of data science to education and, recently, cyber-security. Seth’s work involves the application of machine learning techniques in the development of and research into tools that enhancing students learning in Mathematics and Elementary Computer Programming. In recent times, Seth has collaborated with Cyber Security experts to explore the application of machine learning in detecting Cybersecurity Threats around the dissemination of information about the COVID Pandemic.

Talk Title: Genetic Ramp Sequences Increase Gene Expression: How ramps of suboptimal codons can affect disease by impacting transcript and protein levels

Abstract: Genetic data are notoriously difficult to analyze because of the breadth of data available for each individual, high natural variation throughout the genome, multicollinearity of genetic variants, interactions between genes and environmental factors, and various other genetic biases. We developed the first and only algorithm to identify one of these genetic biases called ramp sequences. Ramp sequences are small stretches of slowly-translated codons at the beginning of genes that counterintuitively slow ribosomes in order to increase overall protein levels by limiting downstream ribosomal collisions. Ramp sequences are found in all domains of life, but can significantly differ between human populations and tissues based on the relative codon adaptiveness within each cell. We developed The Ramp Atlas (https://ramps.byu.edu) to allow users to interactively explore how ramp sequences relate to cellular gene expression. Additionally, we demonstrate that ramp sequences may partially explain SARS-CoV-2 proliferation patterns and reveal a biological mechanism underpinning some genetic associations with Alzheimer's disease. Ramp sequences are an exciting avenue of research that can facilitate large-scale data analyses that may lead to better understanding the basic biology underlying the relationship between human genetics and disease.

Bio: Dr. Justin Miller is an Assistant Professor in the Division of Biomedical Informatics and affiliated with the Sanders-Brown Center on Aging at the University of Kentucky. Dr. Miller actively collaborates with various researchers at the University of Kentucky, Brigham Young University, and Washington University in St. Louis. He uses machine learning to identify genetic and phenotypic subtypes of Alzheimer's disease that he hopes will lead to subtype-specific treatments of the disease. Dr. Miller has published over 25 peer-reviewed articles, including over 15 first or last author papers, and he is currently supported through the BrightFocus Foundation grant #A2020118F and the University of Kentucky Alzheimer's Disease Research Center funded through the National Institutes of Health grant #1P30AG072946-01.

Talk Title: EduceLab and Heritage Science: Fostering Breakthroughs through Infrastructure and Innovation

Abstract: This talk will define some of the exciting progress being made in Heritage Science at the University of Kentucky, such as the "virtual unwrapping" of ancient documents, with a focus on advanced computational infrastructure and machine learning techniques, which are helping to create new approaches to longstanding problems. The talk will also describe the emerging EduceLab midscale infrastructure, to be commissioned over the next five years, which will encourage the development of new techniques and user communities in Heritage Science.

W. Brent Seales is the Alumni Professor of Computer Science at the University of Kentucky and a Getty Conservation Institute Scholar (2019-20). Seales’ research applies data science and computer vision to challenges in the digital restoration and visualization of antiquities. In the 2012-13 academic year he was a Google Visiting Scientist in Paris, where he continued work on the “virtual unwrapping” of the Herculaneum scrolls. In 2015, Seales and his research team identified the oldest known Hebrew copy of the book of Leviticus (other than the Dead Sea Scrolls), carbon dated to the third century C.E. The reading of the text from within the damaged scroll has been hailed as one of the most significant discoveries in biblical archaeology of the past decade.

http://www2.cs.uky.edu/dri/

Talk Title: Four Ways to Use an NVIDIA GPU

Abstract: NVIDIA GPUS offer huge acceleration capabilities and are used today in a wide range of scientific disciplines. More than 2,000 applications are accelerated on NVIDIA GPUs, and more are being added each day. This event is aimed at researchers and developers interested in harnessing the power of GPUs. In a morning lecture, we'll discuss the four ways you can use a GPU. For a developer focus, we'll go through GPU programming languages, optimized GPU-accelerated libraries, and performance portable programming models. And for the application end-user, we'll show how to access GPU-optimized, pre-compiled containers for some of the most popular scientific applications.

Bio: Kristopher Keipert is a Solutions Architect at NVIDIA, focused on engagements in Higher Education and Research. With a research computing background, Kristopher enjoys assisting scientific developers with porting and optimizing their code for NVIDIA GPUs. Kristopher earned his PhD in physical chemistry from Iowa State University.

Talk Title: Hybrid Multi-cloud HPC: Delivering Better Answers Faster, Everywhere

Abstract: Recently, the ability to build powerful yet affordable HPC systems has made advanced computing more accessible than ever. No longer the exclusive domain of specialists with access to expensive, complicated and proprietary equipment, new teams are seeking to power data-intensive, performance-hungry workloads such as artificial intelligence (AI) and high-performance data analytics (HPDA) for new and innovative use cases. As more and different types of users rush to leverage advanced computing resources, the hybrid cloud model continues to be ideally suited for HPC, AI and HPDA. The ability to pay-per-use for HPC resources in the cloud makes its strategic advantages affordable for almost any organization. In this session, learn how enterprises worldwide are using hybrid cloud offerings to harness breakthrough computational power while employing standards-based technologies that have been tested and validated for industry-specific workloads and applications. All of which helps today’s most visionary engineers, researchers and business executives do what they do best: make the discoveries that fundamentally change the world.

Jay Boisseau is an experienced, recognized leader and strategist in advanced computing technologies, with over 25 years of experience in building and leading computing programs, departments, and organizations. As the AI & HPC Technology Strategist at Dell Technologies, Jay evaluates new technologies and approaches for HPC and machine/deep learning, assesses customers’ workloads needs and plans, and helps design new solutions to increase customers’ innovation, productivity, and efficiency. His current primary focus areas are cloud-enabled HPC for simulation, data analytics (HPC on demand), contributing to the Omnia cluster software project, working with strategic HPC/AI customers, and developing strategies for new HPC/AI solutions in key emerging verticals. Additional projects include evaluations of several new AI & HPC technologies, assessing quantum computing prospects for HPC/AI, leading the Dell Technologies HPC Community, and some confidential (NDA required) projects.
Before coming to Dell Technologies in 2014, Jay created the Texas Advanced Computing Center (TACC) at The University of Texas at Austin and led it (2001-14) to world prominence. Under Jay’s leadership, TACC deployed numerous world-class computing systems for U.S. open scientific research and developed new programs to enable thousands of researchers to accomplish more with these powerful technologies. Prior to TACC, Jay worked as an associate director at the San Diego Supercomputer Center (1996-2001) and at the Arctic Region Supercomputing Center (1994-96). Jay received his doctorate in astronomy from UT Austin for his advanced computational research in modeling the structure supernovae explosions.

Talk Title: Addressing the long-term data preservation challenges for computational workflows with Fujifilm’s Object Archive

Abstract: This presentation will discuss the key elements of Fujifilm’s Object Archive software, how it extends long-term data management capabilities related to computational workflows, scientific research and digital preservation efforts. Object Archive addresses long-term data archiving challenges with its modern tape technology and simple integration with S3-compatible API. Learn how Fujifilm tape technology and software works together with 3rd party products such as iRODS, Starfish, and Spectra Logic to increase data management efficiency and resiliency across research and computational workflows.

Chris Kehoe is head of Infrastructure Engineering and Data Management Solutions, Fujifilm Recording Media U.S.A., Inc. Chris has been working as a data management practitioner for over 26 years. Driving solutions for companies like STK/SUN Microsystems, Spectra Logic, NOAA and Xerox. As an expert in data management solutions, he leads the charge for FUJIFILM’s next gen storage solutions. Chris Kehoe and avid adventurer, resides in Colorado with his family.

Talk Title: Institutional Storage/Archive Systems to Support Research Needs

Abstract: This session overviews the options for storing all forms of research data for both short-term with quick access to long-term archival storage (viable for many years). It will cover multiple storage technologies from Networked Attached Storage (NAS), object storage, to automated robotic tape archives, and hybrid approaches including automating the movement of data to various tiers of storage based upon usage patterns and user policies.

Mr. Fitch has spent 42 years in technology, and 16 years at Spectra Logic helping some of the largest research universities to implement more efficient and cost-effective ways to manage research data. His clients manage from tens of TB’s to hundreds of PB’s of data.

Talk Title: The real world: How Hybrid of both on-premise and cloud accelerates research

Abstract: This session will discuss how the mix of on-premise solutions combined with cloud based solutions can enable researchers to advance science by leveraging the best of both to accomplish more, faster.

Scott Friedman, Ph.D., is a Principal Technical Business Development Manager with the Higher Education Research Group at Amazon Web Services (AWS.) The group enables universities and their researchers to effectively leverage AWS to enhance their research. Prior to joining AWS, Scott was most recently the CTO for Advanced Research Computing at UCLA enabling its research community through advanced computing resources and domain expertise. He has also held appointments as a Lecturer in the Computer Science department and has received grant funding as PI, Co-PI, and Senior Staff from NSF, DOE, NIH, and NEH. Scott holds M.S. and Ph.D. degrees in Computer Science and two professional degrees in Architecture. Research interests include distributed and parallel systems performance, optimization and analysis, and large scale visualization. Two recognition highlights; having a system he designed and built make the Top500 supercomputing list and a special award from the International Supercomputing Conference for “Making it look easy.”

Talk Title: The cornerstone of the next-generation integrated environment for superclusters.

Abstract: Since their beginning in a NASA project around 1994, Beowulf clusters have become the essential class of high performance computing (HPC) architectures. Although the supercluster platform has widely served the community, the innovation paradigm has come to HPC and its system infrastructure—composable disaggregated infrastructure or CDI. Fully disaggregated infrastructure has several benefits over monolithic or stranded resources, eliminating the rigid, hardware-bound silos that have held companies back from their digital transformation strategies. This discussion will outline why CDI is a future-proofing strategy for the enterprise's HPC, AI, and advanced computing needs.

Earl J. Dodd is World Wide Technology's Global HPC Business Practice Leader. He provides HPC/HPDA/Supercomputing strategy, technology enablement, business development, and marketing and sales support to WWT's global enterprises and governments. Earl helps achieve a customer's desired ROI by leveraging HPC technology, extreme data in motion, and the Cloud on secure ultra-scale architectures and collaboration environments. This effort drives the next generation of computationally steered workflows in decision support environments for real-time situational awareness and institutional learning.

Talk Title: HPE Cray AI Development Environment – Scaling Deep Learning Model Development from Cloud to Core

Abstract: As Deep Learning continues to become more widely adopted, it is imperative to have the appropriate software infrastructure to enable scaled model training and experimentation, whether in the Cloud or at the Core. This presentation will discuss the challenges of training Deep Learning models and how the HPE Cray AI Development Environment provides a unified platform for large scale model development from Cloud to Core.

Hoang leads Solution Engineering for Determined AI as part of the Artificial Intelligence Strategy and Solutions group, where he helps customers who are building cutting-edge AI products across a wide range of industries dramatically accelerate their time to market by leveraging the HPE Cray AI Development Environment. Prior to Determined, Hoang was a Solution Architect developing machine learning solutions at Box.

Talk Title: The future of memory in HPC

Abstract: Memory has always been one of the most prized and expensive resources in an HPC cluster. Memory itself hasn’t changed much over the last ten years, yet with the latest speed enhancements, and now the advent of CXL around the corner, the way memory is addressed and used within the datacenter will undergo a dramatic shift. This discussion will address the details of the shift, the new capabilities coming online, as well as the architectural changes needed to take advantage of these.

Matt Demas, CTO, GigaIO. Matt's career spans two decades of experience in architecting innovative IT solutions, starting with the US Air Force. He has built federal, healthcare, and education-based vertical solutions at companies like Dell, where he was a Senior Solutions Architect. Immediately prior to joining GigaIO, he served as Field CTO at Liqid. Matt holds a Bachelor’s degree in Information Technology from American InterContinental University, and an MBA from Concordia University Austin.

Talk Title: The future of memory in HPC

Abstract: Memory has always been one of the most prized and expensive resources in a HPC cluster just like the datacenter. Memory hasn’t changed much over the last 10 years. With speeds enhancing and now the invention of CXL, The way memory is addressed and used within the datacenter will have a dramatic shift. This discussion will address the details of the shift, new capabilities as well as changes needed to take advantage of the new capabilities.

Matt Demas is the CTO at GigaIO. He has been delivering composable disaggregated infrastructure to HPC customers for over 4 years. Matt has been at the edge of building solutions to revolution industries since serving as an enlisted Air Force technician at the Pentagon to his current role. He mission is to deliver cloud like capabilities from edge-to-core-to-cloud for HPC, AI and traditional datacenter workloads.

Session D & G: (1PM – 3PM)
NOTE: If you have interest and plan to attend this workshop you must contact Tony Elam (tony.elam@uky.edu) for further information.

Talk Title: Deploying and Using Open OnDemand in CloudyCluster in Google Cloud

Abstract: In this session we will be walking through the installation and configuration of Open OnDemand on CloudyCluster in Google Cloud, the session will be done in a way that enables participants to just watch and learn or participate in a hands-on session using a pre-created google project. We will go through launching CloudyCluster in Google Cloud, accessing Open OnDemand and launching interactive sessions, and launching a basic HPC job. If you would like to participate a laptop will be required.

Boyd Wilson, CEO of Omnibond, has extensive experience and leadership in the areas of software development, system administration, storage architectures and business models. Formerly, Boyd served as Software CTO and also Executive Director over computing operations at Clemson University and had research computing as part of his purview. Prior to that he was Director of Computing for the Engineering School at Miami University. He has participated in various academic grants, papers and workshops in the areas of parallel storage, cloud computing and computational infrastructure.

Talk Title: Learning Data Science using Jupyter Notebooks and the Julia Language

Abstract: Julia is an open-source programming language targeting scientific computing and data science with many abstractions built into the language and ecosystem. It has been gaining popularity since its first stable release in 2018 with the promise of being an approachable language that compiles to efficient native code via LLVM, while providing rich capabilities for interactive data analysis and visualization. This combination of easily accessible abstractions while not sacrificing performance makes Julia an excellent language to explore data analysis, statistics, and machine learning algorithms.

This tutorial will give a gentle introduction to the Julia language. We then cover important data science tools and techniques, using hands-on exercises in Jupyter notebooks covering: i) accessing and manipulating data (eg. using data frameworks and SQL); ii) exploring data using interactive data visualization tools, (eg. using Jupyter and Pluto.jl); iii) exploring statistics, automatic differentiation and machine learning algorithms (eg. using Zygote.jl and Flux.jl).
The objective of this tutorial is to provide the audience with enough hands-on experience in Julia to start applying it to their own projects, or use Julia as a teaching tool in the classroom.

Johannes Blaschke is a computer systems engineer at the National Energy Research Scientific Computing Center, where he is engaging with scientists to help them optimize their software for the next-generation supercomputers. His work focuses on enabling the real-time data analysis using extreme-scale computing environments.

Session D & G: (1PM – 3PM)
NOTE: If you have interest and plan to attend this workshop you must contact Tony Elam (tony.elam@uky.edu) for further information.

You are invited to submit an abstract for 5th Annual Commonwealth Computational Summit at the University of Kentucky. Presentations are being sought across all areas of computational sciences and engineering, High Performance Computing, Textual Analytics and Data Mining, Artificial Intelligence, Machine Learning and Deep Learning, as well as exciting new related technologies! Guidelines for presentation of a Lightning Talk: Presentations will be strictly limited to 10 minutes. You should describe your research area, innovation, and challenges. Your goal is to quickly inform the audience, create interest in collaboration, and solicit follow-up discussions. Presentations will focus on exciting computational oriented research conducted by faculty throughout the Commonwealth of Kentucky and our region. The audience for presentations will be other faculty researchers, graduate students and industry scientists and engineers working in computational fields.

Information on Submission: The following information must be submitted by Monday, October 11, 2022. Selection of our Lightning Talk speakers will be completed by October 13, 2022. A complete program will be distributed within a short time after this date. See Summit website for registration and submission of materials for speakers. You will be prompted for information such as: name, email & contact Info, affiliation/institution, title/position, area/topic of research (see below), title of the presentation and an abstract (description of research to be presented, challenges, background of speaker (mini-bio), approximately 1 page in length (total)). In addition, you will be asked to pick a category for your Lightning Talk such as bioinformatics and biosciences; textual analytics and data mining; artificial intelligence and machine learning; High Performance Computing/Computational Science and Engineering; New HPC related Technologies, etc.
Note: Optional PDF upload is also available; Laptop projector equipment will be available.

The CCS Technical Program Committee and Session Chairs will review all submissions and select presentations based upon quality of abstracts submitted, research interest/impact and mixture of topics. It should be noted that Post-Summit, all slides (PDF) for the speakers will be posted in our CCS Online Summit Proceedings.

Are you a graduate student (including masters and postdocs) in the Commonwealth of Kentucky (or in our region) doing research related to Bioinformatics and Biosciences; Textual Analytics and Data Mining; Machine Learning or AI; or High Performance Computing – Computational Science or Engineering? Would you like to present a poster at our Summit, expand your resume/vita and win a Gift Card prize? (NOTE: Gift Card prizes will be awarded to the top four rated posters)? If so, please register and submit a title and abstract for your poster on this web page.

Title – current/planned working title for poster
Abstract – 1-2 page abstract that list institution, research participants, describes the research addressed with a brief description of the project’s goals/objectives, design/methods, and findings (outcomes/impact). Also please indicate which of the areas this would fall into: Bioinformatics/Bioscience; Textual Analytics/Data Mining; HPC/Computational Sci & Eng; Machine Learning/AI; New Technologies/Other.

Following your submission, you will be contacted regarding any further needed details. Later, you will receive the poster selection results. If selected, you will be given further instructions and the judging criteria.