The ICDS Symposium will include many activities designed to share knowledge or encourage interdisciplinary collaboration.

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Please note: all sessions will be recorded.

Agenda for Wednesday, October 21

Session 1: 8:00 – 9:30 a.m. ET

TopicPresenter(s)Zoom Link
Introductory RemarksJenni Evans, Director, Institute for Computational and Data Sciences

Lora Weiss, Senior Vice President for Research
Join Session 1
Keynote Presentation: "ZettaScale Computing on Exascale Platforms!" (View Abstract)Shantenu Jha, Chair, Computation and Data Driven Discovery (C3D) Department at Brookhaven National Laboratory, and Professor of Computer Engineering at Rutgers University

Session 2: Noon – 1:30 p.m. ET

TopicPresenter(s)Zoom Link
Panel Discussion 1: Big Data, Agriculture & Food Supply (View Abstract)Moderator:
Asad Azemi, Professor and Chair, Engineering, Mathematics and Science, University of Wisconsin Platteville (Formerly Associate Professor of Engineering, Penn State Brandywine)

Panelists:
David Hughes, Associate Professor of Entomology & Biology, Penn State University

Paul Esker, Assistant Professor, Epidemiology and Field Crop Pathology, Penn State University

Long He, Assistant Professor, Agricultural and Biological Engineering, Penn State University
Join Panel 1
Panel Discussion 2: Artificial Intelligence and Machine Learning in Manufacturing (View Abstract)Moderator:
Soundar Kumara, Allen E. Pearce and Allen M. Pearce Professor of Industrial Engineering

Panelists:
Josh Siegel, Assistant Professor of Computer Science and Engineering, Michigan State University

Paul Witherell, Mechanical Engineer, Systems Integration Division, National Institute of Standards and Technology (NIST)

Maja Vukovic, Distinguished Research Staff Member and Research Manager at IBM Research

Jimmy Pike, SVP/Senior Fellow, OCTO, Integrated Systems Group, Dell Technologies
Join Panel 2

Session 3a: 3:00 – 3:30 p.m. ET

TopicPresenter(s)Zoom Link
Product Briefings from Industry SponsorsAWSJoin AWS Briefing
Product Briefings from Industry SponsorsDellJoin Dell Briefing
Product Briefings from Industry SponsorsIBMJoin IBM Briefing

Session 3: 3:30 – 5:00 p.m. ET

TopicPresenter(s)Zoom Link
ICDS Connects:
"State of the Industry" Talks
“The AI Ladder,” presented by Bill Higgins, Distinguished Engineer, Watson AI Platform, IBM

"Democratization of HPC/AI," presented by Thierry Pellegrino, Vice President of the Data-Centric Workloads and Solutions Organization, Dell EMC

Kam Syed, Senior Manager of Research Business Development, Amazon Web Services (AWS)
Join Session 3
ICDS Connects: Faculty Lightning Talks View lightning talk speakers

Faculty Lightning Talk Presenters

PresenterEmailTitle of Lightning Talk
Jean-Paul Armache, Assistant Professor of Biochemistry and Molecular Biologyjza449@psu.eduWebSciV - Incorporation and visualization of various scientific sources using AI in a multi-layer web-based platform
Huanyu "Larry" Cheng, Assistant Professor of Engineering Science and Mechanicshuc24@psu.eduDegradable, tattoo-like sensors for biomedical applications
Erika Ganda, Assistant Professor of Food Animal Microbiomesevk5387@psu.eduFood Animal Microbiomes: Using Sequencing Technologies to Provide Solutions for the Agricultural Industry
Michael Hillman, L. Robert and Mary L. Kimball Assistant Professor, College of Engineeringmhillman@psu.eduDirect Image-based Numerical Simulation
Abdullah Konak, Professor of Information Sciences and Technology, Penn State Berks auk3@psu.edu“Reset for Success” (RSS): A Student Retention Support System Based on Persuasive Technologies
Steven Nixon, Research and Development Engineer, Applied Research Laboratorysxn5077@arl.psu.eduData science for condition based maintenance
Sabahattin Gokhan Ozden, Assistant Professor of Information Sciences and Technology, Penn State Abingtonsgo7@psu.eduThe Opioid Epidemic: Searching for Information Efficiently
Bing Pan, Associate Professor of Recreation, Park, and Tourism Managementbingpan@psu.eduUnderstanding Visitor Demographics and Experience in Yellowstone National Park through Social Media
Vittaldas Prabhu, Professor and Charles and Enid Schneider Faculty Chair in Service Enterprise Engineeringvittal.prabhu@psu.eduEngineering the 21st-Century Service Economy
Meng Su, Associate Professor of Computer Science and Software Engineering, Penn State Behrendmus11@psu.eduIntegrate AI Cloud Services in Undergraduate Course

Agenda for Thursday, October 22

Session 1: 8:00-9:30 a.m. ET

TopicPresenter(s)Zoom Link
Introductory Remarks

Jenni Evans, Director, Institute for Computational and Data Sciences

Nicholas Jones, Executive Vice President and Provost, Penn State
Join Session 1
Keynote Presentation: "The Landscape of Data Science: Basic Research to Impact" (View Abstract)Chaitan Baru, Senior Science Advisor, Convergence Accelerator, Office of the Director, National Science Foundation

Session 2: Noon – 1:30 p.m. ET

TopicPresenter(s)Zoom Link
Panel Discussion 1: Social Engineering with Data - Disinformation and Destabilization of Geo-political Order (View Description)Moderator:
Anne Toomey McKenna, Affiliate Faculty, Penn State Institute for Computational and Data Sciences

Panelists:
Maurice Turner, Senior Advisor, U.S. Election Assistance Commission, Technologist

Anthony C. Robinson, Associate Professor, Director, Online Geospatial Education, Assistant Director, GeoVISTA center, Department of Geography, Penn State

Maria D. Molina, Assistant Professor, Department of Advertising + PR, Michigan State University

Kevin Munger, Assistant Professor of Political Science and Social Data Analytics, Department of Political Science, Penn State
Join Panel 1
Panel Discussion 2: Data, Genetics, and DNA - Value, Ethics, and Risks (View Description)Moderator:
Aleksandra (Sesa) Slavkovic, Professor; Associate Dean for Graduate Education, Eberly College of Science

Panelists:
Andrew Read, Director, Huck Institutes of the Life Sciences, Evan Pugh Professor of Biology and Entomology, Eberly Professor of Biotechnology

Daniel Susser, Assistant Professor of Information Sciences and Technology and Philosophy, Research Associate, Rock Ethics Institutes

Margaret Hu, Professor, Penn State Law and School of International Affairs, and ICDS Faculty Fellow

Nilam Ram, Professor, Departments of Communication and Psychology at Stanford University.
Join Panel 2

Keynote Descriptions

ZettaScale Computing on Exascale Platforms, presented by Shantenu Jha

Abstract: We outline the vision of  “Learning Everywhere,” which captures the impact of learning methods coupled to traditional HPC methods. We: (i) discuss effective performance improvements for traditional HPC simulations that learning (MLforHPC) provides; (ii) provide a taxonomy of the modes by which MLforHPC can impact computational science, including scenarios: MLinHPC, MLoutHPC and MLaroundHPC; and (iii) identify and survey recent problems that benefit from MLforHPC. We will also outline software systems developed for ML driven simulations and discuss how learning methods and HPC simulations are being integrated. We identify a spectrum of challenges and requirements that MLforHPC presents for both new cyberinfrastructure and application developments.

The Landscape of Data Science: Basic Research to Impact, presented by Chaitan Baru

Abstract: Over the past three years, via its Harnessing the Data Revolution Big Idea (aka HDR), and other related programs, the National Science Foundation has launched a series of multidisciplinary programs covering foundations, systems, applications, and education in Data Science. For example, the Transdisciplinary Research In Principles Of Data Science (TRIPODS) program explores the foundations of data science at the nexus of computer science, statistics, and mathematics. The TRIPODS+X program explores how the data challenges and concepts from various science domains (the “X”) might interact with and influence foundational issues. The NSF HDR Institutes program seeks to establish center-scale activities in data science encompassing aspects of theory, systems, and applications of data science methods across various disciplines and applications. The Data Science Corps program supports the development of experiential learning curricula in undergraduate data science education. The NSF Convergence Accelerator is a new, unique program to support use-inspired convergent research characterized by deep multidisciplinary collaborations and partnerships among academia, industry, government, non-profit and other sectors, with the goal of accelerating ideas from research into practice. In 2019, its first pilot year, the NSF Convergence Accelerator is supporting projects in two tracks that involve data science: the Open Knowledge Network and AI and Future Work.

The range of new, data science-related programs and the variety of programmatic approaches being taken at NSF reflects the excitement and experimentation underway in academia as well, where a variety of new data science paths are being explored…almost as many as there are universities!

In this discussion, we will explore the landscape of research programs and activities in data science; examine what makes data science new and different from programs we have seen thus far; and consider future directions.

Panel Descriptions

Wednesday Panel 1: Big Data, Agriculture & Food Supply

Organized by Asad Azemi, Professor and Chair, Engineering, Mathematics and Science, University of Wisconsin Platteville (Formerly Associate Professor of Engineering, Penn State Brandywine

Panelists:

  • David Hughes, Associate Professor of Entomology & Biology, Penn State University
  • Paul Esker, Assistant Professor, Epidemiology and Field Crop Pathology, Penn State University
  • Long He, Assistant Professor, Agricultural and Biological Engineering, Penn State University

Data that is unstructured or time sensitive or simply very large cannot be processed by relational database engines. Such datasets in agriculture often include numerous weather and soil measurements as well as corresponding plant or animal performance assessments under multiple management regimes over multiple years. This type of data requires a different processing approach called big data, which uses massive parallelism on readily-available hardware. 

Predictive models derived from big data can help to identify best management practices for getting the best crop and livestock performance under various environmental conditions, and help to make decisions that will tackle inefficiencies in planting, harvesting, water use and energy, and increase yields and deliver safe, nutritious food to communities around the world. 

Join us as interdisciplinary panelists address how big data and AI are improving food and agriculture from farm to table. 

Wednesday Panel 2: Artificial Intelligence and Machine Learning in Manufacturing

Organized by Soundar Kumara, Allen E. Pearce and Allen M. Pearce Professor of Industrial Engineering

Panelists:

  • Josh Siegel, Assistant Professor of Computer Science and Engineering, Michigan State University
  • Paul Witherell, Mechanical Engineer, Systems Integration Division, National Institute of Standards and Technology (NIST)
  • Maja Vukovic, Distinguished Research Staff Member and Research Manager at IBM Research
  • Jimmy Pike, SVP/Senior Fellow, OCTO, Integrated Systems Group, Dell Technologies

This panel aims to facilitate a discussion on exploring the application of artificial intelligence (AI) and machine learning (ML) in the future of manufacturing systems. Manufacturing systems have evolved from the early computer integrated manufacturing to the current Department of Energy’s Smart Manufacturing. In recent years, the proliferation of sensors and data analytics have resulted in research ideas and implemented systems that are termed, “smart.” AI and ML have evolved in the last decade to be the most important technologies to enhance every aspect of human life. Given the speed with which AI and ML have evolved in the last five years, disciplinary areas such as manufacturing have not moved at a commensurate speed. In this context, it is necessary to look at manufacturing from the perspective of AI and ML driven society of the future and prepare to lay the fundamental tenants of research, development and education. Manufacturing needs to take not only process and system level aspects but also socio, economic and political changes that are influencing the way we live, work and collaborate. The objective of this panel is to bring together researchers and practitioners to discuss and generate a roadmap for AI and ML driven manufacturing research, development and education.

Thursday Panel 1: Social Engineering with Data: Disinformation & Destabilization of Geo-Political Order

Organized by Anne Toomey McKenna, Affiliate Faculty, Penn State Institute for Computational and Data Sciences

Panelists:

  • Maurice Turner, Senior Advisor, U.S. Election Assistance Commission, Technologist
  • Kate Hammerberg, Unit Chief, Analytics and Research Directorate, Global Engagement Center, U.S. Department of State; Recipient of President Barack Obama’s National Public Service Award; American Security Project Fellow; former Elliott School Merit Fellow
  • Anthony C. Robinson, Associate Professor, Director, Online Geospatial Education, Assistant Director, GeoVISTA center, Department of Geography, Penn State
  • Maria D. Molina, Assistant Professor, Department of Advertising + PR, Michigan State University
  • Kevin Munger, Assistant Professor of Political Science and Social Data Analytics, Department of Political Science, Penn State

The U.S. and other nations are the testing and proving grounds for large-scale social engineering with data. These efforts arguably are transforming existing geo-political order and threaten the foundations of democracy, including fair and accurate elections. Harnessing vast quantities of consumer data, social engineering is the online manipulation of citizens via disinformation and targeted behavioral messaging (using data) on social media platforms (information eco-systems). These intentional efforts by nation states and private actors include:

  • manipulation of social groups and vulnerable populations
  • engineering election results
  • erosion of confidence in the electoral process
  • undermining democracy
  • altering geo-political order

Join us as four interdisciplinary panelists address how researchers, citizens, and governments are using AI, cyber measures and other security technologies, and the law, to investigate and identify disinformation, secure electoral systems, mitigate destabilization, educate the public about social engineering with data, and create policies that combat malicious social engineering.

Thursday Panel 2: Data & Genetics/DNA: Value, Ethics, and Risks

Organized and moderated by Aleksandra (Sesa) Slavkovic, Professor; Associate Dean for Graduate Education, Eberly College of Science

Panelists:

  • Andrew Read, Director, Huck Institutes of the Life Sciences, Evan Pugh Professor of Biology and Entomology, Eberly Professor of Biotechnology
  • Daniel Susser, Assistant Professor of Information Sciences and Technology and Philosophy, Research Associate, Rock Ethics Institutes
  • Margaret Hu, Professor, Penn State Law and School of International Affairs
  • Nilam Ram, Professor, Departments of Communication and Psychology at Stanford University.

About:

The data deluge brought forth a great deal of discussion about the four V’s of big data: Volume, Variety, Velocity, and Veracity. But intertwined with these aspects are:

  • VALUE — to what extent are the data needed and insights gained from analyses via statistical, ML, or AI models and systems impactful?
  • ETHICS — what are the normative issues in generating, analyzing and disseminating data? 
  • RISKS — how do we think about and define risks in the scientific enterprise that relies on data?

While these issues most naturally arise within contexts that deal with human data, any scientific discipline should consider these dimensions to enable sounds scientific progress and decision making. We will hear perspectives on these three topics, including related opportunities and challenges, from our interdisciplinary panel of experts. We will discuss how the human screenome project aims to capture our digital lives, and investigate its relation to genomics. We will also discuss new directions in privacy, transparency and reproducibility using census data.  More broadly, we will examine how technology aids in online manipulation and is impacting our autonomy, and how life sciences research spanning the bench, modeling and the field impacts our understanding of human disease and public health.

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