The ICDS Symposium will include many activities designed to share knowledge or encourage interdisciplinary collaboration.
Please note: all sessions will be recorded.
Agenda for Wednesday, October 21
Session 1: 8:00 – 9:30 a.m. ET
|Introductory Remarks||Jenni 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
|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)
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
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
|Product Briefings from Industry Sponsors||AWS||Join AWS Briefing
|Product Briefings from Industry Sponsors||Dell||Join Dell Briefing|
|Product Briefings from Industry Sponsors||IBM||Join IBM Briefing|
Session 3: 3:30 – 5:00 p.m. ET
|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
|Presenter||Title of Lightning Talk||Zoom Link for Follow-up Q&A|
|Jean-Paul Armache, Assistant Professor of Biochemistry and Molecular Biologyemail@example.com||WebSciV - 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 Mechanicsfirstname.lastname@example.org||Degradable, tattoo-like sensors for biomedical applications||N/A (please contact via email)|
|Erika Ganda, Assistant Professor of Food Animal Microbiomesemail@example.com||Food Animal Microbiomes: Using Sequencing Technologies to Provide Solutions for the Agricultural Industry||N/A (please contact via email)|
|Michael Hillman, L. Robert and Mary L. Kimball Assistant Professor, College of Engineeringfirstname.lastname@example.org||Direct Image-based Numerical Simulation||https://psu.zoom.us/j/91878608446|
|Abdullah Konak, Professor of Information Sciences and Technology, Penn State Berksemail@example.com||“Reset for Success” (RSS): A Student Retention Support System Based on Persuasive Technologies||https://psu.zoom.us/j/99251505607|
|Steven Nixon, Research and Development Engineer, Applied Research Laboratoryfirstname.lastname@example.org||Data science for condition based maintenance||N/A (please contact via email)|
|Sabahattin Gokhan Ozden, Assistant Professor of Information Sciences and Technology, Penn State Abingtonemail@example.com||The Opioid Epidemic: Searching for Information Efficiently||
|Bing Pan, Associate Professor of Recreation, Park, and Tourism Managementfirstname.lastname@example.org||Understanding Visitor Demographics and Experience in Yellowstone National Park through Social Media||
|Vittaldas Prabhu, Professor and Charles and Enid Schneider Faculty Chair in Service Enterprise Engineeringemail@example.com||Engineering the 21st-Century Service Economy||N/A (please contact via email)|
|Meng Su, Associate Professor of Computer Science and Software Engineering, Penn State Behrendfirstname.lastname@example.org||Integrate AI Cloud Services in Undergraduate Course||
Agenda for Thursday, October 22
Session 1: 8:00-9:30 a.m. ET
|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
|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
The Honorable Tom J. Ridge, First Secretary of U.S. Department of Homeland Security; First Director of the Office of Homeland Security; and 43rd Governor, Commonwealth of Pennsylvania
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
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|
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.
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
- 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
- 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
- The Honorable Tom J. Ridge, First Secretary of U.S. Department of Homeland Security; First Director of the Office of Homeland Security; and 43rd Governor, Commonwealth of Pennsylvania
- 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
- 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.
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.