Conference Programs & Tracks
Group Vice President and General Manager, IDC Government and Health Insights, IDC
Mona Siddiqui, MD
Chief Data Officer, U.S. Department of Health & Human Services
Tolga Kurtoglu, PhD
William Mark, PhD
President, Information and Computing Sciences, SRI
David Bray, PhD
Executive Director at the Institute for Human-Machine Cognition; Senior Fellow, People-Centered Internet Coalition
Federal Chief Information Officer, U.S. Office of Management and Budget
Anthony Scriffignano, PhD
Senior Vice President & Chief Data Scientist, Dun & Bradstreet
Agencies have been accumulating data for many years. However, organizations also realize they have not gained many benefits from the datasets. Along with an increase in unstructured data, there has also been a rise in the number of data formats. Administrative data, such as notes and articles, as the primary data type have expanded to include images, audio, video, and sensors.
Many organizations fail to consider how quickly a big data project scales. Constantly pausing a project to add additional resources cuts into time for data analysis. Assessing what data exists and its integrity – completeness, accuracy, bias and trust – prolong the analysis effort. This challenge is further compounded by integrating disparate data sources and securing big data.
This track addresses the major challenges faced by Big Data environments with an emphasis on identifying what data you have, how to source additional data, how to organize it, how to clean it, how to prepare the data for use in a machine learning application, and ultimately, how to integrate and scale the application into the Agency’s IT systems.
Government Data Center Analytics
Shawn McCarthy, Research Director, IDC Government Insights
Finding Early Success with Intelligent Automation and Big Data
Edward Preble, PhD, Research Data Scientist, Center for Data Science, RTI International
In evaluating the potential applications for intelligent automation, fundamental questions revolve around “How do I get started in Artificial Intelligence and what are the best applications where AI can and should be deployed?” In many cases, the answers have less to do with technology choices and more to do with evolving the organization’s culture and mindset. As processes transition from Business Intelligence and Performance Management to AI- and data-driven strategic roles and functions, agencies and departments will face common opportunities to refine the future of work.
This track looks at alternatives to building Data Science teams and strategies for enabling a data-driven workforce.
Personnel, Supply Chain & Logistics
Lindsey Sheppard, Associate Fellow, International Security Program, Center for Strategic & International Studies (CSIS)
Bridging Policy and the Mission with Computer-Based Models
Corey Lofdahl, PhD, Principal Engineer, Systems & Technology Research (STR)
Leveraging AI in the Automation of Government Accounting and Reporting
Harnessing AI to Maximize Value in Public Sector Procurement
Track Chair: Curt Savoie, Program Manager, Global Smart Cities Strategies, IDC
To achieve the title of Smart City, municipalities must enhance existing services, while at the same time innovate and deploy new applications and capabilities. For existing services, organizations are utilizing predictive models to gain operational efficiency, such as using data to enhance asset location. Big data is also aiding in the delivery of a better user experience (UX). Artificial intelligence can also be applied in a host of other specific areas, such as the preparation for autonomous vehicles and smart mobility systems, as well as planning and regulating of new service delivery.
This track examines the design and governance of the Smart City utilizing data and intelligent automation. Focus is given to three specific aspects of the Smart City: digital government and citizen services, transportation, and public safety.
Delivering Effective Citizen Services
Madelene Stolpe, Head of Digital Strategy, Health & Human Services, City of Oslo, Norway
The City of Oslo is utilizing chatbots, machine learning models, and a mapping tool to more effectively communicate and engage with its citizens related to available health and human services. This talk examines the three projects to discuss learnings and plans to scale pilots across the municipality.
Identifying Targeted Public Safety Applications for Your AI Digital Transformation
Alison Brooks, PhD, Research Director, Smart Cities Strategies & Public Safety, IDC
Strategies for Developing AI-Based Applications & Services for Transportation
Mark Zannoni, Research Director, Smart Cities & Transportation, IDC
Panel: AI in Smart Cities, Campuses, and Communities
Moderator: Ruthbea Clarke, Vice President IDC Government Insights, IDC
Once the initial Big Data challenges have been overcome, what does an organization do with the data? How can it use AI to accelerate digital transformation strategies? Having more data doesn’t necessarily lead to actionable insights. A key challenge for data science teams is to identify a clear objective and determine the most impactful questions. Once key patterns have been identified, agencies must also be prepared to act and make necessary changes in order to demonstrate value from them.
This track explores the delivery of services and applications powered by learning systems.
Panel: Adoption, Best Practices, and Successful Deployment of Process Automation
The federal government is facing unprecedented operating challenges as they manage mounting budget constraints while trying to be more agile to increase mission objectives. Unable, in many cases, to hire more employees, federal agencies are forced to spend dollars on contractor support or shift resources away from mission-critical work to handle routine, manual tasks. Robotic process automation (RPA) provides federal agencies the capability to operate more efficiently with reduced resources. Hear from government thought leaders and subject matter experts who will discuss their adoption, best practices, and successful deployment of RPA.
Using NLP and Big Data to Deliver High-Value Decision Making
Abhivyakti Sawarkar, MD, Biomedical Informatician, Office of Translational Sciences (OTS), Center for Drug Evaluation and Research (CDER), U.S. Food & Drug Administration
Planning for Desired Outcomes with Recommender Systems
Sung-Woo Cho, PhD, Senior Associate/Scientist, Social and Economic Policy, Abt Associates
Despite the recent interest in using algorithmic models for data analysis and insight, the underlying methodologies and protocols have been proven for decades. Researchers are experimenting with new ideas that leverage these time-tested frameworks.
This track provides attendees with a roadmap for the evolution of AI technologies in the next few years. How will trust and explainability be resolved by the industry to become integral components of future machine learning solutions? Which emerging AI solutions and technologies will be evolving out of research labs in the near term, enabling new classes of productive applications? What will the next generation of AI-optimized hardware look like? What can we expect from the next generation of biometric technologies?
Explainable AI: The Need for Transparency and Auditability of “Black Box” Systems
Panel: Implementing Advanced AI Technologies
Machine learning (ML) is currently viewed as a single tool. However, ML is not a static environment. Researchers have already developed advanced technology to evolve ML to process larger amounts of data even faster. Some developers, for example, are examining how ML can incorporate blockchain for safety and security within the ML model. ML in its various forms are being integrated into and with other highly advanced intelligent systems such as NLP, image processing, etc. for multitudes of applications. This panel of AI and data science researchers is pushing the bleeding edge of emerging technology and identifying the future of ML.
Organizations can effectively leverage automation in governance, risk management, compliance and security as they move to a digital platform for the future. Change in stewardship of data is afoot including how data ownership, retention, and public records are managed. Algorithmic modeling solutions deliver efficient analysis, though the “black box” question of how insights are arrived at remains an open issue where transparency and auditability are needed.
This track highlights the opportunity to use AI and automation to meet existing compliance reporting, as well as prepare for new legislation on data privacy and protection.
De-Identification of Video Data for Public Sector Research
David Kuehn, Program Manager, FHWA Exploratory Advanced Research (EAR) Program, Federal Highway Administration, U.S. Department of Transportation
The Regulatory Landscape and Designing Trust into Data-Driven Systems
Daniel Wu, JD, PhD, Privacy Counsel and Legal Engineer, Immuta
Creating Organizational Value from Machine Learning
Jun Heider, CTOO, RealEyes Media
* The program is subject to change without notice, due to unforeseen reason.