Big Data EdCon 2019

Agenda At A Glance: Monday, June 3, 2019

Registration Opens

7:30 am

Continental Breakfast
8:00–9:00 am

Opening Remarks
9:00–9:20 am

  • Elena Gortcheva, Program Chair Master of Science in Data Analytics, University of Maryland University College

  • Javier Miyares, President, University of Maryland University College

  • Michael Leahy, Secretary, Department of Information Technology at State of Maryland

9:20–10:00 am Keynote: “Present and Future of AI” - David Cox, Director MIT-IBM Watson AI Lab (watch video)

Abstract: Recent years have seen rapid progress in machine learning and artificial intelligence, which has enabled a wide range of applications across many industries. At the same time, as powerful as today’s artificial intelligence technologies are, these technologies have important limitations that temper their ability to address many important real world problems. This talk will cover foundational research ongoing at the MIT-IBM Watson AI Lab—a first of its kind hybrid academic-industrial lab—that is aimed at breaking down barriers to broad adoption of AI.

10:10–10:50 am “Ethical and Responsible Use of Data” - Natalie Evans Harris, COO of BrightHive (watch video)

10:50–11:00 am - Break

11:00–11:30 am “Toward Human-Centered Machine Learning” - Patrick Hall, Senior Director of Products at (watch video)

Abstract: Machine learning systems are used today to make life-altering decisions about employment, bail, parole, and lending. Moreover, the scope of decisions delegated to machine learning systems seems likely only to expand in the future. Unfortunately serious discrimination, privacy, and even accuracy concerns can be raised about these systems. Many researchers and practitioners are tackling disparate impact, inaccuracy, privacy violations, and security vulnerabilities with a number of brilliant, but often siloed, approaches. This presentation illustrates how to combine innovations from several sub-disciplines of machine learning research to train explainable, fair, trustable, and accurate predictive modeling systems. Together these techniques create a new and truly human-centered type of machine learning suitable for use in business- and life-critical decision support.

11:30 am–12:00 pm “Let's go Quantum - A Review of Quantum Computing Promise" - Murthy Rallapalli, Augusta University (watch video)

Abstract: Our most advanced classical computers, even the fastest ones, seem unable to withstand the needs of the enormous quantity of data produced in our digital society. Scientists are working on computers using quantum physics, which promise to be able to solve problems hard for conventional computers. This session reviews the concept of Quantum computing and its future.

12:00–12:45 pm

12:45–1:15 pm “Big Data: Fire, Ready, Aim” - Jerry Rosenbaum, DAMA National Capital Region (watch video)

Abstract: In a two-man gun duel, the strategy: fire, ready, aim does not tend to make one successful. So, why do so many companies employ the equivalent of a fire, ready, aim strategy for their (first) big data project? The success rate for big data projects is poor. The Wall Street Journal reported that the average return on investment for big data projects of 55 cents on the dollar, and it has been reported that as high as 80% of the big data projects fail. We will focus our attention on some fundamental steps a company should take in preparation for a big data project. Doing these steps does not guarantee success, but doing them will significantly improves the chances that one will succeed. In addition to discussing the key steps, we will look at several big data projects (some successful, and others not) and see how they handled the key steps and how it affected the success (or failure) of the project.


Breakout A-1

1:30–1:50 pm “George Mason University's Interdisciplinary Data Analytics Engineering Master’s Program” Harry Foxwell, James Baldo, Ioulia Rytikova, George Mason University, USA

Abstract: In 2014, George Mason University became one of the first handful of U.S. universities to offer an interdisciplinary Data Analytics Master’s degree program. Mason’s Volgenau School of Engineering, School of Business, and College of Health and Human Services collaborate on the data analytics engineering degree, tapping into the faculty’s extensive industry experience across disciplines. The Masters in Data Analytics Engineering is designed to provide students with an understanding of the technologies and methodologies necessary for data-driven decision-making. Topics cover data mining, information technology, statistical models, predictive analytics, optimization, risk analysis, and data visualization. Aimed at students who wish to become data scientists and analysts in finance, marketing, operations, business intelligence, and other information-intensive groups that generate and consume large amounts of data, the program also has wider applications, including concentrations in digital forensics, financial engineering, and business analytics. Mason's DAEN program begins with core courses in statistics, algorithms, and databases, and introduces students to the tools of data analytics, including R, Python, and SQL. Follow-up courses provide deeper studies of Metadata, Data Analytics in Social Media, Knowledge Mining, and the Laws and Ethics of Big Data. The program concludes with a capstone project that provides an environment within which MS students apply learning and demonstrate mastery of the data analytics problem identification and conversion to results by integrating tools, methods, experience, and data to deliver -- and professionally communicate -- results, insights, and value.

1:55–2:15 pm “QualiChain: Decentralised Qualifications’ Verification and Management for Learner Empowerment, Education Reengineering and Public Sector Transformation” -
Ingo Keck, Lambert Heller, Maria-Esther Vidal, Leibniz Information Centre for Science and Technology, Germany

Abstract: QualiChain (“Decentralised Qualifications’ Verification and Management for Learner Empowerment, Education Reengineering and Public Sector Transformation,” 2018) is a 36-month EU H2020 project starting from January 2019; it aims at revolutionizing the domains of education and labor market by making academic credentials machine readable, authentic, privacy aware and persistent. Exploiting the features of the decentralized technology blockchain, semantic technologies and data analytics, QualiChain will enable public education institutions and other accreditation authorities to authenticate academic and other qualification awards on blockchains. Private data of qualifications will be stored in secure and privacy preserving storage facilities like Solid (Inrupt, Inc., 2018), considering Self-Sovereign Identity (SSI) principles (as coined by Christopher Allen in 2016), building upon technology developed by the open badges movement (IMS Global Learning Consortium, 2017). Storing data in structured and machine readable form together with the development of an ontology will help to easily gain knowledge from this information. QualiChain will not only provide the infrastructure to transmit and store qualifications and relevant metadata like curricula, but also provide tools for analysis and knowledge extraction. The QualiChain framework will be available as open source, and be adjustable to diverse stakeholders and needs. The social impact of QualiChain will be assessed in four pilots in different countries and institutions: i) cross-university degree equivalence verification; (ii) smart curriculum design; (iii) staffing the public sector; and (iv) consultancy and competency management in human resources. Thus, QualiChain will make available effective solutions to allow for the use of data analytics on all sides of the field of qualifications. It will help educators to improve curricula, employers to better understand and transmit their requirements and students to find out about skills they should invest training in.

2:20–2:40 pm “Lessons Learned from Teaching an Analytics Capstone Course” - Steve Knode, Jon McKeeby, UMUC, USA

Abstract: A presentation and discussion outlining the lessons learned from several years of teaching an analytics capstone course. Several important insights gained from the experience of teaching a course where each student is developing an advanced analytics project from start to finish.

2:45–3:05 pm “STEM, IoT, Video Analytics – A Novel Approach to Big Data Analytics at Concordia International School Shanghai” - Peter Tong, Christopher Carter, Concordia International School Shanghai, China

Abstract: Concordia International School Shanghai (CISS) pioneered the Big Data Analytics high school Applied Course in 2014. Improvements and expansions are continuously being implemented into the course. The students’ data analytics projects carried out in this course used datasets that are available online when the course was first developed. With the advancement of technology and the current interest in STEM courses, (CISS) has integrated engineering, Internet of Things (IoT) and video analytics from the various branches of the high school Applied Courses curriculum into its Big Data Analytics course. Moving further aft of the data analytics projects process, this presentation will discuss how a two-week "Exploration" class of 9th and 10th graders designed sensors to measure key environmental factors in the classroom, sent sensor data to an on-site server, and pulled that data through a data visualizer (Tableau) to create a dashboard of real-time monitoring and access to historical information was being designed and built for air quality data collection. A discussion on how raw data from student built sensors and video footages that were collected in-house for data analyses and acted on for on-campus facilities improvement and for sports development respectively will also be presented.

3:05–3:15 pm Coffee Break, Second Floor Lobby

Breakout A-2

3:15–3:35 pm “Business Analytics as Part of Marketing Education in Business Schools Settings” - Stefan Thalmann, Rene Kerschbaumer, Thomas Foscht, University of Graz, Austria

Abstract: Big Data and Data Science are emerging topics currently shaping our society as well as the world of business. In context of management, Big Data and Data Science can be subsumed under the term business analytics, investigating how current technologies and methods from data science (including Big Data) can be applied to businesses and how these technologies and methods transform the business world. Degree programs in higher education emerged over the years focusing mostly on technical aspects. However, the question remains open how business analytics can be integrated into already existing programs of business schools. For this purpose, we selected an existing marketing course in the master program of an Austrian business school and developed a specific educational concept for incorporating business analytics. The course was designed as a seminar and linked to a real business case of high precision measurement devices. Thereby, the goal for students was to combine already known marketing approaches together with methods from business analytics. Specifically, the task was to use web data and to apply web mining. Overall, we divided our students into three groups and organized the course into three phases containing (1) theoretical foundations, (2) business understandings and (3) project work. While theoretical foundations were built by literature review, students were briefed on business understandings by a company representative. Project work consisted of data extraction, cleaning and segmentation and was concluded by final project presentations. The company representative was positively impressed by project results and the feedback from the students was very positive as well. The students could catch up with the newest technologies from business analytics and gather first practical application experience in the field of marketing. The close interaction with the company partner made the experience more realistic and encouraged the students.

3:40–4:00 pm “Teaching Data Analytics in Business Schools: Pedagogical Challenges and Opportunities” - Anteneh Ayanso, Goodman School of Business, Brock University, Canada

Abstract: In recent years, there has been a proliferation of new programs in data analytics at business schools across Canada and the USA. The Goodman School of Business is among those schools in Canada who started this journey early. This trend, coupled with strong market demands for data-literate graduates, has raised a number of discussion issues within the academic community. In particular, the reliance of analytics in several fields such as information systems, computer science, statistics and operations research, presents several pedagogical challenges as well as opportunities for business schools. In this seminar, Dr. Anteneh Ayanso reflects on several years of his experiences in course development and teaching in this area. He reviews Goodman’s journey in the training of both undergraduate and graduate students and discusses the current challenges, initiatives as well as future potential for Brock University in this exciting field of study.

4:05–4:25 pm “Best Practices in Teaching Data Science to High School Students” -
Emanuel Santos, Neil Whitehead, Concordia International School Hanoi, Viet Nam

Abstract: There are very few high schools in the world offering Data Science as a course. Consequently there are no textbooks that tell people how to teach data science to high school students. This becomes a chicken and egg dilemma where high schools do not offer data science because there is no guide or template to follow, while no one will write text book as there are no schools offering the class. My colleagues (Dr. Peter Tong, Dr Emanuel Santos) and I are developing not only the content but also the methodologies and best practices in teaching data science to high school students. Last year, I presented the curriculum that we are using which focused on what we teach to students. The focus this year is on how we introduce data science to high school students. This presentation will focus on building community partnerships, techniques that we us to teach the students, as well as a website that we are building with a repository of materials for teaching data science to HS students:, The purpose of this presentation is to build partnerships between high schools teaching data science and universities.

4:30–4:50 pm “The Need for Big Data Analytics in Elementary Education” -
Peter Tong, Michelle Wee, Concordia International School Shanghai, China, Craig Gingerich, Lori Gingerich, American School of Doha, Qatar

Abstract: Big data is exploding and we are not sure how to appreciate to so much information. Education is a pipeline process; how can educators better prepare students for higher data analytics education? Concordia International School Shanghai pioneered the applied learning course Big Data Analytics for high school in 2014. In 2016, a middle school Big Data Analytics course was developed and taught as an elective while the high school Big Data Analytics was also delivered via e-learning to Concordia Hanoi to kick start their Big Data Analytics program. The vision was to develop a Big Data Analytics program for K-12 education that would span all divisions. Both the high school and middle school big data analytics program have been successfully developed and taught. Moving down the education pipeline and to support the data analytics program at Concordia Shanghai, a pilot program was carried out on how to deliver Big Data Analytics to the Elementary School in 2018. It is important for elementary students to become data literate of the data information, analytics and in the beauty in numbers for them to gain an appreciation of what data information can tell us. This presentation will describe how analytics can be introduced to third and fourth grade students as an after-school co-curricular activity and the challenges encountered in delivering analytics to younger minds. In this presentation, we will discuss the activities used to engage elementary students in how to collect data using electronic devices and sensors, how to perform simple data wrangling and how to present their findings (analytics). This co-curricular activity also incorporates other areas of education such blended learning, information and communications technology in education and the power of storytelling using video discussion platform and digital creative tools such as Flipgrid and Seesaw.

4:55–5:15 pm “Integrating SAP HANA - Big Data in Memory Technology to IS Education” -
Ming Wang, California State University, USA

Abstract: SAP HANA is an innovative in-memory database management system (DBMS) with data warehouse features. The changes made on SAP HANA have become the trends in the database and data warehouse industries. Many database vendors have recently updated their DBMSs with data-in-memory and columnar data storage features in their new products. To catch the database’s industrial trends and to meet the global challenges in data science, the author introduced SAP HANA technology into her database and data warehousing courses using SAP University Alliances curriculum materials. The author taught the Database and Data Warehouse Systems course at the MSIS graduate level. She taught the relational database and object-relational database using Oracle DBMS and data warehousing using SAP HANA. SAP HANA course materials are downloaded from SAP University Alliances Learning Portal. The study provides the importance, design and delivery of integration of SAP HANA technology in IS education. The presentation focuses on SAP HANA data storage, data modeling and implementation. Global Bike, Inc (GBI) case study will be demonstrated.


Breakout B-1

1:30–1:50 pm “A Novel Deep Learning System for Patient Disease Extraction in Clinical Notes" -
Jinhe Shi, Yi Chen, New Jersey Institute of Technology, Guodong Gao, P Kenyon Crowley, University of Maryland College Park, Chenyu Ha, William C. Kinsman, Inovalon, USA 

Abstract: Accurately recording a patient’s medical conditions in an EHR (Electronic Health Record) system is the basis of effectively documenting patient status, coding for billing, and supporting data-driven clinic decision making. However, patient disease information is often not fully captured in structured EHR systems during administrative processes. Alternatively, physicians provide detailed documentation of a patient’s disease, disease-related evidence, history, treatment plans, and clinical outcomes in unstructured clinical notes. In practice, healthcare industry spends a lot of resource to hire human experts to read clinical notes, manually extract disease information for each patient, map it to code and supplement the structured EHR. However, this is highly labor intensive and time consuming. While it is easy to find mentions of a disease in clinical notes, it is challenging to judge whether the disease mention is positive, i.e. it indeed belongs to the patient. For instances, the disease mentions in the sentences like “I did talk to the family about surveillance of the meningioma”, and “his family history includes cancer in his father” are actually not the disease that the patient has. In other words, both examples are negative disease mentions. This paper, we present an advanced attention based deep learning approach that considers the characteristics of clinic notes to extract patient disease information from clinical notes. This model builds upon and combines the advantages of Convolutional Neural Network (CNN) and a Long Short-term Memory (LSTM) Network.

1:55–2:15 pm “Case Study to Develop Deep Learning Image Recognition and Classification Models for Fashion Items” - Bharatendra Rai, University of Massachusetts –Dartmouth, USA

Abstract: Large scale image recognition and classification is an interesting and challenging problem. This case study uses fashion-MNIST dataset that involves 60000 training images and 10000 testing images. Several popular deep learning models are explored in this study to arrive at a suitable model with high accuracy. Although convolutional neural networks have emerged as a gold-standard for image recognition and classification problems due to speed and accuracy advantages, arriving at an optimal model and making several choices at the time of specifying model architecture, is still a challenging task. This case study provides the best practices and interesting insights.

2:20–2:40 pm “Artificial Intelligence (AI) in Health Care – The Future is NOW!” -
Mike Nestor, Cliff Wilke, Trevor Harris, OptumServe, USA

Abstract: As the use of machine learning and artificial intelligence (AI) becomes more mainstream (and less about robots taking over patient care from doctors), it’s easy to consider the potential impact AI could have on the health care industry. However, AI is already at work in the health care industry today — including helping patients with diabetes regulate their blood sugar, refilling prescriptions, and many back-office or administrative processes executed by providers. As technology continues to rapidly transform and disrupt many industries, the health care industry has certainly leveraged technology to enable AI. And with the convergence of algorithmic advances, data proliferation and tremendous increases in computing power and storage, the opportunities for AI in health care will only increase — enabling health care to become more proactive and less reactive. But what is the future of AI in health care? Or is the future already upon us, and we don’t yet realize it.

2:45–3:05 pm “Criminal Activities in Chicago Analyzed with Watson Analytics” -
Alan Whitehead, Neil Whitehead, Concordia International School Hanoi, Viet Nam

Abstract: This paper investigates the rates of Chicago Crime data by using Watson Analytics to discover patterns and trends in the data. Understanding crime data is important as it allows people to avoid times and areas where crimes occur, while also helping law enforcement to make informed decisions on the allocation of resources to reduce crime. Crime data is of interest to all people because a feeling of safety is a basic need, as shown by Maslow’s Hierarchy of Needs in his famous paper from 1943.

3:05–3:15 pm Coffee Break, Second Floor Lobby

Breakout B-2

3:15–3:35 pm “Data Analytics Career Roles: Guidance for Educators, Students, and Professionals” - Russell Walker, DeVry University, USA

Abstract: Because data analytics is a relatively new and highly dynamic field, a clear consensus on job titles and career roles has yet to emerge. A profusion of varied terms may be used for data analytics roles, creating confusion among those seeking to enter the profession and educators who seek to prepare them. This paper presents a review of recent published writing on analytics career roles, and identifies the most commonly described role titles, how these titles are used, and what skills they most frequently require. Each role is then positioned along a spectrum of technical versus business orientation, to disambiguate whether preparatory programs could best be situated within business schools or science and technology schools within an institution. The results are a step toward developing a consensus terminology for describing career roles in the analytics profession, directing students toward appropriate educational programs for their desired roles, and targeting the right skills sets within those programs.

3:40–4:00 pm “Data Analytics on Teacher’s Net Income for 0-10 years of Teaching Experience” -
Felicia Yong, Nipissing University, Canada; Peter Tong, Concordia International School Shanghai, China

Abstract: When new teachers research where to apply for teaching jobs, two of the top factors they may consider are location and salary. This data analytics study examines location and salary, and potential correlations between the two as they affect my decision as a new teacher to stay and work in Ontario, or to teach abroad – out of province or internationally. The objective of this study is to compare the full-time teacher’s net income salaries per year for 0-10 years of teaching experience in selected locations around the world to determine at which location a teacher will have the highest net income during 10 years of teaching. The analytics will examine the trends at x years of experience during the 10-year span and address some misconceptions or assumptions about “where will teachers earn more” in any given year. The study uses seven data sets ranging from 2016-2018 for selected school boards in Australia, Austria, Canada and China. The locations were chosen based on available data and personal interest. This analytics study ultimately begins to answer an important question for my teaching career: will I make more money by staying in Ontario, or by teaching abroad, either out-of-province, or internationally, and at what year should I move locations? This analytics study is ultimately a quantitative analysis, and factors such as culture, distance, school quality, climate, and safety contribute to a qualitative analysis that must accompany this type of career decision. This study has illuminated the importance of conducting a quantitative analysis to inform the qualitative variables a new teacher must consider before making career decisions about where to teach.

4:05–4:25 pm “How Management Teams Impact Employee Readiness for the AI Technological Revolution” - Allyson Jones, University of Maryland University College, USA

Abstract: Technology and the utility it produces in our daily lives and the global marketplace has occurred at a phenomenal speed in the past decades. The introduction of analyzing large data sets composed of Big Data and Internet of Things (IoT) has afforded organizations the ability to become razor focused on strategic planning and evidence-based decision making due to advanced predictive modeling. In order for organizations to build efficacy, increase productivity, and maintain their competitive edges during this digital transformative era they must first access the skill sets of their existing workforce. This innovative digital transformation era has created a new interdependency on Artificial Intelligence (AI). Additionally, the collaboration with intelligent machinery is now commonplace in today’s global marketplace. The emphasis on the automation of the organization’s daily practices must also include prioritizing the upskilling and development of their workforce through this phase of digital transformation. 

4:30-4:50 pm “Data Analytics Implementation Across Multiple Disciplines” - Juan Prieto-Valdes, Miami Dade College, USA

Abstract: Different approaches towards discovering the rules that govern the behavior of big data are in progress, but the greatest nuisance lies in the lethargy to embed analytics as part of the curricula, and apparently, this situation will remain as long as we do not realize the need of transforming current teaching philosophy. It is imperative to decide the most appropriate channel to bring to all these disciplines the knowledge to deal with data analysis and evidence based decisions. In this presentation is discussed the idea of incorporating the big data awareness in all math levels, as a basic method to support its application in other disciplines. This work discusses an approach to prepare school and university students for the today data driven society. The incorporation of understanding and extract insights from big data in traditional mathematics courses is a need for the future well-being of humanity. Application projects related to big data analysis were included as a part of this study in the traditional business calculus course. A group of computer aide exercises for systems with infinite number of elements, introductory examples related to chaotic dynamics, game theory, infinite series and improper integrals, combined optimization, and statistics among other examples are discussed. Five hundred years ago the rank of a man was valued by the amount of land he owned, a hundred years ago, and even today, for the amount of money, but from now, the level is, and will be priced, by the amount of data he handles.

4:55–5:15 pm “Role of Meetups in Data Science and Analytics Training” Linesh Dave, AT&T, USA

Abstract: When pursuing and gaining expertise in any emerging technology such as Data Science and Analytics, continuous personal development becomes mandatory. A school degree or corporate certification scratches the surface or merely provides an introduction to it. Other avenues are required to grow and learn further especially when the new field of technology is maybe rapidly growing. This is where a social group outside of school, workplace or family proves to very useful and important. I learnt this through my personal experience three years back when I completed my Masters Degree in Data Analytics through this institution. So, I want to share my story and experience about meetups with the audience.

Reception in the Lower Level Arts Program Gallery
5:30–6:30 pm

  • Remarks by Dr. Alan Drimmer, Senior Vice President and Chief Academic Officer, University of Maryland University College


Agenda At A Glance: Tuesday, June 4, 2019

Registration Opens
7:30 am

Continental Breakfast
8:00–9:00 am

Opening Remarks
9:00–9:10 am
  • Kathryn Klose, Vice Provost and Dean of The Graduate School, University of Maryland University College

9:10–9:50 am Keynote: “Data Literacy For All” - Kirk Borne, Principal Data Scientist, Booz Allen Hamilton (watch video)

Abstract: This talk will present several informative and accessible examples of data science, machine learning, and statistics that can be used in various settings where you need to communicate the power, uses, and possible misuses of data to non-data scientists, including students (of all ages), potential business clients, and colleagues who want to know what makes data literacy so much fun and such a valuable ally in any organization or career!

9:50–10:20 am “Ethics of Good Intentions” - Cortnie Abercrombie, Founder AI Truth (watch video)

Abstract: Data scientists are going down in history for new fields of work they are doing in AI. But many are not thinking about the impact this work has on people. As creators of AI systems we have a responsibility to understand that we are creators of systems that will be used in much broader ways than we can currently foresee. It’s time to learn how to think critically and broadly about the applications of projects you take on, challenge yourself and everyone around you, and find the courage to speak up and push back when ethical boundaries get crossed. This session will focus on real life examples of data science and AI that have gone bad and what to do about it if you find yourself facing the same situations.

10:20–10:30 am Break

10:30–10:50 am “Connection and Convergence: Education, Training and Data Science” - 
Sylvia Spengler, Program Director Information and Intelligent System, National Science Foundation (watch video)

10:50–11:10 am "Supercharge Machine Learning" - Sameer Joshi, President & CEO Datanova Scientific (watch video)

Abstract: Machine learning and Artificial Intelligence (A.I.) are essential to take advantage of the large and heterogeneous data. A pervasive problem in doing so it the siloed nature of data sources. Individually, these data sources can offer only so much insight. However, if they are fused together, then they can show the big picture and create an appropriately rich data baseline to supercharge automated insight. This talk is structured into three segments (1) Different types of cyber data. The changing face of cybersecurity and ISR is characterized by sensor generated datasets. These are fast, large, and sparsely structured. These must be negotiated with traditional data that is slow, very rich, and highly structured. We discuss the underlying principles of fusing heterogeneous data. (2) What is good data for Machine Learning and A.I. We discuss different categories of automated methods and what type of data they need. We also discuss how automated methods can keep pace with changing data. (3) Case study. We present a cybersecurity case study showing how we corralled heterogenous data into a unified data baseline, and how we used artificial intelligence and natural language processing to provide rapid ‘big picture’ understanding.

11:10–11:20 am Break

11:20–11:40 am “The Art of the Possible: Reimagining Higher Education in the Age AI & the Fourth Industrial Revolution (4IR)” - Curtis B. Charles, Data Scientist, Microsoft (watch video)

Join Microsoft’s Data & AI thought leader, and former HiED administrator, Curtis B. Charles, Ph.D., as he discusses how HiED Institutions are partnering with Microsoft to leverage AI to enable new processes that accelerate curriculum exploration and research breakthroughs. As the Fourth Individual Revolution (4IR) unfolds, and the blurring the lines between the physical, digital, and biological spheres deepens, the way we learn and live, would continue to be impacted at exponential rates. To add, the dynamism and fluidity of current trends and those yet unimagined will continue to disrupt, and shape higher education’s future. To meet the workforce needs of the 4IR, where, 65% of students in grade school will perform jobs that have not been invented yet, and, where the percentage of jobs requiring technology skills will increase to 77% in less than a decade, President Aoun of Northeastern University, purports that Colleges and Universities would need to develop new literacies where students would need to attain knowledge of mathematics, coding and basic engineering principles; the capacity to understand and utilize Big Data through analysis; and fluency in digital communication. In addition, educating students to scale in an age of AI and the 4IR would further require that students are equipped with new cognitive capabilities such as: systems thinking, entrepreneurship, cultural agility, and Critical Thinking. The degree to which institutions can harness their resources to achieve their objectives will depend upon the clarity of these objectives and the institution’s willingness to set priorities and solve its problems. Dr. Charles will discuss how Microsoft is partnering with Colleges and Universities to reimagine education, in an age of Artificial Intelligence, and the 4th Industrial revolution.

11:40 am–12:00 pm “DevOps and Machine Learning” - Yuriko Horvath -Solutions Architect Manager, Amazon Web Services (watch video)

12:00–12:20 pm “Leveraging Innovative Data and AI Capabilities to Improve Healthcare Outcomes” - Will Kinsman, Sr Manager AI Solutions, Inovalon (watch video)

12:20 pm

12:45–1:15 pm “Closing Panel Discussion ” - Moderators: Elena Gortcheva, Program Chair, University of Maryland University College & Peter Aiken, Professor, Virginia Commonwealth University | Panelists: Janet Dobbins, Vice President for Strategic Partnerships and Business Development, The Institute for Statistics Education at; Jeff Hale, Data Scientist and Entrepreneur; Kristin Abkemeier, Data Scientist, Improvix Technologies (for U.S. Department of State);  Dr. Kirk Borne, Principal Data Scientist, Booz Allen Hamilton; Harry Foxwell, Associate Professor, George Mason University (watch video)

2:10–4:20 pm Global Analytics Competition

  • 2:10–2:40 pm “Natural Water Resources: G-7 versus Next 11” - Kyle Jacobson, Anthony Saikali, Justin Zuccon, Dalhousie University, Canada (watch video)

  • 2:45–3:05 pm “The Catastrophic Effects of Global Warming-The Forces at Play” - Barbara Lucas Johnson, Carl Johnson, Prasanthi Lingamallu, University of Connecticut, USA (watch video)

  • 3:10–3:30 pm “Effects of Pesticide Usage, Cell Phone Towers on Bee Colonies in the United States” - Megan Brumbaugh, Rajesh Kumar Gnanasekaran, University of Maryland University College, USA (watch video)

  • 3:35–3:55 pm “Waste Management Report-Erase the Waste” - Nidhishant Dixit, Sithara K, Roshan Jain, State University of New York at Buffalo, USA (watch video)

  • 4:00–4:20 pm “Tracing the Sources of Green House Gas Emissions, Globally and in our Backyard” - David Silberman, Ellen Tappin, Isaac Asiedu, University of Maryland University College, USA (watch video)

4:20–4:40 pm Break

4:40-5:00 pm Award Ceremony and Closing