Each month, Harrisburg University hosts a free lecture open to the community on Data Analytics and Applied Artificial Intelligence. Organized by Dr. Jay Liebowitz, Distinguished Chair of Applied Business and Finance at Harrisburg University, these events occur monthly on Tuesday evenings. Presentations & Q&A are 4:30-5:30 pm, followed by networking/light refreshments.
Each event takes place at Harrisburg University’s Philadelphia Location, 1500 Spring Garden Street, Philadelphia, PA. Click here to go to the Harrisburg University Philadelphia website.
Tuesday, September 25, 2018, 4:30 pm-5:30 pm (Presentation and Q&A); Networking/Light Refreshments 5:30-6 pm
Distinguished Speaker: Dr. Ron Daniel, Director, Elsevier Labs
Title: Semi-automated exploration and extraction of data in scientific tables
Abstract: Most of the experimental results reported in scientific articles, and recorded in databases or in supplements to the article, are provided in tables. Unfortunately, the amazing recent progress in natural language understanding is of little help if we want to automatically understand those tables. Tables are, after all, not your grandmother’s natural language. Despite this, we believe significant progress can be made towards the goal of combining tables of related information into larger sets that can be analyzed, visualized, understood, and used as the basis for decisions. Elsevier Labs is prototyping tools to help guide people in the exploration of tables from many articles and the extraction and merging of the data they contain. This talk will show examples of what has been accomplished by manually merging such data. With those as examples of the desired outcomes, we will describe our experiments to duplicate such examples, the work flow in which they operate, and our most recent results.
Ron Daniel is the Director of Elsevier Labs, an R&D group which concentrates on smart content and on the future of scholarly communications. Educated as an electrical engineer, Ron has done
extensive work on metadata standards such as the Dublin Core, RDF, and PRISM. Before joining Elsevier in 2010, he worked at a startup that was acquired for its automatic classification technology, and consulted on taxonomy and information management issues for nine years. Ron received his Ph.D. in Electrical Engineering from Oklahoma State University, and was a postdoctoral researcher at Cambridge University and at Los Alamos National Laboratory. Ron is bemused by the way technology reincarnates itself, specifically in the way that parallel implementations of neural networks for machine vision are currently in vogue, just as they were more than 20 years ago when he was working on them in grad school.
Tuesday, October 16, 2018: 4:30-5:30 pm (Presentation and Q&A); 5:30-6 pm (Networking/Light Refreshments)
Distinguished Speaker: Dr. Scott Nestler, Academic Director, MS in Business Analytics Program, University of Notre Dame
Title: “Should We?” Not Just “Can We?”: Ethical Considerations In Data Science And Business Analytics
Abstract: Data-informed decision making creates new opportunities, but also expands the set of possible risks to organizations when technical capabilities get too far ahead of ethical considerations. Concerns should extend beyond individual privacy to issues of identity, ownership, and reputation. In this presentation, motivating examples of ethical dilemmas and algorithmic bias are explored using data from behavioral science, social media, wearable devices, health care, and human resources. Roles of public laws (to include regional/national differences), government regulations, professional codes, organizational approaches, and individual ethics are presented as ways to address such issues when performing and managing analytic activities. Key questions considered include: (1) Just because I can do something (with data and analytics), does that mean that I should?; (2) How can I help guide ethical decision making at my organization, while still accomplishing business objectives?; and (3) What extra precautions should I consider when dealing with human (medical and behavioral) data?
Bio: Scott Nestler, PhD, CAP, PStat, is an Associate Teaching Professor in the IT, Analytics, and Operations Department in the Mendoza College of Business, at the University of Notre Dame. He is the incoming Academic Director of the new MS in Business Analytics program and the dual-degree MBA/MSBA program, both located on campus in South Bend. These are in addition to the existing MSBA offered by ND at their Chicago campus. Before joining Notre Dame, Scott was an operations research analyst in the U.S. Army, teaching at the Naval Postgraduate School and the U.S. Military Academy at West Point. Nestler has a Ph.D. in management science from the University of Maryland, College Park. He is the vice chair for programs of the INFORMS SpORts (Operations Research in Sports) Section and is the past chair of the Analytics Certification Board.
Tuesday, November 13, 2018: 4:30-5:30 pm (Presentation and Q&A); 5:30-6:00: Networking/Light Refreshments
Distinguished Speaker: Dr. Joshua Schnell, Director at Clarivate Analytics
Title: Applying Data Analytics and AI to Understand Citation Context
Abstract: Authors cite references in their papers for a variety of reasons (e.g. confirmatory vs. critical), and when doing so they use wide ranging language. With the increasing availability of machine readable research articles, natural language processing (NLP) and machine learning (ML) techniques are being used to determine the meaning of references at scale. This lecture will introduce the concept of citation context, review the latest research on applying NLP and ML to determine the meaning of a cited reference, and highlight areas of opportunity for future study.
Bio: Dr. Joshua Schnell is a Director at Clarivate Analytics, with expertise in science planning and assessment, the evaluation of research and development (R&D) programs, and in science and technology (S&T) policy. Previously, he oversaw a team of analysts conducting program evaluations of research and training programs and developing tools for data-driven science management. Before joining Clarivate Analytics, Dr. Schnell directed the analytics group at a science management start-up in the Washington DC area, worked in research administration at Northwestern University, and was an S&T Policy Graduate Fellow at the US National Academies of Science. He holds a PhD from Northwestern University.
Tuesday, December 4, 2018: 4:30-5:30 pm Presentation and Q&A; 5:30-6 pm: Networking/Light Refreshments
Distinguished Speaker: Dr. Jan Neumann, Director of the Comcast Applied Artificial Intelligence Research Group
Title: How AI allows Comcast to Reinvent the Customer Experience
Abstract: Comcast uses AI and Machine Learning in many of its products from the Emmy winning voice remote for the X1 entertainment product, to anticipating the needs of the customer in the digital home. We will show how machine learning is an essential part of our content discovery platform to help our customers find the content they love, and how it allows our smart media analytics solutions to enable a richer navigation and search experience. We will also talk about a major recent effort by Comcast to reinvent the customer service experience for its customers using artificial intelligence. We will explain how Comcast uses deep learning to build virtual assistants that allow its customers to contact the company with questions or concerns and how it uses contextual information about customers and systems in a reinforcement learning framework to identify the best actions that answer these customers’ questions or resolve their concerns.
Bio: Jan Neumann leads the Comcast Applied Artificial Intelligence Research group with team members in Washington, DC, Philadelphia, Chicago, Denver and Silicon Valley. His team combines large-scale machine learning, deep learning, NLP and computer vision to develop novel algorithms and product concepts that improve the experience of Comcast’s customers such as the X1 voice remote and personalization features, virtual assistants and predictive intelligence for customer service, as well as smart video and sensor analytics. Before Comcast, he worked for Siemens Corporate Research on various computer vision related projects such as driver assistance systems and video surveillance. He has published over 20 papers in scientific conferences and journals, and is a frequent speaker on machine learning and data science. He holds a Ph.D. in Computer Science from the University of Maryland, College Park.