NSF Track 2 CRESH: Summer School on AI and Smart Health 2022
Monday July 25 - Wednesday July 27, 2022. Lecture Details Below
Lecture Details
Introduction to Machine Learning
This module begins by introducing what can be achieved today with Machine Learning. It will then overview the basics of supervised learning. In particular, it will discuss the notions of features, linear predictors, loss minimization, and stochastic gradient descent. It will do so within the context of a typical machine learning workflow that entails the collection of data, the learning, and the validation prior to a practical deployment of model predictors.
Speaker(s)
Dr. Gianfranco Doretto
Gianfranco Doretto joined West Virginia University in 2010, where he is now an Associate Professor. His research interests span several areas of computer vision and machine learning, with a current focus of using deep learning techniques for learning representations that supports applications such as video analysis, biometrics, robotics, and smart health. Before joining WVU, Dr. Doretto was a lead scientist at GE Global Research. He holds a D.Eng. degree in Electronics Engineering from the University of Padua, Italy, and a M.S. and a Ph.D. in Computer Science, both from UCLA.
Social Network Analysis
Social media has become an indispensable communication tool giving rise to global online social networks. Analyzing social media data helps study the properties and behaviors of individuals, communities, and organizations and advance our understanding of social and cultural dynamics through the lens of modern information and communication technologies (ICTs). The knowledge thus extracted helps in harnessing business intelligence and guides decision making. The course will introduce basic social science concepts, theories, and principles relevant to guide model development, data analysis, and inference making. Data collection from social media sites using crawlers and APIs will be discussed. Interfacing the collected data with visualization and analytical tools will be taught to enable better understanding of data and extracting actionable knowledge. Specifically, social network analysis (SNA) techniques will be taught to meaningfully study the data to identify trends, leaders, and groups. These skills can be used to analyze social media data about products, services, campaigns, markets, events, customers, and employees; segment audience by geography or demographics, influencers, recommenders or detractors; and measure social media activities. Case-studies will be discussed to demonstrate the impact even the most basic analyses could afford.
Lesson plan for SNA
Social Network analysis course- Intro/Motivation (30 mins)
- Social Network Analysis + Graph theory (lecture) - centrality, clustering (60 mins)
- Break (10 mins)
- Case Study (Gephi visualization) (40 mins)
- Twitter Data Collection (10 mins)
- Twitter Gephi Visualization (20 mins)
- YouTube Trending videos visualization (10 mins)
Speaker(s)
Dr. Nitin Agarwal
Dr. Nitin Agarwal is Maulden-Entergy Endowed Chair and Distinguished Professor of Information Science at University of Arkansas - Little Rock. He is the founding director of the Collaboratorium for Social Media and Online Behavioral Studies (COSMOS). His research contributions lie at the intersection of social computing, behavior-cultural modeling, collective action, social cyber forensics, artificial intelligence, data mining, and machine learning. His research aims to push the boundaries of our understanding of digital and cyber social behaviors that emerge and evolve constantly in the modern information and communication platforms. From Saudi Arabian women's right to drive cyber campaigns to Autism awareness campaigns to ISIS' and anti-West/anti-NATO disinformation campaigns, at COSMOS, he is directing several projects with over $15 million in funding that have made foundational and applicational contributions to social and computational sciences, particularly in understanding coordinated cyber campaigns. He has published 10 books and over 200 articles in top-tier peer-reviewed forums including the NATO’s Defense StratCom Journal, with several best paper awards and nominations. His most recent book explores the deviant behaviors on the Internet and is published by Springer in their series on cybersecurity. His work has been covered by local, national, and international media including Bloomberg, US News, KUAR, Arkansas Business, Arkansas Times, Arkansas Democrat Gazette, and many others. Over the last several years, Dr. Agarwal has spoken at various public and professional, national and international forums such as the NATO’s StratCom COE (Riga, Latvia), DARPA, US Department of State, US Naval Space and Warfare (SPAWAR), US Pentagon’s Strategic Multilevel Assessment groups, US National Academies of Sciences Engineering and Medicine, US Office of the Director of National Intelligence, Facebook Asia Pacific HQ, Twitter Asia Pacific HQ, US Embassy in Singapore, Singapore Ministry of Communication and Information, NATO Senior Leadership meetings, USIP, among others. Visit https://profiles.ualr.edu/na10 for more details.
Introduction to Deep Learning Architectures
This lecture provides a broad introduction to the basic concept of the classical neural networks (NN) and its current evolution to deep learning (DL) technology. The primary goal of this lecture is to introduce the well-known deep learning architectures and their applications. This lecture will describe the history of neural networks and its progress to current deep learning technology. It covers several deep learning architectures such the classical multi-layer feed forward neural networks, multi-layer auto-encoders, and examples of popular convolutional neural network-based architectures such as AlexNet, VGGNet, GooGleNet (inception modules), and ResNet. Advanced architectures such as Siamese deep networks, coupled neural networks will also be covered.
Speaker(s)
Nasser M. Nasrabadi
Nasser M. Nasrabadi is a professor in the Lane Computer Science and Electrical Engineering Department at West Virginia University. He was senior research scientist (ST) at US Army Research Laboratory (ARL). He is actively engaged in research in deep learning, biometrics, computer vision and image processing. He has published over 300 papers in journals and conference proceedings. He has been an associate editor for the IEEE Transactions on Image Processing, IEEE Transactions on Circuits and Systems for Video Technology and IEEE Transactions for Neural Networks. He is a Fellow of IEEE and SPIE.
Privacy, Fairness, and Explainable AI
The remarkable development of AI in healthcare domain presents obvious privacy and fairness issues, when data analytics models are built on users’ personal and highly sensitive data, e.g., clinical records, user profiles, and biomedical images. In this talk, we start with the general problem of privacy, medical ethics, and their significance in digital health. We then concentrate on research of differential privacy preserving machine learning. Differential privacy ensures that the adversary cannot infer any information about any particular record with high confidence (controlled by a privacy budget) from the released learning models. We introduce the concept of differential privacy and present several mechanisms, including Laplace mechanism, exponential mechanism, input perturbation, and functional perturbation, that have been developed to enforce differential privacy in machine learning models, and discuss how to apply and adapt those mechanism to preserve differential privacy in deep learning models. Next we focus on fairness aware machine learning as decision models can cause discrimination against individuals and groups beyond damages to privacy. We introduce a unified causal modeling and counterfactual inference based framework to capture and measure discrimination accurately and present challenges and several efficient algorithms to discover and remove various types of discrimination such as direct/indirect and individual/group/system discrimination from historical training datasets as well as predictive models while preserving data utility. Some recent work of fairness aware generative adversarial networks and fair recommendation will also be covered. We conclude the talk with challenges of building trustworthy AI systems in digital health.
Speaker(s)
Dr. Xintao Wu
Dr. Xintao Wu is the professor and the Charles D. Morgan/Acxiom Endowed Graduate Research Chair in Database and leads Social Awareness and Intelligent Learning (SAIL) Lab in Computer Science and Computer Engineering Department at the University of Arkansas. He got his BS degree in Information Science from the University of Science and Technology of China in 1994, ME degree in Computer Engineering from the Chinese Academy of Space Technology in 1997, and Ph.D. in Information Technology from George Mason University in 2001. Dr. Wu's major research interests include data mining, privacy and security, fairness aware learning, and big data analysis. Dr. Wu has published over 120 scholarly papers and served on editorial boards of six international journals and many program committees of top international conferences in data mining and AI. Dr. Wu is also a recipient of NSF CAREER Award (2006) and several paper awards including PAKDD'13 Best Application Paper Award, BIBM'13 Best Paper Award, CNS'19 Best Paper Award, and PAKDD'19 Most Influential Paper Award.
Dr. Lu Zhang
Lu Zhang is an Assistant Professor in Computer Science and Computer Engineering Department at University of Arkansas. He received the BEng degree in computer science and engineering from University of Science and Technology of China, in 2008, and the PhD degree in computer science from Nanyang Technological University in 2013. His research interests lie in the field of data mining, machine learning, artificial intelligence, and distributed computing, particularly in discrimination/fairness-aware machine learning, causal modeling and inference, and data mining and privacy for genetic data.
Effective Communication
Communicating the excitement of science is a specialized skill. This course is geared towards scientists who want to learn how to best engage an audience, targeting effective approaches to presenting scientific discovery and fundamentals.
Speaker(s)
Dr. Rebecca 'Becky' Thompson
Rebecca Thompson is the Head of Education and Public Outreach at the Fermi National Accelerator Lab. After receiving her Ph.D. in physics from the University of Texas at Austin, she joined the staff of American Physical Society where she led their public engagement efforts for 11 years. While there she authored the popular Spectra, The Laser Superhero series of physics comic books. She is a Fellow of the American Physical Society and a member of the Sigma Xi honors society. Her first book, Fire, Ice, and Physics; The Science of Game of Thrones, was released in October 2019.
Multimodal AI and Self-supervised learning: Theory and Applications in Smart Health
In this three-part tutorial, I will cover 1) the theoretical foundation of multimodal AI, including representation learning and information fusion perspectives. Various competing approaches to joint embedding and multimodal fusion will be compared and criticized; 2) the algorithmic foundation of self-supervised learning (SSL). The focus will be on the latest development in contrastive SSL including the construction of proxy tasks. 3) Two exemplar applications of multimodal AI and SSL – social media data mining for disrupting illicit supply networks and home-based early screening of autism disorder spectrum (ASD).
Speaker(s)
Dr. Xin Li
Dr. Xin Li received the B.S. degree with highest honors in electronic engineering and information science from University of Science and Technology of China, Hefei, in 1996, and the Ph.D. degree in electrical engineering from Princeton University, Princeton, NJ, in 2000. He was a Member of Technical Staff with Sharp Laboratories of America, Camas, WA from Aug. 2000 to Dec. 2002. Since Jan. 2003, he has been a faculty member in Lane Department of Computer Science and Electrical Engineering. His research interests include image/video processing and computer vision. Dr. Li was elected a Fellow of IEEE in 2017.
Hands-on with Practical AI platforms
This session will focus on the use three platforms or frameworks for big data science and artificial intelligence. This will be a hands-on session and participants will examine and use the discussed frameworks to analyze large datasets and experiment with machine learning methodologies. As this is a hands-on session, participants will need to have access to a laptop or desktop computer (rather than a phone or Ipad). Also, since some of the activities will use tools from Google, each participant should have a Google account (such as gmail).
Speaker(s)
Don McLaughlin
Don McLaughlin is a project manager for the CRESH project and an instructor in the Lane Department of Computer Science and Electrical Engineering at West Virginia University. His teaching portfolio include data science, artificial intelligence and computer graphics. Formerly, he managed the high performance computer facility at WVU. He was also West Virginia University’s representative to Internet2 for a number of years.
For more information, please contact Don Adjeroh, PhD, Professor and Associate Chair of LCSEE, Project Lead PI.