Web-Based Lectures





Title: Toward Protecting the Privacy of Individuals When Disseminating Data: Challenges in Disclosure Risk Assessment
Presenters: Tom Krenzke, Westat, and Jianzhu Li, Westat
Moderator: Aleksandra Slavkovic, Pennsylvania State University
Date and Time: Wednesday, February 6, 2019, 12:00-1:30 p.m. EST
Sponsor: ASA Committee on Privacy and Confidentiality
Twitter Hashtag: #ASAwebinar

Registration Deadline: Tuesday, February 5, at 12:00 p.m. Eastern time

Description:
We discuss the theory and background of useful approaches for determining the level of data disclosure risk prior to disseminating data. We focus on challenges experienced from several case studies and provide insights into the ways that the risks can be estimated. These challenges relate to risk estimate diagnostics and sensitivity analysis, longitudinal risk, when public use data already exists, variable selection, setting risk thresholds, impact of weights and missing values, query tools, geography, attribute disclosure, risk of synthetic data, and external data. Some discussion of differential privacy is provided in this context. Once the key elements that trigger the relatively high risks are understood, data protection approaches can be determined and processed while balancing the risk reduction with retention of data utility.

Tom Krenzke, senior statistician and Associate Director in the Westat statistical group. He has led research on data disclosure limitation methods working toward solutions for several Federal agencies and Disclosure Review Boards. Mr. Krenzke is an ASA Fellow and President of the Washington Statistical Society, and serves on the ASA Privacy and Confidentiality Committee and Westat Institutional Review Board.

Jianzhu Li, senior statistician at Westat. Dr. Li has 16 years of experience in survey research, including sample design, nonresponse adjustment, imputation, data confidentiality and disclosure protection, and small area estimation. She has worked on several statistical confidentiality projects over the past few years for Government agencies such as NSF, NCHS, NCES, Census Bureau, and USDA.

Registration Fees:
This webinar is free to anyone who would like to attend. However, registration is limited so you must register to receive the access information. The access information will be emailed to everyone who has registered the afternoon of Tuesday, February 5.

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Registration is closed.




Title: Social Network Analytics for Fraud Detection
Presenter: María Óskarsdóttir, KU Leuven
Date and Time: Tuesday, February 19, 2019, 1:00 p.m. – 2:30 p.m. Eastern time
Sponsor: Section on Statistical Consulting

Registration Deadline: Friday, February 15, at 12:00 p.m. Eastern time

Description:
Social networks appear all around us and by nature they contain a lot of valuable data and information that is hidden in the underlying network structure. The information can be used to gain insights by means of analytical techniques, which offer the possibility to incorporate a representation of relationships, human interaction and influence in predictive models. Fraud in particular has a social character, since the probability of someone committing fraud may depend on the people they are connected to. The fraud detection domain can therefore benefit greatly from the analysis of social networks.

In this webinar, we will introduce networks and their application in a fraud detection setting. We will show how to translate unstructured network information into useful and meaningful characteristics of a subject. We will extract features from the subject’s neighborhood as well as the network as a whole and demonstrate how these network-based features can be used to enrich traditional data analysis techniques.

Bio:
María Óskarsdóttir holds a Ph.D. in Business Analytics from KU Leuven, Belgium, and a Master’s degree in Mathematics form the Leibniz Universität Hannover in Germany. She is currently a post-doctoral researcher in insurance analytics at the Faculty of Economics and Business, KU Leuven, where her research puts focus on applying social network analytics techniques for predictive modeling in marketing, credit scoring and insurance. She has published papers in international peer-reviewed journals like Expert Systems with Applications and Business & Information Systems Engineering.

Registration Fees:
Member of the Section on Statistical Consulting: $20
ASA Member: $65
Nonmember: $85

Each registration is allowed one web connection and one audio connection. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Friday, February 15, with the access information to join the webinar and the link to download and print a copy of the presentation slides.




Title: Wearable and Implantable Technology (WIT) with Biopharmaceutical Applications
Presenter: Ciprian Crainiceanu, Professor, Department of Biostatistics at Johns Hopkins University
Date and Time: Wednesday, February 20, 2019, 12:00 p.m. – 2:00 p.m. Eastern time
Sponsor: Biopharmaceutical Section

Registration Deadline: Monday, February 18, at 12:00 p.m. Eastern time

Description:
Wearable and Implantable Technology (WIT) is rapidly changing the data analytic landscape due to their reduced bias and measurement error as well as to the sheer size and complexity of the recorded signals. In this talk I will review some of the most used and useful sensors in the ever-expanding WIT analytic environment and their potential impact on Biopharmaceutical research. I will describe the use of accelerometers, heart and glucose monitors, as well as their combination with ecological momentary assessment (EMA) for improved patient reported outcomes. Several case studies highlighting the application of WIT in clinical trials will be provided. I will introduce an array of scientific problems that can be answered using WIT and describe methods designed to analyze the WIT data from the micro- (sub-second-level) to the macro-scale (minute-, hour- or day-level) data. Based on a better understanding of the WIT data, I will show how the design of experiments can be improved for specific Biopharmaceutical interventions.

Bio:
Dr. Crainiceanu received his PhD from Cornell University in 2003 and is currently a Professor in the Department of Biostatistics at Johns Hopkins University. He leads two research groups: The Statistical Methods and Applications for Research in Technology (SMART, http://www.smart-stats.org) and the Wearable and Implantable Technology (WIT). His research is focused on wearable devices (accelerometers, heart monitors, gps devices, glucometers, ecological momentary assessment) as well as clinical brain imaging, especially CT and structural MRI. Dr Crainiceanu is a Fellow of the American Statistical Association and a recipient of the Myrtro Leftkopoulou award.

Registration Fees:
Biopharmaceutical Section Members: $0
ASA Members: $59
Nonmembers: $74

Each registration is allowed one web connection. Sound is received via audio streaming from your computer’s speakers. Multiple persons are encouraged to view each registered connection (for example, by projecting the webinar in a conference room).

Register

Access Information
Registered persons will be sent an email the afternoon of Monday, February 18, with the access information to join the webinar and the link to download and print a copy of the presentation slides.