Other UCLA departments frequently hold seminars related to Statistics and of likely of interest to our members. Here are links to UCLA Biostatistics seminars and UCLA Biomath seminars:

Join the UCLA Statistics seminars mailing list by sending an email to with “subscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is to be subscribed. After doing that please respond to the email that you receive. An automated email will be sent which confirms that you have been added.

You may be receiving our seminar emails because you are subscribed to our seminars mailing list (or one of our other mailing lists). You can determine which is the case by looking at the subject line of a seminar email. You may unsubscribe from the seminar mailing list by sending an email to with “unsubscribe stat_seminars” (without quotation marks) in the subject field and the message body blank. This needs to be done from the address that is subscribed. After sending that email please follow the directions in the email response that you receive. If you are getting our seminar emails because of a subscription to one of our other mailing lists then the word “seminars” in the subject field must have the appropriate replacement.

Tuesday, 12/12/2017, 2:00PM – 3:30PM
Dynamic Modeling for Health in the Age of Big Data

4240 Public Affairs Building
Prof. Nathaniel Osgood, Professor, Department of Computer Science, Associate Faculty at Department of Community Health & Epidemiology and Bioengineering Division
University of Saskatchewan

Traditional approaches to public health concerns have conferred great advances in the duration and quality of life. Public health interventions – from improved sanitation efforts, to vaccination campaigns, to contact tracing and environmental regulations – have helped reduce common risks to health throughout many areas of the world. Unfortunately, while traditional methods from the health sciences have proven admirably suited for addressing traditional challenges, a troubling crop of complex health challenges confront the nation and the world, and threaten to stop – and even reverse the – rise in length and quality of life that many have taken for granted. Examples include multi-factorial problems such as obesity and obesity-related chronic disease, the spread of drug-resistant and rapidly mutating pathogens that evade control efforts, and “syndemics” of mutually reinforcing health conditions (such as Diabetes and TB; substance abuse, violence and HIV/AIDS; obesity & stress). Such challenges have proven troublingly policy resistant, with interventions being thwarted by “blowback” from the complex feedbacks involved, and attendant costs threaten to overwhelm health care systems. In the face of such challenges public health decision makers are increasingly supplementing their toolbox using “system science” techniques. Such methods – also widely known as “complex systems approaches” – provide a way to understand a system’s behavior as a whole and as more than the sum of its parts, and a means of anticipating and managing the behavior of a system in more judicious and proactive fashion. However, such approaches offer substantially greater insight and power when combined with rich data sources. Within this talk, we will highlight the great promise afforded by combining of Systems Science techniques and rich data sources, particularly emphasizing the role of cross-linking models with “big data” offering high volume, velocity, variety and veracity. Examples of such data include fine-grained temporal and spatial information collected by smartphone-based and wearable as well as building and municipal sensors, data from social media posts and search behaviour, helpline calls, website accesses and rich cross-linked databases. Decision-oriented models grounded by such novel data sources can allow for articulated theory building regarding difficult-to-observe aspects of human behavior. Such models can also aid in informing evaluation of and judicious selection between sophisticated interventions to lessen the health burden of a wide variety of health conditions. Such models are particularly powerful when complemented by machine learning and computational statistics techniques that permit recurrent model regrounding in the newest evidence, and which allow a model to knit together holistic portrait of the system as a whole, and which support grounded investigation of between intervention strategies tradeoffs.