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:

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Tuesday, 02/20/2018, 2:00PM – 3:15PM
IoES / Statistics Faculty Search Job Talk
Characterizing and Quantifying Benefits of Mitigation and Avoided Impacts – Challenges and Opportunities

Location: Franz Hall 2258A
Claudia Tebaldi, Project Scientist at National Center for Atmospheric Research

The research topic of avoided impacts or, alternatively framed, benefits of mitigation – i.e., the comparison of outcomes across alternative possible climate futures — is a compelling framework for the analysis of expected future outcomes from climatic changes. It offers a fertile ground for analysis ranging from physical climate impacts to their repercussions on natural and human systems (e.g., heat and precipitation extremes and their effects on human well-being, agricultural yield changes and food security, tropical cyclone changes in intensity or frequency and ensuing damages on infrastructures). Furthermore, the necessity of quantifying differential impacts opens up interesting statistical questions, from basic signal-to-noise characterizations, to the construction of econometric models to determine dose-response functions for the system impacted.

Over the last couple of years, I have been contributing to an activity at NCAR framed in terms of the characterization of “Benefits of Reduced Anthropogenic Climate change”: BRACE. We have relied on a set of large initial condition ensembles run with NCAR-DOE’s earth system model, CESM. The different ensembles explore climate outcomes under several scenarios, some of them newly designed low-warming pathways, addressing specifically the Paris targets of 1.5C and 2.0C global warming above preindustrial. I will present some examples from the analyses that I conducted, in collaboration with colleagues within and outside of NCAR, specifically about the analysis of changes in heat extremes under different scenarios utilizing extreme value statistics, and the quantification of expected changes in global crop yields from warming temperatures utilizing an empirical model of the relation between weather and yield shocks. These analyses span a wide range of the methodological challenges that uncertainty quantification encounters, and offer perspectives on exciting future work.

Claudia Tebaldi has been a project scientist in the Climate Change Research section of the Climate and Global Dynamics laboratory at NCAR since October 2013 and she is Senior Science Advisor at Climate Central Inc. She holds a Ph.D. in Statistics from Duke University, and she was a postdoc and then a project scientist at NCAR from 1997 to 2007. From 2008 to 2013 she worked as a research scientist for Climate Central, a research and communication organization. Her research focuses on the analysis and statistical characterization of climate change projections and their uncertainty, as derived from climate models, extending from the impacts on the physical climate system, with particular interest in the characterization of changes in extremes, to impacts on human and natural system, like agricultural yields, water resources, health. She is also interested in the detection of observed changes and their attribution to anthropogenic influences. She was a lead author in Working Group 1 of the Intergovernmental Panel on Climate Change, Fifth Assessment Report, Chapter 12, Long Term Projections, Commitments and Irreversibility, and is a co-chair of the World Climate Research Program ScenarioMIP group, responsible for the experimental design of the forthcoming Coupled Model Intercomparison Project, Phase 6, experiments exploring future scenarios, and a member of the Scientific Steering Committee of DAMIP, a similar group responsible for organizing experiments relevant to the Detection and Attribution research community. She is also member of the Scientific Steering Committee of the International ad-hoc Detection and Attribution Group.

Wednesday, 02/21/2018, 12:00PM – 1:30PM
Heterogeneous Causal Effects: A Propensity Score Approach

Location: 4240 Public Affairs Building (Joint with CCPR and CSS)
Yu Xie, Professor at Princeton University

Heterogeneity is ubiquitous in social science. Individuals differ not only in background characteristics, but also in how they respond to a particular treatment. In this presentation, Yu Xie argues that a useful approach to studying heterogeneous causal effects is through the use of the propensity score. He demonstrates the use of the propensity score approach in three scenarios: when ignorability is true, when treatment is randomly assigned, and when ignorability is not true but there are valid instrumental variables.

Friday, 02/23/2018, Time: 2:00PM – 3:15PM
IoES / Statistics Faculty Search Job Talk

Location: 4242 Young Hall
Daniela Castro Camilo, Postdoctoral Fellow at King Abdullah University of Science and Technology

Abstract: TBA

Friday, 02/23/2018, 2:00PM – 3:00PM
Designing a “Cyber-Based” Article Bank to Enhance Statistics Education

Location: 1100 Terasaki Life Sciences Building (CEILS Journal Club)
Mahtash Esfandiari, Senior Continuing Lecturer at UCLA Statistics

Dr. Esfandiari will share the results of a study she conducted on using in-person versus virtual office hours when teaching statistics in a presentation titled, “Designing a “Cyber-Based” Article Bank to Enhance Statistics Education.”

Monday, 02/26/2018, Time: 2:00PM – 3:15PM
IoES / Statistics Faculty Search Job Talk

Location: 4242 Young Hall
Claire McKinnon

Abstract: TBA

Tuesday, 02/27/2018, Time: 2:00PM – 3:30PM
Title: TBD

Location: Franz Hall 2258A
Caiming Xiong (Metamind / Salesforce / Google)

Abstract: TBA