In this half-day workshop, we will present an honorable lecture and an interactive panel discussion on research in statistics and data science.

SWS is committed to fostering an inclusive environment that enables a diverse community to be involved in and contribute to SWS. All audience are welcome, with priority given to SWS members, graduate students and early-stage researchers of all genders. Registration is required for attendance.

More details are available here.

UCLA Faculty Center, Sequoia Room

We are pleased to announce that the Society of Women in Statistics (SWS) is hosting its first ever Industry Event this quarter, featuring a panel of female statisticians from Google, FabFitFun, Jukin Media, and Kukuyeva Consulting. The evening will begin with networking, followed by a moderated panel discussion and Q&A. Discussion topics will include advice for students, projects our panelists have worked on, team dynamics and more.

The event will be held on Thursday, January 30, 2020 from 4:00 pm – 6:00 pm in the Sequoia Room of the Faculty Center. There will be informal networking and refreshments for graduate students only from 4:00 pm – 5:00 pm, and a panel discussion/Q&A from 5:00 pm – 6:00 pm. The second hour will be opened to a select number of undergraduate statistics students.

All are welcome! Please RSVP here by January 23rd.

This event is organized by your SWS Industry Co-Chairs: Ashley Chiu and Larissa Jong

Redwood Room, UCLA Faculty Center
Amy Braverman, Principal Statistician
Jet Propulsion Laboratory, California Institute of Technology

Dr. Brand presented on May 6th at 4:30 pm in the Sierra Room of the Faculty Center. Dinner followed the talk.

Abstract: Variation in social science effects across subpopulations of interest is ubiquitous. Social scientists routinely partition their samples into subgroups to explore how the effects of particular events or interventions vary, or treatments, often by variables like race and gender. Causal methodologists also explore how effects vary by selection into the treatment. In both cases, the key subpopulations are determined by the researcher based on theoretical priors. Developing machine-learning techniques, however, allow researchers, to explore sources of variation they may not have previously considered or envisaged, i.e. to explore data-driven treatment effect heterogeneity. In this paper, we analyze an important topic in the literature on social inequality, the effects of higher education on unemployment and low wage work, with well-defined theoretical guidelines as to effect heterogeneity of interest, and compare what we learn from conventional interaction and propensity methods to machine learning methods. We encourage researchers to follow similar practices in their work on variation in effects, and offer simple yet powerful tools by which to do so.

Bio: Jennie E. Brand is Professor of Sociology and Statistics at the University of California, Los Angeles (UCLA). She is Director of the California Center for Population Research (CCPR) and Co-Director of the Center for Social Statistics (CSS) at UCLA. She is Chair-Elect of the Methodology Section of the American Sociological Association (ASA) and an elected Board Member of the International Sociological Association (ISA) Research Committte on Social Stratification and Mobility (RC28). Prof. Brand is a member of the Board of Overseers of the General Social Survey (GSS) and a member of the Technical Review Committee for the National Longitudinal Surveys Program at the Bureau of Labor Statistics. She received the ASA Methodology Leo Goodman Mid-Career Award in 2016, and honorable mention for the ASA Inequality, Poverty, and Mobility William Julius Wilson Mid-Career Award in 2014. Prof. Brand studies social stratification and inequality, mobility, social demography, education, and methods for causal inference.

The Distinguished Women in Statistics Lecture happens quarterly.

SWS hosted this luncheon for female faculty, staff, and graduate students of the UCLA Statistics Department. It was at Wolfgang Puck in Ackerman Union.

Hacienda Room, Faculty Center
Karen McKinnon, Assistant Professor of Statistics
UCLA

Over the course of my lifetime, anthropogenic climate change has moved from a plausible idea to near-scientific certainty to something that we experience in our own lives through nuisance flooding and unprecedented heat waves. Understanding the climate system is thus not only of scientific interest, but is also critically important for policies surrounding adaptation and mitigation. But the climate system remains tantalizingly complex, especially at the small scales at which we live, demanding that we use a wide range of methods in order to understand its physics and dynamics, and to separate the signal from the noise. In this lecture, I will discuss some examples of my own and others’ work at the intersection of climate science and statistics, including the use of statistical methods to improve long-lead predictions of extreme weather and better understand the contribution of internal variability to recent observed trends in regional climate. There remain many open questions at the junction of statistics, machine learning, and climate science; I will conclude with some thoughts on promising future directions.

The Distinguished Women in Statistics Lecture happens quarterly.

This is a link to photos taken at this event.

SWS hosted this luncheon for female faculty, staff, and graduate students of the UCLA Statistics Department. It happened at the UCLA Faculty Center.