#4 Finding one's path in statistics and data science. An Interview with Prof. Jennifer Hill, NYU

Jennifer Hill Professor of Applied Statistics at New York University, Steinhardt School of Culture, Education, and Human Development, and Director of the Center for Practice and Research at the Intersection of Information, Society, and Methodology (PRIISM). She is a co-founder of the first ever professional society devoted to causal inference research Inaugural leadership committee for the Society of Causal Inference (2019-present). She is also an Associate editor for the American Statistician, Journal of Causal inference. She has won numerous awards, among others Hill was the 2020–2021 winner of the New York University Distinguished Teaching Award. Jennifer develops and evaluates methods to help answer the types of causal questions that are vital to policy research and scientific development. Most recently Hill has been pursuing two intersecting strands of research. The first focuses on Bayesian nonparametric methods that allow for flexible estimation of causal. The second line of work pursues strategies for exploring the impact of violations of typical causal inference assumptions such as ignorability (all confounders measured) and common support (overlap).

Given his extensive experience, we discuss his perspective on academic excellence, leadership, with a special focus on applied statistics including data science.

I hope you enjoy it!

Adel

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About me, the interviewer. At the time of producing this interview, I was an Associate Professor at the Institute for Analytical Sociology, Linköping University, and an
Affiliated Associate Professor in Data Science and Artificial Intelligence for the Social Sciences, Department of Computer Science and Engineering, Chalmers University of Technology, Gothenburg, Sweden. Previously he held positions at Harvard University, University of Cambridge, Max Planck Institute for the Studies of Societies, and the Alan Turing Institute.

My research has both a social scientific and methodological orientation. For the social sciences, I research the effects of economic, political, and natural shocks on living standards and health, globally and locally. I implement and develop novel methodologies in machine learning and causal inference to analyze the causes and consequences of poverty and health. I have published in journals such as PNAS, Science Advances, World Development, International J of Epidemiology, American J of Epidemiology, and Ecological Economics, and in machine-learning conferences as Association for the Advancement of Artificial Intelligence (AAAI) and The North American Chapter of the Association for Computational Linguistics (NAACL). More information at www.adeldaoud.se.

I am leading The AI and Global Development Lab. The vision of the Lab is to “combine AI, earth observation, and social theory to analyze sustainable development globally and locally.” More information is provided at www.global-lab.ai )


Music by Vincent Rubinetti
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00:00 - Introduction
04:01 - Managing your time
08:54 - When to yes and no
18:00 - Jennifer's trajectory towards statistics
29:18 - Handling a burnout
34:54 - Choosing a nontraditional path
38:49 - Taking and giving
42:54 - Leadership style
51:31 - Micro strategies
1:01:09 - time management
1:04:41 - Handling emails
1:09:25 - Future plans