Adel Daoud is the host and organizer of a new podcast series, The Journeys of Scholars. He writes:

Many of us academics are fascinated by the pursuit of academic excellence and mastery, yet it can remain a puzzle how to achieve this pursuit optimally. There exist many scholarly and popular books, articles, and presentations about excellence and mastery, but only a few sources systematically interview those that are on the path of excellence and make those interviews publicly available. Thus, there is a gap between the insight one can gather from reading such books versus hearing from a top performer reflecting on their journey. 

The Journeys of Scholars (JoS) is a podcast, which I have recently created, to fill that gap by aiming to decipher the pursuit of academic excellence through interviews about the trajectories, macro–micro strategies, habits, and advice from top-class academic performers. The interviews are about topics that are usually not discussed as systematically in public academic settings as they perhaps should be. While there is no single blueprint for academic success, these interviews are intended to provide advice and guidance for scholars striving toward excellence by reflecting on the ways different top performers achieved their goals. After all, we all need advice, assistance, and inspiration from our forerunners that paved the way toward new frontiers. The aim of this podcast is to supply these pieces of advice, assistance, and inspiration. 

Most of the interviews currently available at the JoS YouTube Channel—an audio-only version will be made available on Spotify and similar podcast media—are currently with statisticians and computational social scientists. The reason for this current focus is simple: my scholarly network currently is situated in this disciplinary overlap. However, the podcast will eventually expand to include scholars with other disciplinary backgrounds, as I am curious to find out and compare what trajectories scholars within and between disciplines tend to follow. At some point, I might write a book, encapsulating some of the key advice, but for now, the focus of JoS is to conduct interviews with top academic performers and make them available for students, early-career professors, or full professors—anyone interested in the topics of JoS.

Recently, the JoS featured Prof. Xiao-Li Meng, who will be familiar to readers of the IMS Bulletin. While I have known Prof. Meng for several years, I was pleasantly surprised to hear several new stories about his academic journey. Although we were both Harvard affiliated—at that time, I was a postdoc at the Center for Population Studies at Harvard University—we first met in 2019 outside of Harvard by chance. He and I, among others, were invited to the UN Global Pulse in New York for a workshop. The purpose of the workshop was to evaluate how data science can be used for research and policy issues on global sustainable development—mitigating poverty, handling climate change, and fostering global cooperation. I knew of Prof. Meng’s many statistical contributions already before we met, but it struck me that although he had focused mainly on theoretical topics in statistics, he was still actively and deeply participating in all these challenging applied topics, presented in the workshop. Despite the fact that all these applied topics seemed to be residing outside of his area of expertise, he was deeply curious, reflective, and interested. In his JoS interview, my opening question to Prof. Meng was, “How do you stay curious and open to new theoretical and applied ideas, while also making deep contributions to numerous fields in statistics and data science?” His response is intriguing and has stayed in my mind ever since, as a guiding principle for venturing outside one’s main discipline. I will refrain from giving spoilers here and encourage the readers of the IMS Bulletin to listen to the full interview ( 

Currently, the JoS features the following interviews, and others are planned to appear on a regular basis:

Pursuing excellence, with Professor Gary King, Harvard University:

How to combine academia and entrepreneurship. Continuing the conversation with Prof. Gary King at Harvard:

Following your curiosity, with Xiao-Li Meng, Professor of Statistics, Harvard University:

Establishing your research program, with Prof. Stephen Raudenbush, University of Chicago:

Choosing your academic path, with Professor Christopher Winship, Harvard University:

Building excellent research environments, with Professor Peter Hedström, Linköping University:

Finding one’s path as a statistician or data scientist, with Prof. Jennifer Hill, New York University:

Having completed seven interviews with esteemed scholars, each lasting for about an hour and a half, it strikes me how the trajectories towards mastery in one’s field can vary, often with a twist. Professor Jennifer Hill, for example, started her Bachelor’s studies with a social science background and then moved into a PhD in statistics. Interestingly, she said that at the beginning of her graduate student years, she found the mathematics courses to be challenging, and was struggling to keep up with her peers, who often had a traditional mathematics background. So she dedicated herself to deeper training in mathematics and statistics and thereby overcame those challenges. Today, she has risen to become one of the leading statisticians of her generation. Professor Steven Raudenbush at the University of Chicago had a nonconventional path toward excellence. He revealed that he had a non-academic career before his academic journey, and thus embarked on a PhD at Harvard at an older age than is expected of top performers. Like Professor Hill, he dedicated himself deeply to the study of mathematics and statistics, and ended up making critical contributions to multilevel modeling and its application to neighborhoods and education research. 

And there are many more stories. There are several gems of insight to be derived from how Prof. Gary King pioneered political methodology, how Prof. Christopher Winship contributed to the causal inference revolution, and how Prof. Peter Hedström initiated the field of analytical sociology. 

While all the scholars I have interviewed exhibit a similar level of perseverance and dedication to their work, they also testify to the fact that nurturing excellence may require different approaches. That variation is natural: as humans are born into different conditions, they grow and flourish differently. Creativity and exploration take time, and so does nurturing excellence. Yet currently academia puts a premium on early bloomers and fast success—which is perhaps a feature of modern society. Although early flourishing is a strong indicator of remarkable contributions, perhaps academia needs to maintain space for a wider variety of academic trajectories.

The JoS is a long-term project of mine that I have just started and will be pursuing as a side project, in parallel to my research. I will continue interviewing scholars across multiple fields—about one every month. While the number of academic masters walking the face of the earth is relatively small (compared to the size of the global population), this number is still sufficiently large to keep me busy for the rest of my career—and beyond. 


About Adel Daoud

Adel Daoud is an Associate Professor at Institute for Analytical Sociology, Linköping University, and 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, the University of Cambridge, Max Planck Institute for the Studies of Societies, and the Alan Turing Institute. His research has both a social-scientific and methodological orientation. For the social sciences, he researchers the effect of international development interventions (e.g., anti-poverty policies) on global poverty, but also the impact of sudden shocks (e.g., economic, political, and natural disasters). Daoud implements novel methodologies in machine learning and causal inference to analyze the causes and consequences of poverty. He has published in journals such as PNAS, Science Advances, World Development, International J of Epidemiology, and Ecological Economics, and machine-learning conferences such as the Association for the Advancement of Artificial Intelligence (AAAI) and the North American Chapter of the Association for Computational Linguistics (NAACL). 

Daoud leads The AI and Global Development Lab ( The vision of the Lab is to “combine AI, earth observation, and socio-economic theories to analyze sustainable and human development globally.”

In 2022, Daoud was awarded the Hans L. Zetterberg Prize in Sociology which is given annually to young researchers, who with their scholarly work in sociology, preferably by fruitfully combining theory and practice, have advanced the research front. More info at