The team of the Global Lab AI

The Lab funds researchers at the Institute for Analytical Sociology, Linköping University, and the Division of Data Science and Artificial
Intelligence for the Social Sciences, Department of Computer Science and
Engineering, Chalmers University of Technology, Gothenburg, Sweden. 

The Core Team

Adel Daoud, principal investigator

Adel Daoud is the PI of the Lab. He is an Associate Professor at the 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, University of Cambridge, Max Planck Institute for the Studies of Societies, and the Alan Turing Institute. He researchers the impact of economic, political, and natural shocks on global poverty and health. Daoud implements novel methodologies in machine learning and causal inference to analyze the causes and consequences of human development. He has published in journals such as PNAS, World Development, Cambridge J of Economics, Food Policy, Epidemiology, International J of Epidemiology, and Ecological Economics.

Fredrik Johansson


Fredrik Johansson is a co-PI of the Lab. His research interests are primarily focused on causal inference in machine learning and decision-making with applications in healthcare. Fredrik is the principal investigator for Health AI Lab.
Devdatt Dubhashi

Devdatt Dubhashi

Devdatt Dubhashi is a co-PI of the Lab. His research is in design and analysis of randomized algorithms, machine learning for Big Data

Core team

Connor Jerzak

Connor Jerzak

Together with Adel, Connor Jerzak is researching how to adapt causal inference methods using satellite images. Connor received his Master’s in Statistics and PhD in Government from Harvard University, where he was advised by Gary King, Kosuke Imai, and Xiang Zhou. During graduate school, he also worked as an intern at Adobe Research with Nikos Vlassis. His research has appeared or is forthcoming in statistics, economics, and political science journals. Connor is currently doing a one-year postdoc in the Lab at Linköping University (Sweden) while serving as a Visiting Scholar in the Program on Governance and Local Development (GLD) at the University of Gothenburg. In 2022, he will be joining the University of Texas at Austin as an Assistant Professor in the Department of Government.

Mohammad Kakooei

Mohammad Kakooei 

Mohammad Kakooei is a researcher at the postdoctoral level in the Lab. He received the B.S., M.Sc., and Ph.D. degrees in electronic engineering from Shahid Beheshti University (SBU), Tehran, Iran University of Science and Technology (IUST), Tehran, and Babol Noshirvani University of Technology (BNUT), Babol, in 2011, 2014, and 2020, respectively. Then he became a postdoc researcher with the Department of Computer and Electrical Engineering, BNUT, Babol, Iran. He is currently a Researcher with the Department of Data Science and Artificial Intelligence, Chalmers University of Technology, Gothenburg, Sweden. His research interests include image processing, machine learning, remote sensing, parallel processing, GPGPU, and data mining applications.
Markus Pettersson

Markus Pettersson

Markus Pettersson is a PhD student in the Lab. He wrote his masters thesis in the Lab at Data Science and AI at Chalmers University of Technology. He has previously been on exchange at the University of Minnesota. Together with Julia Ortheden, Markus wrote his master thesis (Spring 2021) Using Self-Supervised Deep Learning to Predict Poverty from Satellite Data at the Division of Data Science and AI, Chalmers Technical University.

James Bailie

As of September 2020, I am a PhD student in statistics at Harvard, advised by Prof. Xiao-Li Meng and with the support of a Fulbright Future Scholarship. Read more about James work.

Xiao-Li Meng

Xiao-Li Meng 

Through the research interest of the Lab, Xiao-Li Meng and Adel are researching the relationship between data science and statistics—what we denote deep statistics. Statistics is about the science of summarizing data. Deep statistics is that, but in the age of data science. Data science almost always consists of the overlap of three components: a domain of interest (e.g., economics, medicine, or sociology), computer science, and statistics. Deep statistics is then the statistical component of data science. In this course, we present a framework for a statistical science that tailors inference for multi-source, multi-phase, and multi-resolution. Deep statistics perpetuates all data-heavy substantive domains, and in this year’s course, the principles of deep statistics will be unpacked in the context of Artificial Intelligence (AI) and Earth Observations (EO) to analyze some of humanities most pressing global challenges: sustainable development. We are planning to give a course at Dep of Statistics, Harvard University on these topics.

Collaborators (selected)

Ellen Lust and Governance and LocalDeveloment (GLD)

Ellen is the Founding Director of the Program on Governance and Local Development at Yale University (est. 2013), and then at the University of Gothenburg (est. 2015), and is Professor in the Department of Political Science at the University of Gothenburg.

Felipe Jordán

Felipe Jordán and Adel Daoud conducted the foundational data work while both were employed at Harvard University in 2019. Felipe Jordán is a Postdoctoral Scholar at UCSB's Environmental Markets Lab. He obtained a Ph.D. in Political Economy and Government from Harvard University. Before his doctoral studies, he completed my B.S and M.S in Economics at Universidad de Chile and worked for a year as a lecturer at the Department of Economics of Universidad de Chile.

His research focuses on the political economy of sustainable development. He leverage machine learning methods to construct novel data from historical records and satellite imagery. He use these data to study the impact of policies and institutions on economic prosperity and environmental sustainability at the local and national levels, as well as the impact of environmental conditions on human populations. He is particularly interested in how indigenous cultural and institutional backgrounds interact with Western institutions to shape sustainable development and culture in indigenous communities. His doctoral dissertation explores the long-term impacts of three national-level institutions imposed upon the Mapuche people of Southern Chile: the reservation system that allocated land to Mapuche when forced to settle in the late 19th century, the system of directed industrial development that has promoted the expansion of plantation forests in Mapuche's homeland since 1974, and the system of courts that dealt with property rights within reservations between 1931 and 1979. His Job Market Paper exploits quasi-random variation in reservations' access to these courts to estimate the impact of national-level property rights enforcement on reservations' long-term development. Visit his web page to read about the projects he is currently working on 

Subhashis Banerjee

Subhashis Banerjee is a Professor in Computer Science and Engineering at Indian Institute of Technology Delhi Hauz Khas, New Delhi. With Professor Banerjee, the Lab is extending its scope to India. 

Sourabh B Paul

Sourabh Paul received his PhD in economics from the University of British Columbia, Vancouver and MS in Quantitative Economics from Indian Statistical Institute, Kolkata. Sourabh's work broadly focuses on how some recent changes in the Indian economy have affected the most vulnerable sections of Indian society. His research encompasses issues such as castes and labour mobility, distributional aspects of trade policy, preferences, nutrition, gender, and violence, among others.

Heather Reese

Heather Reese’s primary research interest is in remote sensing of subarctic alpine vegetation types and changes in the vegetation of these areas. She combines multiple data sources such as optical data from Sentinel-2 or Landsat, but also incorporate 3D data from LiDAR or photogrammetry.

Hans Ekbrand

Hans Ekbrand is a senior researcher at the Department of Sociology, University of Gothenburg. He, Björn Halleröd, David Gordon, Shailen Nandy and Adel, conducted researcher on the relationship between good governance and global child poverty during 2013 to 2019.

Björn Halleröd

Björn Halleröd is a professor in sociology at University of Gothenburg. During the past 25 years I have worked with issues related to poverty, wellbeing, and general living conditions. Over this period I have contributed to bridging the gap between theoretical concepts, e.g. poverty and more lately, wellbeing, and the empirical measurement of the concepts. Björn led several project – where Adel was a researcher – on global child poverty that is foundational to the Lab’s research.

David Gordon

David Gordon currently hold the posts of Professor of Social Justice, Director of the Bristol Poverty Institute and Director of the Townsend Centre for International Poverty Research at the University of Bristol.  He provide research leadership for multidisciplinary poverty relevant research across the University of Bristol. Together with Professor Halleröd, Daoud, and others, Professor Gordon contributed to a joint project on global poverty funded by the Swedish Research council. 

Shailen Nandy

In collaboration with Adel and others, Shailen Nandy contributed to a project on global child poverty. He joined Cardiff University in September 2016 and teach on a number of undergraduate and post-graduate Social Science modules, and convene modules on International and Comparative Social and Public Policy. He is currently the Social Policy subject lead. Over the past 20 years my research has focused on different aspects of international development, and on poverty analysis and anti-poverty strategies. 


Collaborators of the Lab

The Lab works with a number of highly skilled collaborators.


Julia Ortheden

Together with Markus Pettersson, Julia wrote her master thesis (Spring 2021) Using Self-Supervised Deep Learning to Predict Poverty from Satellite Data at the Division of Data Science and AI, Chalmers Technical University. On August 2021, she was recruited to Capgemini – Cloud Infrastructure Services, Oslo, Norway. 

SarathM Raveendran

Sarath M Raveendran was a master student in the Lab, writing his master thesis during Fall 2021. His thesis focused on extending our deep-learning methods for measuring human development from Africa to India.


Benjamin Vinnerholt was a master student in the Lab.


Jesper Strömberg  was a master student in the Lab.

Alumni of the Lab

We have the pleasure of working with several students and collaborators at the Lab. Some of them stay in academia and others continue to industry position.

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