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School of Business and Management

Dr Valentin Danchev


Lecturer in Business Analytics

Twitter: @valdanchev


Dr Valentin Danchev is a Lecturer (Assistant Professor) in Business Analytics at the School of Business and Management. He holds a DPhil in Development Studies from the University of Oxford (Oxford Department of International Development), where he was also affiliated with the network science group at the Mathematical Institute. Prior to joining Queen Mary, he held postdoctoral positions at the Stanford University School of Medicine and the University of Chicago, and was a Lecturer in Computational Social Science at the University of Essex.



  • BUS265: Machine Learning and Digital Technology
  • BUS346: Social Network Analysis

Valentin teaches data science and analytics with an emphasis on open reproducible research and responsible analysis of real-world data. His open learning materials on Reproducible Data Science with Python provide an accessible introduction to modern open-source computational tools, reproducible workflows, hands-on Python coding, and data science techniques (from exploratory data analysis (EDA), machine learning, causal inference, and network analysis) necessary to perform open, reproducible, and ethical data analysis.


Research Interests:

Valentin’s research combines computational methods from social data science and network analysis, with approaches from reproducible research and metascience to study the transparency, reproducibility, bias, and social impact of data-intensive research. His current focus is on evaluating and improving the robustness and reproducibility of research innovations and applying artificial intelligence and machine learning in the social and health sciences.

In another stream of research, he uses computational social science and network analysis to examine health-related misinformation, digital-health interventions, and inequality in network structures of global migration.

Centre and Group Membership

Valentin is a member of the Centre for Globalisation Research (CGR).


Research publications

Yacine Jernite, Huu Nguyen, Stella Biderman, Anna Rogers, Maraim Masoud, Valentin Danchev, Samson Tan, Alexandra Sasha Luccioni, Nishant Subramani, Isaac Johnson, Gerard Dupont, Jesse Dodge, Kyle Lo, Zeerak Talat, Dragomir Radev, Aaron Gokaslan, Somaieh Nikpoor, Peter Henderson, Rishi Bommasani, and Margaret Mitchell. 2022. Data Governance in the Age of Large-Scale Data-Driven Language Technology. In 2022 ACM Conference on Fairness, Accountability, and Transparency (FAccT '22). Association for Computing Machinery, New York, NY, USA, 2206–2222.

Danchev, V., Min, Y., Borghi, J., Baiocchi, M. and Ioannidis, JPA., (2021). Evaluation of Data Sharing After Implementation of the International Committee of Medical Journal Editors Data Sharing Statement Requirement. Jama Network Open. 4 (1), e2033972-e2033972

Naudet, F., Siebert, M., Pellen, C., Gaba, J., Axfors, C., Cristea, I., Danchev, V., Mansmann, U., Ohmann, C., Wallach, JD., Moher, D. and Ioannidis, JPA., (2021). Medical journal requirements for clinical trial data sharing: Ripe for improvement. PLOS Medicine. 18 (10), e1003844-e1003844

Hardwicke, TE., Serghiou, S., Janiaud, P., Danchev, V., Crüwell, S., Goodman, SN. and Ioannidis, JPA., (2020). Calibrating the Scientific Ecosystem Through Meta-Research. Annual Review of Statistics and Its Application. 7 (1), 11-37

Janiaud, P., Axfors, C., van't Hooft, J., Saccilotto, R., Agarwal, A., Appenzeller-Herzog, C., Contopoulos-Ioannidis, DG., Danchev, V., Dirnagl, U., Ewald, H., Gartlehner, G., Goodman, SN., Haber, NA., Ioannidis, AD., Ioannidis, JPA., Lythgoe, MP., Ma, W., Macleod, M., Malički, M., Meerpohl, JJ., Min, Y., Moher, D., Nagavci, B., Naudet, F., Pauli-Magnus, C., O'Sullivan, JW., Riedel, N., Roth, JA., Sauermann, M., Schandelmaier, S., Schmitt, AM., Speich, B., Williamson, PR. and Hemkens, LG., (2020). The worldwide clinical trial research response to the COVID-19 pandemic - the first 100 days. F1000Research. 9, 1193-1193

Danchev, V., Rzhetsky, A. and Evans, JA., (2019). Centralized scientific communities are less likely to generate replicable results. eLife. 8, e43094

Danchev, V. and Porter, MA., (2018). Neither global nor local: Heterogeneous connectivity in spatial network structures of world migration. Social Networks. 53, 4-19

Book chapters

Danchev, V. and Porter, M., (2021). Migration networks: applications of network analysis to macroscale migration patterns. In: Research Handbook on International Migration and Digital Technology. Edited by Marie McAuliffe. Edward Elgar Publishing. 70–90.


Valentin welcomes prospective PhD students interested in the following topics: responsible data science and business analytics, computational social science, social networks, causal inference, science policy and innovation, data governance, digital health interventions and evaluations of transparency, reproducibility and robustness of applications of machine learning and artificial intelligence in health and social domains.

Public Engagement

Valentin is a Fellow of the Software Sustainability Institute.

Press Coverage

Many researchers say they’ll share data — but don’t”, Nature. 21 June 2022. 

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