Valentin Danchev

Valentin's research focuses on evaluating research evidence and biases across scientific fields using large-scale data and computational approaches at the intersection of network science, computational sociology, and machine learning. His work is part of ongoing efforts to develop techniques for scalable (yet tractable), rapid, and updating estimation of research replicability. He is also interested in leveraging network experiments and methods of causal inference to evaluate the adoption of research norms and practices towards open and credible science. Valentin’s background is interdisciplinary. Before joining METRICS, he was a Postdoctoral Scholar at Knowledge Lab and the Department of Sociology, University of Chicago, where he conducted a large-scale multilayer network evaluation of published biomedical results, with a focus on research replicability and robustness. He holds a DPhil (PhD) in Development Studies from the University of Oxford, where he was also affiliated with the networks research group at the Mathematical Institute. Prior to his DPhil, he received his MA in Sociological Research from the University of Essex and his BA in Sociology from the University of Sofia.