Gal Wachtel graduated from the MSc in Social Data Science in 2021.

Through this course I hope to better position myself to apply computational techniques to population health, particularly towards improving resource allocation to underserved communities. To this end, I look forward to exploring how causal inference methods and spatial network analysis can enhance traditional epidemiological methods. Specifically, I am interested in how these methods can help understand what we do not know, thus expanding the ability of population health analysis to capture the experiences of diverse populations and reducing bias. For example, I find the work of Gregg Gonsalves and Raj Chetty, both of whom use spatial analysis to investigate distribution of resources, very compelling. Questions that address the social determinants of health, such as identifying patterns in opioid usage in order to detect hotspots, are interesting to me. Having worked with federal agencies to design data driven interventions, such as PEPFAR and the National Institute of Health’s National Cancer Institute, I am also interested in exploring how the improvement of these methods can influence systemic justice when fed into larger public mechanisms. I have found this question particularly poignant during my recent work on the response to COVID-19 where the desire for data was unyielding, yet there were nascent standards governing its usage. Lastly, as mentioned in my correspondence with the program, I hope to have a joint supervision by a member of the OII and by Dr. Moritz Kraemer, a researcher at Oxford’s zoology department. Dr. Kraemer investigates the spatial dynamics of infectious diseases and I believe that a co-advising will allow me to tackle the questions above in the most complete manner.