Professor Ridhi Kashyap honoured with early career award in population research
Congratulations are in order for Professor Ridhi Kashyap, who was last week presented with the Early Achievement Award at the Population Association of America's annual conference.
The award is given biennially to scholars who have made distinguished contributions to population research in the first ten years after receipt of their PhD.
The Population Association of America (PAA) promotes and supports high-quality population research. Its members include demographers, sociologists, economists, public health professionals, and other individuals interested in research and education in the population field.
After receiving the Award at the PAA annual conference in St Louis, Missouri, Professor Kashyap said:
I was honoured to receive the PAA Early Achievement Award and am so grateful to my wonderful mentors and colleagues who supported my nomination.
I am so proud to be a part of the population research community and the work showcased at PAA was testament to the thriving and innovative directions of the field.
Professor Kashyap's research spans different areas of demography, including questions linked to mortality and population health, gender inequality, marriage and family, and migration and ethnicity.
A central interest has been to leverage computational approaches for demographic research, and forge links between demography and a growing interdisciplinary community of computational social science.
From a methodological standpoint, Professor Kashyap is interested in how computational methods (such as agent-based models, microsimulation, machine learning) and new data streams (including digital trace data from the web and social media) can contribute to the study of population dynamics and social inequalities.
An example of this is the Digital Gender Gaps project, where social media and survey data are used to nowcast global digital gender inequalities in internet and mobile access, a global sustainable development goal (SDG) indicator for which there is a significant data gap.
