Aditi Nag is a postdoctoral researcher at the Indian Institute of Technology Kharagpur. She studies stress, resiliency, and governance in historic towns to prevent tourism- and transportation-related issues before they happen. In this Postdoc Portrait interview, she shares how she is paving the way in this field with two first-of-their-kind fellowships.
Predicting Urban Stress in Heritage Towns
Q | What drew you to urban planning?
Although I was drawn to architecture because of its capacity to solve problems, my interest in science resulted from a recurring discrepancy between urban complexity and the methods typically used to address it. Even when the effects were long-term and structural, decisions impacting residents, tourists, and mobility were frequently presented in a descriptive manner, particularly in heritage towns.
I grew curious in how these stacked demands could be examined more analytically as I progressed into planning and research. I started incorporating structured assessment and modeling methodologies into heritage and tourism studies during my PhD thesis, which was funded by the Department of Science & Technology-Innovation in Science Pursuit for Inspired Research (DST-INSPIRE) fellowship—the first awarded in this field of study. That experience informed my research style, which is rigorous, evidence-based, and attentive to living situations. It also defined my overarching goal: to establish legacy research as a discipline capable of reaching high scientific and methodological standards.
Q | What scientific problem are you trying to solve?
My current postdoctoral study focuses on stress thresholds in heritage towns under pressure from tourism and transportation. The primary goal is to understand when cumulative stress leads to functional breakdown and how that moment might be identified early enough to guide interventions.
Instead of focusing on individual factors, the study examines how pressures build up across behavior, infrastructure, and governance. So far, one crucial observation has been that stress manifests itself initially through subtle signs such as resident tiredness, service strain, and temporal congestion, long before physical deterioration becomes apparent.
This work is supported by the Anusandhan National Research Foundation-National Post Doctoral Fellowship (ANRF-NPDF), which I am the first to receive in this field. This assistance has made it possible to move away from retrospective evaluation and toward anticipatory planning techniques, which can help heritage towns survive under growing strain.
Advancing Methodological Rigor in Urban Planning
Q | What has been the most rewarding part of your research journey?
The most thrilling aspect of my experience has been contributing to the methodological legitimacy of a field of study that frequently lies on the periphery of "hard" science. Being the first in my field to be awarded both the ANRF-NPDF and the DST-INSPIRE fellowships marked significant turning points—not as conclusions, but as indications that this line of investigation was both essential and reliable.
Equally enjoyable has been the steady transition of my work from diagnosis to anticipation. Moving from reporting consequences to calculating stress and resilience has significantly altered my perspective on research responsibilities. The importance of accuracy, moderation, and methodological accountability has been emphasized, especially at the postdoctoral stage.
What excites me most is not recognition itself, but the opportunity it creates—to push the field forward with tools and frameworks that others can test, adapt, and improve.
Q | If you could be a laboratory instrument, which one would you be and why?
I would be an artificial neural network (ANN)-based prediction software platform. This system does more than simply record current conditions—it learns from patterns, adapts to new inputs, and predicts consequences before they occur, which is similar to my research methodology. Partial, noisy, and unequal signals—early signs of stress that are simple to ignore yet crucial when taken as a whole—are what I'm interested in. ANN-based systems are particularly good at capturing non-linear relationships, adjusting to different situations, and improving predictions as new information becomes available.
The transition from explanation to foresight is what most appeals to me. Predictive software is made to function in complicated and uncertain environments; it is not concerned with perfect conditions. That ability is crucial to my work. The objective is to predict risk trajectories early enough to allow for informed intervention, rather than classifying systems as sustainable or unsustainable.
Responses have been edited for length and clarity.
Are you a researcher who would like to be featured in the “Postdoc Portraits” series? Send in your application here.
