Julia Kowalski
I am an enthusiastic Computational Science and Engineering researcher interested in developing computational methods to study and predict complex engineering systems and environmental processes, such as geohazards. My specific interests include data-integrated simulation models for shallow flow, contact, and phase-change processes, as well as surrogate models for real-time prediction, applied uncertainty management, and Bayesian model selection. I am an open science and FAIR data advocate, passionate about modern research software engineering, holistic computational workflow management, and ensuring 'analysis-readiness' of data to foster sustainability in CSE and to maximize our knowledge return from computational investments. I am similarly passionate about promoting a diverse working culture, convinced that bringing together a multitude of perspectives not only enriches innovation but also paves the way for broadly accepted scientific solutions.
Session
Computational Science and Engineering (CSE) integrates engineering, applied mathematics, and computer science to enable model-based design, knowledge generation, and decision support. While CSE has become a key enabler of sustainable products and operations, sustainability is still often treated as an afterthought in computational method development. This talk reflects on sustainability as a core design principle for CSE—one that aligns naturally with scientific relevance, long-term usability, and enduring research value. Drawing on representative examples, we present a holistic perspective spanning resource consumption, digital infrastructure, and organizational practices. We examine how these aspects interact in modern computational research and conclude with directions for impact through sustainable CSE.