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May 12, 2025Data and Software Drivers in Computational Sensing: Key Takeaways from Dr. Stefan Robila’s Lecture
Speaker: Dr. Stefan Robila, Ph.D.
Data, Innovation, and Community Engagement Distinguished Lecture Series
Introduction
In this lecture, Dr. Stefan Robila discusses how computational sensing and scientific workflows are transforming the way we manage, analyze, and interpret large-scale data. His research emphasizes the importance of scalable, energy-aware systems that make big data analysis both efficient and sustainable, with real-world applications in remote sensing and green computing.
Key Insights
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Cyber-Physical Modeling: Complex problems are tackled today using a combination of cyber and physical modeling.
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Scalable Intelligent Workflows: Investigation of large physical phenomena requires the development of adaptable and highly scalable intelligent workflows that leverage the vast computing resources available today to speed up discovery.
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Reproducible Research: Scientific workflows contribute not only to faster discoveries but also improve the trustworthiness of the research process by strengthening the reproducibility of the results.
“The work I presented contributes to the development of scalable, energy-aware scientific workflows—a critical infrastructure for managing and extracting insight from big data in a sustainable and efficient manner.”
Why It Matters
Dr. Robila’s lecture highlights how computational sensing has evolved from single-event data analysis to a powerful engine for discovery within cyber-physical systems. By connecting instruments with advanced computing resources, scalable workflows are driving innovation, improving data reliability, and enabling sustainable scientific progress across disciplines.
IDRIS-AWS Research Day: Engaging with Rutgers University – Newark Students and Faculty
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