Chiara Bordin
Stillingsbeskrivelse
Associate Professor
Optimization & analytics applied to integrated sustainability systems
Advancing human-centered and reflexive computational modelling in energy and environmental systems
Chiara Bordin is an interdisciplinary energy and environmental systems scientist working at the interface of computer science, mathematical optimization, and sustainable resource systems. Her research focuses on computational optimization and data-driven modelling for complex sustainability systems, with major contributions in smart energy and power systems modelling and emerging applications in water-resource and aquaculture systems. In parallel, her work contributes to interdisciplinary sustainability education and research training, bridging technical modelling with critical reflection and collaborative practice.
Her core expertise lies in the development of mathematical optimization models, stochastic and multihorizon decision-support frameworks, and predictive analytics methods applied to integrated energy systems. Her work has contributed to advancing storage integration, network design and restructuring, reliability-oriented planning, microgrid coordination, electric vehicle management, and the integration of machine learning into operational energy modelling.
Beyond energy systems, her research program expands into broader sustainability domains, including water quality modelling, aquaculture systems analytics, and environmental risk assessment. Across these domains, a unifying methodological spine is maintained: the application of advanced computational optimization and data-driven decision frameworks to coupled socio-technical systems.
Her research therefore spans:
- Energy informatics, optimization and smart energy systems modelling (core domain)
- Data-driven decision support and predictive analytics for complex resource systems
- Interdisciplinary environmental and water-resource applications
- Pedagogical research on interdisciplinary teaching and sustainability integration in computer science
Mathematical modelling and optimization serve as the conceptual backbone of her research program. By combining computational methods with power systems engineering, environmental analytics, economics, and machine learning, she contributes to the evolving field of Computational Sustainability — addressing how digital and analytical tools can support resilient, low-carbon, and resource-efficient systems.
Building on her expertise in optimization, in recent years, her research has also focused on formalizing the human and interdisciplinary dimensions of computational modelling. This includes the development of reflexive and decision-oriented modelling frameworks that make explicit the assumptions, interpretative choices, and collaborative dynamics underlying energy system optimization. Through this work, she contributes to advancing more transparent, context-aware, and decision-relevant modelling practices within Energy Informatics.
In addition to her methodological contributions, she is actively engaged in interdisciplinary collaboration across engineering, environmental science, and sustainability education. She contributes to shaping the next generation of Energy Informatics and Computational Sustainability specialists through research-driven teaching and cross-disciplinary program development.
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Forskningsinteresser
Associate Professor
Optimization & analytics applied to integrated sustainability systems
Advancing human-centered and reflexive computational modelling in energy and environmental systems
Chiara Bordin is an interdisciplinary energy and environmental systems scientist working at the interface of computer science, mathematical optimization, and sustainable resource systems. Her research focuses on computational optimization and data-driven modelling for complex sustainability systems, with major contributions in smart energy and power systems modelling and emerging applications in water-resource and aquaculture systems. In parallel, her work contributes to interdisciplinary sustainability education and research training, bridging technical modelling with critical reflection and collaborative practice.
Her core expertise lies in the development of mathematical optimization models, stochastic and multihorizon decision-support frameworks, and predictive analytics methods applied to integrated energy systems. Her work has contributed to advancing storage integration, network design and restructuring, reliability-oriented planning, microgrid coordination, electric vehicle management, and the integration of machine learning into operational energy modelling.
Beyond energy systems, her research program expands into broader sustainability domains, including water quality modelling, aquaculture systems analytics, and environmental risk assessment. Across these domains, a unifying methodological spine is maintained: the application of advanced computational optimization and data-driven decision frameworks to coupled socio-technical systems.
Her research therefore spans:
- Energy informatics, optimization and smart energy systems modelling (core domain)
- Data-driven decision support and predictive analytics for complex resource systems
- Interdisciplinary environmental and water-resource applications
- Pedagogical research on interdisciplinary teaching and sustainability integration in computer science
Mathematical modelling and optimization serve as the conceptual backbone of her research program. By combining computational methods with power systems engineering, environmental analytics, economics, and machine learning, she contributes to the evolving field of Computational Sustainability — addressing how digital and analytical tools can support resilient, low-carbon, and resource-efficient systems.
Building on her expertise in optimization, in recent years, her research has also focused on formalizing the human and interdisciplinary dimensions of computational modelling. This includes the development of reflexive and decision-oriented modelling frameworks that make explicit the assumptions, interpretative choices, and collaborative dynamics underlying energy system optimization. Through this work, she contributes to advancing more transparent, context-aware, and decision-relevant modelling practices within Energy Informatics.
In addition to her methodological contributions, she is actively engaged in interdisciplinary collaboration across engineering, environmental science, and sustainability education. She contributes to shaping the next generation of Energy Informatics and Computational Sustainability specialists through research-driven teaching and cross-disciplinary program development.
Member of the Arctic Centre for Sustainable Energy (ARC): ARC Website
Member of the Aurora Center MASCOT (Mathematical Structures in Computation): MASCOT Website
Projects: Smart Senja, TENORS
Interview for the Springer Nature Journal
Interview for the AIMMS community
Member of the editorial board of the Energy Informatics Journal by Springer Open
Member of the editorial board of the Discover Applied Science Journal by Springer Nature
I was pleased to be one of the guest editors of the special issue titled “Smart and Sustainable Energy Hubs for a Future Integrated Energy System” published in the Energies Journal by MDPI.
Member of the technical program committee of the following conferences:
- Energy Informatics.Academy Conference (EI.A)
- DACH+ Energy Informatics Conference
- KES Sustainability in Energy and Buildings Conference
Undervisning
INF-3010 / INF-8010: Energy Informatics - Smart Energy and Power Systems Modelling (Master level and PhD level course)
INF-3993 Special Curriculum: Mathematical optimization and machine learning for the Vehicle Routing Problem
INF-2700 Computer Networks and communication
INF-3701/INF-8701 Advanced Datatbase Systems (Master level and PhD level course)
INF-2700 Database Systems
INF-XXXX: I am available to develop and providing any tailored special curriculum course for master students interested in specialized applications of mathematical modelling, optimization, prescriptive analytics, decision science, decision support systems tools, as well as broader topics within energy informatics, energy systems, power systems, sustainability, and environmental intelligence. Feel free to get in touch to discuss your specific needs and a potential tailored course design.
Capstone Projects: get in touch to discuss applications of mathematical optimization, prescriptive analytics, decision science, decision support systems tools
Medlem i forskningsgruppe
CV
Short CV
Chiara Bordin received her Master’s degree in Industrial Engineering from the University of Bologna (Italy) and her PhD in Automation and Operational Research from the same institution, with a dissertation focused on mathematical optimization applied to thermal and electrical energy systems.
Following her PhD, she held research positions at the University of Durham (UK), in collaboration with the Durham Energy Institute and the Engineering Department at the University of Cambridge. She later joined NTNU (Norwegian University of Science and Technology) as a Postdoctoral Researcher and subsequently worked as Research Scientist at SINTEF Energy, the largest energy research institute in Scandinavia. She is currently Associate Professor in Energy Informatics at the Department of Computer Science (IFI), UiT – The Arctic University of Norway.
Her research program centers on computational optimization and data-driven modelling for complex sustainability systems. She has made significant contributions to smart energy and power systems modelling, particularly in storage integration and degradation modelling, strategic network design and restructuring, microgrid coordination, reliability-oriented planning, electric vehicle charging infrastructure, and hydrogen integration within decarbonized energy systems.
Methodologically, her work combines mathematical optimization, stochastic and multihorizon decision-support models, and machine learning techniques to address operational and strategic challenges in coupled socio-technical systems. In recent years, her research has expanded toward broader environmental and water-resource applications, including predictive modelling for aquaculture systems and environmental risk assessment, under the broader umbrella of Computational Sustainability.
Throughout her career, she has been actively involved in interdisciplinary and international research collaborations spanning computer science, power systems engineering, environmental analytics, and sustainability studies.
She serves as reviewer for leading international journals (Elsevier, Springer, Wiley, among others), is a member of the Editorial Board of the journal Energy Informatics (Springer), as well as the journal Discover Applied Sciences (Springer), and acts as academic R&D advisor for industry and startups in energy systems and digital energy management.
Her long-term research vision is to advance computational decision-support frameworks for integrated energy–water–environment systems, while actively contributing to interdisciplinary sustainability education, and supporting resilient, low-carbon transitions.
Member of the Arctic Centre for Sustainable Energy (ARC): ARC Website
Projects: Smart Senja
Member of the AURORA Centre MASCOT (Mathematical structures in computation)
Interview for the AIMMS community: Interview
I am pleased to be one of the guest editors of the special issue titled “Smart and Sustainable Energy Hubs for a Future Integrated Energy System” to be published in the Energies Journal by MDPI. The deadline for papers’ submission is the 15th of April 2023.