Min forskning omfatter fem hovedtemaer. Det første temaet er tilpasningsdyktig systemarkitekturer (adaptive system architectures). Mine hovedbidrag innenfor området er utvikling av programmeringsabstraksjoner for adapsjonskontroll og teknikker for å observere og analysere systemoppførsel. Mitt andre tema er støtte for multimedia-applikasjoner, inklusiv kontinuerlige media. Mine hovedbidrag innenfor for dette temaet er å anvende formelle spesifikasjoner av tjenestekvalitetsforvaltning (QoS) til konfigurasjon av kjøreomgivelsen for multimediaapplikasjoner og utviklingen av eksplisitte bindingsarkitekturer for multimedia kommunikasjon. Det tredje temaet er støtte for mobilitet og kontekstsensitive systemer. Dette inkluderer observasjon av endringer i omgivelsene og tilby best mulig tjeneste basert på disse observasjonene (for eksempel i personalisering av tjenester). Mine hovedbidrag er kontekstavhengig konfigurering og rekonfigurering basert på nåværende kontekst og støtte for integrasjon av et vidt spekter av tjenester og informasjonsressurser. Mitt fjerde tema er sikkerhet. Mine hovedbidrag her er forskning rundt tilpasningsdyktige sikkerhetsmekanismer, utvikling av sikker og brukervennlig lagring og deling av informasjon, kombinasjon av sikker flerpartsberegninger (Secure Multiparty Computation, SMC) og offentlig nøkkelteknologi for å løse problemer rundt personvern og analyse av data (inklusiv helsedata), og sikkerhet i kontekst av mobile enheter, NFC og sikre elementer (SIM- kort). Det femte hovedtema er innen AI og maskinlæring (ML). Mine hovedbidrag her er innen system- og programmeringsstøtte ved realisering av løsninger som anvender ML.

Forskningssiden til Anders Andersen

Lab hjemmeside: https://uit-hdl.github.io/

Publikasjoner: https://uit-hdl.github.io/publications.html

Kode: https://uit-hdl.github.io/software.html

Data: https://uit-hdl.github.io/data.html

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

Google Scholar

Linkedin

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