Rannsóknir
Helga Ingimundardóttir
Rannsóknir
Vélrænt nám og bestun: að læra góðar ákvarðanareglur úr gögnum í stað þess að hanna þær í höndunum.
Styrkir
Verkefnastyrkir
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2024–26
HiDef Textiles: bestun textílferla með gervigreind
Rannsóknasjóður Háskóla Íslands
Verkefnisstyrkur sem tengir gervigreind, iðnaðarverkfræði, tölvunarfræði og textílhandverk.
Markmiðið er að lýðræðisvæða tækni og forritun gegnum textíl og tengja listir og vísindi. Við byggjum sjálfbæran námsvettvang til að deila bestu starfsháttum úr iðnaði, með áherslu á þátttöku kvenna í STEM.
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2025
HiDef Textíll: Tæknivæddar prjónavélar
Háskóli Íslands: Styrkir vegna stuðnings við samfélagsvirkni
1.430.000 kr. styrkur til að miðla rannsóknum og sérþekkingu til almennings.
Styrkurinn styður þróun gagnvirks vefsvæðis sem miðlar rannsóknahugbúnaði og nýsköpunarferli snjallprjónavélarinnar til almennings. Verkefnið tengir saman tækni, sjálfbærni og íslenskan textílmenningararf og gerir almenningi kleift að kynnast og prófa íslensk mynstur.
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2024
Rannís, Nýsköpunarsjóður námsmanna
Styrkur til að uppfæra prjónavél frá tíunda áratugnum í sjálfvirka nútímavél — þrír BS-nemar í þrjá mánuði.
Hluti af HiDef Textiles. Verkefnið sameinar sjálfbæra tækni, textílnýsköpun, STEAM-menntun, gervigreind og Internet hlutanna. Í lok sumars var vélin virk og tilbúin til sýningar á ráðstefnum.
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2012
Kennslumálasjóður Háskóla Íslands
Verkefnið Breytt dæmatímafyrirkomulag hjá VON.
Styrkur til að endurbæta heimadæmi í verkfræði og náttúruvísindum: einfaldara verklag sem léttir álag á kennara en veitir nemendum markvissari endurgjöf. Verkefnið var unnið í náminu Starfendarannsóknir.
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2009–12
Doktorsnemastyrkur
Rannsóknasjóður Háskóla Íslands
Þriggja ára styrkur til doktorsnáms.
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Ferðastyrkir
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2024
Erasmus+ ferðastyrkur fyrir háskólastarfsfólk
ERASMUS+
Lærdómsferð til Aalborg og DTU um verkefnabundið nám og samfellu í námskrá.
Ferðin bætir kennsluaðferðir Háskóla Íslands með því að læra af verkefnabundnu námi Aalborg-háskóla og samfellu námskrár hjá Danmarks Tekniske Universitet. Samstarfið styrkir námskrá, greiðir leið nemenda úr BS í MS og eflir alþjóðleg tengsl.
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2010
Ferðastyrkur fyrir háskólastarfsfólk
Fundusz Stypendialny i Szkoleniowy (FSS)
Ferðastyrkur til heimsóknar í Politechnika Śląska í Gliwice, Póllandi.
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2009
Námsstyrkur til framhaldsnáms
Franska sendiráðið, Reykjavík
Styrkur til íslenskra nemenda í meistaranámi í Frakklandi.
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Ritverk
Tímaritsgreinar
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2025
Viðskiptagreind sem brú milli náms og starfs
TímaritsgreinTímarit Kennslumiðstöðvar Háskóla Íslands, 12(1), pp. 46–49, 2025
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2021Tímaritsgrein
Nature Genetics, 53(6), pp. 779 – 786, Springer Nature, 2021
Long-read sequencing (LRS) promises to improve the characterization of structural variants (SVs). We generated LRS data from 3,622 Icelanders and identified a median of 22,636 SVs per individual (a median of 13,353 insertions and 9,474 deletions). We discovered a set of 133,886 reliably genotyped SV alleles and imputed them into 166,281 individuals to explore their effects on diseases and other traits. We discovered an association of a rare deletion in PCSK9 with lower low-density lipoprotein (LDL) cholesterol levels, compared to the population average. We also discovered an association of a multiallelic SV in ACAN with height; we found 11 alleles that differed in the number of a 57-bp-motif repeat and observed a linear relationship between the number of repeats carried and height. These results show that SVs can be accurately characterized at the population scale using LRS data in a genome-wide non-targeted approach and demonstrate how SVs impact phenotypes. © 2021, The Author(s), under exclusive licence to Springer Nature America, Inc.
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2021
Ratatosk: hybrid error correction of long reads enables accurate variant calling and assembly
Tímaritsgrein Opinn aðgangurGenome Biology, 22(1), Springer Nature, 2021
A major challenge to long read sequencing data is their high error rate of up to 15%. We present Ratatosk, a method to correct long reads with short read data. We demonstrate on 5 human genome trios that Ratatosk reduces the error rate of long reads 6-fold on average with a median error rate as low as 0.22 %. SNP calls in Ratatosk corrected reads are nearly 99 % accurate and indel calls accuracy is increased by up to 37 %. An assembly of Ratatosk corrected reads from an Ashkenazi individual yields a contig N50 of 45 Mbp and less misassemblies than a PacBio HiFi reads assembly. © 2021, The Author(s).
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2018Tímaritsgrein
Journal of Scheduling, 21(4), pp. 413 – 428, Springer Nature, 2018
Dispatching rules can be automatically generated from scheduling data. This paper will demonstrate that the key to learning an effective dispatching rule is through the careful construction of the training data, xi(k),yi(k)k=1K∈D, where (i) features of partially constructed schedules xi should necessarily reflect the induced data distribution D for when the rule is applied. This is achieved by updating the learned model in an active imitation learning fashion; (ii) yi is labelled optimally using a MIP solver; and (iii) data need to be balanced, as the set is unbalanced with respect to the dispatching step k. Using the guidelines set by our framework the design of custom dispatching rules, for a particular scheduling application, will become more effective. In the study presented three different distributions of the job-shop will be considered. The machine learning approach considered is based on preference learning, i.e. which dispatch (post-decision state) is preferable to another. © 2017, Springer Science+Business Media New York.
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2018
Insights into imprinting from parent-of-origin phased methylomes and transcriptomes
TímaritsgreinNature Genetics, 50(11), pp. 1542 – 1552, Nature Publishing Group, 2018
Imprinting is the preferential expression of one parental allele over the other. It is controlled primarily through differential methylation of cytosine at CpG dinucleotides. Here we combine 285 methylomes and 11,617 transcriptomes from peripheral blood samples with parent-of-origin phased haplotypes, to produce a new map of imprinted methylation and gene expression patterns across the human genome. We demonstrate how imprinted methylation is a continuous rather than a binary characteristic. We describe at high resolution the parent-of-origin methylation pattern at the 15q11.2 Prader–Willi/Angelman syndrome locus, with nearly confluent stochastic paternal methylation punctuated by ‘spikes’ of maternal methylation. We find examples of polymorphic imprinted methylation unrelated (at VTRNA2-1 and PARD6G) or related (at CHRNE) to nearby SNP genotypes. We observe RNA isoform-specific imprinted expression patterns suggestive of a methylation-sensitive transcriptional elongation block. Finally, we gain new insights into parent-of-origin-specific effects on phenotypes at the DLK1/MEG3 and GNAS loci. © 2018, The Author(s), under exclusive licence to Springer Nature America, Inc.
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2011
Detection of fouling in a cross-flow heat exchanger using wavelets
TímaritsgreinHeat Transfer Engineering, 32(3-4), pp. 349 – 357, Taylor and Francis Ltd., 2011
Detection of fouling in a heat exchanger experiencing perfect steady-state conditions is not very difficult. But the challenge is to detect fouling when all inputs (inlet temperature of the fluids and the mass flow rates) are simultaneously varying. In this paper it has been considered that the mass flow rates can vary in a ratio of 2, and that the inlet temperatures can vary by about ±20%. This first approach is dedicated to show the feasibility of using the wavelet transform. It has been considered that getting simulated data is the best way. In fact, it is then possible to introduce an arbitrary fouling factor. Thus, in the first part of the paper the model of the heat exchanger is presented. It is developed using Simulink. The validation is carried out on an electrical heater, for which it is possible to find an analytical solution for transient states. It is also shown that steady states are accurately computed over a large range of the number of transfer units and heat capacity rate ratios. Then a brief overview of the wavelet transform is given. Then basic examples show that the wavelet transform can help to find the trend of time series. It is then applied to the analysis of the "wavelet- transformed" effectiveness of the heat exchanger. This analysis is carried out on a sliding observation window (to be able to detect fouling on-line). It is shown that fouling is detected at a very early stage. Copyright © Taylor and Francis Group, LLC.
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Ritrýnd ráðstefnurit
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2026
A Scalable Matheuristic for Routing Capacity-Constrained Groundfish Surveys
RáðstefnugreinLearning and Intelligent Optimization. LION 2026, Springer International Publishing, 2026, Accepted
Bottom trawl groundfish surveys are planned around a fixed set of sampling stations, but the order of visits and the timing of port returns strongly affect total travel distance and operational feasibility. Although mixed-integer programming (MIP) models can encode capacity and port-call constraints directly, solving the full model to optimality is often impractical at realistic scales. We propose a matheuristic that combines fast tour construction with repeated calls to a time-limited capacity MIP subproblem. Starting from an initial segmented tour, the algorithm iteratively examines boundaries between adjacent segments separated by a port visit and solves a restricted two-segment capacity MIP on the union of stations in the two segments, with fixed endpoints and a single intermediate port call. The MIP reallocates stations between the two segments subject to capacity, and the best incumbent found within a time limit is accepted if it improves total distance. We evaluate the approach under four initialization strategies and a range of MIP time limits. Across scenarios, the method consistently improves baseline plans and provides a fast, reproducible tool for strategic planning and scenario comparison. These results suggest that restricted-MIP local improvement is an effective mechanism for producing high-quality survey routes under realistic operational constraints.
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2026
Designing Authentic Industry-Engaged Assessment for Professional Competence in Business Intelligence
Ráðstefnugrein Opinn aðgangurProceedings of the 22nd International CDIO Conference, pp. 211 – 220, 2026
Engineering programs increasingly seek to align student learning with professional practice, yet meaningful industry collaboration often exposes misalignment between academic assessment norms and workplace expectations. This paper presents a CDIO Implementation case describing the redesign of an undergraduate business intelligence course to strengthen industrial relevance while supporting student professional development. The redesign integrated semester-long, industry-inspired project work structured around team-based learning and competence-oriented assessment, emphasizing authentic data, reproducible workflows, and professional collaboration tools. A single industrial partner contributed real-world datasets, problem framing, and feedback, enabling students to engage with constraints and quality standards characteristic of professional practice. Assessment and feedback were redesigned to balance team performance with individual contribution through visible artifacts (e.g. version control activity) and structured reflection, addressing persistent challenges related to workload, accountability, and fairness. Evidence from iterative course evaluations, student reflections, graduate feedback, and industry partner input indicates increased student engagement, improved coherence across learning activities, and clearer alignment between assessment criteria and professional expectations. At the same time, the collaboration revealed tensions between academic tolerance for developmental errors and industry demands for reliability and trustworthiness, highlighting the importance of explicitly preparing students to communicate differently with academic and professional audiences. The paper reflects on these tensions and outlines design principles for industry-engaged CDIO courses, including expectation alignment early in the course, sustained use of a shared industrial context, and assessment practices that foreground professional standards without sacrificing formative learning space. The case offers transferable insights for CDIO programs seeking to leverage industrial collaboration as a catalyst for student professional development through innovative assessment and feedback. © 2026 22nd International CDIO Conference. All right reserved.
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2026
Using Pull Requests to Make Collaboration Visible in CDIO Project-Based Courses
Ráðstefnugrein Opinn aðgangurProceedings of the 22nd International CDIO Conference, pp. 296 – 305, 2026
Assessing collaboration, individual accountability, responsiveness to feedback, and reproducibility remains a persistent challenge in open-ended, team-based project courses. Final deliverables often mask how work evolved, how responsibilities were shared, and how decisions were negotiated. This paper presents an assessment design in which pull requests (PRs), a mechanism for reviewing and integrating changes to shared project work, serve as the central mechanism for making collaborative practices visible and assessable within CDIO-aligned project-based courses. Rather than treating GitHub as a programming topic or tooling exercise, the approach positions PRs as assessment infrastructure that binds technical change to process evidence through review discussions, explicit responses to comments, and documented handover. The implementation spans two intentionally sequenced courses in Industrial Engineering. A second-year course introduces structured collaboration practices through short, skills-focused project cycles. A subsequent advanced course assumes GitHub fluency and organizes all project work within a single persistent repository maintained across the semester. Within this environment, PRs function as the primary unit of feedback, peer review, and evaluation. Students make their reasoning, revisions, and communication visible through routine workflow activity, and unresolved feedback carries forward across project cycles into capstone evaluation, enabling cumulative rather than fragmented assessment. Experience across multiple offerings shows that PR-centered assessment improves the scope and specificity of peer review, strengthens documentation and reproducibility practices, and fosters shared responsibility for evolving team artifacts. Students initially expressed concern about perceived overhead, but review quality, documentation habits, and disciplined handover improved markedly as the workflow became routine. Graduating students later identified PR-based communication and accountability as directly transferable to professional settings. The paper highlights key design decisions, trade-offs, and sequencing choices, offering practical and transferable guidance for educators seeking assessment methods that make collaboration, responsiveness, and reproducibility genuinely visible in CDIO project-based courses. © 2026 22nd International CDIO Conference. All right reserved.
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2024
Evolving Submodels for Column Generation in Cutting and Packing for Glulam Production
Ráðstefnugrein2024 IEEE Congress on Evolutionary Computation (CEC), IEEE, 2024
This study introduces a novel optimization approach for the pressing phase in glued laminated timber (glulam) production, integrating column generation techniques with an evolution-ary algorithm within a real-world glulam factory context. At its core, an evolutionary strategy refines parameters in a sub model dedicated to developing cutting patterns via column generation. These patterns are then utilized in a separate, overarching 2D packing problem, targeting the optimization of the pressing process while adhering to pressing capacity constraints. The solutions' effectiveness feeds back into the evolutionary strategy, guiding the optimization of its parameters. This iterative, multi-layered framework presents a significant advancement in glulam manufacturing, aiming to enhance production efficiency, reduce waste, and expedite customer order fulfillment. © 2024 IEEE.
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2016
Evolutionary learning of linear composite dispatching rules for scheduling
RáðstefnugreinStudies in Computational Intelligence, 620, pp. 49 – 62, Springer Verlag, 2016
A prevalent approach to solving job shop scheduling problems is to combine several relatively simple dispatching rules such that they may benefit each other for a given problem space. Generally, this is done in an ad-hoc fashion, requiring expert knowledge from heuristics designers, or extensive exploration of suitable combinations of heuristics. The approach here is to automate that selection by translating dispatching rules into measurable features and optimising what their contribution should be via evolutionary search. The framework is straight forward and easy to implement and shows promising results. Various data distributions are investigated for both job shop and flow shop problems, as is scalability for higher dimensions. Moreover, the study shows that the choice of objective function for evolutionary search is worth investigating. Since the optimisation is based on minimising the expected mean of the fitness function over a large set of problem instances which can vary within the set, then normalising the objective function can stabilise the optimisation process away from local minima. © Springer International Publishing Switzerland 2016.
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2015
Generating training data for learning linear composite dispatching rules for scheduling
RáðstefnugreinLecture Notes in Computer Science, 8994, pp. 236 – 248, Springer International Publishing, 2015
A supervised learning approach to generating composite linear priority dispatching rules for scheduling is studied. In particular we investigate a number of strategies for how to generate training data for learning a linear dispatching rule using preference learning. The results show, that when generating a training data set from only optimal solutions, it is not as effective as when suboptimal solutions are added to the set. Furthermore, different strategies for creating preference pairs is investigated as well as suboptimal solution trajectories. The different strategies are investigated on 2000 randomly generated problem instances using two different problem generator settings. © Springer International Publishing Switzerland 2015.
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2014
Evolutionary Learning of Weighted Linear Composite Dispatching Rules for Scheduling
RáðstefnugreinInternational Joint Conference on Computational Intelligence, 1, pp. 59 – 67, Science and Technology Publications, Lda, 2014
A prevalent approach to solving job shop scheduling problems is to combine several relatively simple dispatching rules such that they may benefit each other for a given problem space. Generally, this is done in an ad-hoc fashion, requiring expert knowledge from heuristics designers, or extensive exploration of suitable combinations of heuristics. The approach here is to automate that selection by translating dispatching rules into measurable features and optimising what their contribution should be via evolutionary search. The framework is straight forward and easy to implement and shows promising results. Various data distributions are investigated for both job shop and flow shop problems, as is scalability for higher dimensions. Moreover, the study shows that the choice of objective function for evolutionary search is worth investigating. Since the optimisation is based on minimising the expected mean of the fitness function over a large set of problem instances which can vary within the set, then normalising the objective function can stabilise the optimisation process away from local minima. © 2014 SCITEPRESS (Science and Technology Publications, Lda.).
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2012Ráðstefnugrein
Lecture Notes in Computer Science, 7219 LNCS, pp. 408 – 412, 2012
Many heuristic methods have been proposed for the job-shop scheduling problem. Different solution methodologies outperform other depending on the particular problem instance under consideration. Therefore, one is interested in knowing how the instances differ in structure and determine when a particular heuristic solution is likely to fail and explore in further detail the causes. In order to achieve this, we seek to characterise features for different difficulties. Preliminary experiments show there are different significant features that distinguish between easy and hard JSSP problem, and that they vary throughout the scheduling process. The insight attained by investigating the relationship between problem structure and heuristic performance can undoubtedly lead to better heuristic design that is tailored to the data distribution under consideration. © 2012 Springer-Verlag.
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2011
Sampling strategies in ordinal regression for surrogate assisted evolutionary optimization
RáðstefnugreinInternational Conference on Intelligent Systems Design and Applications, ISDA, pp. 1158 – 1163, 2011
In evolutionary optimization surrogate models are commonly used when the evaluation of a fitness function is computationally expensive. Here the fitness of individuals are indirectly estimated by modeling their rank with respect to the current population by use of ordinal regression. This paper focuses on how to validate the goodness of fit for surrogate models during search and introduces a novel validation/updating policy for surrogate models, and is illustrated on classical numerical optimization functions for evolutionary computation. The study shows that for validation accuracy it is sufficient for the approximate ranking and true ranking of the training set to be sufficiently concordant or that only the potential parent individuals should be ranked consistently. Moreover, the new validation approach reduces the number of fitness evaluation needed, without a loss in performance. © 2011 IEEE.
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2011
Supervised learning linear priority dispatch rules for job-shop scheduling
RáðstefnugreinLecture Notes in Computer Science, 6683 LNCS, pp. 263 – 277, 2011
This paper introduces a framework in which dispatching rules for job-shop scheduling problems are discovered by analysing the characteristics of optimal solutions. Training data is created via randomly generated job-shop problem instances and their corresponding optimal solution. Linear classification is applied in order to identify good choices from worse ones, at each dispatching time step, in a supervised learning fashion. The method is purely data-driven, thus less problem specific insights are needed from the human heuristic algorithm designer. Experimental studies show that the learned linear priority dispatching rules outperforms common single priority dispatching rules, with respect to minimum makespan. © Springer-Verlag Berlin Heidelberg 2011.
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Ritrýnd útvíkkuð ágrip
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2026
Endurhönnun Upplýsingaverkfræði: Stigvaxandi þróun faglegra vinnubragða
Ráðstefna Kennsluakademíu opinberu háskólanna, Veröld Háskóla Íslands, Reykjavík, 2026, Submitted
Verkefnið fjallar um yfirstandandi endurhönnun skyldunámskeiðsins Upplýsingaverkfræði (IÐN302G) á öðru ári í grunnnámi í iðnaðarverkfræði. Námskeiðið byggir upp tölvu- og gagnalæsi með áherslu á gagnaöflun, gagnavinnslu, GitHub og miðlun niðurstaðna. Námsgögn og námsfyrirkomulag eru birt á opnum námskeiðsvef, Upplýsingaverkfræði.
Endurhönnunin byggir á lærdómi úr lengra komnu valnámskeiði í Viðskiptagreind (IÐN610M) fyrir nemendur á þriðja námsári og meistaranema. Þar vinna nemendur með opnari greiningarverkefni, flóknari raungögn frá samstarfsaðila og hagnýtar aðferðir gervigreindar, auk þess að miðla niðurstöðum til ólíkra hagaðila, þar á meðal stjórnenda fyrirtækja. Markmiðið er að kenna fleiri grunnþætti faglegs vinnuferlis fyrr og á byrjendavænan hátt, svo í framhaldsnámskeiðinu skapist meira rými til að takast á við flókin gagnasöfn, opnari vandamál og faglega miðlun niðurstaðna.
Í Upplýsingaverkfræði er því lögð áhersla á vinnuferlið sjálft: að móta spurningar, afla gagna, skipuleggja þau, greina þau, gera vinnuna endurtakanlega og miðla niðurstöðum með skýrum hætti. Námskeiðið er jafnframt stílað inn á að þessi vinnubrögð nýtist í fjölmörgum öðrum námskeiðum á námsleiðinni, ekki aðeins í Viðskiptagreind. Nemendur vinna með opin og aðgengileg gögn í stað þess að vinna beint með fyrirtæki eða stofnun. Þannig er leitast við að skapa faglega miðaða, en aðgengilega, inngöngu inn í gagnadrifna verkfræðilega starfshætti.
Kennslufræðilega má skilja þessa hönnun sem form stoðkennslu, þar sem stuðningur er lagaður að stöðu nemenda, dreginn stigvaxandi til baka og ábyrgð á vinnuferlinu færð yfir til þeirra (Pol o.fl., 2010). Samræming námsmarkmiða, endurtekinna verkefna og matsviðmiða byggir jafnframt á hugmyndum um uppbyggilega samhæfingu (e. constructive alignment), þar sem athafnir nemenda og námsmat eiga að kalla fram þá frammistöðu sem hæfniviðmið lýsa (Biggs, 1996).
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2026
Erindrekinn vinnur, nemandinn ber ábyrgð
Ráðstefna Kennsluakademíu opinberu háskólanna, Veröld Háskóla Íslands, Reykjavík, 2026, Submitted
AI-erindrekar (e. AI agents) eru að breyta því hvernig gervigreind er notuð í hugbúnaðarþróun, gagnagreiningu og annarri þekkingarvinnu. Ólíkt hefðbundnu spjalli eða sjálfvirkri kóðafyllingu geta erindrekar tekið við markmiði, lesið samhengi verkefnis, notað verkfæri og framkvæmt fjölskrefaaðgerðir, svo sem að breyta skrám, keyra prófanir og undirbúa pull request. Kannanir benda til þess að notkun slíkra kerfa fari hratt vaxandi, þótt innleiðing þeirra sé víða enn á tilraunastigi. Í alþjóðlegri könnun McKinsey sögðu 62% svarenda fyrirtæki sín að minnsta kosti vera að gera tilraunir með AI-erindreka, en fæst höfðu náð að innleiða þá í stórum stíl (McKinsey & Company, 2025). Í könnun JetBrains meðal hugbúnaðarfólks sögðust 74% nota sérhæfð AI-verkfæri við þróun, þar á meðal kóðaaðstoð og erindreka (JetBrains Research, 2026).
Þessi þróun kallar á nýja faglega færni. Það nægir ekki lengur að kunna að fá gervigreind til að framleiða kóða eða greiningu. Notandinn þarf jafnframt að geta afmarkað verkefnið, stýrt vinnuferlinu, metið og sannreynt niðurstöður og borið ábyrgð á lokaafurðinni.
Haustið 2026 var notkun erindreka fléttuð markvisst inn í IÐN302G Upplýsingaverkfræði, skyldunámskeið á öðru ári í grunnnámi í iðnaðarverkfræði við Háskóla Íslands. Námskeiðið byggir upp tölvu- og gagnalæsi með áherslu á faglegt vinnuferli: að móta spurningar, afla og vinna gögn, halda utan um vinnuna í GitHub, gera greiningu endurtakanlega og miðla niðurstöðum með skýrum hætti (Ingimundardóttir, 2026).
Notkun erindreka er hluti af þessari víðari endurhönnun námskeiðsins. Markmiðið er ekki aðeins að kenna nemendum að nota ný AI-verkfæri heldur að kenna þeim að beita þeim sem hluta af faglegu vinnuferli: að afmarka verkefni, fela erindreka viðeigandi verk, rýna afurðina og bera ábyrgð á niðurstöðunni. Áskorunin er því að nýta þá framkvæmdargetu sem erindrekarnir veita án þess að sjálfvirknin taki yfir þá hugsun, ákvarðanatöku og yfirsýn sem námið á að byggja upp.
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2026
Sýnileg samvinna með tólum atvinnulífsins
11. kennsluráðstefna Kennslu- og upplýsingatæknimiðstöðvar Háskólans á Akureyri (KHA), Zoom, 2026
Á tímum þar sem háskólar leita leiða til að efla gagnsæi, ábyrgð og fagmennsku í námsmati geta vinnulag og tól úr atvinnulífinu skapað ný, áhrifarík tækifæri í kennslu. Fjallað verður um hvernig innleiðing pull request (PR) vinnulags, GitHub Classroom og samfellds, endurtekins vinnuferils hefur breytt verkefnadrifinni kennslu í tveimur námskeiðum í upplýsingaverkfræði og viðskiptagreind við Háskóla Íslands. PR miðað mat gerir samvinnu nemenda sýnilega á öllum stigum: rökstuðning, jafningjarýni, viðbrögð við endurgjöf, ábyrgð á gögnum og skipulagða afhendingu. Með því að festa endurgjöf við PR þræði flytjast athugasemdir og umbótakröfur milli lota, sem skapar samfellu, dýpri ígrundun og réttlátara námsmat. Nemendur æfa þannig vinnubrögð sem tíðkast í atvinnulífi, þar á meðal útgáfustýringu, samvinnudrifna greiningu og faglega framsetningu gagna og niðurstaðna. Reynsla úr námskeiðunum sýnir aukna þátttöku, sterkari teymisvinnu, minni laumufarþegavanda og rýmri skilning á fagmennsku í samskiptum við ytri samstarfsaðila. Kennarar fá jafnframt skýrari innsýn í einstaklingsframlög og verja minni tíma í lokaúrvinnslu. Samspil gagnsæis, ábyrgðar og raunhæfs vinnulags varpar þannig ljósi á hvernig kennsluaðferðir sem byggja á skýrum, endurteknum ferlum og traustri endurgjöf geta styrkt það sem telst góð háskólakennsla.
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2024
Endurskoðun á námskeiði í Viðskiptagreind: Hagnýt hæfni í brennidepli
Ráðstefna Kennsluakademíu opinberu háskólanna, Veröld Háskóla Íslands, Reykjavík, 2024
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Greinar á íslensku
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2025
HiDef textíll: Tæknivædd prjónavél og þverfaglegt samstarf
TímaritsgreinRaflost, pp. 10–13, Þriðja árs rafmagns- og tölvuverkfræðinemar við Háskóla Íslands, 2025, Dreift til allra meðlima Verkfræðingafélags Íslands
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2015
Skilningur á röðunarreglum fyrir verkniðurröðun á vélum
TímaritsgreinVélabrögð, 36, pp. 8–11, Þriðja árs véla- og iðnaðarverkfræðinemar við Háskóla Íslands, 2015, Dreift til allra meðlima Verkfræðingafélags Íslands
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Ritgerðir
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2016
ALICE: Analysis & Learning Iterative Consecutive Executions
Ritgerðp. 265, University of Iceland, 2016
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2010
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