Starfsferilskrá
Helga Ingimundardóttir
Starfsferilskrá
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Akademísk staða
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2024–
Félagi í Kennsluakademíu opinberra háskóla
Kennsluakademía opinberra háskóla · Reykjavík
Viðurkenning fyrir framúrskarandi kennslu og nýsköpun í háskólakennslu.
Viðurkenningin byggir á langvarandi vinnu með virkri kennslu: vendikennslu, verkefnadrifnu námsmati og námskrá í samstarfi við atvinnulífið sem tengir nemendur við raunveruleg verkfræðileg vandamál.
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2023–
Lektor
Iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild, Háskóli Íslands · Reykjavík
Kenni viðskiptagreind, stakræna hermun, aðgerðagreiningu og upplýsingatækni — með raunverulegum gögnum frá íslenskum fyrirtækjum.
Áhersla mín er að tengja fræðilega þekkingu við hagnýta færni, með raunverulegum gögnum í samstarfi við íslensk fyrirtæki. Nemendur fá þau verkfæri og þá innsýn sem þarf til að standa sig í iðnaði.
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2023
Nýdoktor
Iðnaðarverkfræðideild, Háskóli Íslands · Reykjavík
Í rannsóknateymi IDeLM (Intelligent Decision Learning Models), styrkt af Rannsóknasjóði.
IDeLM-verkefnið þróar greind ákvarðanamódel með vélrænu námi og gagnagreiningu, með það að markmiði að bæta ákvarðanatöku á ýmsum sviðum. Ábyrgð mín fól í sér rannsóknir, gagnagreiningu, þróun vélnámsmódela og samstarf innan teymisins.
Ekkert að sýna á þessu umfangi.
Menntun
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2009–2016
Ph.D. í reikniverkfræði
Háskóli Íslands · Reykjavík
Ofurleitni (hyperheuristics) fyrir verkniðurröðun, undir leiðsögn prófessors Tómasar Philips Rúnarssonar.
Sjálfvirknivæðing verkniðurröðunar (Job Shop Scheduling) með raðgreindri aðfallsgreiningu (ordinal regression), auk greiningar á „erfiðleikastigi verkefna“ og „fótsporum reiknirita í verkefnarúmi“. Doktorsstyrkur frá Rannsóknasjóði Háskóla Íslands.
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2010–2012
Diplóma í háskólakennslufræði
Menntavísindasvið, Háskóli Íslands · Reykjavík
30 ECTS nám í háskólakennslufræði — einn af fyrstu þátttakendum árið 2010.
Nám undir stjórn prófessors Guðrúnar Geirsdóttur, sérsniðið að akademísku starfsfólki og doktorsnemum sem kenna við Háskóla Íslands. Þrjár námskeiðaraðir um háskólakennslu, námskeiðahönnun og námsmat, og starfendarannsóknir. Lokaverkefnið varð að styrkumsókn sem fékk úthlutun úr Kennslumálasjóði Háskóla Íslands.
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2008–2010
M.Sc. í reikniverkfræði
Háskóli Íslands · Reykjavík
Bylgjuliðun (wavelets) til að greina útfellingu í varmaskiptum, í samstarfi við Université de Valenciennes.
Rannsóknir með prófessor Sylvain Lalot við Université de Valenciennes et du Hainaut-Cambrésis í Frakklandi (nú Université Polytechnique Hauts-de-France). Skoðað hvort bylgjuliðun gæti greint útfellingu í þverstraums varmaskiptum út frá mælingum við venjulegan rekstur. Styrkur frá franska sendiráðinu á Íslandi til námsins í Frakklandi.
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2005–2008
B.Sc. í stærðfræði
Háskóli Íslands · Reykjavík
Kjörsvið í tölvunarfræði.
Ekkert að sýna á þessu umfangi.
Starfsreynsla
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2022–2023
Yfirmaður gervigreindarrannsókna
Travelshift · Reykjavík
Leiddi rannsóknateymi sem besti ferðapakka á GuideToEurope.com — „travelling thief“-verkefnið.
Teymið þróaði gagnadrifnar bestunaraðferðir fyrir ferðapakka á GuideToEurope.com. Kjarnaverkefnið var NP-erfiða „travelling thief“-verkefnið, sem sameinar bakpokaverkefnið og verkefni farandsalans. Einnig kom ég að einkaleyfisumsóknum sem byggðu á rannsóknunum.
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2021–2022
Gagnafræðingur
CCP Games · Reykjavík
Leiddi þróun rauntíma-tillöguvélar fyrir nýja spilara í EVE Online.
Byggði rauntíma tímaraðafitur úr frumatburðum í Kafka-straumum með TimescaleDB-viðbótinni fyrir PostgreSQL, og þróaði sérsniðna mælikvarða til að meta gæði og þátttöku í nýju tillögumódelunum.
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2016–2021
Rannsóknasérfræðingur
deCODE genetics · Reykjavík
Stofnandi Oxford Nanopore-teymisins; hélt uppi raðgreiningarferli sem vann úr 6 petabætum á þremur árum.
Ábyrg fyrir uppsetningu og viðhaldi greiningarferlis fyrir langar raðgreiningar. Samstarf við tilraunastofuna um verklag og við tölvudeild um breytingar á klasa- og diskaarkitektúr til að vinna úr miklu gagnamagni án gagnataps. Tók þátt í ritun rannsóknagreina með ONT-teyminu og gerði rannsóknarniðurstöður reksturshæfar í framleiðsluumhverfi.
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2015–2016
SQL-ráðgjafi
AGR Dynamics · Reykjavík
Innleiddi AGR 5, vefbundið birgðastýringarkerfi, í ERP-kerfi viðskiptavina.
AGR 5 gerir notendum kleift að sjá söluferil og búa til innkaupatillögur með tölfræðilegri spágerð. Vinnan fól í sér innleiðingu, viðhald gagnagrunna og sérsniðnar SQL-lausnir fyrir hvern viðskiptavin.
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2013–2015
Reikniverkfræðingur, rannsóknir og þróun
Valka (nú hluti af JBT Marel) · Kópavogur
Hannaði bútunarreiknirit fyrir fisk út frá röntgenmyndum og alhæfði beinaskynjun yfir margar tegundir.
Valka er leiðandi í tækjum og sjálfvirknilausnum fyrir fiskvinnslu. Auk bútunar- og beinaskynjunarreiknirita kom ég að þrívíðri myndgerð fiskbeina og framkvæmdi afkastamælingar og skýrslugerð.
Ekkert að sýna á þessu umfangi.
Kennsla
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2024–
IÐN302GUpplýsingaverkfræðiHáskóli Íslands
Grunnnámskeið um gagnagrunna, SQL, regex og gagnasögur — kennt með teymisverkefnum og jafningjamati.
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2025–
IÐN403MHermunHáskóli Íslands
Stakræn hermun á biðraðakerfum og framleiðsluferlum, með áherslu á líkangerð út frá raunverulegum rekstrargögnum.
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2023–
IÐN610MViðskiptagreindHáskóli Íslands
Leiðbeint nám (supervised learning), klösun og ferlagreining á raunverulegum gögnum fyrir 3. árs BS- og 1. árs MS-nema.
Verkefnadrifin kennsla með áherslu á virka þátttöku. Nemendur þróa hagnýta færni, svo sem markvissa notkun GitHub og tæknilega skýrslugerð, og fá góðan grunn í helstu aðferðum vélræns náms og notkunarsviðum þeirra.
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2011, 2012, 2024
IÐN401GAðgerðagreiningHáskóli Íslands
Eini kennari 2011 og 2012, meðkennari 2024; endurhannaði verkefni, námsmat og próf í samstarfi við Menntavísindasvið.
Í samstarfi við Guðrúnu Geirsdóttur var námskeiðið endurskipulagt frá grunni. Nýjungar í námskeiðahönnun, námsmati og dæmatímum urðu öðrum kennara í raunvísindum innblástur, og saman fengum við kennslustyrk frá Háskóla Íslands til að þróa aðferðirnar áfram.
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2007–2010
Dæmatímakennari
Verkfræði- og náttúruvísindasvið, Háskóli Íslands
Dæmatímar og yfirferð í línulegri algebru, hermun, aðgerðagreiningu, stærðfræðigreiningu IB og tölulegri greiningu.
Ekkert að sýna á þessu umfangi.
Styrkir
Verkefnastyrkir
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2024–2026
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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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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2009–2012
Doktorsnemastyrkur
Rannsóknasjóður Háskóla Íslands
Þriggja ára styrkur til doktorsnáms.
Ekkert að sýna á þessu umfangi.
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.
Ekkert að sýna á þessu umfangi.
Viðurkenningar
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2015
Tilnefning til bestu greinar
9. alþjóðlega ráðstefnan um Learning and Intelligent Optimization (LION9)
Ein af þremur greinum sem tilnefndar voru til bestu greinar.
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2005
Magna cum laude
Verzlunarskóli Íslands
Þriðja efst í árganginum og viðurkenning fyrir námsárangur í stærðfræði.
Ekkert að sýna á þessu umfangi.
Erindi og fjölmiðlar
Boðsfyrirlestrar
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2025
Fyrirlestur: HiDef Textiles: Reviving Tradition with Innovation
Reykjavík DataBeers, 25. janúar 2025
Að efla sköpun og sjálfbærni í textílframleiðslu með stafrænni umbreytingu — íslensk prjónahefð mætir nútímatækni.
Erindið sýndi hvernig HiDef Textiles-verkefnið nútímavæðir textílframleiðslu með því að sameina hefðbundnar íslenskar prjónaaðferðir og stafræn hönnunartól. Gögn og glærur eru á GitHub.
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2024
Fyrirlestur: Hugbúnaðargerð og mannlegi þátturinn
Skýrslutæknifélag Íslands (SKÝ)
Um gagnsæi og samskipti milli teyma í hugbúnaðargerð, byggt á reynslu úr mörgum atvinnugreinum.
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2023
Fyrirlestur: Pushing Boundaries: A Data-Driven Dive into Ísfólkið
Haustráðstefna Advania, Harpa
Samspil bókmennta og gagnavísinda: innsýn úr íslenskum útgáfum og Storytel-hljóðbókum Ísfólksins.
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2023
Fyrirlestur: The Legend of the Cat People
6. Reykjavík Data Beers, Orkuveita Reykjavíkur
Verkfræðileg greining á Ísfólkinu (með kattamyndum). Öll gögn og kóði á GitHub.
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2022
Panelisti: Áhrif máltækni og gervigreindar á íslenskuna
3. ELRC-vinnustofan á Íslandi
Umræður með þróunaraðilum og notendum um stöðu og framtíð máltækni á Íslandi.
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2010
Gestafyrirlesari: Politechnika Śląska, Gliwice
Erasmus+ ferðaáætlun fyrir kennara
Í boði prófessors Waldemars Grzechca; kynnti doktorsrannsóknina fyrir deild sjálfvirkni, rafeindatækni og tölvunarfræði.
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2009
Gestafyrirlesari: Université de Valenciennes et du Hainaut-Cambrésis
ENSIAME (nú Université Polytechnique Hauts-de-France)
Kynnti meistaraverkefnið fyrir deildinni, í samstarfi við Sylvain Lalot.
Ekkert að sýna á þessu umfangi.
Ráðstefnuerindi
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2016
Stofnvinnustofa: Samtök kvenna í vísindum (SKVÍS)
SKVÍS
Þátttakandi í stofnvinnustofu um að auka sýnileika kvenkyns sérfræðinga í fjölmiðlum.
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2016
Doktorsvörn: ALICE: Analysis & Learning Iterative Consecutive Executions
Háskóli Íslands, 30. júní 2016
Andmælendur: prófessor Edmund Burke og prófessor Kate Smith-Miles.
Ekkert að sýna á þessu umfangi.
Fjölmiðlar
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2024
Fjölmiðlaviðtal: Sjálfvirknivædd prjónavél í bígerð
Morgunblaðið
Umfjöllun um þrjá nemendur sem gera prjónavél frá tíunda áratugnum sjálfvirka — hluti af HiDef Textiles.
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2017–2020
Hlaðvarpsstjórnandi: ÍSKISUR
Alvarpið og Storytel
Las allar 47 bækur Ísfólksins eftir Margit Sandemo með tveimur vinkonum — og sá um kattahornið.
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2016–2017
Fastagestur í hlaðvarpi: Tæknivarpið
Hlaðvarp Kjarnans
Umræður um nýjustu tækni, allt frá iPhone og Star Wars til kynjahalla í tæknigreinum.
Ekkert að sýna á þessu umfangi.
Nefndir og félagsstörf
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2015, 2023–
Fagráðsmaður, Tækniþróunarsjóður
Fagráð um styrkumsóknir til tækniþróunar og nýsköpunar á Íslandi.
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2018–2021
Stjórnarmaður í starfsmannafélagi
deCODE genetics
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2016
Gjaldkeri starfsmannafélags
AGR Dynamics
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2014–2015
Gjaldkeri starfsmannafélags
Valka
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2011–2012
Fulltrúi doktorsnema í vísindanefnd
Verkfræði- og náttúruvísindasvið, Háskóli Íslands
Fulltrúi framhaldsnema í nefnd sem efldi rannsóknastarf og umræðu þvert á fagsvið.
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2011
Stofnandi Arkimedes
Verkfræði- og náttúruvísindasvið, Háskóli Íslands
Félag doktorsnema og nýdoktora á verkfræði- og náttúruvísindasviði.
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2009–2011
Stofnandi og gjaldkeri Heron
Verkfræði- og náttúruvísindasvið, Háskóli Íslands
Sameinaði óstarfhæf nemendafélög í eitt félag framhaldsnema og gætti fjármála þess.
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2009–2010
Gjaldkeri BEST Reykjavík
Board of European Students of Technology (BEST)
Fulltrúi á aðalfundi BEST og skipuleggjandi tveggja námskeiða fyrir 20 evrópska nemendur.
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2006–2007
Formaður Stiguls
Verkfræði- og náttúruvísindasvið, Háskóli Íslands
Nemendafélag stærðfræði- og eðlisfræðinema.
Ekkert að sýna á þessu umfangi.
Ritverk
Ritrýndar tímaritsgreinar
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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.
Ekkert að sýna á þessu umfangi.
Valdar greinar, endurskoðaðar og útvíkkaðar
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2026
A Scalable Matheuristic for Routing Capacity-Constrained Groundfish Surveys
BókarkafliLearning and Intelligent Optimization. LION 2026, Springer International Publishing, 2026, Accepted
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2016
Evolutionary learning of linear composite dispatching rules for scheduling
BókarkafliStudies 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.
Ekkert að sýna á þessu umfangi.
Ritrýnd ráðstefnurit
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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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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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 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 (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 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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Greinar á íslensku
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2015
Skilningur á röðunarreglum fyrir verkniðurröðun á vélum
TímaritsgreinVélabrögð, 36, pp. 8–11, Þriðja árs véla- og iðnaðarverkfræðinemar við iðnaðarverkfræði-, vélaverkfræði- og tölvunarfræðideild Háskóla Íslands, 2015
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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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Málstofur og fyrirlestrar
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2016
ALICE: Analysis & Learning Iterative Consecutive Executions
MálstofaStatistics and Bioinformatics Seminar, deCODE Genetics, Reykjavík, Iceland, 2016
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2013
Supervising Learning Linear Composite Dispatch Rules for Scheduling
MálstofaReiDok13 Symposium on Computational PhD Projects, School of Engineering and Natural Sciences, University of Iceland, 2013
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2012
Creating Meaningful Training Data for Difficult JSSP Instances for Ordinal Regression
MálstofaSeminar for Ph.D. students, School of Engineering and Natural Sciences, University of Iceland, 2012
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2012
Determining the Characteristic of Difficult JSSP Instances for a Heuristic Solution Methods
MálstofaStats colloquium, School of Engineering and Natural Sciences, University of Iceland, 2012
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Tæknikunnátta
- Almenn forritun
- C#, C++, Python
- Töluleg reikning
- MATLAB
- Tölfræði
- R, tidyverse
- Gagnagrunnar
- Microsoft SQL Server, PostgreSQL, SQLite, TimescaleDB
- Bestun
- Gurobi, GLPK
- Skriftun og verkfæri
- awk, grep, sed, make, git, Quarto
Tungumál
- Íslenska
- móðurmál
- Enska
- reiprennandi
- Danska
- samtalshæfni
- Franska
- samtalshæfni
Áhugamál
- Fagleg
- leitniaðferðir, gervigreind, þróunarreikningar, altæk bestun, tölfræðilegt nám, vélrænt nám, stórgögn, sjálfvirknivæðing, gagnamyndræn framsetning og raunveruleg notkun
- Persónuleg
- prjón, fatasaumur, handverk, garðyrkja, hlaðvörp, internetkettir og rússnesk blá
Aðild
- Kennsluakademía opinberra háskóla