Ketil Malde's picture

Ketil Malde

Associate Professor
  • E-mailketil.malde@uib.no
  • Visitor Address
    HIB - Thormøhlens gate 55
    5006 Bergen
  • Postal Address
    Postboks 7803
    5020 Bergen
Academic article
  • Show author(s) (2023). Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary. Proceedings of the Northern Lights Deep Learning Workshop. 8 pages.
  • Show author(s) (2023). Machine learning in marine ecology: an overview of techniques and applications. ICES Journal of Marine Science. 1829-1853.
  • Show author(s) (2023). Annotating otoliths with a deep generative model. ICES Journal of Marine Science. 55-65.
  • Show author(s) (2023). Age interpretation of cod otoliths using deep learning. Ecological Informatics. 11 pages.
  • Show author(s) (2023). A contrastive learning approach for individual re-identification in a wild fish population. Proceedings of the Northern Lights Deep Learning Workshop. 8 pages.
  • Show author(s) (2022). The salmon louse genome may be much larger than sequencing suggests. Scientific Reports. 1-14.
  • Show author(s) (2022). DeepOtolith v1.0: An Open-Source AI Platform for Automating Fish Age Reading from Otolith or Scale Images. Fishes. 11 pages.
  • Show author(s) (2021). The salmon louse genome: Copepod features and parasitic adaptations. Genomics. 3666-3680.
  • Show author(s) (2021). Automatic interpretation of salmon scales using deep learning. Ecological Informatics. 10 pages.
  • Show author(s) (2021). A real-world dataset and data simulation algorithm for automated fish species identification. Geoscience Data Journal.
  • Show author(s) (2021). A deep learning-based method to identify and count pelagic and mesopelagic fishes from trawl camera images. ICES Journal of Marine Science. 3780-3792.
  • Show author(s) (2020). Microbial communities associated with the parasitic copepod Lepeophtheirus salmonis. . Marine Genomics. 4 pages.
  • Show author(s) (2020). Machine intelligence and the data-driven future of marine science. ICES Journal of Marine Science. 12 pages.
  • Show author(s) (2020). Genome wide analysis reveals genetic divergence between Goldsinny wrasse populations. BMC Genetics. 1-15.
  • Show author(s) (2020). Acoustic classification in multifrequency echosounder data using deep convolutional neural networks. ICES Journal of Marine Science. 1391-1400.
  • Show author(s) (2019). Levels and temporal trends of persistent organic pollutants (POPs) in Atlantic cod (Gadus morhua) and haddock (Melanogrammus aeglefinus) from the southern Barents Sea. Environmental Research. 89-97.
  • Show author(s) (2019). An efficient protocol and data set for automated otolith image analysis. Geoscience Data Journal. 1-9.
  • Show author(s) (2019). Airgun blasts used in marine seismic surveys have limited effects on mortality, and no sublethal effects on behaviour or gene expression, in the copepod Calanus finmarchicus. ICES Journal of Marine Science. 2033-2044.
  • Show author(s) (2018). Judging a salmon by its spots: Environmental variation is the primary determinant of spot patterns in Salmo salar. BMC Ecology. 1-13.
  • Show author(s) (2018). Fish species identification using a convolutional neural network trained on synthetic data. ICES Journal of Marine Science. 342-349.
  • Show author(s) (2018). Automatic interpretation of otoliths using deep learning. PLOS ONE. 1-14.
  • Show author(s) (2017). Whole genome resequencing reveals diagnostic markers for investigating global migration and hybridization between minke whale species. BMC Genomics. 1-11.
  • Show author(s) (2015). Characterization of a novel RXR receptor in the salmon louse (Lepeophtheirus salmonis, Copepoda) regulating growth and female reproduction. BMC Genomics. 22 pages.
  • Show author(s) (2014). Simulating a population genomics data set using FlowSim. BMC Research Notes.
  • Show author(s) (2014). Human-induced evolution caught in action: SNP-array reveals rapid amphi-atlantic spread of pesticide resistance in the salmon ecotoparasite Lepeophtheirus salmonis. BMC Genomics. 18 pages.
  • Show author(s) (2014). Gene expression in five salmon louse (Lepeophtheirus salmonis, Krøyer 1837) tissues. Marine Genomics. 39-44.
  • Show author(s) (2014). Estimating the information value of polymorphic sites using pooled sequences. BMC Genomics. 11 pages.
  • Show author(s) (2013). Increasing Sequence Search Sensitivity with Transitive Alignments. PLOS ONE. 7 pages.
  • Show author(s) (2013). How does sequence variability affect de novo assembly quality? Journal of Natural History. 901-910.
  • Show author(s) (2013). Filtering duplicate reads from 454 pyrosequencing data. Bioinformatics. 830-836.
  • Show author(s) (2012). Maternal 3 ' UTRs: from egg to onset of zygotic transcription in Atlantic cod. BMC Genomics. 14 pages.
  • Show author(s) (2011). The genome sequence of Atlantic cod reveals a unique immune system. Nature. 207-210.
  • Show author(s) (2011). Systematic exploration of error sources in pyrosequencing flowgram data. Bioinformatics. I304-I309.
  • Show author(s) (2011). Identification of vimentin- and elastin-like transcripts specifically expressed in developing notochord of Atlantic salmon (Salmo salar L.). Cell and Tissue Research. 191-202.
  • Show author(s) (2011). EST resources and establishment and validation of a 16 k cDNA microarray from Atlantic cod (Gadus morhua). Comparative Biochemistry and Physiology - Part D:Genomics and Proteomics. 23-30.
  • Show author(s) (2010). Characteristics of 454 pyrosequencing data-enabling realistic simulation with flowsim. Bioinformatics. i420-i425.
  • Show author(s) (2010). Calcium from salmon and cod bone is well absorbed in young healthy men: a double-blinded randomised crossover design. Nutrition & Metabolism. 9 pages.
  • Show author(s) (2009). Identification of immune related genes in Atlantic halibut (Hippoglossus hippoglossus L.) following in vivo antigenic and in vitro mitogenic stimulation. Fish and Shellfish Immunology. 729-738.
  • Show author(s) (2008). The effect of sequence quality on sequence alignment. Bioinformatics. 897-900.
  • Show author(s) (2008). Repeats and EST analysis for new organisms. BMC Genomics. 7 pages.
  • Show author(s) (2006). RBR: library-less repeat detection for ESTs. Bioinformatics. 2232-2236.
  • Show author(s) (2006). Calculating PSSM probabilities with lazy dynamic programming. Journal of functional programming. 75-81.
  • Show author(s) (2005). Masking repeats while clustering ESTs. Nucleic Acids Research (NAR). 2176-2180.
  • Show author(s) (2005). A graph based algorithm for generating EST consensus sequences. Bioinformatics. 1371-1375.
  • Show author(s) (2003). Fast Sequence Clustering Using a Suffix Array Algorithm. Bioinformatics. 1221-1226.
  • Show author(s) (2003). A Fast Algorithm for EST Clustering using Suffix Arrays. Bioinformatics.
  • Show author(s) (2021). Fisheries acoustics and Acoustic Target Classification - Report from the COGMAR/CRIMAC workshop on machine learning methods in fisheries acoustics. 2021 - 25. 2021 - 25. .
  • Show author(s) (2020). Big Data in Marine Science. 6. 6. .
  • Show author(s) (2019). Machine learning to improve marine science for the sustainability of living ocean resources: Report from the 2019 Norway - U.S. Workshop. .
  • Show author(s) (2018). Report of the Workshop on Machine Learning in Marine Science (WKMLEARN). .
  • Show author(s) (2022). Artificial Intelligence methods for fisheries management.
  • Show author(s) (2019). Using a CNN trained on synthetic data for fish species identification.
  • Show author(s) (2019). Using a CNN trained on synthetic data for fish species identification.
  • Show author(s) (2016). Big Data og Big Analysis - hvordan drikke fra brannslangen.
  • Show author(s) (2015). Insitute of Marine Research - a research and advisory institute.
Academic lecture
  • Show author(s) (2023). Measuring Adversarial Robustness using a Voronoi-Epsilon Adversary.
  • Show author(s) (2023). A story about data extraction and deep learning applied to fishery acoustic data.
  • Show author(s) (2022). Using deep learning models to count and identify fish species from in-trawl images.
  • Show author(s) (2022). Selecting maximally informative frequency subsets for acoustic surveys.
  • Show author(s) (2022). Exploring imaging protocols and neural network architectures for automated otolith analysis.
  • Show author(s) (2022). An online otolith age reader using deep neural networks: Perspectives and challenges.
  • Show author(s) (2022). A deep learning approach for individual re-identification (re-ID) of fish in the wild.
  • Show author(s) (2021). Combined trawl-mounted optic and acoustic methods to study the mesopelagic ecosystem.
  • Show author(s) (2019). The COGMAR project.
  • Show author(s) (2018). Drowning in data: Can deep learning approaches be the solution?
  • Show author(s) (2013). Identifying diagnostic SNPs in the presence of sequencing errors.
  • Show author(s) (2013). Can Software Transactional Memory make concurrent programs simple and safe?
  • Show author(s) (2012). Transcriptomic analysis of the salmon louse.
  • Show author(s) (2012). The Salmon Louse Genome Project.
  • Show author(s) (2012). Attempts to induce sterility in Atlantic salmon by morfolino and zink finger techniques.
  • Show author(s) (2011). Maternal 3'UTRs: from egg to onset of zygotic transcription in Atlantic cod.
  • Show author(s) (2009). Using Bloom Filters for Large Scale Gene Sequence Analysis in Haskell.
  • Show author(s) (2002). A Fast Algorithm for EST Clustering using Suffix Trees.
Short communication
  • Show author(s) (2011). Flower: extracting information from pyrosequencing data. Bioinformatics. 1041-1042.
Masters thesis
  • Show author(s) (2023). Object Tracking Approach for Catch Estimation on Trawl Surveys.
  • Show author(s) (2022). Selecting Maximally Informative Frequency Subsets for Acoustic Surveys.
Doctoral dissertation
  • Show author(s) (2023). On the Significance of Distance in Machine Learning.
  • Show author(s) (2005). Algorithms for the Analysis of Expressed Sequence Tags.
  • Show author(s) (2023). Escaping the black box: explicit annotation of otolith growth rings with deep learning.
  • Show author(s) (2023). Annotating Otoliths with a Deep Generative Model.
  • Show author(s) (2020). Automatic interpretation of salmon scales using deep learning.
  • Show author(s) (2019). Otoliths as life history indicators.
  • Show author(s) (2017). Automatisk klassifisering av svømming i not .
  • Show author(s) (2013). A data storage strategy for generic, heterogenous scientific data.
Article in business/trade/industry journal
  • Show author(s) (2021). Machine Learning + Marine Science: Critical Role of Partnerships in Norway. Journal of Ocean Technology. 1-9.

More information in national current research information system (CRIStin)