Bilde av Johansen, Håvard D.
Bilde av Johansen, Håvard D.
Department of Computer Science havard.johansen@uit.no +4777646738 +47 91110987 Tromsø REALF A 261

Håvard D. Johansen


Professor

Job description

Håvard D. Johansen teaches and researches computer security and privacy topics. His work in overlay networks received great acclaim in the research community providing a completely different way of approaching the problem of Byzantine fault tolerance. Johansen is currently focused on secure remote execution of code using Intel SGX, reactive information-flow policies, and energy-efficient blockchains based on Byzantine consensus. Johansen is a hands-on full-stack software developer. He has up-to-date expertise with modern software technologies like GO, TypeScript, iOS/Android platforms, and Docker. Dr. Johansen has also competence in machine learning, and interdisciplinary experiences in the medical, sports, and psychology domains.


  • Aakash Sharma, Thomas Bye Nilsen, Sivert Johansen, Dag Johansen, Håvard D. Johansen :
    Designing a Service for Compliant Sharing of Sensitive Research Data
    Lecture Notes in Computer Science (LNCS) 2022 ARKIV / DOI
  • Enrico Tedeschi, Tor-Arne Schmidt Nordmo, Dag Johansen, Håvard D. Johansen :
    On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern
    ACM Transactions on Internet Technology 2022 ARKIV / DOI
  • Enrico Tedeschi, Tor-Arne Schmidt Nordmo, Dag Johansen, Håvard D. Johansen :
    On Optimizing Transaction Fees in Bitcoin using AI: Investigation on Miners Inclusion Pattern
    ACM Transactions on Internet Technology 2022 ARKIV / DOI
  • Debesh Jha, Ashish Rauniyar, Håvard D. Johansen, Dag Johansen, Michael Alexander Riegler, Pål Halvorsen et al.:
    Video Analytics in Elite Soccer: A Distributed Computing Perspective
    Proceedings of the IEEE Sensor Array and Multichannel Signal Processing Workshop 2022 ARKIV / DOI
  • Joakim Aalstad Alslie, Aril Bernhard Ovesen, Tor-Arne Schmidt Nordmo, Håvard D. Johansen, Pål Halvorsen, Michael Riegler et al.:
    Áika: A Distributed Edge System for AI Inference
    Big Data and Cognitive Computing 2022 ARKIV / DOI
  • Pietro Randine, Aakash Sharma, Gunnar Hartvigsen, Håvard D. Johansen, Eirik Årsand :
    Information and communication technology-based interventions for chronic diseases consultation: Scoping review
    International Journal of Medical Informatics 2022 ARKIV / DOI
  • Debesh Jha, Michael Riegler, Håvard D. Johansen, Dag Johansen, Jens Rittscher, Pål Halvorsen et al.:
    FANet: A Feedback Attention Network for Improved Biomedical Image Segmentation
    IEEE Transactions on Neural Networks and Learning Systems 2022 ARKIV / DOI
  • Areeg Samir Ahmed Elgazazz, Håvard D. Johansen :
    Technical Viewpoint of Challenges, Opportunities, and Future Directions of Policy Change and Information-Flow in Digital Healthcare Systems
    International Academy, Research and Industry Association (IARIA) 2022
  • Lars Brenna, Isak Sunde Singh, Håvard D. Johansen, Dag Johansen :
    TFHE-rs: A library for safe and secure remote computing using fully homomorphic encryption and trusted execution environments
    Array 2022 DOI
  • Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Bjørn Aslak Juliussen, Steven Hicks, Vajira L B Thambawita, Håvard D. Johansen et al.:
    Njord: a fishing trawler dataset
    ACM Publications 2022 DOI
  • Tor-Arne Schmidt Nordmo, Ove Kvalsvik, Svein Ove Kvalsund, Birte Hansen, Pål Halvorsen, Steven Hicks et al.:
    Fish AI: Sustainable Commercial Fishing
    Nordic Machine Intelligence (NMI) 2022 ARKIV / DOI
  • Areeg Samir Ahmed Elgazazz, Håvard D. Johansen :
    Technical Viewpoint of Challenges, Opportunities, and Future Directions of Policy Change and Information-Flow in Digital Healthcare Systems
    International Academy, Research and Industry Association (IARIA) 2022
  • Vajira L B Thambawita, Steven Hicks, Jonas L Isaksen, Mette H. Stensen, Trine B. Haugen, Jørgen K Kanters et al.:
    DeepSynthBody: the beginning of the end for data deficiency in medicine
    IEEE conference proceedings 2021 DOI
  • Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Håvard D. Johansen, Michael Alexander Riegler, Pål Halvorsen, Dag Johansen :
    Dutkat: A Multimedia System for Catching Illegal Catchers in a Privacy-Preserving Manner
    ACM Digital Library 2021 DOI
  • Aril Bernhard Ovesen, Tor-Arne Schmidt Nordmo, Håvard D. Johansen, Michael Alexander Riegler, Pål Halvorsen, Dag Johansen :
    File system support for privacy-preserving analysis and forensics in low-bandwidth edge environments
    Information 2021 ARKIV / DOI
  • Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Håvard D. Johansen, Dag Johansen, Jens Rittscher et al.:
    Real-Time Polyp Detection, Localization and Segmentation in Colonoscopy Using Deep Learning
    IEEE Access 2021 ARKIV / DOI
  • Debesh Jha, Pia Smedsrud, Dag Johansen, Thomas de Lange, Håvard D. Johansen, Pål Halvorsen et al.:
    A Comprehensive Study on Colorectal Polyp Segmentation with ResUNet++, Conditional Random Field and Test-Time Augmentation
    IEEE journal of biomedical and health informatics 2021 ARKIV / DOI
  • Aakash Sharma, Thomas Bye Nilsen, Katja Pauline Czerwinska, Daria Onitiu, Lars Brenna, Dag Johansen et al.:
    Up-to-the-Minute Privacy Policies via Gossips in Participatory Epidemiological Studies
    Frontiers in Big Data 13. May 2021 ARKIV / DOI
  • Debesh Jha, Sharib Ali, Steven Hicks, Vajira L B Thambawita, Hanna Borgli, Pia Smedsrud et al.:
    A comprehensive analysis of classification methods in gastrointestinal endoscopy imaging
    Medical Image Analysis 2021 ARKIV / DOI
  • Debesh Jha, Anis Yazidi, Michael Alexander Riegler, Dag Johansen, Håvard D. Johansen, Pål Halvorsen :
    LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
    Springer Nature 2021 DOI
  • Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven Hicks, Vajira L B Thambawita, Enrique Garcia-Ceja et al.:
    Kvasir-Instrument: Diagnostic and Therapeutic Tool Segmentation Dataset in Gastrointestinal Endoscopy
    Springer 2021 DOI
  • Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Michael Alexander Riegler, Dag Johansen, Håvard D. Johansen et al.:
    Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
    IEEE (Institute of Electrical and Electronics Engineers) 2021 DOI
  • Nikhil Kumar Tomar, Debesh Jha, Sharib Ali, Håvard D. Johansen, Dag Johansen, Michael Riegler et al.:
    DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
    Springer Nature 2021 DOI
  • Abhishek Srivastava, Debesh Jha, Sukalpa Chanda, Umapada Pal, Håvard D. Johansen, Dag Johansen et al.:
    MSRF-Net: A Multi-Scale Residual Fusion Network for Biomedical Image Segmentation
    IEEE journal of biomedical and health informatics 2021 ARKIV / DOI
  • Debesh Jha, Steven Hicks, Krister Emanuelsen, Håvard D. Johansen, Dag Johansen, Thomas de Lange et al.:
    Medico Multimedia Task at MediaEval 2020: Automatic Polyp Segmentation
    2020
  • Vajira Lasantha Bandara Thambawita, Steven Hicks, Hanna Borglia, Håkon Kvale Stensland, Debesh Jha, Martin Kristoffer Svensen et al.:
    PMData: a sports logging dataset
    Association for Computing Machinery (ACM) 2020 ARKIV / DOI
  • Håvard D. Johansen, Dag Johansen, Tomas Kupka, Michael Riegler, Pål Halvorsen :
    Scalable Infrastructure for Efficient Real-Time Sports Analytics
    Association for Computing Machinery (ACM) 2020 DOI
  • Debesh Jha, Michael Alexander Riegler, Dag Johansen, Pål Halvorsen, Håvard D. Johansen :
    DoubleU-Net: A Deep Convolutional Neural Network for Medical Image Segmentation
    IEEE International Symposium on Computer-Based Medical Systems 2020 DOI
  • Vajira Lasantha Bandara Thambawita, Debesh Jha, Hugo Lewi Hammer, Håvard D. Johansen, Dag Johansen, Pål Halvorsen et al.:
    An extensive study on cross-dataset bias and evaluation metrics interpretation for machine learning applied to gastrointestinal tract abnormality classification
    ACM Transactions on Computing for Healthcare (HEALTH) 22. June 2020 DOI
  • Aakash Sharma, Katja P Czerwinska, Lars Brenna, Dag Johansen, Håvard D. Johansen :
    Privacy Perceptions and Concerns in Image-Based Dietary Assessment Systems: Questionnaire-Based Study
    JMIR Human Factors 2020 ARKIV / DOI
  • Debesh Jha, Pia Smedsrud, Michael Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen et al.:
    Kvasir-SEG: A segmented polyp dataset
    Lecture Notes in Computer Science (LNCS) 2020 ARKIV / DOI
  • Svein Arne Pettersen, Håvard D. Johansen, Ivan Andre Matias Do Vale Baptista, Pål Halvorsen, Dag Johansen :
    Quantified Soccer Using Positional Data: A Case Study
    Frontiers Media S.A. 2020 FULLTEKST / DOI
  • Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Bjørn Aslak Juliussen, Steven A. Hicks, Vajira Thambawita, Håvard D. Johansen et al.:
    Njord: A Fishing Trawler Dataset
    2022
  • Dag Johansen, Håvard D. Johansen, Michael Riegler :
    Muligheter og skranker for bruk av teknologi og kunstig intelligens ved forebygging av fiskerikriminalitet
    2022
  • Dag Johansen, Bjørn Aslak Juliussen, Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Pål Halvorsen, Håvard D. Johansen et al.:
    Sustainable commercial fishing: Digital inspectors to the rescue
    Arctic Frontiers : Abstracts 2022
  • Michael Alexander Riegler, Dag Johansen, Bjørn Aslak Juliussen, Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Pål Halvorsen et al.:
    Njord: An out-in-the-wild real world fish vessel catch analysis dataset
    Arctic Frontiers : Abstracts 2022
  • Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Bjørn Aslak Juliussen, Steven A. Hicks, Vajira Thambawita, Håvard D. Johansen et al.:
    Njord: A Fishing Trawler Dataset
    2022
  • Håvard D. Johansen, Dag Johansen, Areeg Samir Ahmed Elgazazz, Thomas Bye Nilsen, Aakash Sharma :
    Datainnsamling med personvern og datasikkerhet
    2022
  • Michael Alexander Riegler, Dag Johansen, Bjørn Aslak Juliussen, Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Pål Halvorsen et al.:
    Njord: An out-in-the-wild real world fish vessel catch analysis dataset
    2022
  • Håvard D. Johansen, Dag Johansen, Bjørn Aslak Juliussen, Tor-Arne Schmidt Nordmo, Aril Bernhard Ovesen, Pål Halvorsen et al.:
    Sustainable commercial fishing: Digital inspectors to the rescue
    2022
  • Areeg Samir Ahmed Elgazazz, Håvard D. Johansen :
    Technical Viewpoint of Challenges, Opportunities, and Future Directions of Policy Change and Information-Flow in Digital Healthcare Systems
    2022
  • Aakash Sharma, Thomas Bye Nilsen, Lars Brenna, Dag Johansen, Håvard D. Johansen :
    Accountable Human Subject Research Data Processing using Lohpi
    2021 ARKIV
  • Debesh Jha, Sharib Ali, Krister Emanuelsen, Steven Hicks, Vajira L B Thambawita, Enrique Garcia-Ceja et al.:
    Kvasir-Instrument: Diagnostic and Therapeutic Tool Segmentation Dataset in Gastrointestinal Endoscopy
    2021
  • Debesh Jha, Nikhil Kumar Tomar, Sharib Ali, Michael A. Riegler, Håvard D. Johansen, Thomas de Lange et al.:
    NanoNet: Real-Time Polyp Segmentation in VideoCapsule Endoscopy and Colonoscopy
    2021
  • Aakash Sharma, Thomas Bye Nilsen, Håvard D. Johansen :
    Compliant Sharing of Sensitive Data with Dataverse and Lohpi
    2021 ARKIV
  • Debesh Jha, Sharib Ali, Nikhil Kumar Tomar, Håvard D. Johansen, Dag Johansen, Michael Riegler et al.:
    Exploring Deep Learning Methods for Real-Time Surgical Instrument Segmentation in Laparoscopy
    2021 FULLTEKST
  • Nikhil Kumar Tomar, Debesh Jha, Sharib Ali, Håvard D. Johansen, Dag Johansen, Michael Riegler et al.:
    DDANet: Dual Decoder Attention Network for Automatic Polyp Segmentation
    2021
  • Håvard D. Johansen, Dag Johansen, Tomas Kupka, Michael Riegler, Pål Halvorsen :
    Scalable Infrastructure for Efficient Real-Time Sports Analytics
    2020
  • Debesh Jha, Anis Yazidi, Michael Alexander Riegler, Dag Johansen, Håvard D. Johansen, Pål Halvorsen :
    LightLayers: Parameter Efficient Dense and Convolutional Layers for Image Classification
    2020
  • Debesh Jha, Smedsrud Pia H, Michael Riegler, Pål Halvorsen, Thomas de Lange, Dag Johansen et al.:
    Kvasir-SEG: A Segmented Polyp Dataset
    2020

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