Post-doc and Ph.D. Students

Currently supervised PostDocs

• Anuja Vats (2024 – ): Postdoctoral Fellow in the SmaRTWork project
• Betül Bayrak (2025 – ): Postdoctoral Fellow at the SFI NorwAI research center

Currently supervised Ph.D. students (main supervision)

• Abdul Kazeem Shamba (2023 – ): Hybrid AI for Anomaly Detection and Predictive Maintenance on Wireless Embedded Sensor Nodes – co-supervised by Gavin Taylor
• Stuart Galina Ottersen (2023 – ): Using machine learning to predict response and dropout in RCT data from digital sleep therapy – co-supervised by Håvard Kallestad

Currently supervised Ph.D. students (co-supervision)

• Tomasz Szczepanski (2024 – ): Improved diagnosis by digital gold mining in historical neurophysiological data – main supervisor Kristian Bernhard Nilsen
• Sophia Sylvester (2023 – ): Radar Data and Machine Learning in Psychiatric Care: Novel Approaches for Advancing Sleep/Wake Estimation and Aggression Prediction – main supervisor Håvard Kallestad
• Anna Rodum Bjøru (2021 – ): Explainable AI with Deep Bayesian Networks – main supervisor Helge Langseth
• Emanuel Lorenz (2022-2025): Multimodal brain-computer interface (BCI) for Neuromotor Rehabilitation – main supervisor Xiaomeng Su
• Fabienne Rössler (2018 – ): – main supervisor Nadav Bar

Alumni (Ph.D. and Post-docs)

• Sverre Herland (2020 – 2025): Reinforcement Learning for Robotic Control and Manipulation in Ocean Space Applications – co-supervised by Helge Langseth and Ekrem Misimi
• Hafiz Areeb Asad (2021 – 2025): Machine Learning for Cognitive Power Management in IoT – main supervisor Frank Kraemer
• Aleksej Logacjov (2021 – 2025): Large-scale Self-supervised Learning for Enhancing Accelerometer-based Human Activity and Sleep Recognition – co-supervised by Paul Jarle Mork
• Betül Bayrak (2022 – 2025): Post-hoc eXplainable Artificial Intelligence Methods: Counterfactuals and XCBR Applications – co-supervised by Helge Langseth
• Tom Hermann (2023 – 2025): Using Artificial Intelligence for Understanding Health Risks in Elderly – co-supervised by Håvard Skjellegrind
• Fredrik Granviken (2020 – 2024): Thesis: Personalized Decision Support in the Management of Musculoskeletal Pain Disorders in Primary Physiotherapy Care – main supervisors Ingebrigt Meisingset and Ottar Vasseljen
• Eirik Lund Flogard (2020 – 2024): Thesis: Improving Labour Inspection Efficiency via Machine Learning – main supervisor Ole Jakob Mengshoel
• Lena Jedamski (2023 – 2024): Topic: Building a Recommendation System for Trustworthy Methods in AI Applications – co-supervised by Andreas Hafver
• Bjørn Magnus Mathisen (Postdoctoral Fellow, 2021 – 2024): SFI Exposed Aquaculture
• Abdulmajid Murad (Postdoctoral Fellow, 2023 – 2024): NorwAI WP Data
• Tiago Veiga (Postdoctoral Fellow, 2020 – 2023): ERCIM & AI4EU
• Paola Marin Veites (Ph.D., 2021 – 2023): Topic: The Use of Case-Based Reasoning in Muscoloskeletal Pain Complaints – co-supervised by Ottar Vasseljen
• Håkon Måløy (Ph.D., 2018 – 2023): Thesis: Learning neural representations for the processing of temporal data in deep neural networks – main supervisor Keith Downing
• Abdulmajid Murad (Ph.D., 2018 – 2023): Thesis: Uncertainty-Aware Autonomous Sensing with Deep Reinforcement Learning – main supervisor Frank Kraemer
• Deepika Verma (Ph.D., 2017 – 2022): Thesis: Using Case- based Reasoning for Creating Intelligent Systems in Healthcare – co-supervised by Paul Jarle Mork
• Elise Klæbo Vonstad (Ph.D., 2017 – 2022): Thesis: Improving Exergame Technologies for Older Adults Using Machine Learning – main supervisor Jan Harald Nilsen
• Johannes Rehm (2020 – ): – main supervisor Odd Erik Gundersen
• Di Wu (Ph.D., 2017 – 2022): Thesis: Computational Risk Analysis for Digitizing Sustainable Urban Water Supply Systems – main supervisor Hao Wang
• Hoda Nikpour (Ph.D., 2015 – 2021): Thesis: Problem solving in uncertain domains by a Bayesian supported knowledge-intensive case-based reasoning method – main supervisor Agnar Aamodt
• Bjørn Magnus Mathisen (Ph.D., 2016-2021): Thesis: Using Similarity Learning to Enable Decision Support in Aquaculture – co-supervised by Agnar Aamodt and Helge Langseth
• Irina Reshodko (Post-doc, 2020 – 2021): Automated feedback and instruction system for passenger car driving schools, co-supervised by Odd Erik Gundersen
• Ilya Ashikhmin (Post-doc, 2017 – 2021): selfBACK project & Back-Up project
• Tomasz Szczepanski (Researcher, 2016-2021): selfBACK project & Back-Up project

Ph.D. Committees (internal and external)

• Maedeh Nasri, Leiden University, The Netherlands (2024): A compass towards equity: a data analysis framework to capture children’s behaviour in the playground context [opponent]
• Viktor Eisenstadt, University of Hildesheim, Germany (2024): Supporting Early Phases of Conceptual Design in Architecture Using Case-Based Reasoning and Distributed Artificial Intelligence [opponent]
• Patrick Klein, University of Trier, Germany (2024): Combining Expert Knowledge and Deep Mining with Case-Based Reasoning for Predictive Maintenance [opponent]
• Andrea Marheim Storås, OsloMet, Norway (2024): Beyond the Black Box: Transparent Machine Learning Systems for Medical Applications [opponent]
• Mir Riyanul Islam, Märladalen University, Sweden (2024): Explainable Artificial Intelligence for Enhancing Transparency in Decision Support Systems [opponent]
• Sven Myrdal Opalic, University of Agder, Norway (2023): Advanced Warehouse Energy Storage System Control Using Deep Supervised and Reinforcement Learning [opponent]
• Claudio Díaz, University of Sydney (2023): Mining Activity Tracker Data To Analyse Physical Activity Behaviours And Provide Personalised Feedback In Health Education Programmes [opponent]
• Tor Gunnar Høst Houeland, NTNU (2020): Automated lazy metalearning in introspective reasoning systems [administrator and member of the committee]
• Pascal Reuss, University of Hildesheim, Germany (2019): Case Factories: A Maintenance Cockpit for distributed structural Case-Based Reasoning Systems [opponent]
• Patrick Schuch, NTNU (2019): Deep Learning for Fingerprint Recognition Systems [administrator and member of the committee]
• Thomas Falch, NTNU (2018): ImageCL and Other Techniques and Tools for Optimizing Applications Utilizing Heterogeneous Computing [administrator and member of the committee]