Adane Nega Tarekegn

Adane Nega Tarekegn
Adane Nega Tarekegn
Adane N. Tarekegn
Adane N. Tarekegn
  • Location Bergen, Norway

    • About me

    I’m Adane, an AI researcher working at the intersection of machine learning, generative AI, and multimodal learning, with applications in media content understanding, large-scale data analysis, and decision support systems.

    My work focuses on building intelligent systems that can solve complex tasks. Currently, I am a postdoctoral researcher at SFI MediaFutures (University of Bergen, Norway), where I work on multimodal AI, LLMs, and deep generative models for industry-driven research and innovation.

    I obtained my Ph.D. in Modeling and Data Science from the University of Torino, Italy. During that time, I spent a period as a visiting researcher at the Wroclaw University of Economics and Business, Poland, where I collaborated on a machine learning research project.

    International Projects

    Below are three selected international research projects I have contributed to, focused on AI, machine learning, and real-world applications across media, healthcare, and environmental sensing.

    • Multimodal AI for Video Content Analysis and Generation (WP3), MediaFutures
    • Data Aggregation, AI Analytics, Deep Learning for Monitoring Neurological Disorders (WP4), Alameda Project
    • Image Preprocessing and Computer Vision for Underwater Environment Recognition and Sensing (WP4), ADRIATIC Project

    Selected Publications

    • A. N. Tarekegn et al. (2026).
      Multimodal Video Summarization with Mamba and Bayesian Approach.
      32nd International Conference on Multimedia Modeling (MMM2026), Lecture Notes in Computer Science, vol 16412. Springer, Singapore. https://doi.org/10.1007/978-981-95-6950-2_39.
    • A. N. Tarekegn, F. Rabbi, L. Steskal, B. Tessem (2025).
      Automated News Clip Generation via Robust Video Summarization
      In Proceedings of the 37th IEEE International Conference on Tools with Artificial Intelligence (ICTAI), Athens, Greece, 03–05 November 2025.
      DOI: 10.1109/ICTAI66417.2025.00103.
    • A. N. Tarekegn, B. Tessem, F. Rabbi (2025).
      A New Cluster Validation Index Based on Stability Analysis
      In Proceedings of the 14th International Conference on Pattern Recognition Applications and Methods – ICPRAM, ISBN 978-989-758-730-6; ISSN 2184-4313, SciTePress, pages 377–384.
      [Paper] [Code] [PyPI]
    • A. N. Tarekegn, M. Sajjad, F. Alaya Cheikh, M. Ullah, K. Muhammad (2023), Efficient Human Gait Activity Recognition Based on Sensor Fusion and Intelligent Stacking Framework
      IEEE Sensors Journal, 23(22), 28355–28369
      [Paper] [Code]
    • A. N. Tarekegn, Mario Giacobini, Krzysztof Michalak (2021).
      A Review of Methods for Imbalanced Multi‑Label Classification
      Pattern Recognition, Volume 118, 2021, Article 107965, ISSN 0031-3203.
      [Paper]
    • M. Munsif, M. Sajjad, M. Ullah, A. N. Tarekegn, F. A. Cheikh, P. Tsakanikas (2024), Optimized Efficient Attention‑Based Network for Facial Expressions Analysis in Neurological Health Care
      Comput. Biol. Med.
      [Paper] [Code]
    • A. N. Tarekegn, K. Michalak, G. Costa, F. Ricceri, M. Giacobini (2024), Predicting Multiple Outcomes Associated with Frailty Based on Imbalanced Multi‑Label Classification
      J. Healthc. Inform. Res., 8, 594–618
      [Paper]
    • A. N. Tarekegn, M. Ullah, F. A. Cheikh, M. Sajjad (2023), Enhancing Human Activity Recognition Through Sensor Fusion and Hybrid Deep Learning Model
      ICASSPW – IEEE Int. Conf. on Acoustics, Speech, and Signal Processing Workshops
      [Paper] [Code]
    • A. N. Tarekegn, F. A. Cheikh, M. Ullah, E. T. Sollesnes (2023), Underwater Object Detection Using Image Enhancement and Deep Learning Models
      EUVIP – Eur. Workshop on Visual Information Processing
      [Paper] [Code]
    • Y. Taifour, H. Afridi, A. N. Tarekegn, M. Ullah (2023), Self‑Supervised Animal Detection in Indoor Environment
      IPTA – Int. Conf. on Image Processing Theory, Tools and Applications
      [Paper]
    • A. N. Tarekegn, F. Ricceri, G. Costa, E. Ferracin, M. Giacobini (2020), Predictive Modeling for Frailty Conditions in Elderly People: Machine Learning Approaches
      JMIR Med. Inform., 8(6):e16678
      [Paper] [Code]

    Reviewing

    Conference Reviewing

    • ACM International Conference on Multimedia (ACM MM), 2025
    • European Conference on Artificial Intelligence (ECAI), 2024–2025
    • European Workshop on Visual Information Processing (EUVIP), 2025
    • International Conference on Artificial Neural Networks (ICANN), 2023
    • International Conference on Advancements of Science and Technology (ICAST), 2022–2025

    Journal Reviewing

    • Pattern Recognition
    • IEEE Transactions on Multimedia
    • IEEE Sensors Journal
    • Applied Soft Computing
    • IEEE Transactions on Knowledge and Data Engineering
    • IEEE Transactions on Circuits and Systems for Video Technology

    Thesis Examination

    • External Examiner – MSc Theses on AI/ML, University of Gondar, 2022/2023
    • Internal Examiner – Several MSc Theses on AI/ML, Bahir Dar University, 2021/2022

    Supervision

    I have supervised MSc theses on topics in Machine Learning, Artificial Intelligence, Deep Learning, and Data Science, spanning diverse application domains.

    • Institutions: Bahir Dar University (BDU), University of Gondar (UoG), and Norwegian University of Science and Technology (NTNU).
    • Timeframe: 2021–2024.

    Teaching & Mentorship

    • MSc: Deep Learning & Computer Vision – University of Wollo
    • BSc (Seminar Leader):AI Ethics – University of Bergen
    • BSc/MSc:Machine Learning, Programming, Human-Computer Interaction (HCI) – Bahir Dar University
    • Thesis Supervision: Machine Learning, Deep Learning, and Data Science projects,