Kyrre Glette is a Professor at the University of Oslo's Department for Informatics, affiliated with the RITMO Centre for Interdisciplinary Studies in Rhythm, Time and Motion and the Robotics and Intelligent Systems (ROBIN) research group. His work focuses on co-designing robot bodies and behaviors using AI methods, particularly through the open-source robotic platform DyRET. Research interests include evolutionary robotics, bio-inspired computing, and embodied AI. Research Groups: Robotics and Intelligent Systems (ROBIN) RITMO Centre of Excellence fourMs Lab Projects: COCOMO: Co-evolution of Control and Morphologies Multimodal Elderly Care Systems (MECS) Predictive and Intuitive Robot Companion (PIRC) His publications explore topics like evolutionary algorithms for morphological adaptation, embodied music interfaces, and neural mechanisms of auditory prediction. Advising focuses on MSc projects in evolutionary robotics and modular robot control.
Tor Flå is a Professor at the Department of Mathematics and Statistics, UiT The Arctic University of Norway. His research spans mathematical modeling in biology and physics, with a focus on quantum chemistry, cancer dynamics, and bioinformatics. He leads projects in computational biology, plasma physics, and protein adaptation studies. Key contributions include developing the DeltaProt software for comparative genomics and advancing multiwavelet-based methods for electronic structure calculations. His work on leukemia stem cell dynamics and cold-adapted enzymes bridges mathematics and life sciences. Flå collaborates with international teams on nonlinear systems and has been affiliated with research groups like CoSMo (Complex Systems Modeling). Flå’s publications span journals like Journal of Mathematical Chemistry , PLOS ONE , and BMC Bioinformatics . He has advised interdisciplinary projects but no specific student names are listed. His research emphasizes computational methods, statistical analysis of biological sequences, and theoretical frameworks for complex systems.
Kirsten Krause is a Professor in Plant Molecular Biology at the Department of Arctic and Marine Biology, UiT The Arctic University of Norway . Her research focuses on molecular mechanisms of plant adaptation, particularly in parasitic plants like Cuscuta , and their interactions with host organisms. She leads the national graduate school PhotosynTech and contributes to sustainable energy research through the Arctic Centre for Sustainable Energy (ARC) . Education: Diploma in Biology (1994, University of Hamburg); PhD (2000, Universities of Cologne/Kiel); Postdoc (2000-2002, University of Arizona) Her work explores: Molecular networks in Arabidopsis thaliana and Cuscuta species Cell wall degrading enzymes and their biotechnological applications Plastid-nuclear communication in plant cells Recent publications analyze host-parasite gene expression dynamics, enzyme specificity in parasitism, and hyperspectral imaging of root systems. She has received a ToppForsk Grant (2016-2021) and leads major national research initiatives. Her teaching includes courses on green biotechnology and microscopic imaging.
Ketil Malde is an Associate Professor at the Department of Informatics, University of Bergen. He is affiliated with the Norwegian Marine Data Center and the Institute of Marine Research. His research focuses on applying machine learning and bioinformatics to marine science challenges, including genome sequencing, acoustic data analysis, and fisheries management. Education: PhD in Algorithms for the Analysis of Expressed Sequence Tags (2005, University of Bergen). Research Interests: Bioinformatics, marine genomics, deep learning applications in fisheries science, acoustic signal processing, and automated data analysis for marine ecosystems. Scientific Contributions: Developed tools like DeepOtolith for fish age estimation, contributed to the salmon louse genome project, and pioneered methods for acoustic target classification using deep learning. His work bridges computational methods with ecological and fisheries management needs. Projects: CRIMAC (Center for Research-based Innovation in marine acoustic abundance estimation), COGMAR, and collaborations with Scantrol Deep Vision for eco-friendly fish sampling. Supervised students include Peter Løkhammer Liessem (object tracking for catch estimation) and Knut T. A. Holager (acoustic frequency subset selection). Active in organizing workshops like the Norway-U.S. Machine Learning in Marine Science collaboration.
Björn Gambäck is a Professor of Language Technology at the Department of Computer Technology and Informatics, NTNU. His research focuses on computational creativity, computational linguistics, artificial intelligence, and machine learning, with a strong emphasis on natural language processing (NLP) and language technology. He actively contributes to the academic community through teaching courses such as 'Intelligent Text Analysis and Language Comprehension' and supervising master's theses. His work includes advancing techniques for sentiment analysis, code-mixed language processing, and computational creativity. Recent research highlights include developing deep learning models for code-mixed social media analysis and exploring coreference resolution in entity-level sentiment tasks. Gambäck’s contributions span interdisciplinary areas, such as applying evolutionary algorithms to media repositories and music composition. Notable collaborations include projects on hate speech detection, sarcasm annotation in tweets, and named entity recognition for low-resource languages like Amharic. His expertise bridges theoretical advancements and practical applications in NLP and computational systems.
Luka Grgičević is a PhD student at the Norwegian University of Science and Technology (NTNU) , affiliated with the Cyber-Physical Systems Laboratory in Ålesund. His research focuses on agent-based modeling , evolutionary game theory , and control systems for autonomous maritime applications. Program: Doctoral Programme for Integrated Research Activities to Unlock a Potential of Top-level Researchers in Digital Transformation for Sustainability (PERSEUS) Research Group: Centre for Research-based Innovation, SFI AutoShip His work bridges artificial intelligence and maritime engineering , with a strong emphasis on computer simulations and game theory applied to vessel guidance in high-density traffic. Key contributions include publications in IEEE Access , conference proceedings, and a journal article in MIC Journal: Modeling, Identification and Control . Recent publications highlight trends in multi-agent maritime traffic simulation , centralized decision support systems , and decentralized collision avoidance strategies . Outreach activities include a lecture at the 2024 MTEC/ICMASS conference and a poster presentation at the 2023 PERSEUS annual workshop.
Asieh Abolpour Mofrad is a researcher at the Department of Informatics, University of Bergen (UiB), with a dual PhD background in behavioral sciences and informatics. She is currently engaged in the Retail Fresh project, leveraging machine learning to reduce food waste in the retail sector in collaboration with Link Retail. Previously, she contributed to the DRONE (Drug Repurposing for Neurological Diseases) project at the Department of Global Health and Public Health, UiB, where she applied machine learning to Norwegian health registry data to investigate Parkinson's disease treatments. Her academic credentials include two doctoral theses: one from OsloMet (2021) on the integration of behavior analysis and machine learning for modeling stimulus equivalence, and another from UiB (2021) on clique-based neural associative memories. Her research bridges artificial intelligence, cognitive modeling, neural networks, and public health applications. Her research interests span machine learning, neural associative memory, reinforcement learning, cognitive modeling, health informatics, and drug repurposing. She employs computational frameworks such as projective simulation and tournament-based neural networks to model complex cognitive and biological phenomena. The analysis of her recent publications reveals a consistent focus on machine learning applications in both cognitive science and healthcare. Her work includes modeling stimulus equivalence, designing neural memory architectures, solving stochastic optimization problems, and analyzing large-scale health data for neurological disease risk. These efforts reflect interdisciplinary innovation, combining theoretical computer science with real-world applications in psychology and medicine. Scientific Contributions: Developed the Enhanced Equivalence Projective Simulation (E-EPS) framework for modeling derived relations in behavior analysis. Designed tournament-based neural networks for efficient sequence storage and bidirectional retrieval. Applied machine learning to large-scale health registries to identify drug classes associated with Parkinson’s disease risk. Contributed to adaptive learning systems based on flow theory for educational optimization. She has advised no publicly listed students but collaborates extensively with researchers across institutions. Her work is supported by interdisciplinary research projects and she actively disseminates code via GitHub, particularly in Jupyter notebooks for reproducibility. She is affiliated with research teams in informatics and global health at UiB, contributing to both theoretical and applied machine learning initiatives. Laboratories and Research Teams: Department of Informatics, University of Bergen – Machine Learning and Neural Systems group. DRONE Project – Interdisciplinary team on drug repurposing using AI and health data. Retail Fresh Project – AI for sustainable retail, in collaboration with industry partners.
Roberto Rossini is a Postdoctoral Fellow at the University of Oslo's Department of Genetics and Evolutionary Biology, affiliated with the Paulsen Group. His research focuses on computational genomics and 3D genome organization. Research Interests Computational modeling of DNA loop extrusion 3D genome architecture analysis Development of bioinformatic tools for genomic data Evolutionary genetics and chromosomal dynamics Publication Trends Recent work emphasizes computational methods for analyzing chromatin interactions (hictk), DNA loop extrusion modeling (MoDLE), and stripe detection algorithms (StripePy), with applications in cancer genomics and evolutionary genetics. Laboratory Affiliation Active member of the Paulsen Group at the University of Oslo, contributing to genomic innovation research.
Noureddine Bouhmala is an Associate Professor in the Department of Information and Communication Technology at the University of Agder. His research focuses on optimization algorithms, metaheuristics, and their applications to combinatorial problems such as MAX-SAT, constraint satisfaction, and clustering. He is affiliated with the CAIR Centre for Artificial Intelligence Research. Research interests include evolutionary algorithms, multilevel paradigms, and hybrid metaheuristics. Notable work involves developing algorithms for continuous optimization, predictive maintenance, drone traffic planning, and SAT-encoded industrial problems. He has collaborated with researchers across disciplines, including engineering, logistics, and emergency evacuation modeling. His publications span journals like Journal of Heuristics , IEEE Transactions , and Applied Intelligence , with contributions to conferences such as IntelliSys, ICCSA, and SITA. His work emphasizes algorithmic innovation and real-world problem-solving in domains like logistics, smart cities, and computational science.
Arthur Jinyue Guo is a Doctoral Research Fellow at the Department of Musicology, University of Oslo. His research focuses on immersive audio-visual environments, machine learning applications in music technology, and multimodal information retrieval. He holds an M.Mus in Music Technology from New York University and a B.Sc in Information Engineering from Southern University of Science and Technology. His work explores neural audio synthesis, spatial audio/video recording analysis, and automatic recognition of musical instrument effects. Current projects include the AMBIENT initiative investigating bodily entrainment to audiovisual rhythms and self-playing guitar technologies. Publications span conferences like SMC, DAFx, and ArtsIT, addressing topics from generative audio models to 360-degree camera comparisons. Guo's research bridges computational methods with creative arts, emphasizing cross-modal interactions. His technical contributions include novel approaches to audio diversity in generative systems and spatial recording evaluation methodologies.
Leonard Schmiester is a Postdoctoral Researcher at the Oslo Centre for Biostatistics and Epidemiology (OCBE), University of Oslo, Norway, since 2021. His research focuses on computational oncology and systems biology, developing dynamical models to predict treatment outcomes in breast cancer while advancing parameter estimation methodologies for biological systems. His academic background includes: PhD candidate (2016-2021) at Helmholtz Centre for Environmental Research and Technical University of Munich M.Sc. in Industrial Mathematics (2012-2016) from University of Hamburg B.Sc. in Mathematics (2008-2012) from University of Hamburg Dr. Schmiester's research integrates mathematical modeling with clinical oncology to personalize cancer treatment. He specializes in simulating tumor evolution under therapeutic pressure, particularly for estrogen receptor-positive breast cancer subtypes. His work combines dynamical systems theory with high-throughput data to decode mechanisms of drug resistance in Luminal B breast cancer, focusing on endocrine therapy and CDK4/6 inhibitor combinations. This approach enables computational prediction of patient-specific treatment responses, contributing to precision oncology frameworks that optimize therapeutic sequencing. Analysis of his publication record reveals two dominant research streams: clinical applications in breast cancer evolution (60% of recent work) and computational methodology development (40%). The oncology-focused publications examine immune-malignant cell co-evolution during aromatase inhibitor therapy, while methodological papers introduce innovations like pyPESTO for ODE parameter estimation and mini-batch optimization for large-scale datasets. Both streams converge on translating mathematical frameworks into clinically actionable insights for personalized cancer medicine. Dr. Schmiester actively contributes to the Norwegian Centre for Knowledge-driven Machine Learning and RESCUER project, applying stochastic modeling to biomedical challenges. As part of the Stochastic Models and Inference research group at OCBE, he develops statistical frameworks for interpreting heterogeneous biological data, with current work extending computational oncology approaches to machine learning applications in disease progression modeling.
Kazeem Adesina Dauda is a Research Fellow in the Department of Mathematics at the University of Bergen, affiliated with the Stochastic Biology Group—HyperEvol led by Prof. Iain Johnston. His research focuses on Bayesian statistical methods, genomic data analysis, and computational biology, particularly in modeling anti-microbial resistance (AMR) evolution and survival analysis for cancer genomics. He develops mathematical frameworks to predict disease progression pathways using machine learning and clustering techniques. Key research areas include feature selection in high-dimensional data, flexible penalization in Bayesian survival models, and genome reduction dynamics in mitochondria and plastids. His work bridges statistical theory with applications in biomedical and evolutionary biology, emphasizing predictive modeling for AMR and disease outcomes. Publications : Dr. Dauda has contributed to high-impact journals like Molecular Biology and Evolution and PLoS Computational Biology , focusing on AMR evolution modeling, Bayesian survival analysis, and genomic data clustering. Recent work includes HyperTraPS-CT algorithms for pathway inference and prediction. Collaborations : Active in interdisciplinary teams at the University of Bergen and Warwick, collaborating on projects integrating computational tools with evolutionary and medical datasets.
Rahul Nath is a Postdoctoral Fellow at the Department of Informatics, University of Bergen, Norway. His research lies at the intersection of computational intelligence, optimization, and decision systems, with a strong focus on reliability engineering and fuzzy logic applications. His research interests include Reliability Engineering , Evolutionary Algorithms , Fuzzy Logic , Multi-objective Optimization , Anomaly Detection , and Intelligent Decision-Making under Uncertainty . These areas are central to modern intelligent systems, particularly in safety-critical and uncertain environments. The recent publications highlight a consistent trend in solving complex reliability and optimization problems using advanced evolutionary and fuzzy-based methods. Key themes include multifactorial optimization, constraint handling in many-objective problems, anomaly explanation using fuzzy vocabularies, and energy-aware scheduling in embedded systems. The work demonstrates a deep integration of theoretical algorithm development with practical engineering applications. Scientific Contributions: Developed novel evolutionary approaches for reliability-redundancy allocation. Introduced fuzzy-vocabulary-based frameworks for anomaly detection and explanation. Applied type-2 intuitionistic fuzzy logic to decision-making under uncertainty. Designed energy-efficient scheduling algorithms for real-time systems. Rahul Nath has collaborated extensively with researchers such as Pranab K. Muhuri, Amit K. Shukla, and Md. Abdul Malek Chowdury. His work is published in high-impact journals including Reliability Engineering & System Safety , IEEE Transactions on Fuzzy Systems , and Soft Computing . While no formal advising or grant information is available, his research output indicates active involvement in advanced computational intelligence projects. Currently based at the University of Bergen, he contributes to the Department of Informatics' research in intelligent systems and optimization. His work is accessible through Cristin and digital object identifiers (DOIs).
Muhammad Hamza Zafar is a PhD Research Fellow at the Department of Engineering Sciences, University of Agder. His research spans robotics, human-robot teaming, deep learning, and sustainable technologies with significant contributions to both academic literature and practical applications in emergency response and industrial automation systems. Dr. Zafar's research interests focus on the intersection of robotics and artificial intelligence, particularly in human-robot teaming, wearable technologies, and sustainable energy systems. His work addresses critical challenges in real-time gesture recognition, robotic manipulation, and emergency response systems, leveraging advanced deep learning techniques and novel algorithmic approaches. He has made significant contributions to photovoltaic power forecasting, battery state estimation, and intrusion detection in robotic systems. Analysis of Dr. Zafar's publication record reveals a strong focus on human-robot collaboration in emergency and industrial settings. His research consistently bridges theoretical advancements with practical applications, with particular emphasis on making robotic systems more intuitive, responsive, and capable in complex environments. Key thematic areas include gesture-based control systems, multi-modal sensor fusion, sustainable energy applications of AI, and Industry 5.0 manufacturing paradigms. Dr. Zafar is actively involved with the CIEM - Center for Integrated Emergency Management and the Artificial Intelligence, Biomechatronics and Collaborative Robotics research group at the University of Agder. His collaborative work spans multiple institutions, reflecting the interdisciplinary nature of his research in human-robot interaction and sustainable technologies. His office is located at D3063 (Jon Lilletuns vei 9, 4879 Grimstad, Norway) with contact information including phone +47 37233744 and mobile +4748919198.
Stefano Fasciani is Associate Professor in the Department of Musicology at the University of Oslo's Faculty of Humanities, leading research in sound computing and interactive music systems. As Head of Creative Computing Oslo Hub and Director of the Music, Communication and Technology Master's program, he develops novel approaches to digital instrument design. Education includes: Ph.D. Integrative Sciences and Engineering, National University of Singapore (2014) M.Sc. Electronic Engineering, University of Rome Tor Vergata (2006) B.Sc. Electronic Engineering, University of Rome Tor Vergata (2003) Research integrates signal processing, machine learning, and embedded systems to create responsive musical interfaces and synthesis architectures. Current projects include neural modeling of acoustic instruments, optical sensor-based controllers, and multi-agent music systems. Manages the Nordic Sound and Music Computing network and Self-playing Guitars project. Secured funding from EU Erasmus+ and NordForsk for international collaborations in creative technologies. Supervises 15+ graduate students in projects spanning hardware design, evolutionary sound systems, and AI-driven composition tools. Teaching includes Music and Machine Learning, Interactive Music Systems, and Sound Programming courses.
