Evolutionary Algorithms Professors and Supervisors in Norway

Lars Vidar Magnussonمشاهده پروفایل

دانشیار

Østfold University CollegeNorway

Lars Vidar Magnusson is an Associate Professor at the Department of Computer Science and Communication, Østfold University College. His research focuses on machine learning, image analysis, natural language processing, and explainable AI, with applications in healthcare decision support and computer graphics. He leads the Machine Learning research group and is affiliated with The Digital Society research priority area. Education: PhD from the University of Oslo, Master’s from Østfold University College. Research Interests: Includes evolutionary optimization for medical diagnosis (e.g., deep vein thrombosis), recurrent neural networks for industrial prediction, and AI-driven education tools (AI4AfL project). His recent work bridges machine learning with healthcare, emphasizing clinical decision support systems and interpretable AI models. Key Projects: AI4AfL (AI for Assessment for Learning) and Decision Support in Healthcare using AI. Active in conferences like IEEE Congress on Evolutionary Computation and Scandinavian Conference on Artificial Intelligence (SCAI).

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Roland Olssonمشاهده پروفایل

دانشیار

Østfold University CollegeNorway

Roland Olsson is an Associate Professor at the Department of Computer Science and Communication, Oslo and Akershus University College of Applied Sciences (HIOF). He holds a doctorate from the University of Oslo (UiO) and previously served as an Associate Professor at Chalmers University of Technology in Gothenburg, Sweden. His research focuses on machine learning, automatic programming, and algorithm design through large-scale combinatorial search, particularly via his ADATE system. Olsson’s work emphasizes meta-machine learning, including the automatic design of neural network architectures. His research group explores applications in image processing, symbolic regression, and optimization. Key areas include improving algorithms for edge detection, oil well event prediction, and real-time CNC machine control through imitation learning. Publications span domains like evolutionary computation, industrial informatics, and smart grids. His work frequently appears in journals like IEEE Open Journal of the Industrial Electronics Society and conferences such as IEEE International Conference on Big Data and Smart Computing. Though no specific awards are listed, his contributions to automatic programming and machine learning algorithms are widely recognized in the academic community. Olsson’s advising and grant involvement is implied through collaborative projects with industry partners (e.g., CNC machine operators, oil well monitoring). He leads the Machine Learning group at HIOF and teaches courses in machine learning. His research extends to interdisciplinary applications, including IoT architectures for smart grids and healthcare activity recognition.

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Aliaksandr Hubinمشاهده پروفایل

دانشیار

Østfold University CollegeNorway

Aliaksandr Hubin is an Associate Professor of Statistics at Østfold University College (OUC), affiliated with the Section for Research Administration. He provides statistical support to researchers at OUC while conducting methodological research in Bayesian statistics, machine learning, and operations research. His academic background includes a PhD from the University of Oslo (2014–2018), a Master's from Molde University College (2012–2014), and a Specialist degree from Belarusian State University (2008–2013). His research focuses on Bayesian model selection, nonlinear regressions, neural networks, and weak supervision in machine learning. Hubin has held positions at the Norwegian Computing Center (2018–2020) and the University of Oslo as a postdoc (2021–2022). His work spans methodological advancements in MCMC algorithms, variational inference, and applications in epigenetics, econometrics, and healthcare. Key achievements include developing the GMJMCMC algorithm for Bayesian logic regression and the 'skweak' framework for weak supervision in NLP. His awards include the NIMA 2014 award for best MSc graduate, Belarusian Ministry of Education First Award (2013), and recognition at the Graybill 2017 conference. His research has been published in journals like Bayesian Analysis , Neural Computing & Applications , and Scientific Reports , with a focus on model uncertainty quantification and scalable Bayesian methods.

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Kosio Beshkovمشاهده پروفایل

پژوهشگر ارشد

University of OsloNorway

Kosio Beshkov is a Postdoctoral Fellow in Condensed Matter Physics at the University of Oslo, specializing in the intersection of topological data analysis and machine learning. His research focuses on theoretical frameworks for understanding neural network representations and biological neural systems. His primary research interests include: Theory of deep neural networks in overparametrized regimes Topological data analysis of neural manifolds De novo protein design using evolutionary algorithms and geometric modeling Connections between network representations and topological spaces Recent publications demonstrate strong trends in computational neuroscience, with 7 papers from 2021-2025 spanning journals like PLoS Computational Biology and iScience. His work consistently applies polyhedral geometry, quotient spaces, and homology to neural representation problems, while expanding into protein language models and gene therapy applications. Current technical approaches combine: Topological data analysis for high-dimensional neural data Geometric deep learning for robust representations Biophysically-detailed neuron modeling Protein structure-geometry relationships

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Lex Nederbragtمشاهده پروفایل

دانشیار

University of OsloNorway

Lex Nederbragt is an Associate Professor at the University of Oslo , affiliated with the Faculty of Mathematics and Natural Sciences and the Centre for Ecological and Evolutionary Synthesis . His work bridges genomics , bioinformatics , and STEM education , focusing on integrating computational methods into bioscience teaching. Email: lex.nederbragt@ibv.uio.no Phone: +47 22844132 Room: 3404 Lex specializes in genomics , particularly teleost fish genetics , genome assembly , and bioinformatics tool development . His research explores evolutionary biology , including immune gene evolution , hybrid speciation , and horizontal gene transfer . He also advances computational education through active learning , reverse instructional design , and Software Carpentry . His recent publications address graph-based genome analysis , genomic repeat landscapes , and pedagogical strategies . He received the Olav Thon Foundation national award for excellence in teaching (2025) and was recognized as a Merittert Utdanner (2023). Co-developer of tools like NucBreak , NucDiff , and khmer . Key contributor to Atlantic cod and grayling genome projects.

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Muhammad Hamza Zafarمشاهده پروفایل

پژوهشگر ارشد

University of AgderNorway

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.

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Nina Mc Tiernanمشاهده پروفایل

پژوهشگر ارشد

University of BergenNorway

Nina Mc Tiernan serves as a Postdoctoral Fellow at the Department of Biomedicine, University of Bergen, specializing in protein N-terminal acetylation mechanisms and their clinical implications. Her research spans molecular biochemistry, genetic disorder pathogenesis, and translational cell signaling within the Systems Biology and Translational Cell Signaling research groups. Her primary research focuses on N-terminal acetyltransferases (NATs), particularly NAA10 and NatA complex functionality. She investigates how pathogenic variants disrupt enzyme activity, leading to neurodevelopmental disorders including intellectual disability, developmental delay, and syndromic features like hemihypertrophy and cardiomyopathy. Her work integrates biochemical assays, clinical case analysis, and systems biology approaches to elucidate genotype-phenotype correlations and molecular pathomechanisms. Analysis of her 13 publications (2018-2025) reveals an evolving research trajectory from clinical case reports toward mechanistic studies of protein degradation pathways and physiological impacts. Early work characterized novel NAA10 variants and their clinical presentations, while recent publications explore acetylation-degradation crosstalk and physiological consequences, indicating maturation of this field where her contributions are pivotal. Her research has received funding from Health West RHF (Grants 912176, F-12540-D11382), The Cancer Society (Grant 171752—PR-2009-0222), and the Research Council of Norway (Grant 249843). She collaborates extensively within the Translational Cell Signaling and Metabolism and Translational Protein Research groups, utilizing advanced proteomics and structural biology methodologies. Dr. Mc Tiernan maintains active collaboration with Haukeland University Hospital's clinical departments, bridging basic research with patient diagnostics. Her current work on acetylation's role in protein stability and degradation pathways holds significant therapeutic potential for genetic disorders.

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Ahmad Rafiمشاهده پروفایل

استاد

UiT The Arctic University of NorwayNorway

Professor Ahmad Rafi is a faculty member in the Department of Clinical Bioinformatics at UiT The Arctic University of Norway, located in Tromsø. He leads research focused on rapid clinical diagnostics of bacterial infections and antimicrobial resistance (AMR), leveraging advanced technologies like nanopore sequencing and machine learning. His work emphasizes One Health perspectives, integrating human, animal, and environmental health domains. Research Interests: Development of rapid diagnostic tools for AMR and pathogen identification Integration of genomic sequencing (WGS) and real-time taxonomic analysis Optimization of clinical workflows for sepsis and urosepsis management Multi-omic approaches combining quantitative phase microscopy and machine learning Recent Publications Highlight: His team has achieved diagnostic turnaround times as low as 4–9 hours using nanopore sequencing for sepsis and urinary tract infections. Innovations include culture-independent methods and SERS nanowire chips for strain-level bacterial classification. Advising & Grants: While specific student names or grant details aren’t listed, his involvement in projects like AMR-Educate reflects collaborative efforts in education and innovation around antimicrobial resistance. Labs/Teams: Active member of the Clinical Bioinformatics Research Group, contributing to translational research bridging computational methods with clinical applications.

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Iain Johnstonمشاهده پروفایل

استاد

University of BergenNorway

Iain Johnston is a Professor in the Department of Mathematics at the University of Bergen, Norway. His research focuses on computational and theoretical approaches to understanding mitochondrial genetics, evolutionary biology, and stochastic processes in biological systems. He is affiliated with the Computational Biology Unit (CBU) and The Alan Turing Institute. His work integrates mathematical modeling, bioinformatics, and experimental data to study organelle genome evolution, disease progression, and evolutionary dynamics. Key research areas include mitochondrial DNA heteroplasmy, organelle gene retention, and the development of computational tools like HyperTraPS for analyzing evolutionary pathways. He collaborates internationally on topics such as antimicrobial resistance modeling, cancer progression systems, and plant mitochondrial dynamics. His interdisciplinary approach bridges mathematics, biology, and computer science. Johnston’s publications span high-impact journals like *Science Advances*, *Proceedings of the National Academy of Sciences*, and *Genome Biology and Evolution*. He has contributed to understanding mitochondrial bottlenecks, organelle network dynamics, and the application of stochastic models to real-world biological systems.

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Kazeem Adesina Daudaمشاهده پروفایل

پژوهشگر ارشد

University of BergenNorway

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.

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Inge Jonassenمشاهده پروفایل

استاد

University of BergenNorway

Dr. Inge Jonassen is a Professor and currently serves as the Head of Department at the Department of Informatics, University of Bergen. She has overall responsibility for the department's activities including research and education. Her leadership role indicates her significant standing within the academic community and her department. Dr. Jonassen's primary research focus is in bioinformatics , specifically developing and applying informatics methods for the analysis of molecular biology data. Her work spans several key areas: Development of methods for automatic discovery of patterns in biological data Application of data analysis techniques and algorithms to molecular biology problems Machine learning approaches for biological data interpretation Analysis of diverse biological data types including DNA sequences, protein structures, and gene expression data Integration of data and tools within bioinformatics Her research demonstrates a strong interdisciplinary approach, with tight collaboration between her computational group and experimental groups across various biological and medical research fields. This collaborative model enables translation of computational methods into practical biological insights. Dr. Jonassen's publication record shows consistent output in high-impact journals, with research spanning marine biology, cancer research, toxicology, and genomics. Her work frequently involves analysis of next-generation sequencing data and development of computational tools that address specific biological challenges. A notable trend in her recent work is the increasing focus on single-cell analysis techniques and spatial biology, reflecting broader trends in the field. She teaches primarily bioinformatics courses, though she has also taught user courses in informatics and operating systems, demonstrating her versatility as an educator. Her group page indicates active research with numerous current projects.

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Alexander Johan Nederbragtمشاهده پروفایل

مدرس ارشد

University of OsloNorway

Dr. Alexander Johan Nederbragt ("Lex Nederbragt") is a Senior Lecturer at the University of Oslo's Faculty of Mathematics and Natural Sciences, affiliated with the Centre for Ecological and Evolutionary Synthesis (CEES) and former member of the Centre for Computational Inference in Evolutionary Life Science (CELS). He leads the "Computing in Science Education" initiative within the Bioscience bachelor's program, developing courses like BIOS1100 that integrate Python programming with biological modeling. His educational philosophy emphasizes reverse instructional design and constructive alignment, with a focus on student-active learning and AI-driven pedagogical innovation. Researchwise, Nederbragt specializes in Bioinformatics and Genomics , particularly Evolutionary genomics of fish (Atlantic cod, haddock, grayling) Graph-based reference genome methodologies Genomic repeat element analysis Horizontal gene transfer in diatoms Computational microbiome studies His work spans comparative genomics, genome assembly validation, and bioinformatics software development (NucDiff, NucBreak). Scientific contributions reveal expertise in High-throughput sequencing Genomic architecture Evolutionary adaptation Computational methods Publication trends from 2012-2023 His research frequently intersects with ecological and evolutionary questions, particularly in teleost fishes. Scientific Awards Merittert Utdanner (Excellent Teaching Practitioner), University of Oslo (2023) Olav Thon Foundation National Award for Excellence in Teaching (2025)

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Ane Kristine Espesethمشاهده پروفایل

پژوهشگر ارشد

University of OsloNorway

Ane Kristine Espeseth is a Research Fellow at the Center for Computing in Science Education (CCSE), University of Oslo, where she conducts doctoral research at the intersection of cognitive science and artificial intelligence. Her work focuses on developing interpretation frameworks for large language models to understand cognitive processes in both biological and artificial systems, while actively serving as student representative for the International Society for Artificial Life. Her academic foundation includes: Bachelor of Arts (Honours) in Computer Science from the University of Cambridge, United Kingdom Master of Science in Cognitive Science from Seoul National University, South Korea Dr. Espeseth's research employs concept-based and mechanistic approaches—including contextual embeddings and neural circuit analysis—to decode how large language models process information, drawing parallels between artificial systems and human cognition. Her methodology spans language acquisition modeling in children, evolutionary algorithms for socially adaptive chatbots, bio-inspired computing paradigms, and abstract mathematics, reflecting a deeply interdisciplinary approach to cognitive-AI convergence. Her scientific recognition features: First place at the European Contest for Young Scientists (2016) She maintains active research collaborations through the Human Factors Lab and BioIntelligence Lab at Seoul National University, while currently advancing computational science education initiatives at the University of Oslo's CCSE, where she also instructs HON2200 – Data-driven projects for the spring 2025 semester.

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