جست‌وجوی استاد و پژوهشگر

Naomi Hallasمشاهده پروفایل

استاد

• plasmonics
• biomedical engineering
• optoelectronics
• +۹ مورد دیگر

Naomi Hallas is a Professor at Rice University, holding appointments in the Department of Electrical and Computer Engineering, Biomedical Engineering, Chemistry, and Physics. Her pioneering work in plasmonics bridges disciplines, with applications in biomedicine, optoelectronics, chemical sensing, photocatalysis, and solar energy sustainability. Her research spans plasmonic photothermal therapies for cancer (via Nanospectra Biosciences) antenna-reactor photocatalysts for industrial processes solar energy conversion for off-grid water purification and desalination machine learning integration in chemical detection surface-enhanced Raman spectroscopy advancements Key scientific contributions include over 30 years of fellowships in societies like APS, OSA, IEEE, SPIE, MRS, AAAS, NAI , and leadership in translating nanotechnology to clinical and environmental solutions. Recent publications highlight trends in plasmonic-enhanced photocatalysis for methane reforming and pollutant degradation machine learning for SERS and environmental analysis stretchable metamaterials and vacuum ultraviolet light generation biomedical applications of gold nanoshells and nanomatryoshkas Scientific awards and honors include Elected Fellow of American Physical Society (APS) Fellow of Optical Society of America (OSA) Fellow of IEEE, SPIE, MRS, AAAS, and National Academy of Inventors (NAI)

Jonas Alexander Finklerمشاهده پروفایل

پژوهشگر ارشد

• Atomistic Simulation
• Computational Materials Science
• Glass Structure Mechanics
• +۴ مورد دیگر

Jonas Alexander Finkler is a Research Fellow in the Department of Chemistry and Bioscience within the Faculty of Engineering and Science at Aalborg University, Denmark. His research focuses on computational methods for disordered materials, particularly glass structure mechanics and atomistic simulations. His primary research interests include Atomistic Simulation , Electronic Structure Calculation , and Machine Learning Potentials for materials science. He develops advanced computational techniques for modeling disordered systems, with emphasis on charge distribution, energy surfaces, and mechanical properties of glasses. His work bridges quantum chemistry and materials engineering through high-dimensional neural network potentials and density functional theory innovations. Recent publications demonstrate expertise in Noise-Tolerant Force Calculations , Fourth-Generation Machine Learning Potentials , and Boson Peak Analysis in metal-organic frameworks. His research output shows consistent annual productivity with 15 publications between 2020-2025, including high-impact journals like Journal of Chemical Physics and Journal of Physical Chemistry A. He operates within the Section of Disordered Materials Glass Structure and Mechanics, utilizing advanced computational infrastructure for large-scale simulations. His collaborative network spans international institutions with significant connections to Swiss research groups through Swiss National Science Foundation projects.

Rafal Wisniewskiمشاهده پروفایل

استاد

• Control Theory
• Hybrid Systems
• Quantum Computing
• +۱۲ مورد دیگر

Rafal Wisniewski is a Professor in the Department of Electronic Systems at the Technical Faculty of IT and Design, Aalborg University. He is a leading researcher in control theory, hybrid systems, and quantum computing applications, with extensive involvement in national and international research projects. He leads the Automation & Control Learning and Decisions Lab and is affiliated with the Aalborg University Space Center. His research interests span Control Theory , Quantum Computing , Optimization , Fault Diagnosis , Decentralized Control , and Automation . He applies these to domains such as wind turbines, water distribution, and Power-to-X systems. His work integrates geometric models, regulation theory, and machine learning for cyber-physical systems. Recent publications show a strong trend toward quantum algorithms for optimization and control, fault diagnosis in renewable energy systems, and decentralized decision-making using game theory and reinforcement learning. His research bridges theoretical advances with practical applications in critical infrastructure. Weighted Feedback-Based Quantum Algorithm for Excited States Calculation (2024) Wisniewski supervises PhD students and hosts guest researchers, contributing to academic training and international collaboration. He has secured significant research grants, including from the Danish Research Council (DFF). His lab, the Automation & Control Learning and Decisions Lab, focuses on intelligent control systems. He also contributes to public discourse on AI, cybersecurity, and sustainable energy through media engagement.

Arghya Bhowmikمشاهده پروفایل

دانشیار

• Computational Materials Science
• Electrochemical Energy Engineering
• Machine Learning for Materials Design
• +۳ مورد دیگر

Arghya Bhowmik is an Associate Professor in the Department of Energy Conversion and Storage at the Technical University of Denmark (DTU), College of Engineering. His research focuses on computational materials science, electrochemical energy systems, and machine learning applications for materials design.

Andreas Rønne Stokholmمشاهده پروفایل

پژوهشگر

• AI
• Earth Observation
• Satellite Data
• +۶ مورد دیگر

Andreas Rønne Stokholm is a Postdoctoral Researcher at DTU Space, the National Space Institute of the Technical University of Denmark, specializing in the Department of Space Research and Technology with focus areas in Geodesy and Earth Observation. He previously completed his Ph.D. at the same institution in 2024 and served as a Visiting Researcher at the European Space Agency's (ESA) ϕ-lab, where he contributed to innovative research combining AI with satellite data for Earth monitoring. His research interests center on applying artificial intelligence to Earth observation data, particularly for sea ice mapping in the Arctic. He focuses on developing automated systems using satellite data from Synthetic Aperture Radar (SAR) to replace manual sea ice charting processes. His work has significant implications for maritime navigation safety in polar regions and enabling Northern Trade Routes through improved ice monitoring. He specializes in machine learning applications for cryosphere studies, quantum technology applications in gravimetry, and dynamic sensor systems for atmospheric phenomena. Stokholm actively contributes to multiple research projects including greenhouse gas emission monitoring using satellite remote sensing and multi-approach methane emissions monitoring for the solid waste sector, where he serves as a supervisor for Ph.D. students. His research aligns with UN Sustainable Development Goals related to climate action and life below water. As part of his professional activities, Stokholm collaborates with international research teams across Europe and has contributed to significant datasets including Aurora DVS observations. His work bridges the gap between advanced computing, satellite technology, and practical applications for societal benefit in polar regions and climate monitoring.

• Structural Biology
• Biophysics
• Computational Biology
• +۷ مورد دیگر

Kresten Lindorff-Larsen is a Professor in Computational Protein Biophysics at the Department of Biology , University of Copenhagen , and leads research at the Linderstrøm-Lang Centre for Protein Science. His work integrates biochemistry, biophysics, structural biology , and machine learning to study protein dynamics and variant effects in disease. He oversees the Biocomputing Core Facility (2013–present) and directs major initiatives like the PRISM Centre (2019–present) and ERC Synergy Grant DynaPLIX (2023).

• Natural Language Processing
• Multimodal Understanding
• Machine Translation
• +۳ مورد دیگر

Israfel Salazar is a Research Fellow at the Department of Computer Science , University of Copenhagen, affiliated with the Natural Language Processing research group . His work focuses on multimodal understanding and representation, bridging natural language processing, machine translation, computer vision, genomics, and historical data analysis. Email : israfel.salazar@di.ku.dk Phone : +4535337068 Research interests include: Culturally aware multimodal machine translation Massively multilingual vision evaluation Genomic offset statistics Temporal data extraction from historical documents His recent publications highlight interdisciplinary approaches combining language and vision systems, statistical modeling for biological data, and historical record analysis. He contributes to benchmarking frameworks and evaluation methodologies for modern AI applications. Israfel's work spans both foundational and applied research domains, with a focus on cross-modal learning, computational methods in bioinformatics, and automated analysis of historical archives. His projects often require hybrid techniques integrating linguistic and visual modalities. He operates within the Natural Language Processing section at DIKU, which also hosts research into probabilistic programming applications in bioinformatics, computer vision integration with NLP, and energy-efficient computational frameworks. Collaborations with other DIKU groups (e.g., FUTHARK for GPU acceleration, quantum programming) may inform his technical implementations.

• Machine Learning
• Artificial Intelligence
• Multimodal AI
• +۲ مورد دیگر

Zhaochong An is a Research Fellow in the Pioneer AI (P1AI) section at the Department of Computer Science , University of Copenhagen. His work focuses on machine learning applications in multimodal semantic segmentation , quantum computing , and medical data analysis . University: University of Copenhagen Department: Department of Computer Science (DIKU) Section: Pioneer AI Contact: Email zhan@di.ku.dk , Phone +4535322423

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۴ مورد دیگر

Adrián Avelino Sousa-Poza is a Researcher at the Department of Computer Science , University of Copenhagen, specializing in Machine Learning and its applications across diverse domains including Artificial Intelligence , Medical Data Analysis , and Quantum Computing . His research intersects with the SCIENCE AI Centre , focusing on both theoretical and applied aspects of machine learning. Recent work explores environmentally sustainable AI , quantum-enhanced models , and cross-cultural adaptation systems , reflecting his interdisciplinary approach. Key themes in his publications include large language models , quantum computing applications , and healthcare informatics , with a particular emphasis on interpretability , fairness , and hardware optimization in AI systems.

• Machine Learning
• Artificial Intelligence
• Computational Biology
• +۳ مورد دیگر

Ian Walter Orzel is a PhD Fellow in the Machine Learning section at the Department of Computer Science, University of Copenhagen . He is affiliated with the SCIENCE AI Centre and contributes to interdisciplinary research bridging machine learning with quantum computing, healthcare diagnostics, and environmental sustainability.

• Machine Learning
• Computational Biology
• Structural Biology
• +۳ مورد دیگر

Valentina Sora is a Postdoc in the Machine Learning section at the Department of Computer Science , University of Copenhagen , focusing on interdisciplinary research at the intersection of machine learning and structural biology. Research Interests Developing machine learning algorithms for quantum-classical simulations in computational chemistry . Modeling biological data with applications in autophagy , cancer genomics , and protein structure networks . Designing computational tools for protein interaction network analysis and high-throughput mutational scans . Key Trends in Publications Her work bridges machine learning and quantum computing with structural biology , emphasizing protein dynamics , mutation effects , and biomolecular simulations . Recent studies apply deep learning to gene expression and genomic variant interpretation , alongside quantum embedding for molecular assemblies. Collaborations span computational biology, quantum chemistry, and cancer research with institutions like the SCIENCE AI Centre at the University of Copenhagen and researchers such as Yevgeny Seldin and Andrea Krogh .

• Machine Learning
• Quantum Computing
• AI Fairness
• +۲ مورد دیگر

Simon Krogh Anderson serves as a Lecturer at the Department of Computer Science (DIKU), Faculty of Science, University of Copenhagen. He is an active member of the Machine Learning section which focuses on theoretical foundations and applications across domains including natural language processing, medical image analysis, and biological data modeling. The department participates in the SCIENCE AI Centre and maintains powerful compute resources including the TreeSense platform for remote sensing. His research spans multiple cutting-edge areas in artificial intelligence with particular emphasis on machine learning, quantum computing applications, and algorithmic fairness. Key interests include sustainable AI development, reproducibility in recommender systems, and cross-cultural adaptation frameworks. His work often bridges theoretical computer science with practical applications in environmental monitoring, healthcare analytics, and quantum information processing. Recent publications demonstrate strong interdisciplinary connections across quantum computing, sustainable AI, and fairness metrics. Trends show increasing focus on environmentally conscious AI development, integration of quantum methods with classical machine learning, and ethical considerations in recommendation systems. His work frequently leverages Denmark's extensive health registries and environmental data resources. Anderson actively contributes to the department's research ecosystem through teaching and collaboration within the Machine Learning section. While specific grants aren't detailed in available materials, his publications indicate involvement in projects related to quantum computing infrastructure, environmental monitoring systems, and AI ethics frameworks. The department provides significant computational resources including a dedicated cluster and specialized labs like TreeSense for remote sensing applications. His work appears connected to the SCIENCE AI Centre's initiatives in sustainable computing and quantum information processing.

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۳ مورد دیگر

Dongyu Gao serves as an Instructor in the Machine Learning section at the Department of Computer Science (DIKU), University of Copenhagen. His position places him within one of Scandinavia's leading computer science departments, which hosts the SCIENCE AI Centre and maintains strong connections with both theoretical and applied machine learning research. Dr. Gao's research interests center around machine learning with applications spanning information retrieval, medical data analysis, remote sensing, sustainability, and biological data modeling. His work appears to bridge theoretical foundations with practical implementations, as evidenced by publications addressing quantum computing applications, environmentally sustainable AI practices, and advanced neural network architectures. The Machine Learning section at DIKU provides substantial computational resources including a powerful dedicated cluster and specialized initiatives like TreeSense for remote sensing applications. Analysis of recent publications associated with Dr. Gao reveals a diverse research portfolio spanning multiple cutting-edge AI domains. His work demonstrates particular strength in quantum machine learning applications, sustainable computing practices, and interpretable AI systems. The publications show a consistent pattern of interdisciplinary collaboration, connecting computer science with healthcare, environmental science, and quantum physics. Notably, several publications address the critical challenge of making AI systems more environmentally sustainable without sacrificing performance. The Machine Learning section operates within DIKU's broader research ecosystem, which includes strong connections to the SCIENCE AI Centre. This environment provides access to substantial computational resources and fosters collaboration across various AI subfields including natural language processing, computer vision, and theoretical machine learning. The department's location in Copenhagen positions it at the intersection of European AI research initiatives with strong connections to both academic and industry partners across the continent.

• Machine Learning
• Quantum Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Marcus Friis Klausen is a Lecturer at the Department of Computer Science , University of Copenhagen, affiliated with the Machine Learning section. His work intersects theoretical and applied machine learning across interdisciplinary domains. Research interests include: Quantum machine learning and hardware acceleration Explainable AI and model interpretability Clinical and healthcare applications of NLP Neuroscience-informed language modeling Environmentally sustainable AI practices Geometric and non-Euclidean deep learning Recent publications show trends in: Quantum computing applications for molecular simulations Medical imaging and clinical decision support Algorithmic fairness and ethical information retrieval Neural network optimization for energy efficiency Biological data modeling and remote sensing

• Machine Learning
• Artificial Intelligence
• Information Retrieval
• +۴ مورد دیگر

Lucas Alexander Kock is an Instructor at the Department of Computer Science , University of Copenhagen . His research spans Machine Learning and its applications in diverse domains including medical data analysis, quantum computing, and sustainable AI. Role: Lecturer in Machine Learning Affiliation: SCIENCE AI Centre, University of Copenhagen Research interests focus on: Quantum machine learning Neuroscience applications Cross-cultural AI systems Environmental sustainability in computing Medical informatics Deep learning explainability Recent publications demonstrate expertise in quantum computing applications , neural signal interpretation , and ethical AI frameworks . No formal awards or advisees are listed in available public data.