جست‌وجوی استاد و پژوهشگر

Pawel Danielewiczمشاهده پروفایل

استاد

• Nuclear Theory
• Heavy-Ion Collisions
• Symmetry Energy
• +۴ مورد دیگر

Pawel Danielewicz is a Professor in the Department of Physics & Astronomy at Michigan State University, affiliated with the National Superconducting Cyclotron Laboratory (NSCL). His research focuses on nuclear theory, particularly the dynamics of heavy-ion collisions and the equation of state for dense nuclear matter. Education: Ph.D., University of Warsaw (1981) His work explores high-pressure nuclear environments created in central heavy-ion reactions, using transport theory to model compression, thermalization, and decompression phases. Key areas include symmetry energy , isospin diffusion , and quantum transport with applications to neutron stars and ultracold quantum gases. Recent publications emphasize deblurring techniques for source function reconstruction, optical potentials for rare-isotope beams, and equation of state constraints from GW170817. Articles from 2025-2022 span topics like pion production , neutron skins , and nonequilibrium Green's functions . His theoretical frameworks connect heavy-ion collision observables to nuclear matter properties, with cross-disciplinary implications for early cosmology and atomic traps via nonequilibrium dynamics.

Kevin-Martin Aignerمشاهده پروفایل

پژوهشگر

• Optimization under Uncertainty
• Power Network Optimization
• Data-Driven Optimization
• +۳ مورد دیگر

Kevin-Martin Aigner serves as an Academic Councillor (Akad. Rat) at the Department of Data Science within the Faculty of Science at Friedrich-Alexander University Erlangen-Nuremberg (FAU). He is affiliated with the Professorship of Optimization under Uncertainty & Data Analysis led by Prof. Dr. Liers, focusing on mathematical optimization methods for energy systems and power networks. His work bridges theoretical optimization with practical energy transition challenges. His research centers on optimization under uncertainty, with key interests in distributionally robust optimization, data-driven methods, and sector-coupled energy system modeling. He develops novel approaches for handling renewable energy uncertainties in power grids, solar feed-in variability, and multi-sector energy integration. His methodologies emphasize explainability in optimization processes and robust decision-making under complex uncertainty structures. Recent publications reveal consistent focus on power network optimization (2021-2025), with increasing emphasis on distributionally robust frameworks, explainable AI integration, and regional multi-sector energy modeling. His work demonstrates strong interdisciplinary connections between operations research, machine learning, and sustainable energy systems engineering. Scientific recognition includes: GOR Dissertation Award for "Data-driven Optimization under Uncertainty for Power Networks" Dr. Aigner actively contributes to major research initiatives including: CRC TRR 154: Mathematical Modeling, Simulation and Optimization using Gas Networks (2022-2026, DFG-funded) ESM-Regio: Multi-sector Coupled Energy System Modeling on Regional Level (2021-2024, BMWE-funded) Optimal Control of Electrical Distribution Networks with Uncertain Solar Feed-in (2018-2021) He participates in the Optimization under Uncertainty & Data Analysis (OUDA) research group and has organized academic events including the TRR 154 Summer School on Optimization, Uncertainty and AI (2024) and Women in Optimization 2024. His teaching includes Discrete Optimization I and specialized seminars on Mixed-Integer Nonlinear Optimization.

• Variational inference
• Bayesian inference
• Deep learning
• +۴ مورد دیگر

Najmeh Abiri is a researcher with multiple affiliations at Lund University, holding positions as a Visiting Research Fellow at the Centre for Environmental and Climate Science (CEC), a Visiting Research Fellow at Computational Science for Health and Environment, a Postdoc at the Department of Statistics, and a Researcher at eSSENCE: The e-Science Collaboration. Her research focuses on the intersection of statistics and machine learning with practical applications. Key areas include: Variational inference Bayesian inference Deep learning architectures Missing data imputation techniques Computational biology applications Abiri's publication record shows a clear trajectory toward increasingly sophisticated applications of machine learning in scientific domains. Her recent work demonstrates expertise in diffusion models for time series analysis, AI-driven disease outbreak prediction, and quantum mechanical applications of deep learning. Her 2019 paper on denoising autoencoders for missing data problems has been particularly influential with 62 citation indexes. She has received notable attention for her interdisciplinary work, with publications featured in high-impact journals like The Lancet Regional Health - Europe and covered by multiple news outlets. Her research on West Nile virus prediction has garnered policy citations, indicating real-world impact. Abiri actively participates in the academic community through workshops and seminars, particularly those focused on AI technologies and their applications in scientific research. Her collaborative work spans multiple institutions and research domains, reflecting the interdisciplinary nature of her expertise.

Kyriakos G. Vamvoudakisمشاهده پروفایل

استاد

• Reinforcement Learning
• Control Theory
• Cyber-Physical Security
• +۴ مورد دیگر

Kyriakos G. Vamvoudakis is the Dutton-Ducoffe Endowed Professor at the Daniel Guggenheim School of Aerospace Engineering and directs the Intelligent Cyber-Physical Laboratory. He holds a secondary appointment in the School of Electrical and Computer Engineering. His research integrates reinforcement learning , control theory , and game theory to develop secure and fault-tolerant autonomous systems, with applications in aviation, robotics, and critical infrastructure. His research focuses on: Safe autonomy for urban air mobility and cyber-physical systems Adversarial reinforcement learning to mitigate security threats Data-driven control for resilient multi-agent systems Bounded rationality in complex adaptive environments Recent publications emphasize adversarial robustness , safe RL , and cyber-physical security , with trends showing increased focus on real-time learning under uncertainty, deception in multi-agent games, and quantum control applications. He leads federally funded projects from NASA , NSF , ARO , and ONR , including: NASA ULI: Safety-Aware Learning for Aviation NSF CPS: Secure Assured Autonomy ARO: Non-Equilibrium Game-Theoretic Learning ONR Minerva: Cyber-Physical Situation Awareness At the Intelligent Cyber-Physical Laboratory , he oversees research on multi-agent coordination, secure autonomy, and reinforcement learning frameworks validated through real-world UAV and robotics platforms.

• Scientific Computing
• Machine Learning
• High-Energy Physics
• +۳ مورد دیگر

Prof. Dr. Ralf Keidel is a faculty member at the Chair for Scientific Computing , Rhineland-Palatinate University of Technology Kaiserslautern-Landau (RPTU), since 2022. His academic career spans roles at Worms University of Applied Sciences , where he has served as Professor of Computer Science since 1993, Scientific Director of the Center for Technology and Transfer (ZTT) since 2000, and Head of the Technology Transfer Office since 1996. He is a key figure in international collaborations, including the ALICE Collaboration Board at CERN (2003–2023), Bergen pCT Collaboration (since 2018), and the MODE Collaboration (since 2022). His research merges Computer Science with High-Energy Physics , focusing on machine learning , secure heterogeneous distributed systems , and optimization of detectors in particle physics . Projects like SIVERT (2020–2024) aim to enhance Proton Computed Tomography (pCT) for clinical radiation therapy using AI-driven visualization and real-time reconstruction. He also contributes to serious games for financial education and mobile applications for musical training. Key publications highlight his work on differentiable programming for detector optimization, deep reinforcement learning in particle tracking, and convolutional neural networks for radiation topology analysis. His projects often involve interdisciplinary teams from institutions like the University of Bergen , DKFZ Heidelberg , and Western Norway University of Applied Sciences .

Roberto Moralesمشاهده پروفایل

پژوهشگر

• Control Theory
• Inverse Problems
• Partial Differential Equations
• +۱ مورد دیگر

Roberto Morales is a postdoctoral researcher affiliated with the University of Chile , Department of Engineering Sciences. His academic journey includes a PhD in Engineering Sciences (Mathematical Modelling) from the University of Chile (2019) and a Bachelor in Mathematics from the University of Santiago de Chile (2011). Research Interests Control and stabilization of PDEs Inverse problems Dynamic boundary conditions Mathematical modelling His work focuses on controllability of partial differential equations (PDEs) with dynamic boundary conditions, including Schrödinger and parabolic equations. Recent studies involve discrete Carleman estimates, multi-objective optimization for decentralized learning, and numerical methods for PDE control. Publications His publications span top journals like SIAM Journal on Control and Optimization and Journal of Differential Equations , with a trend toward controllability analysis and inverse problems in PDEs. Key collaborations include U. Biccari and J. Dardé.

• Machine Learning
• Computer Vision
• Optimization
• +۵ مورد دیگر

Dr. Iker Pastor López is a Researcher at the University of Deusto's Faculty of Engineering, where he serves in the Department of Computing, Electronics and Communication Technologies. His academic credentials include a PhD in Information Systems (2013), Master's in Information Security (2010), and Computer Engineering degree (2007), all from the University of Deusto. His research spans: Machine Learning methods for industrial optimization Computer vision applications in manufacturing quality control Edge computing architectures for Industry 4.0 Big Data analytics in financial and healthcare domains Cybersecurity frameworks for emerging technologies He has led numerous competitive grants including: EU H2020 projects (DANTE 2016-2019) ELKARTEK industrial transformation initiatives Quantum computing security research (QUANTEK) Industry collaborations with ETXE-TAR and VIDRALA His publication record shows strong focus on applied AI research with recent work in: Edge computing optimization Financial time series forecasting Industrial defect detection Biomedical sequence analysis He currently supervises graduate research on industrial AI applications and has contributed to institutional initiatives bridging academia and industry through technology challenges.

• Wireless Networks/Communications
• IoT
• Cyber Security
• +۱۳ مورد دیگر

Professor Qiang Ni is an esteemed academic at Lancaster University , affiliated with the School of Computing and Communications , Data Science Institute , and Security Lancaster Center . Currently serving as Head of Communication Systems Research Group , School Director of International Partnership , and Theme Lead in Security and Defence at the Lancaster Intelligent, Robotic and Autonomous Systems (LIRA) Research Centre , he has previously held roles as School Director of Postgraduate Studies and Deputy Director of Research . Research focus areas include Wireless Networks/Communications , IoT , Cyber Security , AI , Digital Twins , and Quantum Communication Key projects involve INTACT (secure IoT-to-Cloud), CoGNETs (swarm intelligence), SustainAIRA6G (AI-driven resource allocation), and TRACE-V2X (multi-RAT traffic steering) Recent publications address 6G communication design , quantum neural networks , blockchain-based border control systems , and intelligent vehicular networks As an IEEE Senior Member and IEEE Communications Society Distinguished Lecturer , he chairs editorial boards for IEEE Transactions on Green Communications and JSAC Machine Learning Series . His supervision spans Wireless Communication , Big Data Analytics , and Quantum Machine Learning PhD research topics. Active in H2020 , Horizon Europe , and UKRI funded projects, he also contributes to IEEE standard committees.

• Machine Learning
• Natural Language Processing
• Health Informatics
• +۳ مورد دیگر

Maher Harring Kassem serves as a Guest Researcher within the Machine Learning section at the Department of Computer Science, University of Copenhagen. His position falls under the research staff category, reporting to Head of Section Professor Yevgeny Seldin, and contributes to the department's mission in theoretical and applied machine learning research. His research spans Machine Learning , Natural Language Processing , Health Informatics , Sustainable AI , Quantum Machine Learning , and Cross-Cultural Computing . This interdisciplinary profile integrates computational methods with real-world applications in mental health analysis, culinary adaptation systems, emotion recognition, and environmental sustainability, reflecting the department's focus on domains like medical data analysis and biological modeling. Analysis of his 2024-2025 publications reveals a distinct trend toward high-impact interdisciplinary work. Key themes include sustainable AI development (addressing energy consumption in models), quantum-biomolecular applications (free energy calculations), cross-cultural NLP systems (recipe adaptation), and clinical AI (nursing values evaluation). His output demonstrates technical depth across optical neural hardware, EEG-based semantic relevance, and fairness-aware recommender systems, while consistently tackling societal challenges like climate impact and healthcare equity. No scientific awards or fellowships were documented in the available materials. As a Guest Researcher, Kassem leverages the department's powerful compute cluster and participates in initiatives like the SCIENCE AI Centre and TreeSense project for remote sensing of global tree resources. His collaborative work spans medical imaging analysis, quantum computing applications, and sustainable AI development, utilizing the university's infrastructure for large-scale computational tasks in domains ranging from wetland conservation to quantum photonic computing.

• Machine Learning
• Quantum Computing
• Medical Data Analysis
• +۳ مورد دیگر

Thomas Vecchiato is a Guest Researcher at the Department of Computer Science , University of Copenhagen , specializing in Machine Learning . His work intersects theoretical foundations with applications in quantum computing, sustainability, and biomedical data analysis. His research focuses on: Quantum-inspired machine learning architectures Ethical and sustainable AI development Medical imaging and clinical data analysis Language models for emotion recognition and healthcare applications Recent publications highlight advancements in: Optical neural network hardware Quantum biomolecular energy calculations Federated learning for personalized medicine Explainability techniques for AI models

• Algorithms
• Machine Learning
• Computational Complexity
• +۲ مورد دیگر

Benjamin Bogø is a Research Fellow at the Department of Computer Science, University of Copenhagen. His work bridges algorithmic complexity, machine learning, and quantum computing, with a focus on sustainable AI and cross-cultural applications. He is affiliated with the SCIENCE AI Centre and contributes to the department's compute cluster initiatives. Benjamin's research interests include: Quantum computing applications in neural networks AI explainability and ethical frameworks Algorithm optimization for complex systems Climate-aware machine learning Interdisciplinary biomedical and cultural data analysis His recent publications analyze: Quantum-classical hybrid systems (15% of articles) Large language model interpretability (20% of articles) Medical and ecological applications (30% of articles) Quantum hardware optimization (25% of articles) Algorithmic fairness in recommender systems (10% of articles)

• Machine Learning
• Computer Science
• Quantum Computing
• +۳ مورد دیگر

Mathias Nygaard Larsen is an Instructor at the Department of Mathematical Sciences and Department of Computer Science (DIKU) at the University of Copenhagen. His research spans interdisciplinary domains including Machine Learning , Quantum Computing , and Computational Modeling , reflecting collaborations between mathematical and computer science communities. His publications highlight innovative approaches in Quantum-enhanced computational methods Explainable AI systems Biomedical data analysis Cross-cultural algorithmic frameworks Current work focuses on environmentally sustainable AI practices and quantum-classical hybrid models for biomolecular simulations, utilizing Copenhagen's advanced compute infrastructure.

• Machine Learning
• Artificial Intelligence
• Data Analysis
• +۳ مورد دیگر

Nadja Petersen is an Instructor at the Department of Computer Science , University of Copenhagen. Her work is affiliated with the Machine Learning Section and the SCIENCE AI Centre, focusing on theoretical foundations and applied research in domains like healthcare, environmental sustainability, and quantum computing. Email: nape@di.ku.dk Research Interests: Nadja contributes to areas including neural network hardware, sustainable AI practices, emotion recognition in conversational systems, and quantum computing applications for biomolecular analysis. Her work intersects machine learning with interdisciplinary challenges such as climate modeling, medical diagnostics, and computational biology. Recent Publications: Her research spans optical neural network processors, sustainable AI frameworks, and quantum-enhanced molecular simulations. Key trends include AI interpretability, cross-cultural recipe generation, and brain-computer interface applications. Labs & Teams: She collaborates with the Machine Learning Section at DIKU, a hub for foundational and applied research connected to the SCIENCE AI Centre. The section explores quantum machine learning, medical imaging, and environmental data analysis.

• Topological Photonics
• Photonic Crystals
• Semiconductor Lasers
• +۵ مورد دیگر

Dr. Sang Soon Oh is a Lecturer at the School of Physics and Astronomy, Cardiff University, and a Sêr Cymru II Rising Star Fellow (2017-2023) funded by the Welsh government. His research spans theoretical and experimental aspects of topological photonics , photonic crystals , semiconductor lasers , and chiral metamaterials , with a focus on graphene-based systems and quantum emitters . Education: PhD in Physics from KAIST (2007), with a research visit at the University of St Andrews (2004-2005). Postdoctoral: ETRI (2007-2010), University of Surrey (2010), Imperial College London (2010-2017). His research interests center on: Topological protection mechanisms in photonic systems Non-Hermitian and non-linear photonic dynamics Quantum dot and nanowire laser integration Chiral and plasmonic light-matter interactions Selected publication trends reveal: Advances in nonlinear topological photonics (2025) Non-Abelian nodal line physics in photonic and mechanical systems (2024-2022) Graphene plasmonics and 2D material applications (2024) Machine learning for topological phase diagram analysis (2023) Scientific recognition: Sêr Cymru II Rising Star Fellowship (£983K) Outstanding reviewer for Light: Science & Applications (2022, 2023) Supervision includes: Current PhD students: Ghada Alharbi (optical bistability), Kyle Netherwood (quantum filtering), Ion Wood-Thanan (aperiodic materials) Former PhD students: Stephan Wong (topological lasing), Zeeshan Ahmad (graphene plasmonics), Joe Mahoney (quantum dot modulators), Shaikhah Almousa (resonator theory) Collaborative supervision of Jan Olthaus (Münster University) and 3 external PhD examining roles

• Combustion
• Molecular Spectroscopy
• Laser Diagnostics
• +۳ مورد دیگر

Thomas Häber is a Senior Scientist and Research Fellow at the Karlsruhe Institute of Technology (KIT), where he leads research in the Institute for Chemical Technology and Polymer Chemistry. He holds a PhD in Physical Chemistry from the University of Göttingen (2000) and an MS in Chemistry from the University of Paderborn (1997). His work focuses on combustion diagnostics, molecular spectroscopy, and catalysis, with expertise in laser-based techniques like PLIF and high-resolution FTIR. He has pioneered studies on flame-wall interaction, ignition mechanisms, and computational fluid dynamics modeling. Key achievements include developing tomographic multispecies visualization methods and analyzing NOx storage catalysts. His research projects include collaborations within the SFB 150 and FOR 1447 frameworks. He has received awards such as the Richard Zsigmondy Award and Humboldt Foundation fellowship. Research interests span combustion dynamics, laser diagnostics, and computational tools like machine learning. His contributions to understanding flame quenching, hot particle ignition, and catalytic oxidation have advanced both fundamental science and engineering applications. He collaborates across disciplines, integrating experimental and numerical approaches to solve complex fluid flow and reaction problems.