جستجوی استاد راهنمای دانشگاهی

• Control Systems
• Neural Networks
• Embedded Systems
• +۶ مورد دیگر

Dr. Eng. Marek Krok is a Lecturer at the Department of Automation within the Faculty of Electrical Engineering, Automation and Computer Science at Opole University of Technology. His academic focus lies in control systems, neural networks, and energy-efficient algorithm design, with significant contributions to perfect control theory and FPGA-based implementations. Role: Lecturer Department: Automation Contact: Room P3-414, m.krok@po.edu.pl His research emphasizes perfect control systems , including energy-optimal algorithms for LTI systems, inverse model control, and pole-free control strategies. He has explored practical applications in servomechanism systems and CNC machining, leveraging neural networks and FPGA hardware for improved performance. The most recent 15 publications highlight trends in control theory , neural network integration , energy optimization , and hardware implementations . Key subfields include model-based control, recurrent neural networks, FPGA acceleration, multivariable systems, Moore–Penrose inverse alternatives, and real-time control validation. In teaching, Krok supervises consultations and maintains a personal website: m.krok.po.opole.pl . His work bridges theoretical control system design with industrial applications in automation and robotics.

Jun Xiaمشاهده پروفایل

دانشیار

• Multiphase Flows
• Turbulent Combustion
• Transport Phenomena
• +۴ مورد دیگر

Dr Jun Xia serves as a Reader in Mechanical and Aerospace Engineering within Brunel University's College of Engineering, Design and Physical Sciences. Holding a BEng and MSc from Zhejiang University and a PhD from the University of Southampton, he directs the MSc Advanced Mechanical Engineering program while leading research in high-fidelity simulation of multiphase flows and combustion systems. His research spans Multiphase flow dynamics in energy systems Turbulent combustion with alkali-metal emissions Molecular dynamics for CO 2 /H 2 storage Machine learning-enhanced fluid simulations Droplet/spray combustion modeling Notable methodologies include Lattice Boltzmann methods, interface-capturing techniques, and chemistry tabulation for complex fuels. Recent publications (2021-2024) demonstrate strong focus on multicomponent droplet dynamics, CO 2 /H 2 transport in porous media, and alkali-metal emissions in solid fuels, primarily published in Physics of Fluids , Combustion and Flame , and Journal of Chemical Physics . Professional memberships include: Combustion Institute British Section (CIBS) American Society of Mechanical Engineers (ASME) Supported by EPSRC funding, his work integrates molecular dynamics with macroscopic modeling for underground energy storage. He collaborates extensively with international groups on coal/biomass combustion and has developed simulation tools for dense-to-dilute spray regimes. His IEF research group focuses on physics-guided machine learning for transport phenomena.

• Complex Networks
• Artificial Intelligence
• Urban Networks
• +۵ مورد دیگر

Dr. José Francisco Vicent Francés serves as an Associate Professor in the Department of Computer Science and Artificial Intelligence at the University of Alicante's Higher Polytechnic School. He directs the Master's Degree in Data Science and leads the ANVIDA research group (Data Analysis and Visualization in Complex Networks), with active roles in teaching, research supervision, and interdisciplinary projects. His educational foundation includes a Doctorate in Computer Engineering and Computing from the University of Alicante (2007) and a Degree in Physical Sciences from the Complutense University of Madrid (1987), specializing in Automatic Computing. Research focuses on complex network analysis, AI applications in urban systems, and evolutionary algorithms for simulation. His work bridges theoretical computer science with practical urban challenges, emphasizing data visualization and spatio-temporal modeling. Recent efforts target explainable AI frameworks and network-based approaches to environmental and transportation problems. Current publications (2024-2025) reveal strong interdisciplinary trends: deep learning models for urban noise pollution, traffic accident forecasting, and carbon concentration monitoring using graph-based spatio-temporal methods. These works integrate computer science with environmental science and urban planning, demonstrating consistent innovation in network analysis and AI-driven prediction systems. Dr. Vicent has directed four doctoral theses and 17 master's/undergraduate theses in the past five years. His research funding includes seven public projects since 2020, notably the ongoing 'Development of AI and Complex Networks Techniques for Urban Sustainability Management' (2024-2025) and the 'Data Analysis for COVID-19 Prevention' initiative. As director of the ANVIDA research group, he fosters collaborations across computer science, urban planning, and environmental sciences, maintaining strong ties with municipal authorities and international research teams through projects like the 'City as a Multiple Data Network' study.

Stefan Lüdtkeمشاهده پروفایل

استادیار

• Neuro-symbolic machine learning
• Machine learning for heterogeneous data
• Machine learning for marine ecology
• +۵ مورد دیگر

Stefan Lüdtke is an Assistant Professor (Juniorprofessor) for Marine Data Science at the University of Rostock (since July 2023) and concurrently serves as a junior research group leader at ScaDS.AI Leipzig . Previously, he was a postdoctoral researcher at the Institute for Enterprise Systems, University of Mannheim (2021-2023) and completed his PhD at the University of Rostock (2016-2021). Education & Career Timeline: 2023 – present: Juniorprofessor (Assistant Professor) for Marine Data Science, University of Rostock 2023 – present: Junior research group leader, ScaDS.AI Leipzig 2021 – 2023: Postdoc, Institute for Enterprise Systems, University of Mannheim 2016 – 2021: PhD studies, University of Rostock Research Interests: Dr. Lüdtke’s research integrates neuro-symbolic machine learning with practical applications spanning heterogeneous tabular data , marine ecology , and underwater technology . His work bridges symbolic reasoning and modern gradient-based learning to tackle complex real-world problems such as hyperspectral imaging for environmental monitoring, knowledge-graph completion, and robust human-activity recognition. Key focus areas include: Design of memory-augmented decision-tree ensembles and gradient-based tree learning. Data-centric evaluation and quality assessment of machine-learning models on tabular and sensor data. Domain adaptation and self-training techniques for activity recognition in changing environments. Application of AI to marine robotics and glacier-dynamics mapping. Publication Trends: Across 40+ peer-reviewed works (2017-2025), Lüdtke demonstrates a steady shift from foundational probabilistic-filtering and lifted-inference methods toward cutting-edge neural-symbolic hybrids and tabular-data-centric learning. Recent high-impact venues show contributions in machine-learning theory , computer vision , and environmental informatics , with a notable uptick in interdisciplinary projects combining AI and marine science. Scientific Awards & Honors: No specific awards are listed in the provided material. Advising & Funding: No named students or explicit grant details are disclosed in the text. Laboratories & Teams: He leads the Marine Data Science junior research group at the University of Rostock and the ScaDS.AI Leipzig junior research group, fostering cross-institutional collaboration in AI and data science.

Banu Günel Kılıçمشاهده پروفایل

استاد

• Signal Processing
• Artificial Intelligence
• Software Engineering
• +۴ مورد دیگر

Prof. Banu Günel Kılıç is a full-time faculty member at Middle East Technical University (METU), where she holds the rank of Professor in the Graduate School of Informatics, Department of Information Systems. She has served in various academic roles since 2004, including previous appointments as Associate Professor (2014-2022) and Assistant Professor (2010-2014), and currently directs the Graduate School of Informatics. Doctorate: Queen's University of Belfast (2001-2004) Postgraduate: University of Bristol (2000-2001) Undergraduate: METU, Electrical Engineering (1995-2000) Her research focuses on signal processing, cybersecurity, and machine learning, with particular emphasis on intrusion detection systems in software-defined networks, acoustic gunshot localization, auditory attention modeling, and security visualization infrastructures. She has supervised 14+ graduate theses, including work on microservice call graphs and institutional investor networks. Recent publications demonstrate strong interdisciplinary work in machine learning and cybersecurity , with specific applications in intrusion detection, cloud resource allocation, and acoustic signal processing. She has contributed to over 25 peer-reviewed publications and holds two standard patents in acoustic source separation technology. Prof. Kılıç has participated in multiple European Union funded projects including EuroCC2 (2023-2026), EUMaster4HPC (2022-2025), and prior FP7 projects like IRIS and DIOMEDES. Her work bridges traditional acoustic signal processing with modern cybersecurity applications in enterprise environments.

Reemt Hinrichsمشاهده پروفایل

پژوهشگر

• Signal Coding
• Cochlear Implants
• Digital Signal Processing
• +۳ مورد دیگر

Reemt Hinrichs is a researcher at the Institute for Information Processing (TNT) at Leibniz University Hannover, where he focuses on signal processing applications across biomedical engineering, audio technology, and structural health monitoring. He completed his Dr.-Ing. (PhD) at TNT in 2023 after working as a research assistant since January 2018. Dr. Hinrichs earned his Master's Degree in Mechatronics from Leibniz University Hannover in May 2017, completing his thesis on "System-theoretical modeling of a structural sound signal path" at the Institute for Information Processing. His academic journey reflects a consistent focus on signal processing theory applied to practical engineering challenges. His primary research interests center around Signal Coding , particularly for Cochlear Implants , along with broader expertise in Digital Signal Processing and Nonlinear System Theory . His work spans multiple application domains including biomedical engineering (cochlear implants), audio processing (guitar effects modeling), and structural health monitoring (acoustic emissions analysis for infrastructure). Dr. Hinrichs' publication record demonstrates a strong focus on compression algorithms for cochlear implants, with numerous papers on neural network-based approaches for zero-delay compression of electrical stimulation patterns. He has also made significant contributions to guitar effects modeling using convolutional neural networks and structural health monitoring through acoustic emission analysis. His research bridges theoretical signal processing with practical applications across diverse domains, showing particular strength in applying deep learning techniques to specialized signal processing challenges. With approximately 60 theses supervised, Dr. Hinrichs has been actively involved in mentoring students across various research topics including cochlear implant technology, structural modeling, and audio signal processing. His supervision portfolio includes work on nonlinear prediction of electrode excitation patterns, geometry-dependent modeling of transfer functions, and automatic extraction of guitar effects. His current research focuses on "deep learning models for the compression of electrode excitation patterns of cochlear implants," continuing his long-standing expertise in this specialized area of biomedical signal processing while expanding into new applications of neural network architectures for real-time signal compression.

Hossein Azizpourمشاهده پروفایل

دانشیار

• Deep Learning
• Computer Vision
• Artificial Intelligence
• +۴ مورد دیگر

Hossein Azizpour is an Associate Professor in Machine Learning at KTH Royal Institute of Technology, affiliated with the Division of Robotics, Perception and Learning (RPL). He holds associated roles at the Science for Life Laboratory, Swedish e-Science Research Centre, WASP, and Digital Futures. His research focuses on deep learning under uncertainty, model interpretability, feature selection, and applications in life sciences, physics, and earth observation. Research Themes : (1) Deep learning under uncertainty (Bayesian methods, semi-supervised learning), (2) Interpretable AI (feature attribution, understanding neural networks), (3) Generative & foundation models (Vision Transformers, diffusion models), (4) Applications in medical imaging, fluid dynamics, and earth observation. Recent Work Trends : His publications emphasize vision transformers, uncertainty quantification, and cross-domain applications. Key areas include model optimization (e.g., Patchdropout), medical AI (e.g., breast cancer risk prediction), and physics-informed deep learning. Scientific Awards : Best Paper Award, CVPR DeepVision Workshop (2015) Google Travel Grant, CVPR DeepVision Workshop (2015) Best Paper Runner-Up, CVPR DeepVision Workshop (2014) Best Industry Paper Prize, BMVC (2013) Outstanding Reviewer, CVPR (2013) Advising & Collaborations : Supervises PhD students in AI for physics applications (e.g., fluid dynamics), medical imaging, and computer vision. Collaborates with institutions like AstraZeneca, Alan Turing Institute, and Meta. Labs & Teams : Leads the Robotics, Perception and Learning (RPL) division’s research and co-organizes the Computer Vision Reading Group at KTH.

• Natural Language Processing
• Machine Learning
• Authorship Attribution
• +۳ مورد دیگر

Nesibe Merve Husejnović serves as a Senior Lecturer in the Software Engineering Department at the International University of Sarajevo, where she also completed her Master's degree in 2013 and is currently pursuing doctoral studies in Computer Science and Engineering. Her educational background includes a Master of Science from the International University of Sarajevo (2013), with ongoing Ph.D. research in the same institution's Computer Science and Engineering Department. Her research specializes in authorship attribution for social media content using advanced machine learning techniques. She develops neural network and SVM-based systems for identifying authors of short messages, contributing significantly to digital forensics and natural language processing. Her work bridges computational linguistics and cybersecurity, with particular focus on Twitter and social network analysis. Publication trends from 2012-2018 show progressive methodological evolution from basic neural networks to recurrent architectures for batch processing, alongside consistent exploration of feature engineering for short-text analysis. The 2012 RFID antenna publication indicates supplementary expertise in hardware design. No scientific awards or honors are documented in the available materials. While she maintains active research output, the source text provides no information about graduate student supervision, research grants, laboratory affiliations, or collaborative teams.

Lucas Hofمشاهده پروفایل

استاد

• Aeronautics and Aerospace
• Sustainable Development
• Circular Economy
• +۶ مورد دیگر

Dr. Lucas Hof is a Professor at the Department of Mechanical Engineering , École de technologie supérieure (ÉTS), University of Québec . He holds a B.Sc. , M.Sc. from Delft University of Technology and a Ph.D. from Concordia University. Research Axes: Aeronautics/Aerospace, Sustainable Development & Circular Economy, Innovative Materials, Intelligent Systems Labs: NUMERIX (Organizational Engineering), DYNAMO (Machine Dynamics), CIRODD (Sustainable Development), LFIC (Smart Manufacturing) His research focuses on advanced manufacturing (SACE micromachining, additive manufacturing), smart corrosion monitoring in bolted joints, industry 4.0/5.0 integration , and circular economy strategies for materials like eggshells and titanium alloys. Recent work explores neural networks for powder coating optimization and electrochemical post-processing of 3D-printed parts. Key publications span glass machining ( Manufacturing Letters ), smart maintenance systems ( IFAC-PapersOnLine ), and circular supply chains ( Journal of Cleaner Production ). He co-supervises numerous PhD/MSc students in topics including solar panel design, medical mask recycling, and metamaterial applications.

• Astrophysics
• Stellar Astronomy
• Ultracool Dwarfs
• +۴ مورد دیگر

William Cooper is a Researcher in the Department of Physics, Astronomy and Mathematics at the University of Hertfordshire's School of Physics, Engineering & Computer Science. His work focuses on ultracool dwarfs, spectral classification, and near-infrared spectroscopy, with significant contributions to the Gaia mission's astrometric and photometric analyses. Specializes in Gaia ultracool dwarf research Active in near-infrared spectroscopy and stellar classification Collaborates internationally on astrometry and photometry projects Recent publications highlight his expertise in applying recurrent neural networks for light curve analysis and characterizing ultracool dwarf candidates through multi-wavelength spectroscopy. He maintains an ORCID profile (0000-0003-3501-8967) for his research outputs.

• Prescriptive Process Monitoring
• Generative AI
• Autonomous Enterprise
• +۳ مورد دیگر

Hans Weytjens is a postdoctoral researcher at the Technical University of Munich (TUM) and a guest professor at KU Leuven in Belgium. He holds a Ph.D. in Machine Learning for Predictive and Prescriptive Process Monitoring, an M.Sc. in Business and Information Science, and an MBA in Finance. His current academic role at TUM involves research in the Department of Information System Development and Operation. Ph.D., KU Leuven (2023) M.Sc., KU Leuven (1990-1991) MBA, University of Chicago (1990-1991) Hans Weytjens specializes in Machine Learning applications for Business Processes , with a focus on Prescriptive Process Monitoring , Generative AI , and Autonomous Enterprise systems. His work bridges theoretical advancements with practical implementations in process optimization and AI-driven decision-making frameworks. Hans Weytjens' publications span topics like Event Log Dynamics , Reinforcement Learning , and Time Series Forecasting , reflecting his expertise in integrating Uncertainty Quantification and Visual Analytics into process mining methodologies. At TUM, Hans contributes to the Chair of Information System Development and Operation , advancing research on AI-driven enterprise automation and predictive process analytics.

• Adaptation
• Concept drift
• Evolving data streams
• +۳ مورد دیگر

Dr. Indrė Žliobaitė is a researcher in the Department of Computer Science at Aalto University. Her work spans interdisciplinary domains, combining computational methods with ecological and biomedical applications. Adaptation Concept drift Evolving data streams Change detection Predictive analytics Discrimination aware data mining Her research includes modeling respiratory motion for medical applications, analyzing fossil records for paleoecological insights, and developing energy-efficient computing architectures. Publications highlight applications of Bayesian models, mobility trace analysis, and approximate computing techniques.

• Telecommunications
• Wireless Communications
• Digital Signal Processing
• +۳ مورد دیگر

Guido Montorsi is a Full Professor in the Department of Electronics and Telecommunications at the Polytechnic University of Turin. He is affiliated with the College of Electronic, Telecommunications and Physics Engineering and the College of Computer, Film and Mechatronics Engineering. His scientific disciplinary sector is IINF-03/A - Telecommunications (Area 0009 - Industrial and Information Engineering). Professor Montorsi has been actively involved in teaching across multiple academic programs since 2005, including the PhD program in Electrical, Electronics and Communications Engineering through the current 39th cycle. He teaches courses such as Signal Processing and Wireless Transmission Lab, Advanced Wireless Communications, and Information and Communication Theory at both Master's and Bachelor's levels. His research focuses on telecommunications with emphasis on wireless communications, digital signal processing, and communication theory. Professor Montorsi leads the Advanced techniques for digital transmission (CommGroup) research group and has been the scientific manager for numerous research projects including RESTART - Spoke 3 (2023-2025) and WiMAGIC (2008-2011), along with multiple commercial research projects extending through 2026. His recent publications demonstrate a trend toward applying machine learning techniques, particularly recurrent neural networks, to traditional communication problems like channel estimation and signal detection. The research spans both theoretical communication concepts and practical implementations for next-generation wireless systems. Signal overlay design and detection for satellite broadband channels (patent) Topcom++ Software (copyright) A method and a node for detecting phase noise in MIMO communication systems (patent) A method for the construction of codes for the frame header of adaptive coding and modulation systems (patent) Analog-digital belief propagation (patent) Professor Montorsi supervises several PhD students including Martina Magnaldi (39th cycle, 2023-present), Barbara Ripani (37th cycle, 2021-2025), and Majid Mosavat (35th cycle, 2019-2023). His research grants span both competitive public funding and commercial contracts with organizations interested in advanced wireless communications, deep space communications, and satellite broadband technologies. He is actively involved in the Advanced techniques for digital transmission (CommGroup) research group, focusing on next-generation wireless communication systems, optical communications, and error correction coding techniques for challenging channel conditions.

• Stock Assessment
• Fish Bioenergetics
• Food-Web Dynamics
• +۷ مورد دیگر

Ji Xiang He is an Adjunct Associate Professor at the Department of Fisheries and Wildlife, College of Agriculture & Natural Resources, Michigan State University. He also serves as a Research Fisheries Biologist at the Michigan Department of Natural Resources (MI DNR) at the Alpena Fisheries Research Station. His professional focus integrates fish bioenergetics, food-web dynamics, and life-history parameter analysis within the context of human-ecosystem interactions in fisheries management. Education: Undergraduate in Fisheries and Aquaculture (specializing in Chinese reservoir systems), with graduate training in Fisheries and Ecology at the State University of New York (SUNY) beginning in 1995. His research bridges ecological frameworks (natural resource geography, landscape ecology, systems ecology) with quantitative fisheries science. While his primary work centers on stock assessment and ecosystem modeling in aquatic environments, recent Google Scholar publications indicate interdisciplinary applications of computational modeling and machine learning in biomedical contexts such as arterial hypertension and aneurysm progression. Key trends in his publications reveal expertise in physics-based machine learning, patient-specific hemodynamic modeling, and longitudinal data analysis for cardiovascular diagnostics. Though seemingly divergent from his fisheries background, these computational techniques may inform ecological modeling practices through cross-disciplinary methodological innovation. He maintains affiliation with the Quantitative Fisheries Center and emphasizes functional connections between ecological systems and human management practices in his work.

• Cyber-Physical Systems
• Automata Theory
• Machine Learning
• +۳ مورد دیگر

Martin Tappler is a researcher at Vienna University of Technology's Faculty of Electrical Engineering and Information Technology, working in the Cyber-Physical Systems research area (Institute of Computer Engineering). Holding a technical doctorate (Dr.techn.), he serves as a Project Assistant conducting advanced research at the intersection of formal methods and machine learning. His research focuses on automata theory , neural network semantics , and reinforcement learning systems . Tappler's work bridges theoretical computer science with practical applications in cyber-physical systems, particularly exploring how formal models can enhance machine learning approaches. His methodology combines rigorous mathematical frameworks with empirical validation in complex decision-making environments. Tappler's recent publications demonstrate significant contributions to understanding the relationship between neural network architectures and formal language structures, as well as developing novel testing methodologies for reinforcement learning systems. His research shows a clear trajectory toward creating more reliable and interpretable AI systems for safety-critical applications. As a researcher at TU Wien, Tappler collaborates with colleagues across multiple institutions, as evidenced by his co-authored publications with researchers from various academic backgrounds. His work appears in prominent computer science venues focusing on formal methods and machine learning.