جست‌وجوی استاد و پژوهشگر

• Embedded Systems
• Machine Learning
• Digital Signal Processing
• +۴ مورد دیگر

Prof. Gerold Bausch is a faculty member at HTWK Leipzig within the Faculty of Engineering . He leads the Electronic Engineering Lab (EEL) and serves as Coordinator for International Relations and Examination Board Member at HTWK Leipzig. His research focuses on Embedded Systems , Machine Learning , and Digital Signal Processing . Specializes in low-power embedded systems with ARM and RISC-V architectures Supervises thesis projects in collaboration with institutions like Fraunhofer-Institut and Max-Planck-Institut Developed innovative measurement systems for infrastructure monitoring Recent research trends include machine learning on microcontrollers , energy-autonomous sensor applications , and low-power wide-area networks . He has received prestigious awards such as the Actemium-Förderpreis and VDI Bezirksverein Leipzig Förderpreis . His work bridges academic research with industry applications through partnerships with companies like Siemens AG and ANDAV Electronics GmbH. Scientific Awards: Actemium-Förderpreis für Angewandte Digitalisierung (1st Prize) VDI Bezirksverein Leipzig e.V. Förderpreis (1st Prize) He supervises numerous Bachelor's and Master's students , guiding projects in embedded systems, signal processing, and biomedical technologies. His research emphasizes practical solutions for industrial challenges and environmental monitoring.

• Natural Language Processing
• Grammatical Error Correction
• Discourse Analysis
• +۳ مورد دیگر

Murathan Kurfalı is an active researcher at Uppsala University's Department of Linguistics and Philology within the Faculty of Languages, specializing in Natural Language Processing with a strong focus on Grammatical Error Correction (GEC), discourse analysis, and multilingual applications. His work bridges computational linguistics with practical language education tools, particularly for Swedish language processing. His research interests center on developing and evaluating NLP systems for language learning, with significant contributions to GEC evaluation methodologies using Large Language Models. He has pioneered reference-free evaluation metrics through LLM-based post-editing and created benchmark datasets like SweSAT-1.0 for assessing Swedish language models. His work extends to climate change applications through ClimateEval, a comprehensive NLP benchmark for climate-related tasks. Analysis of his publication trends shows consistent advancement in discourse analysis (particularly implicit relation classification and connective detection) alongside growing emphasis on climate informatics and Swedish language model evaluation. His methodological approach favors resource-efficient techniques like gradient boosting for connective detection while leveraging LLM capabilities for complex tasks. As a collaborative researcher, he frequently partners with Uppsala colleagues including Joakim Nivre and Mats Wirén, contributing to major workshops like BEA, ClimateNLP, and NLP4CALL. His work demonstrates strong translational impact from theoretical NLP to practical language education and climate science applications.

• Formal Verification
• Concurrency
• Automata Theory
• +۳ مورد دیگر

Bengt Jonsson is a researcher at Uppsala University , Sweden, with a focus on formal verification, concurrency, and automata learning. His work addresses challenges in software testing, protocol verification, and concurrent systems analysis, often collaborating with institutions like KTH Royal Institute of Technology and academic peers such as Parosh Aziz Abdulla and Konstantinos Sagonas. Research Fields: Formal Verification, Concurrency, Automata Theory, Software Testing, Distributed Systems, Protocol Verification Recent publications highlight advancements in stateless model checking, dynamic partial order reduction, and automated detection of state machine bugs in protocol implementations. His 2023 CONCUR Test-Of-Time Award underscores his contributions to theoretical computer science. Collaborative efforts span network protocol analysis, cache performance modeling, and register automata learning, with practical applications in IoT security and multicore parallelization. Scientific Awards: CONCUR Test-Of-Time Award (2023)

Sebastian Hauschildمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Distributed Systems
• Biomedical Signal Processing
• +۳ مورد دیگر

Sebastian Hauschild is a doctoral researcher at Lübeck University of Applied Sciences (TH Lübeck), affiliated with the Department of Electrical Engineering and Computer Science. He works as part of Prof. Horst Hellbrück's team and the Competence Center for Sensor Applications (CoSA), focusing on implementing AI in resource-constrained distributed systems, particularly for smartphone-based medical diagnostics. Current research: AI-driven decentralized data processing for rural healthcare applications Key collaborations: Laboratory for Medical Optics (TH Lübeck) and University of Lübeck Technical focus: Data security, sensor systems, and embedded machine learning

Mariana Kozlowskaمشاهده پروفایل

پژوهشگر

• Materials Science
• Computational Chemistry
• Biophysics
• +۷ مورد دیگر

Dr. Mariana Kozlowska is a Group Leader and KIT Associate Fellow at the Institute of Nanotechnology, Karlsruhe Institute of Technology (KIT). She leads the DNA Unit of the Virtual Materials Design platform, focusing on multiscale modeling of soft matter and natural materials to understand molecular dynamics, assembly, and interfacial phenomena in chemistry, biology, and physics. Her research integrates density functional theory (DFT) , molecular dynamics simulations , and multiscale modeling to investigate spectroscopic properties, photophysical phenomena in metal-organic frameworks (MOFs) , molecular adsorption, and noncovalent interactions. Current projects include virtual design of chiral alignment materials and photoswitchable MOF conductance, supported by the Carl Zeiss Stiftung and DFG Priority Program. Dr. Kozlowska’s work has been recognized through grants like the Brigitte-Schlieben-Lange-Programm and 3D Matter Made to Order Cluster of Excellence. Her interdisciplinary approach bridges computational modeling with experimental validation, particularly in biomolecular assembly and functional materials design . She has also contributed to science communication via initiatives like Creative Discussions about Science and the ADAMED SmartUP program. Research Focus: Multiscale materials modeling, soft matter assembly, MOF electronic properties Key Collaborations: Prof. Burkhard Luy (Virtmat), Prof. Maria Andrea Mroginski (DAAD grant)

Miguel Alves Gomesمشاهده پروفایل

پژوهشگر

• Recommender Systems
• Data Mining
• Data Analytics
• +۸ مورد دیگر

Miguel Alves Gomes is a Scientific Researcher and doctoral student at the Institute for Technologies and Management of Digital Transformation (TMDT) at the University of Wuppertal, where he has been actively contributing since December 2020. He holds an M.Sc. in Computer Science with a focus on Data Analytics and previously studied Information Technology with a focus on Information Science, both at the University of Wuppertal. Bachelor of Science in Information Technology (focus: Information Science), University of Wuppertal Master of Science in Computer Science (focus: Data Analytics), University of Wuppertal His research lies at the intersection of machine learning and data mining, particularly in personalized recommender systems . He investigates the integration of supervised and unsupervised learning methods, with a growing emphasis on Transformer-based architectures and explainable AI . His work aims to enhance model interpretability and adaptability, especially in dynamic environments requiring continuous learning. The 2024 publication highlights a clear trend toward advancing embedding techniques for lifelong learning, focusing on algorithmic strategies for dynamically extending model representations. This reflects a deep engagement with cutting-edge challenges in AI sustainability and model evolution. No scientific awards were mentioned in the provided text. Miguel Alves Gomes has served as a student assistant at TMDT during his studies and is currently a research associate and doctoral candidate. There is no mention of grant leadership or student advisement in the available information. He is affiliated with the Institute for Technologies and Management of Digital Transformation (TMDT), which serves as his primary research unit at the University of Wuppertal.

Nataša Živićمشاهده پروفایل

استاد

• Information and Coding Theory
• Cryptography
• Information Security
• +۱۱ مورد دیگر

Prof. Dr. Nataša Živić is a Professor of Information and Coding Theory and Real-Time Image Processing at the Faculty of Digital Transformation , HTWK Leipzig . Her work bridges theoretical foundations in coding, cryptography, and signal processing with practical applications in IoT, smart grids, automotive systems, and blockchain technologies. Academic background: Dr.-Ing. habil. and Dr.-Ing. in Electrical Engineering, University of Siegen; Magister and Dipl.-Ing. from University of Belgrade. Professional journey: Founder of Secutanta GmbH; Senior Technical Security Engineer at Volkswagen (Cariad); academic roles at University of Siegen. Her research focuses on secure and robust communication , including noise-tolerant data authentication, joint channel coding and cryptography, and real-time image processing. She explores probabilistic AI algorithms and distributed ledger technologies for industrial applications in mobility and energy systems. The 15 most recent publications highlight her leadership in applying blockchain and DLT to automotive and IoT contexts, advancing secure communication in smart metering and charging infrastructure, and integrating machine learning for environmental and financial prediction. Her work spans IEEE, MDPI, and Springer venues, reflecting strong academic impact. Scientific Awards: Best Paper and Innovation Award, IEEE SEGE 2015 Best Paper Award, UBICOMM 2010 She has led DAAD-funded international projects with universities in the USA, Korea, and China, and contributes to standardization as a member of DIN and ISO committees on security (SC 27) and AI (SC 42). She is also an IEEE Senior Member and active reviewer for top journals. Her research addresses real-world challenges in automotive security, smart energy, and digital transformation.

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Jiaqi Liu is an Associate Professor in the Department of Computer Science and Engineering at Southern University of Science and Technology in Shenzhen, China. His research spans multiple domains within artificial intelligence, with a particular focus on autonomous systems, multimodal learning, and computer vision. He maintains active collaborations with researchers across China and internationally, particularly with Jian Sun and Peng Hang on autonomous driving projects. Dr. Liu's research interests encompass a broad spectrum of AI and computer science topics. His work in autonomous driving involves developing sophisticated decision-making frameworks for cooperative vehicle systems. In multimodal learning, he has pioneered approaches for handling missing modalities and creating adaptive fusion networks. His signal processing research includes innovative methods for sleep staging and EEG analysis using dynamic mode decomposition techniques. Additional research areas include photonic computing accelerators, graph neural networks, and quantum neural networks. The publication trend shows significant growth in output quality and quantity, with numerous papers in top-tier venues including IEEE Transactions, CVPR, AAAI, and IJCAI. His work demonstrates a strong interdisciplinary approach, bridging computer science with applications in healthcare, robotics, and telecommunications. Recent publications indicate increasing focus on the integration of large language models with reinforcement learning for autonomous systems. Dr. Liu has received recognition through publications in high-impact journals and conferences including: IEEE Transactions on Intelligent Transportation Systems IEEE Robotics and Automation Letters Expert Systems with Applications CVPR (Computer Vision and Pattern Recognition) AAAI Conference on Artificial Intelligence His research program involves collaborations across multiple domains, with current projects focusing on language-guided autonomous driving, multimodal sentiment analysis, and advanced signal processing techniques. He leads research efforts in developing novel deep learning architectures for complex real-world problems, particularly in the domains of autonomous systems and healthcare applications.

Fernando Magno Quintão Pereiraمشاهده پروفایل

دانشیار

• Compilers
• Code Generation
• Compiler Optimizations
• +۳ مورد دیگر

Fernando Magno Quintão Pereira is an Associate Professor in the Department of Computer Science at the Federal University of Minas Gerais (UFMG), Brazil. He earned his Ph.D. from the University of California, Los Angeles (UCLA) in 2008 under the supervision of Jens Palsberg. His research focuses on compiler technologies, including code generation, optimization techniques, and static program analysis. Pereira's work spans: Binary analysis and optimization Program transformation and synthesis Performance profiling and code size reduction Security-oriented compiler techniques Machine learning applications in compilers His recent publications demonstrate a strong emphasis on practical compiler tools, theoretical frameworks for program analysis, and optimization algorithms for modern computing systems. He actively contributes to the programming languages community through service on program committees for major conferences including PLDI, CGO, ECOOP, and OOPSLA. He has also held organizational roles such as Diversity and Inclusion Co-Chair for PLDI 2023 and Finance Chair for CGO 2025.

• Information Retrieval
• Federated Learning
• Edge Computing
• +۴ مورد دیگر

Nicola Tonellotto is a researcher at the University of Pisa, Department of Information Engineering, focusing on Information Retrieval , Federated Learning , and Edge Computing . He has collaborated extensively with institutions like CNR, City University of London, and Sapienza University of Rome. Key research areas: Query Processing , Conversational Search , Dense Retrieval , Distributed Learning His recent work addresses challenges in Sequential Recommender Systems , Cross-Modal Query Suggestion , and Energy-Aware Resource Management for FaaS platforms. Notable contributions include FedCMD for emotion recognition and ColBERT-PRF for semantic relevance feedback. As a coauthor, he has advanced GPU-based parallelization for tree ensembles and SIMD extensions to accelerate document scoring. He also explores reproducibility in IR research, emphasizing scientific integrity.

• Clinical Natural Language Processing
• Mental Health Informatics
• Temporal Information Extraction
• +۵ مورد دیگر

Dr. Sumithra Velupillai is a Research Fellow at King's College London's Institute of Psychiatry, Psychology & Neuroscience, Department of Psychological Medicine. With a PhD from Stockholm University completed in 2012, she has established herself as a leading researcher in clinical natural language processing with a specific focus on mental health applications. Her work bridges computer science, linguistics, and clinical medicine to extract meaningful insights from electronic health records. Dr. Velupillai's research interests center on clinical natural language processing, particularly for mental health applications. She specializes in temporal information extraction from clinical narratives, uncertainty detection in medical text, and developing NLP methods for analyzing mental health records. Her work has significant implications for improving clinical decision support systems, mental health diagnostics, and patient care through advanced text mining techniques. She has made substantial contributions to developing lexicons, annotation schemes, and computational models specifically tailored for healthcare contexts. Her recent publications demonstrate a strong trajectory in applying cutting-edge NLP techniques to mental health challenges, with particular emphasis on pain detection in mental health records, suicide risk assessment, and temporal reasoning in clinical narratives. Velupillai's work consistently addresses methodological challenges in healthcare NLP while maintaining clinical relevance. Dr. Velupillai maintains active collaborations with leading researchers in mental health informatics, including Robert Stewart, Angus Roberts, and Rina Dutta at King's College London. Her research is frequently published in top venues for biomedical informatics and computational linguistics, reflecting the interdisciplinary nature of her work.

Philipp Terhörstمشاهده پروفایل

استادیار

• Responsible AI
• Biometrics
• Machine Learning
• +۳ مورد دیگر

Dr. Philipp Terhörst is a Research Group Leader at Paderborn University, specializing in Responsible AI for Biometrics . His work focuses on fairness, privacy, explainability, and uncertainty in machine learning systems for biometric applications. Education: Ph.D. in Computer Science (2021) from Technical University of Darmstadt Previous Affiliations: Fraunhofer IGD (2017–2022), ERCIM Fellowship at Norwegian University of Science and Technology His research addresses critical ethical challenges in biometric AI, including demographic bias mitigation , privacy-enhancing technologies , and confidence-aware verification systems . Recent publications explore explainability frameworks, backdoor attacks, and fairness in deepfake detection. Scientific contributions have earned awards from the European Association for Biometrics and International Joint Conference for Biometrics . He actively participates in academic service as a reviewer for TPAMI, CVPR, and IEEE journals. Teaching: Offers courses in Human-Centered Machine Learning Collaborations: Member of Transregional Collaborative Research Centre 318 Management Programs: Alumnus of the BMBF-funded 'Software Campus' initiative

Mike Schwarzمشاهده پروفایل

استاد

• Semiconductor Device Physics
• Schottky Barrier Transistors
• Compact Modeling
• +۵ مورد دیگر

Professor Mike Schwarz is a faculty member at the Technical University of Mittelhessen (THM), where he holds a position in the Department of Electrical Engineering and Information Technology. He has been serving as Professor for Embedded Systems and Simulation since October 2020. Prior to his academic appointment, he worked at Robert Bosch GmbH as Head of Diode Design and Head of Process and Device Simulations MEMS from 2016-2020, and as a MEMS Sensor Design & Simulation Engineer from 2013-2020. His educational background includes a Doctor of Philosophy from Universitat Rovira i Virgili, Tarragona, Spain (2009-2012), where he completed his dissertation on "Two-Dimensional Analytical Predictive Modeling of Schottky Barrier SOI and Multi-Gate MOSFETs." He also holds a Master of Engineering from the same institution (2008-2009) and a Diploma in Engineering from Gießen-Friedberg University of Applied Sciences (2003-2008). Professor Schwarz's research spans semiconductor device physics, particularly Schottky barrier transistors and compact modeling, MEMS sensor design, neuromorphic computing using memristive devices, and machine learning applications in complex plasma physics. His recent work shows a clear trend toward applying AI techniques to plasma particle tracking and advancing cryogenic electronics through detailed physics-based modeling of Schottky barrier devices. He has made significant contributions to understanding device behavior at deep cryogenic temperatures, which has implications for quantum computing applications. His publication record demonstrates consistent productivity across multiple venues, with recent articles appearing in Solid-State Electronics, Machine Learning & Knowledge Extraction, New Journal of Physics, and Nano Futures. His work bridges theoretical device physics with practical applications in sensor technology and neuromorphic computing. Best paper honorable mention award LAEDC conference, Guatemala 2024 IEEE Transactions on Electron Devices List of Golden Reviewers 2015, 2012 IEEE Electron Devices Letters List of Golden Reviewers 2017, 2015 Awarded for the second best dissertation of the year (DEEEA, URV) 2013 Doctorate with distinction 2012 Awarded the Friedrich Dessauer Prize (best diploma thesis of the year) 2008 Professor Schwarz serves on multiple IEEE committees, including as Editor for IEEE Transactions on Electronic Devices since 2025, and as Chair of the IEEE EDS Germany Chapter from 2020-2025. He has been actively involved in organizing the Symposium Schottky Barrier MOS Devices since 2016 and has served on technical program committees for MIXDES and other conferences. His professional activities demonstrate strong engagement with both academic and industrial communities in semiconductor device research.

• Computer Vision
• Deep Learning
• Safe AI
• +۵ مورد دیگر

Prof. Dr.-Ing. Seyed Eghbal Ghobadi is a Professor of Computer Vision and Machine Learning at the Technical University of Central Hesse (Technische Hochschule Mittelhessen), Department of Mathematics, Natural Sciences and Computer Science. He received his PhD in 2010 from the Center for Sensor Systems at the University of Siegen after studying Electrical Engineering at the Technical University of Braunschweig. Research Focus: Deep Learning for Computer Vision, Safe AI, Uncertainty Estimation, Cyber-Physical Systems, and Domain Shift Detection Key Projects: AI-supported highly automated weed control in grassland (KIhUG), AI Safeguarding for Stellantis/Opel Teaching: Courses on Deep Learning for Computer Vision, Pattern Recognition, Fundamentals of AI, and Embedded Systems His recent work explores predictive uncertainty quantification using Dirichlet networks, domain shift detection in cyber-physical systems, and real-time image segmentation with PID Net. He supervises theses on topics including signal denoising and embedded AI applications.

Olaf Wolkenhauerمشاهده پروفایل

استاد

• Systems Biology
• Bioinformatics
• Mathematical Modeling
• +۴ مورد دیگر

Prof. Dr. Olaf Wolkenhauer is a faculty member at the University of Rostock , where he holds the Chair of Systems Biology & Bioinformatics within the Institute of Computer Science . He also serves as an Adjunct Professor at institutions including Case Western Reserve University , Chhattisgarh Swami Vivekanand University of Technology , and the University of Cleveland , as well as a Visiting Professor at the Leibniz Institute for Food Systems Biology at the Technical University of Munich. His research focuses on systems biology, bioinformatics, and machine learning applications in medicine. Chair of Systems Biology & Bioinformatics, University of Rostock Adjunct Professor, Case Western Reserve University Adjunct Professor, Chhattisgarh Swami Vivekanand University of Technology Adjunct Professor, University of Cleveland Visiting Professor, Leibniz Institute for Food Systems Biology Wolkenhauer’s research integrates mathematical modeling , data analysis , and machine learning to address complex biological and medical problems. His recent work explores epigenetic instability in cancer , drug repurposing , synthetic data generation , and collaborative filtering for biomedical applications. He has contributed to advancements in AI-driven diagnostics and functional data analysis . His scientific contributions have earned him recognition, including Fellowship at the Stellenbosch Institute for Advanced Studies (STIAS) and membership in the DFG Review Board 201 Fundamentals of Medicine and Biology . His teaching portfolio includes courses on modeling and simulation in life sciences , data science with Python , and seminars on systems biology and scientific communication . Fellow, Stellenbosch Institute for Advanced Studies (STIAS) DFG Review Board Member, Fundamentals of Medicine and Biology