جست‌وجوی استاد و پژوهشگر

Florian Knollمشاهده پروفایل

استاد

• Medical Imaging
• Machine Learning
• Magnetic Resonance Imaging (MRI)
• +۳ مورد دیگر

Prof. Dr. Florian Knoll is a full professor in Computational Imaging at the Department of Artificial Intelligence in Biomedical Engineering (AIBE) at Friedrich-Alexander-Universität Erlangen-Nürnberg. He leads the Computational Imaging Lab, focusing on machine learning applications in medical imaging, particularly accelerating MRI through innovative reconstruction algorithms and translating them into clinical practice. His research emphasizes improving MRI speed, artifact robustness, and accessibility, alongside developing quantitative biomarkers for disease processes. Knoll's work is funded by NIH grants, including projects on machine learning for musculoskeletal imaging, MR fingerprinting, and deep learning frameworks for MRI reconstruction. He is a key figure in open science initiatives, co-creating the fastMRI dataset with Facebook AI, providing public access to over 1300 knee and 7000 brain MRI scans. He currently serves as deputy editor of Magnetic Resonance in Medicine and chairs the ISMRM Reproducible Research Study Group. His contributions extend to reproducible research, maintaining GitHub repositories with code for image reconstruction techniques (e.g., AGILE, gpuNUFFT) and educational materials. He teaches medical imaging fundamentals at FAU, integrating theoretical and practical insights for students and researchers. Grants: NIH R01EB024532, R21EB027241, P41EB017183, R01EB029957 Labs/Teams: Computational Imaging Lab, fastMRI initiative Software: GitHub repositories for MRI reconstruction (e.g., github.com/FlorianKnoll )

• Business Informatics
• Artificial Intelligence
• Business Intelligence
• +۱۰ مورد دیگر

Prof. Dr. Harald Ritz serves as Professor of Practical Computer Science, especially Business Informatics, at the Technical University of Central Hesse (THM) within the Department of Mathematics, Natural Sciences and Computer Science since 2003. He holds leadership roles as Chair of Examination Committees for B.Sc. and M.Sc. Business Information Systems and Spokesperson for the MNI department in the Business Informatics Working Group (AKWI). His educational background includes a Diplom in Business Informatics (Dipl.-Wirtsch.-Inform.) and doctorate (Dr. rer. pol.) from the Technical University of Darmstadt, following professional experience at SAP SI AG and a professorship at Heilbronn University of Applied Sciences. Ritz's research centers on AI-driven digital transformation for data-driven enterprises, with focus on the “Data to Decision” value chain encompassing Framing, Allocation, Analytics, and Preparation phases. His work integrates business intelligence, data warehousing, machine learning, and SAP ecosystems to address challenges in SME digitalization, operational IT management, and educational technology. Current projects emphasize AI applications in higher education, including intelligent tutoring systems and automated feedback mechanisms. Analysis of his 15 most recent publications reveals a consistent trajectory toward applied AI solutions in business contexts, particularly in intelligent chatbots for educational support, financial trading algorithms, and cloud-based data infrastructure. The research demonstrates increasing integration of no-code platforms, real-time analytics, and domain-specific AI applications across logistics, banking, and procurement sectors. No scientific awards were documented in the source materials. Professor Ritz actively supervises academic development through bachelor’s and master’s theses, doctoral research, and collaborative projects. Current initiatives include the “Winfy” AI chatbot (v4.0, 2025), AI-based feedback systems for educational content (Freiraum 2025 grant), the frits intelligent tutoring project with Prof. Kammer, and doctoral research on AI adoption in SMEs. His work bridges theoretical research with practical implementation in SAP environments and cloud platforms. He operates within THM’s MNI department infrastructure, collaborating through the Business Informatics Working Group (AKWI) and contributing to the Digital Classroom communication platform for online education.

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۳ مورد دیگر

Konstantinos Nikitopoulos is a Professor at the University of Surrey , UK, specializing in Wireless Communications and Signal Processing . His research focuses on MIMO Systems , Open-RAN , and Non-Linear Processing for next-generation wireless networks. His recent work explores Analogue Processing for Tbps Wireless Systems and Neuromorphic Computing in MU-MIMO detection. He has developed frameworks like MIMO-SoftiPHY and SACCESS for software-based radio acceleration and power-efficient network design. Key Publications : Power-Efficient RIC, NL-COMM, NeuroMIMO Collaborators : Rahim Tafazolli, George Katsaros, Marcin Filo His research impacts 6G Network Development through innovations in Beamforming , Channel Estimation , and Software-Defined Radios .

• Business Administration
• Operations Research
• Supply Chain Management
• +۶ مورد دیگر

Prof. Dr. Julia Rieck is a Full Professor of Business Administration at the University of Hildesheim , leading the Department of Business Administration and Operations Research within the Faculty of Mathematics, Natural Sciences, Economics and Computer Science. As Dean of the Faculty , she oversees academic programs, quality management, and research initiatives. Her roles include academic advising for the Business Information Systems (B.Sc./M.Sc.) programs and active participation in examination boards and quality committees. Education: PhD in Political Science (Dr. rer. pol.) with summa cum laude (2008), Habilitation at Clausthal University of Technology (2014), and studies in Business Mathematics (Diploma, University of Hamburg, 2003) and Mathematics (Georg-August-University Göttingen, 2000). Research: Focuses on Operations Research , Supply Chain Management , Project Planning , and Logistics . Her work integrates mathematical modeling , machine learning , and real-world applications , particularly in disaster response , dynamic transportation , and sustainable e-commerce . Projects: Leads third-party funded initiatives like "IT für die sorgende Gesellschaft" (AI in healthcare/social sectors) and contributes to the HULLS real-lab (AI in aging societies). Collaborates with regional companies (e.g., Youco, ADITUS) and institutions (HAWK, University of Hannover). Teaching: Emphasizes practical application through case studies, industry partnerships, and the IT-Speed Dating event for student-company connections. Her courses cover project resource planning , logistics , and digital transformation . Labs & Teams: Active in the Institute of Business Administration & Business Information Systems , contributing to the KET Kompetenzwerkstatt (entrepreneurship support) and interdisciplinary teams in AI and sustainability research.

Gabriele Bavotaمشاهده پروفایل

دانشیار

• Software Quality
• Empirical Software Engineering
• Mining Software Repositories
• +۳ مورد دیگر

Gabriele Bavota is an Associate Professor at the Software Institute of Università della Svizzera Italiana (USI) in Lugano, Switzerland. He leads the SEART (Software Engineering Advanced Research Team) group and serves as Principal Investigator for the DEVINTA ERC starting grant focused on developer intelligence through mining software artifacts. Dr. Bavota's research spans Software Quality, Empirical Software Engineering, and Mining Software Repositories. His work has evolved from foundational studies on code smells and technical debt to cutting-edge research at the intersection of artificial intelligence and software development. He has made significant contributions to understanding API usage patterns, software quality metrics, and developer behavior through empirical studies of large software repositories. His recent publications reveal a strong focus on AI-assisted software development, with extensive research examining code generation, code summarization, and code review automation using large language models. He has also expanded his research to include quality assurance in game development (detecting game stuttering and low engagement events) and voice user interface testing. His work consistently bridges theoretical insights with practical applications for software developers. ACM SIGSOFT Distinguished Paper Award for API compatibility research (MSR 2019) ACM SIGSOFT Distinguished Paper Award for Hugging Face model documentation study (ICPC 2024) ACM SIGSOFT Distinguished Artifact Award for deep learning fault taxonomy (ICSE 2020) As an active member of the software engineering research community, Dr. Bavota serves on program committees for major conferences including ICSE, ASE, FSE, and MSR. He has held leadership roles such as Program Co-Chair for ICSME 2023 and Vision/Reflection Track Co-Chair for ICSE. His SEART research group develops practical tools like the SEART Data Hub that streamline large-scale source code mining and preprocessing for empirical software engineering research.

Thomas Ludwigمشاهده پروفایل

استاد

• High-Performance Computing (HPC)
• Data Storage Systems
• Energy Efficiency in Computing
• +۳ مورد دیگر

Prof. Dr. Thomas Ludwig is the Director of the German Climate Computing Center (DKRZ) and a Professor at the Universität Hamburg. He holds a doctoral degree and habilitation from the Technische Universität München, with expertise in High-Performance Computing (HPC), energy efficiency, and data storage systems. His research focuses on optimizing parallel systems, storage technologies, and computational efficiency for climate science applications. He leads projects like AIMES and PeCoH, advancing HPC storage and energy-aware computing. Education: Doctoral degree and habilitation from TU München (1988–2001). Chair in Parallel Computing at Universität Heidelberg (2001–2009). Research Interests: HPC, data reduction techniques, energy-efficient systems, parallel I/O optimization, and climate modeling infrastructure. Recent Research Trends: His work emphasizes storage system efficiency, machine learning in HPC, and convergence between HPC and Big Data. Key contributions include frameworks for portability (Vecpar), automated performance tools, and energy-aware storage solutions. Awards: Some publications received recognition, e.g., a Best Paper award in 2014 for work on energy efficiency. However, no personal awards are explicitly listed. Advising & Grants: Supervised numerous theses in HPC, I/O optimization, and energy efficiency. Leads major projects funded by national and international initiatives. Labs/Teams: Heads the DKRZ team providing supercomputing and data management for climate research, collaborating with global institutions like the University of Hamburg and European research networks.

• Natural Language Processing
• Large Language Models
• Multilingual Models
• +۵ مورد دیگر

Timothy Baldwin is a Professor at the University of Melbourne, School of Computing and Information Systems, with additional affiliation at Mohamed bin Zayed University of Artificial Intelligence in UAE. His research spans natural language processing, large language models, and multilingual AI systems. His research interests focus on the safety, reliability, and ethical aspects of large language models. He investigates bias evaluation and debiasing techniques, uncertainty quantification methods, fact-checking systems, and multilingual model safety. His work addresses critical challenges in making AI systems more transparent, reliable, and culturally aware, with particular attention to low-resource languages and cross-cultural differences. Baldwin's recent publications demonstrate a strong focus on evaluating and improving the safety of language models across diverse linguistic contexts, developing tools for fact verification, and understanding the internal mechanisms of large language models. His research shows increasing emphasis on practical applications with real-world impact, particularly in multilingual settings and safety-critical domains. His scientific contributions include foundational work on multilingual NLP, bias mitigation techniques, and frameworks for evaluating LLM safety across different cultural contexts. His research has been published in top-tier venues including ACL, NAACL, EMNLP, and ICLR. Baldwin actively mentors students and junior researchers, with frequent collaborations with Haonan Li, Xudong Han, and Fajri Koto, among others. His research group appears to focus on practical applications of NLP with strong ethical considerations, particularly regarding model safety and cultural sensitivity.

• Digital Transformation
• Business Process Management (BPM)
• Robotic Process Automation (RPA)
• +۴ مورد دیگر

Prof. Dr. Ralf Plattfaut is a Professor for Information Systems and Transformation Management at the University of Duisburg-Essen since 2023. He heads the Process Innovation & Automation Lab , focusing on digital transformation, business process management, and robotic process automation (RPA). Prior to this, he was Professor for Business Informatics at the South Westphalia University of Applied Sciences (2019–2023), and a management consultant at McKinsey & Company (2013–2019). Current Role: Professor (since 2023) Lab Leadership: Process Innovation & Automation Lab Consulting: Management consultant and keynote speaker Research Interests span digital transformation, IT governance, RPA adoption, and postcolonial perspectives in information systems. His work addresses organizational challenges in process automation, agile methodologies, and human-AI collaboration. Recent Publications (2024–2025) examine paradoxes in agile transformations, postcolonial IT governance in NGOs, AI adoption in healthcare, and behavioral barriers like status quo bias. These studies often employ computational grounded theory and cross-organizational case analyses. Scientific Recognition includes multiple Best Paper and Best Conference Paper nominations at ICIS, HICSS, and IFIP E-Government Conferences, as well as recognition by Wirtschaftswoche as a top German-speaking management researcher under 40.

Michael Sammlerمشاهده پروفایل

استادیار

• Formal Verification
• Programming Languages
• Systems Programming
• +۳ مورد دیگر

Michael Sammler is an Assistant Professor leading the Programming Languages and Verification Group at the Institute of Science and Technology Austria (ISTA). He holds a PhD from the Max Planck Institute for Software Systems (MPI-SWS) and was a postdoctoral researcher at ETH Zürich. His research focuses on formal verification of low-level systems code, combining foundational proofs with automation. Key projects include RefinedC (C verification), Islaris (assembly code verification), and DimSum (multi-language interoperability). Education: PhD at MPI-SWS/Saarland Informatics Campus, postdoc at ETH Zürich. Research interests emphasize tool development for safety-critical systems, including Rust verification (RefinedRust), OCaml/C interoperability (Melocoton), and decentralized multi-language semantics (DimSum). Awards: Runner-Up for Informatics Europe 2024 Best Dissertation Award, Dr. Eduard Martin Prize, Distinguished Paper Awards at PLDI/POPL/USENIX, and Google PhD Fellowship. Labs/Teams: Programming Languages and Verification Group at ISTA, collaborations with MPI-SWS and international researchers. His work bridges foundational theory with practical tools for industry-relevant verification challenges.

Gil Westmeyerمشاهده پروفایل

استاد

• Molecular Sensors
• Molecular Actuators
• Neuroimaging
• +۷ مورد دیگر

Prof. Dr. Gil Westmeyer is a Professor of Neurobiological Engineering at the Technical University of Munich (TUM), holding joint appointments at the TUM School of Natural Sciences and TUM School of Medicine and Health. He serves as Director of the Institute for Synthetic Biomedicine at Helmholtz-Zentrum München and leads the Chair of Neurobiological Engineering at TUM. His research program bridges molecular engineering, neuroimaging, and synthetic biology to develop next-generation tools for understanding and manipulating cellular networks. Westmeyer's educational background includes medical and philosophical studies in Munich, doctoral work on the molecular basis of Alzheimer's disease under Professor Christian Haass, clinical training at Harvard Medical School, and postdoctoral research with Professor Alan Jasanoff at MIT. His laboratory focuses on creating genetically encoded molecular sensors and actuators that enable non-invasive imaging and remote control of cellular processes across multiple scales. His research spans three primary domains: molecular sensors for multimodal imaging (from electron microscopy to whole-organism optoacoustics), molecular actuators for spatiotemporal control of cellular processes, and neurobehavioral imaging in freely behaving model organisms. The lab's work integrates synthetic biology, nanotechnology, and advanced imaging techniques to create tools that map dynamic signaling processes and manipulate cellular functions with unprecedented precision. Westmeyer's publication record demonstrates consistent innovation in molecular engineering, with recent work focusing on genetically encoded barcodes for electron microscopy, intron-encoded reporting systems, multiplexed optoacoustic imaging, and magnetically responsive cellular compartments. His publications in high-impact journals like Nature Methods, Cell, and Nature Biotechnology reflect the significance of his contributions to molecular imaging and engineering. ERC Proof of Concept 'inteRNAlizer' (2023) ERC Consolidator Grant 'EMcapsulins' (2019) ERC Starting Grant 'MagnetoGenetics' (2013) Helmholtz Young Investigator's Group (2011) Westmeyer actively mentors students and researchers through multiple teaching positions at TUM, including courses in biological chemistry, genetic machine development (iGEM), mammalian cell technology, and neuro-recording methods. His laboratory develops technologies with clear translational potential for future neurotherapies and regenerative medicine applications, particularly through the creation of imaging-controlled cellular interventions. The lab maintains strong collaborations across disciplines and institutions, with research that contributes to multiple UN Sustainable Development Goals related to health and wellbeing.

Heiner Giefersمشاهده پروفایل

استاد

• Cloud Computing
• Hardware Acceleration
• Field-Programmable Gate Arrays
• +۷ مورد دیگر

Heiner Giefers is a Professor for Cloud Computing at the Department of Computer Science and Natural Sciences at Southwestphalia University of Applied Sciences since 2018. Prior to this position, he worked as a Research Staff Member at IBM Research - Zürich (2013-2018), focusing on hardware acceleration in cloud environments, implementation of big data algorithms on FPGAs, and development of hardware platforms for approximate and in-memory computing. Dr. Giefers received his doctorate (Dr. rer. nat.) from Universität Paderborn in 2012 with a dissertation titled "Design and Programming of Reconfigurable Mesh based Many-Cores." His academic journey at Universität Paderborn includes serving as an Academic Council Member (Akademischer Rat a.Z.) from 2008-2013 and as a Scientific Staff Member from 2006-2012, where he taught digital technology and computer architecture. Professor Giefers' research focuses on energy-efficient computing, particularly through hardware acceleration using FPGAs for cloud and AI workloads. His work spans cloud computing infrastructure, hardware-software co-design, approximate computing, in-memory computing, and energy-efficient implementations of machine learning algorithms. He has made significant contributions to the field of reconfigurable hardware for high-performance computing applications. His recent publications show a strong trend toward applying hardware acceleration techniques to artificial intelligence and machine learning workloads, with a particular focus on energy efficiency. His work bridges the gap between theoretical computer science and practical hardware implementation, often resulting in patented technologies that address real-world computing challenges in cloud environments. Best Paper Award for "Stochastic Matrix-Function Estimators: Scalable Big-Data Kernels with High Performance" (2016) Best Paper Award Nomination for "Energy-Efficient Stochastic Matrix Function Estimator for Graph Analytics on FPGA" (2016) Best Paper Award Nomination for "Analyzing the energy-efficiency of dense linear algebra kernels by power-profiling a hybrid CPU/FPGA system" (2014) Best Paper Award Nomination for "A Triple Hybrid Interconnect for Many-Cores: Reconfigurable Mesh, NoC and Barrier" (2010) Professor Giefers actively supervises numerous Bachelor's and Master's students, with over 50 completed theses covering topics from machine learning and cloud computing to IoT systems and hardware acceleration. He leads the "Energy-efficient AI" project (eki), which aims to increase the energy efficiency of AI systems through approximation techniques for FPGA implementation. Additionally, he collaborates with Prof. Dr. Christian Plessl on the "Digital teaching materials with Jupyter Notebooks" project, creating interactive learning materials that integrate teaching content, program code, and results into a single document. His work extends to practical applications through multiple patents related to FPGA implementations, neural networks, and memory systems, demonstrating his commitment to translating research into real-world solutions.

• Mathematical Data Science
• Machine Learning
• Compressed Sensing
• +۵ مورد دیگر

Johannes Maly is an Assistant Professor at the Bavarian AI Chair for Mathematical Foundations of Artificial Intelligence at LMU Munich. He previously held postdoctoral positions at Catholic University of Eichstaett-Ingolstadt and RWTH Aachen University, and completed his PhD at TUM Munich under Prof. Massimo Fornasier. PhD in Mathematics (2019, TUM Munich) M.Sc. in Mathematics (2015, TUM Munich) B.Sc. in Mathematics (2013, TUM Munich) His research focuses on mathematical data science and machine learning, specifically addressing: Robust covariance estimation under quantization Neural network approximation properties Implicit bias in gradient descent training Multi-structured signal recovery Quantization effects in deep learning and compressed sensing His recent publications analyze dithered quantization in covariance estimation, implicit regularization in overparameterized models, and multi-structured data recovery. He applies mathematical rigor to practical challenges in wireless communications (e.g., MIMO systems) and neural network training. Scientific recognition includes: relAI Fellow MCML Associate He supervises code/toolbox development for reproducibility and teaches graduate courses in convex optimization, high-dimensional probability, and mathematical data science. His work bridges theoretical mathematics and applied signal processing.

• Distributed Systems
• Cloud Computing
• Edge Computing
• +۷ مورد دیگر

David Bermbach is a Full Professor at Technische Universität Berlin , leading the Scalable Software Systems group since 2023. His research focuses on distributed systems, serverless computing, and benchmarking, with significant work on edge and fog computing architectures. He is affiliated with the Einstein Center Digital Future and co-chairs interdisciplinary projects like SimRa for bicycle traffic safety. Full Professor, Scalable Software Systems (2023–present) ECDF-Professor, Mobile Cloud Computing (2017–2023) Postdoctoral Researcher (2014–2017) Education : Diploma in Business Engineering (2010) – Karlsruhe Institute of Technology (KIT) PhD in Computer Science (2014, summa cum laude) – KIT Research Interests span distributed systems with emphasis on cloud, edge, and fog computing, serverless architectures, IoT platforms, and benchmarking frameworks. His work addresses consistency-performance trade-offs, resource placement, and interdisciplinary applications in urban mobility and satellite edge computing. Article Trends show a focus on serverless computing (12/15), edge-cloud integration (9/15), and benchmarking methodologies (7/15). Key themes include optimizing function placement, federated learning architectures, and low-earth orbit computing systems. Scientific Awards Best Paper Award – ShutPub (2024) Best Workshop Paper – A Research Perspective on Fog Computing (2017) Best Paper Runner Up – Benchmarking Eventual Consistency (2014) Summa Cum Laude PhD Thesis (2014) Advising & Grants include mentoring students like Tobias Pfandzelter and Trever Schirmer, leading funded projects through the Einstein Center Digital Future, and contributing to 6G network research. His team works on cloud federation, serverless optimization, and real-world IoT applications.

Miryung Kimمشاهده پروفایل

استاد

• Software Engineering
• Software Engineering for Data Intensive Computing
• Automated Testing
• +۷ مورد دیگر

Miryung Kim is a Professor and Vice Chair of Graduate Studies in UCLA's Computer Science Department, where she directs the Software Engineering and Analysis Laboratory. She is renowned for her pioneering work in software evolution, code clone management, and establishing the emerging field of Software Engineering for Data Intensive Computing (SE4DA and SE4ML). Her research focuses on automated testing and debugging for Apache Spark, developer tools for heterogeneous computing, and conducting systematic studies of refactoring practices in industry. She led the first large-scale study of data scientists in industry and developed JDebloat, a Java bytecode debloating tool that made significant tech transfer impact to the Navy. Her recent publications demonstrate strong trends in fuzz testing for big data analytics and heterogeneous computing, with a focus on natural input generation, co-dependence awareness, and leveraging hardware probes for acceleration. Her work bridges software engineering with data-intensive and heterogeneous computing paradigms. ACM SIGSOFT Influential Educator Award (2022) ICSME Most Influential Paper Award (2023 and 2020) NSF CAREER award Google Faculty Research Award Okawa Foundation Research Award Humboldt Fellow ACM Distinguished Member As an academic advisor, she has produced eight tenure-track faculty members at institutions including Columbia, Purdue, and Virginia Tech. Her research has been supported by National Science Foundation, Air Force Research Laboratory, Google, IBM, Intel, Okawa Foundation, Samsung, and Office of Naval Research. She previously served as Program Co-Chair of ESEC/FSE 2022 and has delivered keynotes at ASE 2019 and ISSTA 2022. She maintains active industry collaborations, serving as an Amazon Scholar at Amazon Web Services and having spent time as a visiting researcher at Microsoft Research.

Detlev Marpeمشاهده پروفایل

پژوهشگر

• Video Coding
• Image Compression
• Signal Processing
• +۳ مورد دیگر

Detlev Marpe is a leading researcher at the Fraunhofer Heinrich Hertz Institute (HHI), serving as Head of the Video Coding & Analytics Department and Head of the Image & Video Coding Group. His work focuses on advancing video compression standards, including HEVC (H.265) and its extensions. He has contributed significantly to tools like entropy coding, transform coding, and scalable video coding. His research emphasizes efficient compression techniques, such as adaptive context models and wavelet-based methods, with applications in multimedia communication and low-delay video encoding. Affiliations: Fraunhofer Institute for Telecommunications HHI, Berlin, Germany Roles: Department Head, Research Group Leader, and Adjunct Lecturer at TU Berlin (2013/14) Research Interests: Video coding standards (HEVC, H.264/AVC), entropy coding (CABAC), wavelet-based compression, scalable video coding (SVC), multiview video coding (MVC), and rate-distortion optimization. His work bridges theoretical advancements with practical implementations, addressing challenges in compression efficiency, scalability, and real-time applications. Publications & Awards: Over 200 publications in top-tier journals and conferences, including IEEE Transactions and SPIE. Notable awards include the Chester Sall Best Paper Award and multiple Best Paper Awards from IEEE journals. His contributions to video coding standards have been adopted in global specifications like MPEG and ITU-T. Grants & Labs: Involved in major research projects on HEVC extensions, 3D video coding, and low-delay applications. Collaborates with industry partners and academic institutions globally. His team at HHI develops reference software and test models for emerging standards.