جست‌وجوی استاد و پژوهشگر

Andreas Polzeمشاهده پروفایل

استاد

• Operating Systems
• Middleware
• Distributed Computing
• +۹ مورد دیگر

Prof. Dr. Andreas Polze is a full Professor in the Operating Systems and Middleware group at the Hasso Plattner Institute (HPI) for Digital Engineering, University of Potsdam, Germany. He has held this position since October 2001 and also serves as the speaker of the HPI Research School, a PhD program in Service-Oriented Systems Engineering, since 2005. He is a member of the steering committee of the Future SOC Lab at HPI. His research focuses on the architecture of operating systems, component-based middleware, and predictable distributed and cloud computing. Current research interests include predictable service computing, adaptive system configuration, and end-to-end service availability on standard middleware platforms. He has led collaborative projects with industry and medical partners such as SAP, Deutsche Telekom, GETEMED, and Charité, including the Fontane telemedicine project and research on porting SAP HANA to new processor architectures. Andreas Polze conducted research visits at the Software Engineering Institute, Carnegie Mellon University, and the Real-Time Systems Laboratory at the University of Illinois, Urbana-Champaign. His academic background includes a PhD from Freie Universität Berlin (1994) and a habilitation from Humboldt University Berlin (2001), both in Computer Science. Scientific Awards: Fellow of DFG-graduate college "Communication-based Systems" (1991–1994) He has supervised PhD and master's students through his leadership in the HPI Research School and has been involved in multiple collaborative research grants, particularly in cloud, middleware, and telemedicine systems. He leads research in system predictability and reliability, especially in service-oriented and cloud environments. He is affiliated with the Operating Systems and Middleware group at HPI, which conducts cutting-edge research in system software, distributed systems, and middleware platforms, with strong industry partnerships and interdisciplinary applications in healthcare and enterprise computing.

• Computer Science
• Machine Learning
• IoT
• +۷ مورد دیگر

Ali Hassan is a researcher affiliated with the National University of Sciences and Technology (NUST), School of Electrical Engineering and Computer Science, Department of Computer and Software Engineering. His work spans multiple domains in computer science, engineering, and applied mathematics, focusing on areas such as machine learning, IoT, energy systems, and medical informatics. His research explores: Reinforcement learning applications for battlefield information systems IoT antenna design and performance evaluation Optimization of second-life battery systems in electric vehicles Transformers for real-time vehicle collision avoidance Image hashing techniques using visual attention models Neural network-based phasor estimation for power grids Biomedical sensor systems for non-invasive health monitoring Mathematical modeling of viral dynamics Recent publications demonstrate his emphasis on interdisciplinary approaches combining AI, signal processing, and sustainability. He has collaborated with institutions across Pakistan, France, Saudi Arabia, and the USA, with a focus on practical implementations in cybersecurity, energy optimization, and healthcare technology.

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

Prof. Dr. Mario Fritz is a leading academic at the CISPA Helmholtz Center for Information Security and Saarland University , with a focus on Trustworthy Information Processing . His work sits at the intersection of AI, Machine Learning, Security, and Privacy, addressing challenges in foundation models, health data, and adversarial robustness. Key Projects : ELLIOT (Multi-Modal Foundation Models), ELSA (Secure AI), PriSyn (Synthetic Health Data), AIgency (Generative AI in Cybersecurity), HMSP (Medical Security) Research Themes : AI/ML security, privacy-preserving techniques, causal modeling, healthcare applications, and ethical AI Recent Publications : Focus on LLM sampling, causal inference, model stealing, and privacy-aware document analysis Collaborations : European Laboratory for Learning and Intelligent Systems (ELLIS), BMBF-funded initiatives, GHGA (Human Genome Archive) Academic Leadership : As a Professor , he coordinates large-scale EU projects and mentors emerging researchers in AI ethics and security.

Qian Liمشاهده پروفایل

پژوهشگر

• Database Systems
• Operating Systems
• Distributed Systems
• +۳ مورد دیگر

Qian Li is a researcher working at the intersection of database systems and operating systems, with primary affiliation at DBOS Inc. and academic connections to Stanford University's School of Engineering, Department of Computer Science, and Peking University in Beijing, China. The research focuses on developing the Database Operating System (DBOS) concept, which reimagines operating systems with database technology at their core. Research interests center on database systems, operating systems integration, transaction processing, and serverless computing. The work explores how database principles like ACID transactions can improve system reliability, debugging, and application development, particularly in cloud environments. Key projects include DBOS, Epoxy for cross-data store transactions, and Apiary for transactional serverless computing. The publication record shows a strong trend toward integrating database transaction semantics with modern computing paradigms, particularly serverless architectures. Recent work demonstrates how transactional guarantees can simplify application development, improve debugging, and enable new approaches to cloud-native application design. The research bridges theoretical database concepts with practical systems implementation. As a core contributor to the DBOS project, Qian Li has collaborated extensively with leading researchers including Michael Stonebraker, Matei Zaharia, Christos Kozyrakis, and Peter Kraft. The work has been published consistently in top-tier venues including VLDB, USENIX ATC, and CIDR, reflecting significant impact in the systems research community.

• Distributed Systems
• Peer-to-Peer Networks
• Cloud Computing
• +۴ مورد دیگر

Peter G. Kropf is a Professor in the Department of Computer Science at the University of Neuchâtel, Switzerland, with a distinguished research career spanning over three decades. His academic journey reflects significant contributions to distributed systems, peer-to-peer networks, and cloud computing, with recent focus on IoT analytics and scientific computing applications. Dr. Kropf's research interests center on Distributed Systems , Peer-to-Peer Networks , Cloud Computing , Wireless Mesh Networks , and Scientific Workflows . His work demonstrates a clear evolution from foundational distributed systems research to practical applications in environmental monitoring, IoT analytics, and large-scale scientific computing. He has maintained consistent research productivity throughout his career, with publications appearing regularly from 1990 through 2024. Analysis of his recent publications reveals a strong trend toward real-time data processing for environmental applications, IoT analytics , and cloud-based scientific workflows . His work often bridges theoretical distributed systems concepts with practical implementations, particularly in environmental monitoring and resource management contexts. The interdisciplinary nature of his research connects computer science with environmental science and hydrology. Dr. Kropf has established long-term collaborations with researchers including Gilbert Babin (15 joint publications), Pascal Felber (14 publications), and Sabina Serbu (7 publications), forming a productive research network focused on distributed systems challenges. His work has appeared in prestigious venues including IEEE Internet Computing, Future Generation Computer Systems, and Middleware conference proceedings.

• Network Security
• Cloud Security
• Software Defined Networking Security
• +۶ مورد دیگر

Udaya Kiran Tupakula is an Associate Professor in the Department of Computing within the School of Computer, Data and Mathematical Sciences at Western Sydney University. With over two decades of research experience, his work focuses on cybersecurity with particular emphasis on network security, cloud infrastructure protection, and emerging technologies. He has established himself as a leading researcher through extensive collaborations, particularly with Professor Vijay Varadharajan, and has contributed significantly to securing virtualized environments and next-generation networks. Dr. Tupakula's research spans multiple critical areas in cybersecurity. His early work focused on fundamental security challenges like DDoS mitigation and TCP SYN flood attacks, evolving into more complex domains including Software Defined Networking security, cloud security architectures, and IoT protection. Recent research directions include AI security, privacy-preserving machine learning, and securing 5G infrastructure. His approach combines theoretical frameworks with practical implementations, often developing novel security architectures that address specific vulnerabilities in emerging technologies. Analysis of his publication trends shows a clear progression from traditional network security toward more sophisticated systems. His work has increasingly incorporated machine learning techniques for intrusion detection, expanded into industrial control systems security, and recently embraced AI safety challenges. The breadth of his research demonstrates adaptability to evolving security landscapes while maintaining focus on practical, implementable solutions rather than purely theoretical approaches. Dr. Tupakula has mentored numerous researchers who have become active contributors in the cybersecurity field. His collaborative approach is evident in the extensive co-authorship network spanning multiple institutions and countries. His research has been supported by various grants focusing on critical infrastructure protection, cloud security, and next-generation network security. His laboratory work focuses on practical security testing environments for SDN, cloud infrastructure, and IoT systems. Current research directions include securing AI systems from malicious use, enhancing privacy in federated learning environments, and developing more robust security frameworks for industrial control systems. The applied nature of his research ensures close alignment with real-world security challenges faced by industry and government organizations.

Thomas F. Düllmannمشاهده پروفایل

پژوهشگر

• Computer Science
• Software Engineering
• Data Science
• +۳ مورد دیگر

Thomas F. Düllmann is a Research Associate at the Department of Informatics within the Faculty of Mathematics, Informatics and Natural Sciences at the University of Hamburg. His contact information includes the email address duellmann@hitec-hamburg.de and office location in Building D, Room 234. He is part of a research group led by Dr.-Ing. Sebastian Werner, collaborating with colleagues Stephanie Schulte Hemming, Leif Bonorden, Sebastian Gerdes, Dr. Alireza Hakamian, Kamila Serwa, and Marion Wiese. Research interests in the Department of Informatics focus on computer science fundamentals, software engineering practices, data analysis methodologies, and interdisciplinary applications of information technology. While specific subfields for Mr. Düllmann have not been detailed in the provided content, his affiliation suggests engagement with contemporary computational challenges and system design.

Christian Mendlمشاهده پروفایل

استادیار

• Quantum Computing
• Computational Physics
• Statistical Physics
• +۲ مورد دیگر

Christian Mendl is a Rudolf Mößbauer Tenure Track Assistant Professor at the Technical University of Munich (TUM) , affiliated with the School of Computation, Information and Technology and the Institute for Advanced Study (TUM-IAS) . His career includes a postdoctoral position at Stanford University (2015-2017) under a Feodor Lynen Fellowship from the Alexander von Humboldt Foundation, a Junior Professorship at TU Dresden (2017-2019), and a PhD in Physics from LMU Munich (2012). Education : Physics and Mathematics (TUM) Appointments : Rudolf Mößbauer Assistant Professor (TUM, 2019), Junior Professor (TU Dresden, 2017), Postdoc (Stanford, 2015-2017) Research Focus : Mendl specializes in Quantum Computing , Computational Physics (tensor networks, quantum Monte Carlo, neural-network quantum states), Statistical and Non-Equilibrium Physics , and Numerical Simulation . His work bridges quantum information theory with condensed matter physics, emphasizing efficient quantum algorithms and simulators for complex systems. Scientific Contributions : Recent publications highlight advancements in quantum circuit optimization , tree tensor network simulations , block encoding of operators , and quantum-assisted optimization for problems like the capacitated vehicle routing and Toda lattice dynamics. His methods often integrate machine learning with quantum information to address challenges in Hamiltonian simulation and quantum error analysis . Awards : Alexander von Humboldt Feodor Lynen Fellowship, Boehringer Ingelheim Fonds PhD Fellowship, TopMath Graduate Program, Studienstiftung des deutschen Volkes Grants : Rudolf Mößbauer Tenure Track Fellowship (TUM-IAS), Dieter Schwarz Fellowship

• Seismic Hazard Assessment
• Earthquake Risk Modeling
• Natural Disaster Risk Analysis
• +۵ مورد دیگر

L. Oostwegel is a Researcher at GFZ German Research Centre for Geosciences, working within the Seismic Hazard and Risk Dynamics department. The researcher specializes in earthquake exposure modeling, building stock characterization, and multi-hazard risk assessment using geospatial data and open-source information. Oostwegel's research focuses on seismic hazard and risk assessment, with particular emphasis on building exposure modeling using OpenStreetMap and remote sensing data. Key research areas include earthquake risk modeling, flood risk assessment, landslide risk management, and the development of tools for multi-hazard risk assessment. The researcher has made significant contributions to understanding building stock characteristics at various scales, from individual buildings to global assessments. Analysis of recent publications reveals a strong focus on utilizing volunteered geographic information and Earth observation datasets for risk assessment. Oostwegel has developed methods for assessing the completeness of OpenStreetMap building data globally and has created tools like 'risk-calculator' for multi-hazard risk assessments. The research shows an interdisciplinary approach combining geophysics, geospatial analysis, and disaster risk reduction. Top-down or bottom-up in earthquake exposure modeling (2025) Safe Haven – Landslides: A Serious Game for Enhancing Risk Awareness (2025) A model of European buildings (2024) From Shelters to Skyscrapers: Worldwide Building Exploration (2024) Seismic loss assessment sensitivity study (2023) Oostwegel has collaborated extensively with researchers including Evaz Zadeh, T., Schorlemmer, D., and others across multiple projects focused on natural disaster risk assessment. The research has practical applications in urban planning, disaster risk reduction, and climate adaptation strategies, particularly for coastal megacities and areas prone to seismic activity.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

B Srivathsanمشاهده پروفایل

دانشیار

• Formal verification of real-time systems
• Automata theory
• Games
• +۱ مورد دیگر

B Srivathsan is an Associate Professor in the Computer Science Department at the Chennai Mathematical Institute . His research focuses on formal verification of real-time systems , automata theory , game theory , and concurrency . He has a dual degree in Computer Science and Engineering from IIT Bombay (2004-2009), a PhD from LaBRI, University of Bordeaux (2012), and post-doctoral experience at RWTH Aachen (2012-2013). Education: B.Tech + M.Tech, IIT Bombay Ph.D., University of Bordeaux Post-doc, RWTH Aachen Research Interests: He specializes in formal verification techniques for real-time systems using timed automata, with applications in critical systems like pacemakers. His work explores automata theory, game-theoretic approaches, and efficient algorithms for verification tasks, including zone-based abstractions and handling diagonal constraints. Publication Trends: His recent work (2024-2025) addresses event-clock automata simulations, MITL model-checking, and deterministic suffix-reading automata. Earlier publications (2019-2022) focus on local-time semantics, negotiation modeling, and scalable verification algorithms for timed systems. PhD Supervision: He advises five current students, including part-time student R. Keerthan at TCS Pune, and co-advises others with researchers from IIT Bombay and ENS Paris-Saclay. Three students have completed their PhDs under his guidance. Professional Service: He serves as President of IARCS (2024-present), previously as Secretary (2017-2023), and chairs program committees for conferences like ATVA 2025. He organizes workshops including the IARCS Verification Seminar and Games Workshop 2020 .

Sergei Savitskyمشاهده پروفایل

استاد

• Reconfigurable Computing
• Digital Signal Processing
• Hardware Architectures
• +۳ مورد دیگر

Sergei Savitsky is a Professor at Wedel University of Applied Sciences, where he serves as Head of the Bachelor of Computer Science program and Senate Chairman. He has been a university lecturer at the institution since October 2008, following professional experience at NXP Semiconductors (2006-2008) and Philips Research Europe (2001-2006). His educational background includes: Doctorate in Engineering (Dr.-Ing.) from Technical University of Dresden (2002) with distinction "summa cum laude" Habilitation (Dr.-Ing. habil.) from Technical University of Dresden (2024) with teaching authorization in "Technical Computer Science" Diplom-Informatiker (equivalent to MSc) in Computer Science from Technical University of Dresden (1998) Savitsky's research focuses on reconfigurable computing systems , with particular expertise in FPGA design, hardware acceleration, and error correction coding. His work bridges theoretical computer science with practical hardware implementation, resulting in numerous patents and publications in top venues. He has made significant contributions to the development of adaptive hardware architectures for forward error correction, which are critical for modern communication and storage systems. His recent publications demonstrate a strong trajectory in optimizing hardware design processes, with particular focus on applying machine learning techniques like self-organizing maps and gradient descent algorithms to improve FPGA placement efficiency. His research spans both theoretical foundations and practical applications, with patents filed in collaboration with industry partners like NXP Semiconductors and ST-Ericsson. Among his recognitions is the Best Paper Award at CENICS 2019 for his work on accelerating FPGA placement algorithms. As an educator, Savitsky teaches courses related to digital system design, including "Computer-aided design of digital systems," where he emphasizes algorithmic aspects of Electronic Design Automation beyond basic digital technology concepts.

Irina Munteanuمشاهده پروفایل

استاد

• Electromagnetic Field Theory
• Bioelectromagnetics
• Computational Electromagnetics
• +۳ مورد دیگر

Prof. Irina Munteanu is a Professor at the Department of Electromagnetic Field Theory (TEMF) at Technische Universität Darmstadt. Her research focuses on computational electromagnetics, biomedical applications of electromagnetic fields, and high-frequency systems. She specializes in numerical methods such as the Finite Integration Technique (FIT) for solving complex electromagnetic problems in areas like SAR distribution analysis, aircraft electromagnetic compatibility (HIRF), and anechoic chamber design. Her work bridges theoretical modeling and practical applications in aerospace, biomedical engineering, and consumer electronics safety. Education and Professional Background: Details not explicitly provided in text, though her academic rank and publications suggest advanced training in electrical engineering and computational physics. Research Interests: Her studies emphasize electromagnetic field interactions with human bodies, electromagnetic compatibility in aviation, and advanced simulation techniques for biomedical and aerospace systems. She has pioneered methods in voxel-based human body modeling for SAR assessment and optimized field uniformity in testing environments. Publications: Over 50 peer-reviewed articles since 2000, with recent focus on bioelectromagnetics and aerospace electromagnetic compatibility. Notable contributions include HIRF interaction studies and innovative approaches to 3D electromagnetic simulation. Advising & Labs: While student names are not listed, her research themes suggest involvement in advising graduate students working on computational electromagnetics and biomedical engineering projects. Active in the TEMF department’s research groups focusing on high-frequency systems and biomedical applications.

Zahra Ebrahimi Mamaghaniمشاهده پروفایل

پژوهشگر

• Approximate Computing
• Reconfigurable Accelerator Design
• In-Network Computing for 5G/6G
• +۳ مورد دیگر

Zahra Ebrahimi Mamaghani is an academic researcher affiliated with the Embedded Systems team at the Faculty of Electrical Engineering and Information Technology, Ruhr University Bochum. Her work focuses on approximate computing, reconfigurable accelerators, and energy-efficient embedded systems. She completed her B.Sc. and M.Sc. at Sharif University of Technology (Iran) and is a PhD student at TU Dresden. She managed the X-DNet (BMBF-funded, collaborating with Huawei) and GREEN-DNN (acatech/BMDV-funded) projects, emphasizing distributed and in-network computing for 5G/6G applications. Education: B.Sc. and M.Sc. in Electrical Engineering, Sharif University of Technology, Iran PhD Candidate at TU Dresden (Cfaed Institute, 2018–2024) Research Projects: ReAp (DFG, 2018–2021) Relearning (ESF, 2021–2023) X-ReAp (DFG, 2023–2025) X-DNet (BMBF, with Huawei) GREEN-DNN (acatech/BMDV) Research Interests: Approximate computing, energy-efficient edge-to-cloud systems, SW/HW co-design, and reconfigurable architectures for 5G/6G. Her recent publications emphasize cross-layer approximation techniques, energy-efficient CGRAs, and distributed computing frameworks for multi-kernel applications. She advises students on topics like approximation of ML models for high-throughput systems, requiring expertise in FPGA programming (Verilog/VHDL), Python, and ML frameworks like TensorFlow/PyTorch. Her lab focuses on embedded systems and collaborates with industry partners like Huawei. She holds a notable position in managing interdisciplinary projects bridging academia and industry, particularly in sustainable computing for next-generation networks.

Jürgen Bechtloffمشاهده پروفایل

استاد

• Mechatronics
• Automation Technology
• Digital Production
• +۳ مورد دیگر

Prof. Dr.-Ing. Jürgen Bechtloff serves as Professor of Measurement, Control and Regulation Technology at the South Westphalia University of Applied Sciences since 1998. He has held significant administrative roles including Founding Dean of Mechanical and Industrial Economics (2002-2004), Dean of Engineering and Economics (2004-2012), and Head of Scientific Center for Dual Studies and Continuing Education (2012-2019). His academic career spans industrial experience at Klöckner-Moeller GmbH (1992-1998) and academic research at TU Braunschweig (1987-1992). Education: Diplom from TU Braunschweig (1987), Dr.-Ing. from TU Braunschweig (1992) Laboratory: Operates the TransferFactory Industry 4.0 demonstrator since 2013 Research Focus: Industrial automation systems, particularly in Industry 4.0 implementation, Digital Production methodologies, and Mechatronic Systems simulation. His work emphasizes practical solutions for real-world challenges in: IoT gateway and platform integration Feasibility studies and implementation support Control engineering and measurement technology Electronics cam systems for motion control Robotic programming and interpolation methods Publication Trends: 15 most recent publications (2000-2020) demonstrate consistent focus on Industry 4.0 implementation, Mechatronics , and Digital Production technologies. His work bridges theoretical concepts with practical applications through case studies and demonstrator projects like the TransferFactory system. Teaching Contributions: Coordinates courses in Mechatronic Systems and Simulation and Digital Production for both bachelor and master programs, with emphasis on project-oriented learning and practical implementation of automation concepts using modern tools like MATLAB/Simulink. Technology Transfer: Provides services in measurement technology (data processing/visualization), control engineering (simulation and implementation), and mechatronics (3D motion design, kinematic analysis). Operates with state-of-the-art equipment including Beckhoff TwinCAT, Siemens S7/TIA systems, ABB/Kuka robots, and MES platforms.