جست‌وجوی استاد و پژوهشگر

Franziska Klemsteinمشاهده پروفایل

پژوهشگر

• Monument Preservation
• Digital Humanities
• GDR Architectural History
• +۳ مورد دیگر

Dr. Franziska Klemstein is an active researcher specializing in monument preservation and digital humanities, with significant contributions to understanding heritage practices in the German Democratic Republic (GDR) era. Her work critically bridges historical scholarship and computational methods, particularly through the development of specialized datasets for document analysis and image retrieval in cultural heritage contexts. Her research focuses on: Monument preservation systems under socialist regimes Ethical implementation of AI in heritage documentation Diversity and inclusion challenges in conservation practices Classification methodologies for historical inventories Digital reconstruction of contested heritage sites Gender dynamics in preservation professions Analysis of Klemstein's recent publications reveals a methodological evolution from historical analysis toward technical innovation, with increasing emphasis on dataset creation (DHMTIC, DHREAAL, TexBiG) and evaluation frameworks for heritage computing. Her work consistently addresses the tension between digital efficiency and critical heritage values, particularly regarding sustainable AI implementation and inclusive documentation practices. While specific awards remain undocumented in the provided materials, her editorial leadership in significant volumes like "On the Way to a Place of Remembrance" (2024) demonstrates recognized expertise. Klemstein's collaborative projects with researchers like Tschirschwitz and Schmidgen indicate active participation in interdisciplinary teams focused on digital heritage solutions, though institutional affiliations and grant details aren't specified in the current text.

Volker Hahnمشاهده پروفایل

استاد

• Plant Breeding
• Agricultural Science
• Phenotyping with Drones
• +۴ مورد دیگر

Dr. Volker Hahn is a leading agricultural scientist specializing in plant breeding at the University of Hohenheim's State Seed Breeding Institute in Stuttgart, Germany. Holding a PhD in molecular genetics from Justus Liebig University Giessen (1993) for work on cytoplasmic pollen sterility in sunflowers , he established the institute's sunflower breeding program and now leads soybean breeding with complementary work on chia, buckwheat, and chickpeas. His research focuses on drone-based phenotyping , NIR quality characterization , and phenomic selection as innovative breeding methodologies. Key Research Areas: Soybean and legume crop improvement Drone-assisted phenotyping systems Phenomic selection techniques Plant-based protein development Climate change adaptation in breeding Major Projects: Legume Generation (EU-funded) SENSOJA (Sensor-assisted soybean breeding) InnoSun (Oil yield enhancement in sunflowers) His recent publications (2021-2025) demonstrate expertise in genomic and phenomic selection , QTL mapping , and climate-adapted breeding for soybean and sunflower. The work shows increasing focus on transcontinental genetic comparisons and non-destructive field phenotyping through LED-controlled speed breeding and multi-sensor platforms . Current efforts emphasize biogas feedstock optimization , disease resistance , and nutrient efficiency in crops. Technical Expertise: Utilizes advanced genotyping arrays (25K SNP), DArT markers , and phenomic prediction algorithms in breeding programs. Maintains laboratory facilities at the State Seed Breeding Institute (Fruwirthstr. 21, Stuttgart) with a team specializing in genetic mapping , molecular screening , and agroecosystem optimization .

• Multi-stable Perception
• Perceptual Inference
• Cognitive Neuroscience
• +۷ مورد دیگر

Dr. Alexander Pastukhov is an Assistant Professor at the Department of General Psychology and Methodology, University of Bamberg. His research focuses on multi-stable perception , perceptual inference , and the interplay between attention and microsaccades . He develops open-source tools like saccadr , eyelinkReader , and bistablehistory for analyzing eye movements and perceptual dynamics. Research Interests : Multi-level mechanisms of perception and memory History effects in bistable perception Attentional allocation via microsaccade analysis Bayesian modeling of perceptual decision-making Scientific Awards : 2013 'Best of the Year' award from the Psychonomic Society for work on sensory memory and adaptation in structure-from-motion displays His recent software development includes probabilistic programming tools for 2D/3D transformations and eye-tracking analysis. He also contributes to applied data analysis teaching materials using R and Python. Collaborative projects with industry partners (e.g., BMW) and institutions (e.g., Fraunhofer, Orange Labs) highlight his research transfer efforts.

Leon Yang Zhuمشاهده پروفایل

استاد

• Policy and Mechanism Design
• Game Theory
• Applied Optimization
• +۵ مورد دیگر

Leon Yang Zhu is a Professor of Operations Management at the Cheung Kong Graduate School of Business (CKGSB). Previously, he held a tenured full professor position at the Data Sciences and Operations Department of the Marshall School of Business, University of Southern California. His academic career spans prestigious institutions including UC Berkeley, where he served as a Postdoc and lecturer. Dr. Zhu's educational background includes a Ph.D. in Industrial and Systems Engineering and an M.A. in Economics from the University of Florida, along with a Bachelor's degree from Shanghai Jiaotong University. His research focuses on Policy and Mechanism Design, Game Theory, and Applied Optimization with significant applications in operations management, e-commerce, and supply chain systems. His work bridges theoretical foundations with practical business applications, particularly in marketplace design, pricing strategies, and operational decision-making under uncertainty. Zhu's research demonstrates a consistent pattern of addressing complex operational challenges through rigorous mathematical modeling and empirical validation, with a growing emphasis on the intersection of traditional operations management and digital platform economics. Professor Zhu has established an impressive publication record in top-tier academic journals including the American Economic Review , Management Science , Operations Research , and Journal of Economic Theory . His research output shows a clear trajectory from foundational theoretical work to increasingly applied research with direct business relevance, particularly in e-commerce and digital platform operations. He serves in editorial capacities for several leading journals including Operations Research , Manufacturing and Service Operations Management , and Production and Operations Management . While specific details about his advising activities aren't provided, his extensive publication record and editorial roles suggest significant mentorship of junior researchers. His research collaborations with major Chinese technology companies like Alibaba demonstrate practical impact beyond academia.

Diego Garbervetskyمشاهده پروفایل

دانشیار

• Static analysis
• Program verification
• Smart Contract analysis
• +۵ مورد دیگر

Diego Garbervetsky is an Associate Professor at the Computer Science Department, School of Sciences, University of Buenos Aires, and a Researcher at ICC/CONICET. He also serves as Director of the Institute of Research in Computer Sciences (ICC). His academic career spans multiple roles in software engineering research and education. His educational background is not explicitly stated in the provided texts, but his current position reflects extensive academic achievement. Garbervetsky's research focuses on static analysis techniques for Java-like programs and Smart Contracts, automated program verification , program understanding , and validation . His specific interests include program understanding, testing and verification of programs featuring rich protocols, static analysis for program verification, and automatic symbolic resource analysis (gas consumption, dynamic memory, energy, etc.). His research has significant implications for blockchain technology, particularly in smart contract verification. Analysis of his recent publications reveals a strong trend toward smart contract security and verification , with multiple papers on modal abstractions, predicate abstractions, and tools like VeriSol for Solidity smart contracts. His work bridges formal methods with practical software engineering challenges, particularly in the blockchain domain. His scientific contributions include the development of multiple research tools: Contractor: Automated tool for behavior validation Contractor.NET: Visual Studio extension for .NET validation JConsume2: Compositional analysis for Java heap memory Consume.Net: Compositional analysis for .NET heap memory BudaPest: Automated software verifier VInTime: Verification suite for Real Time systems Garbervetsky has supervised numerous PhD students including Daniel Wappner, Javier Godoy, and Alexis Soifer, with former students now working at companies like Microsoft, Veritran, and Dialpad. He has served on program committees for major conferences including ICSE, FSE, ASE, and ISSTA, demonstrating his standing in the software engineering research community. His service includes chairing workshops and serving on artifact evaluation committees, showing his commitment to research quality and reproducibility. He currently teaches Software Engineering 2 at the University of Buenos Aires, having previously taught courses in algorithms, automatic software validation, program analysis, programming paradigms, and computer organization.

Antinisca Di Marcoمشاهده پروفایل

دانشیار

• Software Quality
• Software Adaptation
• IoT and WSN
• +۱۳ مورد دیگر

Antinisca Di Marco is an Associate Professor in the Department of Applied Clinical Science and Biotechnology at the University of L'Aquila, Italy. She has held this position since October 2017, having previously served as an Assistant Professor in the Department of Computer Science from 2008 to September 2017. Her research spans Software Engineering with specific focus on extra-functional properties and adaptation mechanisms. Key research areas include: Software modeling and performance analysis Performance antipatterns and model-based reconfiguration Context-aware systems and non-functional analysis Bio-inspired paradigms for self-adaptive systems Bioinformatics (since 2013) Dr. Di Marco's recent publications demonstrate a strong interdisciplinary approach, bridging software engineering with healthcare applications, particularly in mobile health systems for cancer treatment monitoring and symptom management. Her work also addresses fundamental challenges in context-aware mobile systems, performance optimization, and wireless sensor networks. She has received significant recognition including: Best Paper Award at FASE 2010 for performance modeling of context-aware mobile systems Best Poster Award at ECSA 2010 for bio-inspired self-adaptive paradigms Dr. Di Marco has advised multiple PhD students and has been actively involved in numerous research projects including iCARE (ERC Proof of Concept), VISION ERC, and CONNECT FET. She serves as Director of the University of L'Aquila Node of the InfoLife CINI Laboratory, focusing on Bioinformatics and Systems Biology research.

Hamid Bagheriمشاهده پروفایل

دانشیار

• Software Engineering
• Security of Mobile Devices and IoT Systems
• Formal Methods
• +۴ مورد دیگر

Hamid Bagheri is an Associate Professor in the School of Computing at the University of Nebraska-Lincoln, where he conducts cutting-edge research at the intersection of software engineering, security, and formal methods. He also serves as a faculty associate of the Institute for Software Research (ISR) at the University of California, Irvine, and co-directs the ESQuaReD Lab (Engineering Software Quality and Reliability through Deductive methods), which focuses on ensuring software quality through rigorous engineering approaches. Dr. Bagheri's research spans several critical areas including security of mobile devices and IoT systems, scaling formal verification with machine learning, software analysis and testing, dependable cyber-physical systems, and automated program repair and fault localization. His work combines theoretical rigor with practical applications, addressing real-world challenges in software security and reliability through publications in top venues like ICSE, FSE, ASE, and IEEE/ACM Transactions journals. His recent publications demonstrate a strong trend toward integrating machine learning techniques with formal methods to enhance software analysis capabilities. This includes leveraging transformers for database design optimization, neuro-symbolic approaches for certified software synthesis, and large language models for repairing formal specifications, reflecting his ability to adapt to emerging technologies while maintaining a strong theoretical foundation. Dr. Bagheri has received numerous prestigious awards including the EPSCoR FIRST Award, the NSF CISE Career Research Initiation Initiative Award, multiple Distinguished Paper Awards from top conferences, and teaching honors such as the SoC Student Choice Outstanding Teaching Award (2023-2024) and the CSE Outstanding Teaching Award (2020-2021). As an advisor, Dr. Bagheri has mentored several successful PhD students including Mohannad Alhanahnah (who joined Chalmers University as a tenure-track faculty member in Fall 2024) and Clay Stevens (who joined Iowa State University as a tenure-track faculty member in Fall 2023). His research has been supported by significant grants from NSF and other funding agencies, with recent work focusing on automated specification repair, security analysis of IoT systems, and verification of network protocols. The ESQuaReD Lab, which Dr. Bagheri co-directs, brings together a multidisciplinary team focused on addressing critical challenges in software quality and reliability through innovative engineering approaches and formal analysis techniques, with applications spanning mobile security, IoT systems, and cyber-physical systems.

Omar I. Al-Batainehمشاهده پروفایل

پژوهشگر

• Automated Program Repair
• Formal Methods
• Software Engineering
• +۳ مورد دیگر

Omar I. Al-Bataineh is a Research Scientist at Gran Sasso Science Institute (GSSI) in Italy, specializing in software engineering and formal methods. His work bridges theoretical foundations with practical applications in automated program repair and software verification. Education: Ph.D. in Computer Science, University of Western Australia Additional degrees from University of New South Wales and Jordan University of Science and Technology His research centers on three interconnected themes: (1) Multi-fault Automated Program Repair addressing complex bug interactions, (2) Formal Methods for Reliable Repair ensuring provable correctness, and (3) Termination-Aware Repair integrating performance considerations. He develops lightweight test oracles and context-sensitive repair techniques to overcome patch overfitting and scalability limitations in real-world systems. Recent publications reveal strong focus on multi-fault scenarios (60% of 2025 output), with growing emphasis on formal verification (30%) and performance-aware repair (10%). Key venues include ASE, ICSME, and SANER where he explores program slicing, oracle design, and fault interaction analysis. Awards: Best Paper Award at QRS 2022 for advancing automated program repair capabilities Prior to GSSI, he held research positions at Simula Research Laboratory, National University of Singapore, and Nanyang Technological University. His teaching experience includes Advanced Computer Security at UNSW and Java Programming at UWA, though current academic instruction isn't emphasized in recent activities. He maintains active contributions to workshops like APR@ICSE and FASE, focusing on practical tool development.

Daniel Zimmerمشاهده پروفایل

استاد

• Consumer Protection
• International Capital Market Law
• International Corporate Law
• +۱ مورد دیگر

Daniel Zimmer serves as Professor and Director of the Institute of Commercial and Economic Law at the Center for Advanced Studies in Law and Economics (CASTLE), University of Bonn, and maintains a Research Fellowship at the German Institute for Economic Research (DIW Berlin). His distinguished career includes chairing the German Monopolies Commission (2012-2016) and serving as Dean of Bonn's Faculty of Law and Economics until 2018. Education: PhD in Legal Studies, University of Göttingen, 1991 His research program integrates legal frameworks with economic analysis across Consumer Protection, International Capital Market Law, International Corporate Law, and Competition Law. He examines regulatory adaptation to global market dynamics and technological innovation, with particular focus on digital transformation challenges in financial and commercial systems. Recent 2021 publications demonstrate his interdisciplinary approach through works on AI algorithm regulation and economic methodologies in European/German merger control, reflecting sustained engagement with contemporary regulatory dilemmas at the law-economics interface. As Principal Investigator and Cluster member at CASTLE since 2019, he contributes to collaborative research initiatives bridging legal scholarship and economic policy. The source material contains no information regarding student advising relationships or research grant funding.

Sahar Qaadanمشاهده پروفایل

پژوهشگر

• Supervised Machine Learning
• Optimization
• Support Vector Machines
• +۳ مورد دیگر

Dr. Sahar Qaadan is a researcher in the Theory of Machine Learning group at the Institute of Neuroinformatics (INI), Faculty of Computer Science, Ruhr University Bochum, Germany. She has been actively working in supervised machine learning and optimization since 2019, following her PhD completion in Tobias Glasmachers' research group at the same institution. Her educational background includes: MSc in Automation and Robotics from Technical University Dortmund, Germany (2011-2014) BSc in Mechatronics Engineering from University of Jordan, Amman, Jordan (2003-2008) Dr. Qaadan's research focuses on developing efficient algorithms for supervised machine learning, particularly optimization techniques for Support Vector Machine (SVM) training. Her work addresses computational challenges in large-scale learning through innovative approaches to budget maintenance, stochastic gradient methods, and convergence acceleration strategies. Analysis of her 2018-2019 publications reveals a consistent emphasis on enhancing SVM training efficiency. Key contributions include multi-merge budget maintenance techniques, precomputed search methods like Golden Section Search, and specialized adaptations for both stochastic gradient descent and coordinate ascent frameworks. These works collectively target resource-constrained environments while maintaining model accuracy. No scientific awards were mentioned in the provided documentation. Information regarding student advising or research grants is not documented in the available materials. She operates within Tobias Glasmachers' Theory of Machine Learning group at INI, with prior industrial collaboration experience including BMW's Autonomous Systems ADAF project and thermal management control systems for data centers using neural networks and PLC technology.

• Digital Signal Processing (DSP)
• Field-Programmable Gate Arrays (FPGA)
• Machine Learning Hardware Acceleration
• +۱۳ مورد دیگر

Gian Carlo Cardarilli is a researcher specializing in digital hardware design and machine learning acceleration. His work focuses on FPGA implementations, Residue Number System (RNS) architectures, and reconfigurable computing for applications in wireless communication, edge AI, and fault-tolerant systems. Key collaborations with institutions like IEEE and ACM through publications. Active in translating theoretical algorithms into practical hardware solutions for real-time systems. Research interests include: Optimizing deep learning models for heterogeneous platforms. Developing radiation-hardened memory systems. Creating energy-efficient signal processing architectures. Advancing reconfigurable functional units for embedded processors. His article analyses span fields like Quantum Cellular Automata , Variable Fractional Delay Filters , and RNS-Based Position Estimation , reflecting a trend toward adaptive, low-power, and domain-specific hardware.

• Music Processing
• Music Information Retrieval
• Audio Signal Processing
• +۱ مورد دیگر

Prof. Dr. Meinard Müller is a Professor at Friedrich-Alexander Universität Erlangen-Nürnberg (FAU), leading the Chair of Semantic Audio Signal Processing within the International Audio Laboratories Erlangen (a joint institute with Fraunhofer IIS). He specializes in Music Processing, Music Information Retrieval, Audio Signal Processing, and Motion Processing. His work bridges fundamental research and practical applications in music technology, including audio decomposition, source separation, and computational musicology. Research Interests: Focus on algorithmic methods for music analysis, audio feature extraction, and real-time audio processing. Teaching: Supervises a large group of doctoral and master’s students, offering courses on audio processing and music technology. Awards: Recognized as an IEEE Fellow (2020) and ACM Senior Member (2020). His research group develops open-source tools like the FMP Notebooks and libfmp, and maintains datasets such as the Wagner Ring Dataset and Dagstuhl ChoirSet. He actively contributes to conferences like ISMIR and ICASSP, with over 200 publications in top-tier journals and conferences.

Peer Neubertمشاهده پروفایل

استاد

• Robotics
• Computer Vision
• Artificial Intelligence
• +۳ مورد دیگر

Prof. Dr. Peer Neubert is a Professor and Head of the Intelligent Autonomous Systems (INTAS) Working Group at the University of Koblenz. He is affiliated with the Department of Computer Visualistics (Department 4) and serves as Head of the Institute of Computational Visualistics. His academic roles include membership in the Department Council and Library Committee of FB4. His research focuses on robotics, computer vision, and autonomous systems, with particular emphasis on visual place recognition, SLAM, hyperdimensional computing, and adaptive navigation in dynamic environments. Neubert's work spans theoretical advancements and practical implementations, such as benchmarking systems (e.g., HS3-Bench), efficient algorithms (FETCH, LocalSPED), and sensor-based solutions (HealthWalk). His team addresses challenges in long-term navigation, environmental adaptation, and data-efficient learning. Collaborations include external PhD students like Kenny Schlegel and projects like MineSweeper, demonstrating interdisciplinary impact. His contributions are reflected in over 50 publications since 2007, covering topics from hyperspectral segmentation to biomedical applications. While no formal educational details are provided, his extensive research output and leadership roles underscore his expertise in computational visualistics and autonomous systems.

• Optimal Control
• Numerical Analysis
• Partial Differential Equations
• +۵ مورد دیگر

Dominik Meidner is a Senior Lecturer at the Chair of Optimal Control at the Technical University of Munich (TUM), part of the School of Computation, Information and Technology. He leads the Department of Mathematics as Department Manager since 2025. His research focuses on optimal control of partial differential equations (PDEs), numerical methods for PDE-constrained optimization, adaptive finite element methods, and software development for scientific computing. He has authored numerous publications and contributed to software like Gascoigne and RoDoBo. His academic roles include teaching Analysis courses and supervising over 30 theses since 2009. Key awards include the 2019 Dozentenpreis and the Felix-Klein-Lehrpreis 2010. His work bridges theoretical developments with practical applications in fluid-structure interaction, fractional diffusion, and error estimation. Education: PhD (2008, Heidelberg University), Diplom (2003, Heidelberg University). Research Interests: Optimal control, numerical analysis, finite elements, PDE-constrained optimization, adaptive discretization. Software Contributions: Gascoigne (finite element toolkit), RoDoBo (optimization library). Key Activities: Co-organizer of OCIP workshops, member of the TUM-MCLQST cluster, and contributor to academic infrastructure projects.

• Hyperdimensional Computing
• Edge AI
• Neural Network Acceleration
• +۳ مورد دیگر

Tajana Rosing is a Professor at the University of California, San Diego, specializing in hyperdimensional computing, neural network acceleration, and edge AI systems. Her work bridges hardware-software co-design for IoT, biomedical applications, and security-enhanced computing. University: University of California, San Diego Research Focus: Hyperdimensional Computing, Edge AI, IoT Systems, Neural Acceleration, Secure Machine Learning Her recent publications focus on energy-efficient neural network acceleration (e.g., Processing-in-Memory architectures), few-shot learning for edge devices, and hyperdimensional methods for sensor data and biomedical applications. She explores robustness against adversarial attacks and federated learning frameworks. Key trends include hyperdimensional computing for multimodal sensor fusion, transformer optimization via hardware-software co-design, and privacy-preserving edge AI. Collaborations often involve co-authors like Xiaofan Yu, Minxuan Zhou, and Onat Güngör. Her work impacts domains such as healthcare (glucose prediction, ECG analysis), cybersecurity (intrusion detection), and bioinformatics (mass spectrometry, viral sequence analysis).