جست‌وجوی استاد و پژوهشگر

Thaleia Dimitra Doudaliمشاهده پروفایل

استادیار

• Operating Systems
• Machine Learning
• Computer Vision
• +۴ مورد دیگر

Thaleia Dimitra Doudali is an Assistant Professor at the IMDEA Software Institute in Madrid, Spain, leading the Muse research lab. She holds a PhD in Computer Science from Georgia Institute of Technology (2021), advised by Ada Gavrilovska, focusing on hybrid memory management with machine learning. Prior to her PhD, she earned a Diploma in Electrical and Computer Engineering from the National Technical University of Athens (2015). Her research interests span systems for machine learning, machine learning for systems, and computer vision applications in resource management. Her work has been recognized with awards including the 'César Nombela' (2024), 'Juan de la Cierva' (2021), and Rising Star in EECS (2020). She serves on program committees for top conferences (ASPLOS, EuroSys) and organizes workshops. Her lab's projects address cloud resource management, carbon efficiency, and sustainable computing. She advises multiple PhD and intern researchers, contributing to impactful publications in SIGMOD, EuroSys, and other venues. Notable contributions include Cronus (CV-based memory management), CaRE (carbon-efficient cloud-edge orchestration), and foundational work on ML-driven resource forecasting. Her research bridges systems and AI, aiming to optimize data center efficiency and sustainability. She actively promotes diversity in STEM through mentorship roles in ACM-W Greece and Women in HPC initiatives.

• Climate Modelling
• Paleoclimate Modelling
• Earth System Modelling
• +۳ مورد دیگر

Dr. Jan Streffing is a Scientific Programmer in the Climate Dynamics department at the Alfred Wegener Institute (AWI) in Bremerhaven, Germany. His work focuses on advancing Earth System Models (ESMs) with an emphasis on coupled climate modeling, paleoclimate simulations, and ensuring model scalability while preserving mass and energy conservation. He coordinates Earth System Model development efforts, particularly with the AWI-CM3 climate model framework. His research interests include high-resolution climate projections, atmosphere-ocean coupling mechanisms, and improving model physics through numerical methods. Streffing contributes to global kilometer-scale simulations using frameworks like IFS-FESOM, addressing challenges in resolving ocean eddies and cloud processes. He has developed ESM-Tools infrastructure to modularize climate modeling workflows and enhance computational efficiency. Key projects involve the MOSAiC expedition evaluation, where nudging techniques were applied to align model outputs with in-situ observations, revealing model deficiencies in cloud dynamics and snowpack representation. His work also explores deep-water formation impacts on climate sensitivity and integrates data assimilation techniques to improve sea ice forecasts in the AWI coupled prediction system. Streffing's contributions bridge climate science and computational practices, supporting advancements in model resolution, scalability, and accuracy for understanding future climate scenarios.

Lutz Precheltمشاهده پروفایل

استاد

• Software Engineering
• Empirical Software Engineering
• Agile Processes
• +۵ مورد دیگر

Lutz Prechelt is Professor of Informatics at Freie Universität Berlin where he heads the Software Engineering research group (AG SE). His academic journey spans theoretical foundations, industry experience, and extensive empirical research in software engineering. He has held significant administrative positions including Executive Director of the Institute of Informatics and member of various academic senates. Prechelt's research interests have evolved from artificial intelligence and neural networks to empirical software engineering, with a recent focus on qualitative approaches to Agile processes and Pair Programming. His work has consistently emphasized methodological rigor, having conducted pioneering controlled experiments on topics like type-checking, inheritance depth, design patterns, and the Personal Software Process. His research has also ventured into plagiarism detection (JPlag), melody recognition, and qualitative studies of pair programming dynamics. His publication record shows a clear transition from quantitative to qualitative approaches in empirical software engineering. Early work featured controlled experiments on specific technical aspects of software development, while more recent publications focus on qualitative analysis of team dynamics, agile methodologies, and research methodology itself. A notable thread throughout his career has been platform comparison studies (Plat_Forms) examining how different technologies affect development outcomes. Ernst Denert Software Engineering Award 2020 Prechelt has served extensively as reviewer for major funding agencies including DFG, EU, ERC, and BMBF, and as editor for journals like Journal of Universal Computer Science and Transactions on Pattern Languages of Programming. He founded the Forum for Negative Results (FNR) to address publication bias in scientific research. His research group has developed several notable software tools including Saros for distributed pair programming, JPlag for plagiarism detection, and tools for maintainable lecture videos.

Moharram Challengerمشاهده پروفایل

استادیار

• Cyber-physical Systems
• Multi-agent Systems
• Domain-specific Modeling Languages
• +۵ مورد دیگر

Moharram Challenger is a tenure-track Assistant Professor in the Department of Computer Science at the University of Antwerp's Faculty of Sciences. Previously, he served as an assistant professor at Ege University (2017-2018) and as a post-doctoral researcher at the University of Antwerp (2019-2020) working on Flanders Make projects PACo and DTDesign. His academic journey includes R&D leadership roles at UNIT IT Ltd. (2012-2016), post-doctoral research at Wageningen University (2016-2017), and tenure-track faculty positions at IAU-Shabestar University (2005-2009). His research spans Cyber-physical Systems , Multi-agent Systems , and Domain-specific Modeling Languages , with recent publications focusing on quantum machine learning, digital twinning, and IoT optimization. Key projects include ITEA ModelWriter, ITEA Assume, and Flanders Make initiatives. His work demonstrates strong integration of model-driven engineering with emerging technologies like quantum computing and reinforcement learning. Challenger actively contributes to the academic community as a member of IEEE and ACM . His publication record shows consistent output across top venues, with 2025 featuring significant work in quantum-enhanced learning and CPS security. Current research emphasizes practical applications in drone energy modeling, medical diagnostics, and industrial IoT systems. His advising activities focus on cyber-physical systems and agent-based modeling, supported by grants from TUBITAK and Flanders Innovation & Entrepreneurship. Key collaborations include European ITEA projects and partnerships with industrial entities through UNIT IT Ltd. Challenger maintains active development through GitHub repositories related to code refactoring, model-driven engineering, and legacy system modernization, reflecting his commitment to practical software engineering solutions.

• Quantum Computing
• Edge Computing
• Distributed Systems
• +۲ مورد دیگر

Dr. Vincenzo De Maio is a PostDoc Researcher at the Department of Computational Sustainability, Faculty of Informatics, Technische Universität Wien. His research spans quantum computing, edge computing, distributed systems, and sustainable computing, with a focus on hybrid quantum-classical systems and edge AI applications. Current projects: HPQC (2023–2025), ThEMIS FWF (2024–2027), TRITON FWF (2023–2027) Previous projects: RUCON (2016–2023), SWAIN (2021–2024) His research explores the integration of quantum computing with classical systems, particularly in software engineering, workflow decomposition, and hyperparameter optimization. He also works on edge computing for smart cities, environmental monitoring, and traffic safety applications. Recent publications analyze quantum compilation bottlenecks, hybrid quantum-classical workflow orchestration, and quantum neural network optimization. He supervises master's theses on topics like quantum edge benchmarking and hyperparameter tuning. Key collaborations with Prof. Ivona Brandic and others Active in IEEE/ACM conferences and Springer publications

• Software Composition
• Web 2.0 Mashups
• Cloud Computing
• +۶ مورد دیگر

Cesare Pautasso is a Full Professor at the Software Institute within the Faculty of Informatics at the Università della Svizzera italiana (USI) in Lugano, Switzerland. His research focuses on software composition techniques, Web 2.0 mashups, cloud computing, business process management, and RESTful API design. He has led projects like JOpera, a service composition tool, and contributed to frameworks such as Liquid Software for adaptive distributed systems. Education: PhD in Computer Science from ETH Zurich (2004) and a Computer Science Engineering Degree (cum laude) from Politecnico di Milano (2000). Research Interests include model-driven engineering, service-oriented architectures, and API analytics. He has authored over 200 publications and serves on numerous program committees, including IEEE and ACM. His work has been funded by grants from Swiss National Science Foundation (SNSF), EU projects like S-CUBE, and industry collaborations. Teaching and Training: Focuses on Web Development, Enterprise Architecture, and emerging technologies. Active in conferences as general chair of ECOWS 2011 and ICWE 2016. Labs/Teams: Leads research groups exploring API design, blockchain applications, and adaptive systems. Collaborates with institutions like HPI Potsdam and ETH Zurich on projects like FC4MC and API-ACE.

• Process Mining
• Healthcare Informatics
• Data Science
• +۳ مورد دیگر

Dr. Yang Lu is a Research Fellow at the Sydney School of Public Health, University of Sydney. They also serve as the Lead Software Engineer for the OCEAN Project. Their work focuses on advancing process mining techniques applied to healthcare data, particularly in clinical process discovery, event log analysis, and improving healthcare outcomes through data-driven frameworks. Research Interests: Process Mining, Healthcare Informatics, Machine Learning, and Clinical Data Analysis. Their studies emphasize improving clinical workflows and decision-making through advanced data analysis and algorithm development. Publications highlight innovative approaches in detecting process drifts, analyzing clinical pathways in ICU settings, and enhancing event log quality for process mining applications. These contributions bridge computational methods with real-world healthcare challenges. Currently involved in the OCEAN Project, which likely relates to software engineering or data infrastructure for health informatics. No specific awards or grants are listed in the provided information.

Daniel Goldbergمشاهده پروفایل

دانشیار

• GIS
• Geocoding
• GeoComputation
• +۹ مورد دیگر

Dr. Daniel Goldberg is an Associate Professor at Texas A&M University and Director of the TAMU GeoInnovation Service Center. His expertise spans GIS, geocoding, and geocomputation, with a focus on health and environmental applications. He holds a Ph.D. in Computer Science (USC, 2010), M.Sc. (USC, 2003), and B.Sc. (Rutgers, 2002). His research interests include CyberGIS, 3D GIS, spatial databases, and environmental exposure assessment. Notable projects involve developing real-time mobile crowdsensing systems and analyzing the impact of natural disasters on public health. He has contributed to over 40 peer-reviewed articles, often leveraging interdisciplinary approaches. Dr. Goldberg has received awards such as the Texas A&M Montague Scholar Award (2015) and the CyberGIS Fellow designation (2014). His work emphasizes geospatial privacy, health data security, and innovative GIS education methods. He leads the TAMU GeoServices team and collaborates with national institutions like the National Center for Supercomputing Applications. Advising highlights include mentoring over 20 graduate and undergraduate students, with notable contributions to projects on cyberGIS frameworks and NTD surveillance. His lab focuses on bridging geospatial technology with societal challenges in health, environment, and urban planning.

Sang-Woo Junمشاهده پروفایل

استاد

• Big Data Analytics
• Hardware Acceleration
• Non-Volatile Memory
• +۳ مورد دیگر

Professor Sang-Woo Jun is a leading researcher in systems and software for big data analytics, focusing on FPGA-based hardware acceleration and non-volatile memory (NVM) storage. His work spans applications such as graph analytics and bioinformatics, with a strong emphasis on cost-effective, high-performance computing architectures. He advises PhD students like Shengquan Ni and Yicong Huang, both of whom have achieved notable milestones (e.g., thesis defense, fellowship awards). Research Interests: Hardware Acceleration for Big Data FPGA-Based System Architectures Non-Volatile Memory Systems Graph Analytics and Bioinformatics Edge Computing and Low-Power Systems Recent Contributions: His articles highlight innovations in edge accelerators (e.g., IceSpy, Eciton), genomics acceleration (Bancroft), and scalable graph processing (Durin, Sting). These works emphasize reconfigurable systems, privacy-preserving techniques, and energy-efficient designs. Lab & Team: As part of the Intelligent Systems Group (ISG), he collaborates on events like the Southern California Database Day. His research bridges hardware-software co-design with real-world applications in IoT, environmental monitoring, and genomics.

Rhema Linderمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Artificial Intelligence
• AI Art
• +۳ مورد دیگر

Rhema Linder is a Teaching Assistant Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee, Knoxville. His academic journey includes a BS in Computer Science and Mathematics from LeTourneau University (2009) and a PhD in Computer Science from Texas A&M University (2019). Prior to his current role, he served as a postdoctoral researcher at the PAIRS lab at UT Knoxville. His research focuses on Human-Computer Interaction (HCI), AI Art, Information Visualization, and Creative Cognition. He explores how AI and software systems can enhance creative productivity, particularly in collaborative online environments. His work integrates theories from creative cognition, social science, and HCI to design tools for engineering, design, and scholarship. Rhema has held internships at Adobe Research and Microsoft Research, contributing to projects in data science and human-computer interaction. His GitHub repositories include open-source projects like kivy-games, demonstrating his engagement with software development and educational tools. His research interests emphasize multidisciplinary approaches, blending art, technology, and social science to innovate in creative online spaces. He is actively involved in the PAIRS lab, focusing on advancing understanding of human-AI collaboration and information management systems.

• Power Electronics
• Control Theory
• Mathematical Optimization
• +۵ مورد دیگر

Petros Karamanakos is an Assistant Professor in the Department of Electrical Engineering at Tampere University's Faculty of Information Technology and Communication Sciences. His research focuses on advanced control methodologies for power electronic systems, emphasizing optimization, mathematical programming, and model predictive control. He holds a Ph.D. and Diploma in Electrical and Computer Engineering from the National Technical University of Athens (2007–2013). Prior to his academic role, he worked at ABB Corporate Research Center (2010–2011) and was a PostDoc Research Associate at Technische Universität München (2013–2016). His mission involves developing control methods that maximize hardware capabilities while minimizing hardware requirements. Key research topics include variable speed drives, grid-connected converters, multilevel power converters, and modulation strategies. He has received prestigious awards, including the Third Best Paper Award in IEEE Transactions on Industry Applications and four IEEE conference prize paper awards. Teaching responsibilities include courses on power electronics and control systems. He actively supervises BSc, MSc, and PhD students. His work bridges theoretical advancements with practical implementations in power electronics, with a focus on real-time control and system optimization.

Sven Scheweمشاهده پروفایل

استاد

• Automata Theory
• Game Theory
• Formal Verification
• +۱۲ مورد دیگر

Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.

• Business Process Management
• Predictive Analytics
• Machine Learning
• +۳ مورد دیگر

Ilya Verenich is a Researcher at Queensland University of Technology (QUT), known for his work in Business Process Management and Predictive Analytics. He completed his PhD in 2018 with a thesis titled 'Explainable predictive monitoring of temporal measures of business processes.' His research focuses on developing machine learning and AI-driven tools for predictive process monitoring, such as the Apromore platform and Nirdizati web tool. Collaborations with international experts like Marlon Dumas and Marcello La Rosa highlight his contributions to process mining, workflow optimization, and predictive modeling. His work bridges computer science, data mining, and business systems, addressing challenges in real-time process analysis, resource allocation, and anomaly detection. Key areas of expertise include temporal process analysis, LSTM neural networks for sequential data, and minimizing operational waste through predictive activity ordering. He has published extensively in top-tier conferences like CAiSE and BPM, with topics ranging from white-box process performance prediction to symbolic sequence clustering for process monitoring. His interdisciplinary approach combines theoretical frameworks with practical applications in industries requiring efficient workflow management.

• Quantum Computing
• Quantum Algorithms
• Quantum Error Mitigation
• +۳ مورد دیگر

Yunseong Nam is an Adjunct Assistant Professor at the University of Maryland. His research focuses on advancing quantum computing through innovations in trapped-ion systems, quantum algorithms, and error mitigation techniques. He is particularly known for developing methods to enhance gate fidelity, optimize quantum circuits, and improve fermionic simulations for quantum chemistry applications. Research Interests: Nam’s work spans quantum hardware optimization, entangling gate implementation, and hybrid quantum-classical computing architectures. He emphasizes practical applications like materials science simulations and fault-tolerant protocols, leveraging symmetries and resource-efficient algorithms to address computational challenges. His contributions include breakthroughs in pulse engineering, SPAM error handling, and the design of robust quantum circuits. Key Research Trends: Recent articles highlight advancements in trapped-ion gate optimization (e.g., small-angle Mølmer-Sørensen gates), error mitigation strategies, and quantum circuit compilers. His work frequently intersects with experimental quantum systems, emphasizing real-world implementations over purely theoretical models. Awards & Grants: No specific awards or grants are mentioned in the provided texts. Labs & Teams: While specific lab affiliations are not detailed, his collaborations likely involve quantum computing hardware groups and theoretical teams focused on algorithm optimization.

• Resource Optimization
• Telecommunication Networks
• VLSI Circuit Design
• +۵ مورد دیگر

Professor Arunabha Sen is a faculty member at Arizona State University (ASU), affiliated with the School of Computing and Augmented Intelligence and the College of Health Solutions as a Health Solutions Ambassador. He joined ASU in 1987 and holds a Ph.D. in Computer Science from the University of South Carolina (1987). His research focuses on resource optimization in telecommunication networks, VLSI circuits, hardware-software co-design, and network security. Key areas include algorithm design, combinatorial optimization, and network processor systems. His work spans wireless, optical, and sensor networks, with contributions to video transmission over mobile ad-hoc networks and interference-aware channel assignment. Notable projects include robust network design against WMD attacks and tools for resilient communication networks. He has served on multiple technical committees for conferences like IEEE and IFIP, and contributed to academic initiatives such as capstone courses on network processors. Grants include NSF, DOD-DTRA, and Motorola Labs funding, emphasizing interdisciplinary research in network science and communications. Teaching responsibilities include courses on algorithms, game theory, and network design. His service roles include Associate Editor for IEEE Transactions on Mobile Computing and leadership in graduate program committees. Research outputs include over 30 peer-reviewed publications, with recent work in algorithmic network design and social computing data mining.