جست‌وجوی استاد و پژوهشگر

Melanie Schmidtمشاهده پروفایل

استاد

• Geometric Data Analysis
• k-means clustering
• Combinatorial Optimization
• +۳ مورد دیگر

Professor Melanie Schmidt is a faculty member in the Department of Computer Science at Heinrich-Heine-Universität Düsseldorf, where she leads the Algorithms and Data Structures research group. Previously, she was affiliated with the University of Bonn's Institute of Computer Science, where she completed her PhD under Prof. Dr. Heiko Röglin and headed a subgroup on "clustering for big data" within his research group. Current Position: Professor at Heinrich-Heine-Universität Düsseldorf Previous Position: Researcher and lecturer at University of Bonn PhD Advisor: Prof. Dr. Heiko Röglin Her research focuses on geometric data analysis, particularly k-means clustering in data streams, combinatorial optimization, and approximation algorithms. Her work bridges theoretical computer science with practical applications in big data processing. She has made significant contributions to understanding the theoretical foundations of clustering algorithms while developing efficient implementations for real-world applications. Professor Schmidt's publication record shows a consistent evolution from theoretical analysis of k-means to practical implementations for big data environments. Her recent work explores fairness in clustering, privacy-preserving techniques, and efficient algorithms for high-dimensional data. She has published in top-tier conferences including SODA, ICALP, ESA, and ITCS, demonstrating both theoretical rigor and practical relevance. Best Student Paper Award at ESA 2012 (joint work with Martin Groß, Jan-Philipp W. Kappmeier, and Daniel Schmidt) She actively supervises numerous Master's and Bachelor's students, with current advisees including Lena Carta, Lukas Drexler, and Anna Arutyunova. Her research group includes members such as Anja Rey, Julian Wargalla, and Annika Hennes. She teaches advanced courses in algorithms and data structures, with a focus on randomized algorithms and efficient algorithm design for big data problems. Professor Schmidt leads the Algorithms and Data Structures research group at Heinrich-Heine-Universität Düsseldorf, which focuses on developing and analyzing efficient algorithms for fundamental computational problems, with particular emphasis on clustering, geometric data analysis, and big data applications.

Ashwin Nayakمشاهده پروفایل

استاد

• Quantum Information
• Quantum Computation
• Algorithms
• +۴ مورد دیگر

Ashwin Nayak is a Professor at the University of Waterloo, affiliated with the Department of Combinatorics and Optimization and the Institute for Quantum Computing (IQC). He holds a B.Tech. from IIT Kanpur (1995) and a Ph.D. from UC Berkeley (1999). Before joining Waterloo in 2002, he held postdoctoral positions at DIMACS, AT&T Labs, and Caltech. He was also an associate faculty member at the Perimeter Institute for Theoretical Physics from 2003 to 2011. His research focuses on quantum information and computation, including quantum algorithms, complexity theory, and communication protocols. He has made significant contributions to topics such as quantum walks, quantum error correction, and the theory of quantum communication complexity. His work bridges foundational aspects of quantum mechanics with computational challenges, emphasizing interdisciplinary connections to theoretical computer science. Nayak has advised numerous graduate students and has taught courses ranging from introductory optimization to advanced quantum information theory. His current supervision includes Ph.D. student Pulkit Sinha and M.Math. student Nicholas Allen. Past students have pursued careers in academia and industry, contributing to fields like quantum algorithms and cryptography. He is actively involved in academic activities, including organizing lectures and conferences on quantum information. His lab at IQC collaborates on cutting-edge projects in quantum computing and information theory.

• Logic Programming
• Knowledge Representation
• Graph Databases
• +۷ مورد دیگر

Ana Edelmira Pasarella Sanchez is a Professor in the Department of Computer Sciences at the Faculty of Mathematics and Statistics, Universitat Politècnica de Catalunya (UPC). She is a member of the ALBCOM research group, focusing on algorithms, bioinformatics, complexity, and formal methods. Her work bridges theoretical computer science with practical data systems. PhD in Computer Science, Universitat Politècnica de Catalunya Her research centers on logic programming, knowledge representation, and graph databases. She explores how formal methods can enhance data processing, particularly through dynamic pipelines and trust-aware access control. Her work integrates Datalog, semantic reasoning, and big data frameworks to improve scalability and correctness in knowledge systems. She has contributed to foundational semantics of logic programs and their applications in security and data integration. Her recent publications highlight a trend toward efficient, adaptive data processing systems, especially for graph analytics and knowledge graphs. She compares paradigms like MapReduce and pipelining, advocating for dynamic, functional approaches to big data. Her work increasingly addresses real-world challenges in federated knowledge graphs and access control. SACMAT 2017 Best Paper Award Pasarella has been involved in multiple competitive R&D+i projects, such as 'Modelos y Técnicas para el Procesamiento de Información a Gran Escala' and 'Modelos y métodos basados en grafos para la computación en gran escala,' indicating sustained funding and collaborative leadership. She advises on research direction within her group and mentors through collaborative publications. She has served on the scientific committee of the Latin American Informatics Conference (CLEI), contributing to the broader academic community. She is part of the ALBCOM research group and collaborates extensively with researchers like Fernando Orejas, Maria-Esther Vidal, and Elvira Pino, working on logic-based frameworks for data and security systems.

Paul Amyمشاهده پروفایل

استادیار مهمان

• Astrophysics
• Galactic Astronomy
• Dark Matter
• +۶ مورد دیگر

Paul Amy is a Visiting Assistant Professor in the Department of Physics at Massachusetts College of Liberal Arts (MCLA). He teaches courses such as General Physics I, Electricity and Magnetism, and Introduction to Astrophysics. His office is located in the Center for Science & Innovation, 110D, and he can be reached at paul.amy@mcla.edu or (413) 662-5458. His educational background includes a Ph.D. in Physics from Rensselaer Polytechnic Institute (2019), an M.S. in Physics from SUNY Albany (2011), and a B.S. in Physics from Union College. Dr. Amy's research focuses on the structure and evolution of the Milky Way's halo, particularly the detection and characterization of tidal streams—stellar remnants pulled from dwarf galaxies or globular clusters. He uses data from large-scale sky surveys like the Sloan Digital Sky Survey (SDSS) and the Gaia mission, with a special emphasis on blue horizontal branch (BHB) stars due to their brightness and reliable distance estimates. His work helps map the gravitational potential of the Milky Way and infer properties of dark matter. The two recent publications highlight his contributions to identifying stellar substructures and modeling galactic dynamics. His current projects include developing a novel algorithm to detect tidal streams using line-of-sight velocity versus heliocentric distance plots and investigating unusual structures in the North Galactic Cap near the Virgo Overdensity. arXiv:1903.10136, ApJ submitted (2019) MNRAS, 477, 2419 (2018) Dr. Amy is actively involved in research but no information is available about student advising, grants, or scientific awards. He works within the physics program at MCLA, contributing to both teaching and cutting-edge astrophysical research.

• Data Mining
• Machine Learning
• Text Summarization
• +۶ مورد دیگر

Mark Last is a Professor at Ben-Gurion University in Beersheba, Israel, with a distinguished career spanning over three decades in computer science research. His work primarily focuses on data mining, machine learning, and natural language processing applications. His research interests encompass stream data mining, text summarization, fuzzy logic systems, and classification algorithms. Last has made significant contributions to developing techniques for analyzing dynamic data streams, multilingual text processing, and applying machine learning to real-world problems in healthcare, social media analysis, and security informatics. His work often bridges theoretical advancements with practical applications, particularly in handling non-stationary data and developing interpretable models. Recent research trends show a continued focus on stream data analysis, with applications expanding into social media monitoring, healthcare prediction systems, and multilingual content analysis. His work demonstrates consistent innovation in adapting machine learning techniques to evolving data environments and practical challenges. Mark Last has maintained a prolific publication record with over 175 publications documented in DBLP, collaborating extensively with researchers including Abraham Kandel, Marina Litvak, and Oded Maimon. His work has been published in top venues including IEEE Access, Machine Learning journal, and Expert Systems with Applications.

Frank Pallasمشاهده پروفایل

استاد

• Privacy Engineering
• Policy-Aligned Systems
• Techno-Legal Implications
• +۷ مورد دیگر

Frank Pallas is a Professor at the Paris Lodron University of Salzburg, affiliated with the Faculty of Digital and Analytical Sciences. His research focuses on Privacy Engineering and Policy-Aligned Systems (PEPSys), integrating technical and legal aspects of emerging technologies. Education: Dipl.-Inform. and Dr.-Ing. in Computer Science from TU Berlin His work emphasizes techno-legal privacy engineering , policy-alignment mechanisms , and experimental assessment of privacy techniques , addressing anonymity, utility metrics, and societal impacts. Recent publications explore data anonymization, GDPR compliance, and privacy in distributed systems. Key projects include co-leading Salzburg's EXDIGIT initiative for digital sciences. Activities span organizing international workshops on privacy engineering, media contributions, and third-mission engagements like public lectures on digital privacy.

Orhan Ecemişمشاهده پروفایل

دانشیار

• Quantitative Decision Methods
• Multi-Criteria Decision Making
• Data Analytics
• +۴ مورد دیگر

Orhan Ecemiş serves as an Associate Professor at Gaziantep University's Technical Sciences Vocational School within the Electronics and Automation Department, specializing in Robotics and Artificial Intelligence. His academic appointment commenced in 2024 following progression from Doctor Lecturer (2022-2024) and Lecturer (2009-2022) positions. He currently holds administrative roles as Head of Department and Deputy Director of Vocational School/College at Gaziantep University, having previously served in these capacities during 2010-2014 and 2020-2022. His educational background includes a Doctorate in Econometrics from Akdeniz University (2011-2016), a Master's in Computer Education from Gazi University (2001-2004), and undergraduate studies in Computer Teaching at Gazi University (1996-2000). Ecemiş specializes in Quantitative Decision Methods, Multi-Criteria Decision Making, Data Analytics, and Operations Research, with significant contributions to Artificial Intelligence applications. His research bridges computer science and economic modeling, focusing on practical decision support systems across diverse domains including energy policy, logistics optimization, and digital transformation. His work consistently applies advanced mathematical frameworks to real-world problems, particularly utilizing neutrosophic logic and machine learning techniques for complex decision environments. His 15 most recent publications demonstrate strong thematic continuity in applying Multi-Criteria Decision Making methods to contemporary challenges, with increasing focus on sustainability metrics, digital technologies, and economic forecasting. The research spans energy policy evaluation, cloud computing selection, green logistics, and international trade analysis, reflecting both methodological sophistication and practical relevance to Turkish and European contexts. As an academic supervisor, he has guided at least one Master's thesis to completion in 2025. His teaching portfolio encompasses computer programming, web technologies, algorithms, and data science applications across undergraduate and associate degree programs since 2014. While specific grant information isn't detailed in the source material, his extensive publication record across international journals and conference proceedings indicates substantial research activity.

• Film Criticism
• Cinema Theories
• Cinema History
• +۲ مورد دیگر

Yezdan Çelebi is a Research Assistant at Üsküdar University's Faculty of Communication, Department of Radio, Television and Cinema. She completed her undergraduate and master's degrees at Istanbul University, with a double major in Political Science and International Relations, and continues her PhD in Radio, Television and Cinema at Istanbul University. Research Interests: Primarily focused on cinema studies, Çelebi examines Kurdish cinema, ecological dystopias, digital inequality, and social media discourses. Her work bridges film criticism with broader socio-political themes through discourse analysis frameworks. Conference Participation: XXIV. Conference on New Trends in Turkish Film Studies (2025) 11th International Communication Days (2024) 3rd International Cinema Symposium (2023) 10th International Communication Days (2023) Administrative Roles: Co-editor for academic publications since 2021 Student Adviser (2022-present) National Accreditation Working Group member (2023-present) Various committee memberships at Üsküdar University

Mathias Deneckeمشاهده پروفایل

پژوهشگر

• Digital Cultures
• Media Studies
• Logistics
• +۴ مورد دیگر

Mathias Denecke is a Postdoctoral Researcher at the Institute for Media Studies at Ruhr University Bochum since March 2022 and an associated member of the Collaborative Research Center 'Virtual Lifeworlds' since February 2024. His academic trajectory includes doctoral research at Leuphana University Lüneburg's Centre for Digital Cultures (2014-2018) and prior work as a Research Associate on media streaming cultures at the University of Hamburg (2018-2022). His educational background features a completed doctorate in 2021 with the project 'Electricity metaphors in digital cultures,' an MA in Literature, Art and Media Studies from the University of Konstanz (2011-2014), and a BA in the same field with a minor in Political Science (2008-2011), including an Erasmus semester in Vilnius. Denecke's research centers on the intersection of digital cultures and material infrastructures, with particular focus on metaphors in theory formation, logistical systems, digitally mediated work, and age-appropriate assistance technologies. His work critically examines how conceptual frameworks like 'flow' and 'circulation' shape our understanding of digital phenomena while investigating the hidden labor within platform economies and care infrastructures. He approaches these topics through media theory, cultural studies, and political economy perspectives, often employing historical and comparative methods. His publications reveal a consistent trajectory exploring the metaphors that structure digital culture, evolving from early work on streaming and data flows to more recent investigations of logistical capitalism, care work in technical environments, and the material conditions of digital labor. Denecke frequently collaborates with other scholars while maintaining a distinctive focus on the epistemological dimensions of digital infrastructure and the social implications of emerging technologies. Denecke teaches courses including 'Data Bodies. People in Computing Environments,' 'Digital Media and Health,' 'Care Work' (at MA level), and 'Artificial Intelligence and Human Work' (at BA level), reflecting his research interests in the relationship between humans and computational systems. His teaching appears to integrate theoretical frameworks with contemporary case studies from platform economies and assistive technologies. As part of the Collaborative Research Center 'Virtual Lifeworlds,' Denecke contributes to interdisciplinary research examining the evolving relationship between digital technologies and human experience, particularly investigating how infrastructures shape everyday life and social relations in increasingly digitized environments.

• Distributed computing
• Parallel computing
• Cloud computing
• +۶ مورد دیگر

Dana Petcu is a Professor at the Computer Science Department of the Faculty of Mathematics and Computer Science at West University of Timisoara. She serves as Director of both the Institute for Advanced Environmental Research and Institute e-Austria Timisoara. With expertise in distributed and parallel computing, she has published over two hundred papers on Cloud, Grid, Cluster, and HPC computing. She is also the chief editor of the open-access journal Scalable Computing: Practice and Experience (SCPE) and has coordinated multiple European Commission-funded projects. Education: Ms. Degree in Computer Science Ph.D. in Numerical Analysis Dana Petcu's research focuses on distributed and parallel computing systems. Her current interests include Cloud & Grid computing, and HPC & Cluster computing. Previously, she worked on Mathematical software, Numerical methods, and Computer graphics. Her work bridges theoretical foundations with practical implementations, particularly in resource management, scheduling algorithms, and scalable computing architectures. She has developed significant expertise in applying these technologies to scientific computing, data-intensive applications, and multi-cloud environments. Her scholarly output demonstrates consistent focus on cloud and distributed computing evolution. Over the past decade, her research has shifted from foundational grid computing to modern cloud technologies, edge computing, and exascale systems. Key themes include resource management across heterogeneous environments, autonomic systems, security SLAs, and multi-cloud portability. Her work shows increasing interdisciplinary connections, particularly with AI/ML techniques applied to resource optimization and anomaly detection in large-scale systems. Scientific Awards: Maria Sibylla Merian-Award (2005) IBM Faculty Award (2009) MLNR Award "Spiru Haret" (2015) Romanian Academy Award "Gheorghe Cartianu" (2015) Dana Petcu has advised numerous graduate students through various master's programs in Distributed and Parallel Computing. She has secured substantial research funding as coordinator of FP7 projects HOST and SPRERS, and as scientific coordinator of mOSAIC. Her grant portfolio includes multiple European Commission-funded initiatives focused on cloud computing infrastructure, resource management, and multi-cloud environments. She has also contributed to EU Research Activities in Cloud Computing as an editor, demonstrating her leadership in shaping European research agendas in this field. She leads the Computer Science Research Center (CCI) and High Performance Computing Service Center (HPC-UVT) at West University of Timisoara. Her teams develop and maintain significant infrastructure for distributed computing research, including simulation environments like CloudSim and iFogSim. She has established strong connections between academic research and practical applications through Institute e-Austria Timisoara, fostering technology transfer and innovation in cloud computing solutions.

Tsichlas Kostasمشاهده پروفایل

دانشیار

• Algorithms and Data Structures for Main and Secondary Memory
• Algorithms and Data Structures in Distributed Environments
• Computational Geometry
• +۵ مورد دیگر

Tsichlas Kostas serves as an Associate Professor in the Department of Computer Engineering and Informatics at the University of Patras, Greece, within the Division of Applications and Foundations of Computer Science. His research spans fundamental and applied computing domains with active involvement in the ML@Cloud laboratory. His expertise centers on algorithmic innovation across multiple dimensions: Memory-optimized algorithms for primary/secondary storage systems Distributed environment data structures and computational geometry Physics-informed computing and complex network analysis Specialized domains including alphanumeric and graph algorithms Recent publication trends (2022-2025) demonstrate concentrated research in historical graph management systems, temporal network analysis, and physics-computing intersections. Key contributions include vertex-centric partitioning strategies, temporal community detection frameworks, and machine learning applications for energy data motif discovery. He maintains active laboratory affiliations with the LARGE-SCALE CLOUD DATA MACHINE LEARNING WORKSHOP (ML@Cloud lab), Combinatorial Algorithms Laboratory, and Distributed Systems and Telematics Laboratory, contributing to Greece's computational research infrastructure.

Mario Marques Freireمشاهده پروفایل

استاد

• Computer Systems and Networks
• Cloud Computing
• Computer Security and Privacy
• +۳ مورد دیگر

Mário Marques Freire is a Full Professor at the University of Beira Interior, where he serves as President of the Faculty of Engineering and Professor in the Department of Computer Science. He is also a Senior Researcher at Instituto de Telecomunicações, leading the Secure and Intelligent Networked Software Systems Lab. His academic qualifications include a Dr. habil. in Computer Science (2007) and Ph.D. in Electrical Engineering (2000) from the University of Beira Interior, complemented by an M.Sc. in Systems and Automation and B.Sc. in Electrical Engineering from the University of Coimbra. Freire's research spans Computer Systems and Networks, with specific expertise in Cloud Systems, Computer Networking, Security & Privacy, and AI applications. His work focuses on infrastructure virtualization, encrypted traffic classification, DDoS attack detection, and malware analysis. Recent publications demonstrate strong emphasis on cloud security, AI-enhanced defect prediction, and IoT security frameworks. He has received several scientific awards including multiple Conference Best Paper Awards and an ACM Certificate of Appreciation for his long-term contributions. Freire has supervised over 35 graduate students (10 PhD graduates) and led numerous research projects such as 'Towards the assurance of SECURity by dESIGN of the Internet of Things' (FCT/COMPETE/FEDER) and 'Cloud Computing Competence Center'. At the University of Beira Interior, Freire has held significant administrative roles including Vice-Rector (2020-2021), Director of Computer Science Services (2009-2013), and multiple terms as President of the Faculty of Engineering. He leads the Network Applications and Services research group at Instituto de Telecomunicações, focusing on secure networked systems.

• Smart Cities
• Semantic Web
• Distributed Systems
• +۴ مورد دیگر

Luis Sanchez Fernandez is a Full Professor at the Department of Telematics Engineering, Carlos III University of Madrid. His research focuses span Smart Cities, Semantic Web, and Distributed Systems. Contact information includes email luis.sanchez@uc3m.es and office location 4.1.F08 in Leganés. His research program integrates Blockchain Governance , Urban Mobility Analysis , and Complex Systems Modeling . Recent work examines approval-based voting mechanisms in decentralized networks and fractional transport equations for physical simulations. Publications demonstrate a strong emphasis on fair algorithm design for societal applications. Key article themes show convergence of Smart City Data Integration Multiwinner Election Algorithms Cellular Automaton Dynamics Semantic Annotation Frameworks As Deputy Director of Teaching Affairs, he leads curriculum innovation in Telematics Engineering. His educational background includes a Doctorate from Universidad de Salamanca, focusing on Wikipedia as a teaching resource in higher education.

Peter Macgregorمشاهده پروفایل

مدرس

• Algorithms
• Data Science
• Machine Learning
• +۴ مورد دیگر

Peter Macgregor is a Lecturer at the School of Computer Science, University of St Andrews, specializing in algorithms for data science and machine learning. His research focuses on graph theory, clustering algorithms, and computational geometry for similarity search. Primary Affiliation: School of Computer Science, University of St Andrews Research Themes: Spectral clustering, Clustering algorithms, Graph algorithms, Similarity search, Theoretical computer science Research Interests: Data Science and Machine Learning are at the core of Peter Macgregor's research, with a particular emphasis on Spectral Clustering, Clustering Algorithms, and Computational Geometry. He explores topics like kernel density estimation for similarity graphs, efficient data structures for dynamic datasets, and theoretical guarantees for clustering methods. Scientific Output Trends: His recent work (2022-2025) spans spectral clustering optimizations, dynamic DBSCAN variants, and similarity graph approximations. Publications focus on algorithmic efficiency, clustering quality metrics, and theoretical foundations of machine learning.

• statistical learning theory
• nonparametric statistics
• information theory
• +۱ مورد دیگر

Dr. Azadeh Khaleghi is a Full Professor of Statistics at École Nationale de la Statistique et de l'Administration Économique (ENSAE) and a permanent member of the Centre de Recherche en Économie et Statistique (CREST). Her research focuses on the mathematical foundations of Statistics and Machine Learning, particularly in devising nonparametric models for long-memory time series and Multi-Armed Bandits problems. Research Interests Statistical learning theory Nonparametric statistics Information theory Long-memory processes Scientific Awards & Grants GRAPH4HEALTH ANR Project (2023) Google Faculty Research Award (2019/20) London Mathematical Society Scheme 4 Grant (2019) Adobe Research Grant (2018) London Mathematical Society Scheme 1 Grant (2017) E.M. Gold Best Paper Award (2013) Teaching Statistics 1 (SE2C1EN) at ENSAE (2022-present) Apprentissage Statistique appliqué (SE308) at ENSAE (2022-present) Machine Learning (MATH336) at Lancaster University (2016-2022) Probability & Stochastic Processes (MATH580/STOR602) at Lancaster University (2015-2021)