جست‌وجوی استاد و پژوهشگر

• Visualization
• Geometric Modeling
• Topological Analysis
• +۵ مورد دیگر

Joshua A. Levine is an Assistant Professor in the Department of Computer Science at the University of Arizona, where he has been since 2016. Previously, he held positions at Clemson University’s School of Computing (2012–2016) and the University of Utah’s Scientific Computing and Imaging Institute (2009–2012). He earned his Ph.D. in Computer Science from The Ohio State University, along with BS and MS degrees in Computer Engineering and Mathematics from Case Western Reserve University. Education: Ph.D., Computer Science, The Ohio State University (2009) M.S., Computer Science, Case Western Reserve University (2005) B.S., Computer Engineering and Mathematics, Case Western Reserve University (2003) Research Interests: His work focuses on visualization , geometric modeling , topological analysis , and computational topology . He develops tools like the Topology ToolKit for analyzing complex datasets and explores applications in mesh generation, vector fields, and performance analysis. His methods bridge theory with practical systems for scientific and engineering domains. Key Contributions: His publications span top-tier venues like IEEE VIS, SIGGRAPH, and Computer Graphics Forum , emphasizing scalable visualization techniques and algorithms for high-performance computing. Notable work includes Neural Volume Compression and Particle Merging-and-Splitting . Awards: DOE Early Career Research Program (2018) Best Paper Honorable Mention (IEEE VIS SciVis 2017) Multiple Best Paper Awards at visualization and meshing conferences (2010–2017) Labs & Collaborations: Leads visualization research at the University of Arizona, contributing to the Topology ToolKit (TTK), an open-source platform for topological data analysis. Engages in interdisciplinary projects with civil engineering and natural language processing communities.

Tariq Yousefمشاهده پروفایل

دانشیار

• Natural Language Processing
• Named Entity Recognition
• Digital Humanities
• +۱۱ مورد دیگر

Tariq Yousef is an Associate Professor in Data Science at the Department of Mathematics and Computer Science, University of Southern Denmark. He is actively engaged in interdisciplinary research combining computer science and the humanities, particularly in natural language processing for ancient and classical languages. His research interests include Named Entity Recognition, Visual Analytics, Deep Learning, and Digital Humanities. He focuses on developing robust NLP models for under-resourced languages such as Ancient Greek and Classical Arabic, leveraging parallel corpora and handwritten text recognition. His work bridges computational techniques with philological and historical research. The recent publications indicate a strong trend in applying AI and visualization tools to ancient language processing, with a focus on annotation, evaluation, and model enhancement. His work frequently appears in computational linguistics and language technology venues, demonstrating sustained contributions to NLP for cultural heritage. Professional Activities and Leadership: Organizer, The First Workshop on Visualization for Natural Language Processing (May 2024) Co-organizer, DH workshop “Data Stories: Analyzing and Visualizing Textual Narratives” (May 2024) Organizer, Ugarit: Translation Alignment Technologies for Under-resourced Languages (July 2022) Research Projects: Carlsberg Foundation - E-Rhetoric: HTR for Medieval Greek (2025–2028) VERNE: Circular Solutions for Sustainable Tourism (EU, 2024–2027) AlgaeProBANOS: Product Development in Baltic/North Sea (EU, 2023–2027) Empowering Humanities Education with Deep Learning (2023–2024) Teaching and Supervision: Teaching: Introduction to Web Data Science (2023–2023), DS838: Web Development (2024) Supervision: Retrieval Augmented Chatbot for Algae Researchers (2024)

Jari Porrasمشاهده پروفایل

استاد

• Software Engineering
• Distributed Systems
• Human-Computer Interaction
• +۵ مورد دیگر

Jari Porras is a Professor of Software Engineering (specializing in Distributed Systems) at Lappeenranta-Lahti University of Technology LUT. He holds visiting professorships at Aalto University (Finland) and the University of Huddersfield (UK). His research spans distributed systems, human-computer interaction, and sustainability in ICT, with a recent focus on human and sustainability aspects of software engineering. D.Sc. (Tech) in Computer Science from LUT (1998) 500+ supervised Master’s theses and 27 examined doctoral dissertations External evaluator for 28 doctoral theses since 2000 His research interests include: Parallel and distributed computing Wireless and mobile systems Sustainable ICT Human and sustainability aspects of software engineering Wearable technology usability Big data and its organizational implications Recent publications focus on security-usability alignment, sustainability integration in software engineering, wearable technology adoption, and big data applications. He collaborates internationally and has co-authored works with researchers from multiple institutions.

Amal Abd El-Raoufمشاهده پروفایل

استاد

• Software Engineering
• Software Quality
• Big Data
• +۴ مورد دیگر

Dr. Amal Abd El-Raouf is a Full Time Professor in the Computer Science Department at Southern Connecticut State University, where she has served since 2005. She teaches undergraduate courses including Introduction to Programming, Data Structures, and Algorithms, along with graduate Software Engineering and Software Quality courses. As an active member of the Greater Hartford Association for Computing Machinery (ACM) Chapter, she contributes to the regional computing community. Her academic credentials include a PhD in Computer Science and Engineering from the University of Connecticut (2005) and Bachelor's and Master's degrees from Cairo University. This foundation supports her dual expertise in theoretical computer science and practical software development. Dr. Abd El-Raouf's research spans critical areas of modern computing: Software Engineering and Quality form the core of her work, extended into Big Data analytics, Real-time Systems, Parallel and Distributed Computing, and Object-Oriented Systems. Her investigations address fundamental challenges in software performance, maintenance, and reliability, with recent work bridging into bioinformatics and machine learning applications. Analysis of her 2008-2019 publications reveals an evolving research trajectory: beginning with foundational work in software restructuring and maintenance, she progressively incorporated Big Data techniques and machine learning. Notable trends include applying clustering algorithms to software architecture, optimizing distributed systems, and developing predictive models for sales and software reliability - demonstrating consistent innovation from theoretical frameworks to practical implementations. Her research program is substantially supported by diverse funding sources: NASA Connecticut Space Consortium grants for bioinformatics initiatives ($2,000, 2024-2025) Microsoft Azure Cloud Grant ($20,000, 2016-2017) CSU Research Grants for Big Data performance optimization ($3,000, 2018-2019) and software reliability prediction ($3,750, 2017-2018) NASA Space Grant for cloud-based gene expression analysis ($7,500, 2015-2016) While specific lab infrastructure isn't detailed in available materials, her collaborative publications with researchers like Hanafi and Fergany indicate active research partnerships across institutions. Her current work on the NASA-funded Women in Bioinformatics Workshop signals ongoing expansion into interdisciplinary computational biology applications.

Rage Uday Kiranمشاهده پروفایل

دانشیار

• Data Mining
• Parallel Computation
• Air Pollution Data Analytics
• +۳ مورد دیگر

Prof. Rage Uday Kiran is an Associate Professor at the University of Aizu in Aizu wakamatsu, Fukushima, Japan, with additional visiting researcher appointments at the University of Tokyo and the National Institute of Information and Communications Technology (NICT) in Tokyo. He holds a PhD in Computer Science from the International Institute of Information Technology, Hyderabad, and has published over 50 articles in top-tier venues including EDBT, SSDBM, PAKDD, and DASFAA. Education PhD in Computer Science - IIIT-Hyderabad, India (2005-2011) MS in IT Agriculture - DA-IICT, India (2003-2005) B.Tech in Agriculture Engineering - Acharya N. G. Ranga Agricultural Engineering College, India (1999-2003) Research Focus Dr. Kiran specializes in developing novel data mining models and high-performance algorithms for temporal, spatiotemporal, and transactional databases. His primary research explores pattern mining techniques for rare item discovery, parallel computation optimizations, and real-world applications in air pollution analysis, traffic congestion prediction, agricultural ICT systems, and recommender systems. His recent work focuses on utility-based pattern discovery in spatiotemporal contexts and periodic pattern detection in temporal databases, emphasizing scalability and efficiency for large-scale datasets. Professional Experience Project Assistant Professor - University of Tokyo (2015-2020) Researcher - NICT Tokyo (2015-2020) Post Doctoral Researcher - University of Tokyo (2012-2015) Technical Contributions Dr. Kiran maintains public datasets for pattern mining research and develops open-source tools including the PAttern MIning Python Kit (PAMI-PyKit). His technical expertise spans C/C++/Java, database systems (MySQL/PostgreSQL), web technologies, and cloud platforms (Azure/AWS).

• Computer Science
• Parallel Computing
• Data Stream Processing
• +۳ مورد دیگر

Dr. Jakub Yaghob is a researcher at the Faculty of Mathematics and Physics , Charles University , specializing in computer science and parallel computing. He teaches advanced programming topics including Compiler Principles , Parallel Programming , and Cloud Computing . Research Interests : Parallel data stream processing, virtualization technologies, semantic web infrastructures, and performance optimization Teaching : Advanced C++ programming, virtualization administration, and computer systems architecture Technical Expertise : Design of parallelization frameworks, astrophysical data analysis, and hybrid CPU-GPU systems His publications focus on: Optimizing stream data processing across distributed architectures Developing domain-specific languages like Bobolang Performance evaluation in educational programming contexts Applications of parallel computing in astrophysics

Christian Fischer Pedersenمشاهده پروفایل

دانشیار

• Signal processing
• Machine learning
• Distributed systems

Christian Fischer Pedersen is an Associate Professor at the Department of Electrical and Computer Engineering , Aarhus University School of Engineering. His research focuses on signal processing, machine learning, and distributed systems, with applications in biomedical engineering and industrial diagnostics. Signal processing for healthcare and mechanical systems Machine learning applied to survival analysis and text mining Distributed systems for medical and industrial applications Recent publications highlight his work in explainable AI for pediatric arthritis detection, probabilistic neural networks for survival analysis, and MEMS sensor applications in mechanical diagnostics. His projects include European collaborations like the PRECISE fall-risk prevention initiative. Contact: cfp@ece.au.dk

Chunbo Chuمشاهده پروفایل

استاد

• Distributed Systems
• Applications of Artificial Intelligence
• Computer Science Education
• +۳ مورد دیگر

Dr. Chunbo Chu is a Professor in the Department of Computing Sciences and Mathematics at Franklin University, where he has served as Lead Faculty of Computer Science since 2008. He holds a Ph.D. in Computer Science from Wayne State University (2008) and B.S. and M.S. in Computer Science from Fudan University (1997, 2000). As academic leader, he chairs both the B.S. and M.S. in Computer Science programs. Ph.D. in Computer Science, Wayne State University, 2008 M.S. in Computer Science, Fudan University, 2000 B.S. in Computer Science, Fudan University, 1997 His research focuses on distributed systems, with significant contributions to predicate detection and network monitoring. He also investigates artificial intelligence applications and leads innovations in computer science education through learning analytics and retention studies. His work bridges theoretical foundations with practical implementations in software architecture and network traffic modeling. Dr. Chu has made 15 publications spanning 1999-2024, emphasizing educational improvement and distributed system reliability. His scholarship includes key contributions to overlay network design (2007), multifractal traffic modeling (2001-2002), and modern educational approaches using BRIDGES platform (2024). Academic Leadership Fellow (2024-25) Robert L. Bailey Teaching Award (2024, graduate faculty) Provost's List for teaching excellence (2020-2021) Dr. Chu actively contributes to computer science education through NSF-funded initiatives, program committee memberships (IEEE Frontiers in Education 2024, EAAI-24), and invited talks on AI's educational impact. He serves as technical reviewer for journals including IEEE Transactions and Journal of Parallel and Distributed Computing .

• Operations Research
• Production and Operations Management
• Scheduling Theory and Applications
• +۳ مورد دیگر

Chuen-Lung Chen is a Professor in the Department of Management Information Systems at National Chengchi University's College of Commerce in Taipei, Taiwan. He holds a Ph.D. in Industrial and Systems Engineering from Auburn University in the United States, with additional graduate and undergraduate degrees from Tamkang University and Chung Yuan Christian University in Taiwan. His research focuses on Applied Operations Research, Production and Operations Management, and Scheduling Theory and Applications . Professor Chen has developed numerous optimization algorithms and heuristics for solving complex scheduling problems in manufacturing and service industries, with applications extending to financial engineering and knowledge management systems. His work bridges theoretical operations research with practical business applications, particularly in production planning and resource allocation. Analysis of his recent publications reveals a consistent focus on flow shop scheduling problems, with increasing applications to financial modeling and knowledge integration systems. His research methodology combines traditional operations research techniques with evolutionary computation approaches like genetic algorithms and particle swarm optimization. Distinguished Professor award (2016-2017, 2015-2016, 2012-2013) Senior Excellent Teacher Award (10 years, 2010) Academic Research Excellence Award (2010) Multiple Internationalization Research Excellence Awards (2007-2008) Outstanding Service Teacher Award (2008) Outstanding Teaching Award (2002) MOST First-Class Research Award (1990) Professor Chen has secured substantial research funding through Taiwan's Ministry of Science and Technology (MOST), directing numerous projects on scheduling algorithms, financial modeling, and management information systems. He has held significant administrative roles including Department Chair (2011-2014), Deputy Dean of the College of Commerce (2008-2016), and Director of the International MBA Program (2011-2016). His leadership extends to directing international programs and contributing to university-wide initiatives for English-taught degree programs. His work demonstrates a sustained contribution to both theoretical advancements in operations research and practical applications in manufacturing and financial systems, with a research trajectory showing increasing interdisciplinary connections between operations management and information systems.

Manuel Lama Penínمشاهده پروفایل

استاد

• Machine Learning
• Process Mining
• Predictive Monitoring
• +۶ مورد دیگر

Manuel Lama Penín is a Full Professor at the Research Center on Intelligent Technologies (CiTIUS) with the University of Santiago de Compostela. His research bridges machine learning, process mining, and semantic modeling to address challenges in healthcare, industry, and e-learning. He has led 29 of 51 R&D projects and authored over 150 scientific works, including 52 journal articles. Research Areas: Process Mining & Conformance Checking Predictive Monitoring & Drift Detection Semantic Modeling & Service Computing Social Workflow Analysis & Data-to-Text AI in Healthcare & Educational Technologies Notable Projects: TRANSFIRESAUDE (2023, EU Health R&I Ecosystems) AQUATECHInn 4.0 (2023, Aquaculture Education) INCEPTION (2021, Concept Drift in Process Mining) Scientific Recognition: Recipient of the Transfer Technology award from the Galician Royal Academy of Sciences for co-founding InVerbis Analytics, a spin-off commercializing process mining research. Article Trends: His recent publications focus on explainable AI integration in EU regulations, drift detection methodologies, and natural language generation for process documentation. Collaborative efforts emphasize cloud-based solutions, healthcare process formalization, and predictive modeling across sectors.

Artan Lumaمشاهده پروفایل

استاد

• Cryptography
• Blockchain Technology
• Cybersecurity
• +۶ مورد دیگر

Artan Luma is a Full Professor at the Faculty of Contemporary Sciences and Technologies at South East European University in Tetovo, Macedonia. His career spans 20+ years in academia, focusing on cryptography, cybersecurity, and blockchain technologies. He has held leadership roles such as Deputy Director of the e-Learning Center since 2005. PhD in Computer Sciences (SEEU, 2010) MSc in Electrical Engineering Science (State University of Prishtina, 2007) Engineer of Informatics (State University of Tetova, 2002) Luma's research bridges theoretical and applied domains, including blockchain-based academic credential verification , IIoT cybersecurity frameworks , and data mining for curriculum-job market alignment . His recent publications highlight smart contract implementations and machine learning-enhanced security systems . As a co-author, Luma collaborates with researchers like Halil Snopce, Azir Aliu, and Ylber Januzaj. His work appears in journals such as IEEE, SAR Journal, and TEM Journal, with a 2025 article on test coverage analysis.

Leticia Arco Garciaمشاهده پروفایل

پژوهشگر

• Natural Language Processing
• Complex Networks
• Deep Learning
• +۵ مورد دیگر

Leticia Arco Garcia is a postdoctoral researcher at Vrije Universiteit Brussel working in the Department of Informatics and Applied Informatics with affiliations at Federated Labs AI and Robotics. Her research spans computational linguistics, complex network analysis, and more recently, interdisciplinary applications in archaeobotany. Her research interests focus on Natural Language Processing , Complex Networks , and Deep Learning methodologies. Early work centered on community detection in complex networks and customer behavior analysis, while recent publications demonstrate expansion into archaeobotanical applications using morphometric analysis of phytoliths. She employs advanced computational techniques including LSTM networks, case-based reasoning, and multi-objective optimization across diverse domains from smart power grids to electronic money systems. Analysis of her 15 most recent publications reveals an interdisciplinary trajectory moving from pure computer science applications toward biological/archaeological domains. The earliest works focus on network construction and community detection, while more recent papers apply similar analytical frameworks to botanical specimens, indicating methodological transfer between fields. Her work consistently applies computational rigor to complex data structures across diverse application areas. Her scientific contributions include: Development of novel network construction methodologies for large purchasing collections Advancements in overlapping community detection using rough clustering Application of deep learning to sentiment analysis and customer behavior prediction Recent innovative work applying morphometric analysis to archaeobotanical specimens Dr. Garcia actively presents her research at international conferences including INTERSPEECH and specialized meetings on phytolith research. Her collaborative network spans multiple institutions, with co-authorship on interdisciplinary projects connecting computer science with archaeology and plant science. While specific grant information isn't detailed in the available text, her consistent publication output across multiple domains suggests successful research funding.

Ashvin Goelمشاهده پروفایل

استاد

• Systems Software
• Cloud Computing
• Storage Systems
• +۵ مورد دیگر

Ashvin Goel is a Professor in the Department of Electrical and Computer Engineering and Department of Computer Science at the University of Toronto , where he leads research at the intersection of Operating Systems , Reliability Engineering , and Computer Security . His work focuses on ensuring software systems can withstand bugs and vulnerabilities, with publications in top venues like SOSP , OSDI , and FAST . He received his PhD in Computer Science and Engineering from Oregon Graduate Institute in 2003, with prior degrees from UCLA (MS) and IIT Kanpur (BS). Current research projects include runtime verification systems like Recon and intrusion recovery frameworks such as Taser , often in collaboration with Professor Angela Demke Brown. His team explores kernel instrumentation, file system consistency, and security recovery methods. NSERC Discovery Accelerator Award 2012 FAST Best Paper Award 2012 Google Faculty Research Award 2011 Netapp Faculty Award 2008 Ontario Early Researcher Award Professor Goel's recent publications address deterministic concurrency control, distributed graph mining, and storage system reliability. He teaches graduate courses in Dependable Software Systems and Distributed Systems , with prior course development in operating systems security and time-sensitive applications.

• Algorithms and Data Structures
• Big Data
• High-Performance Computing
• +۱۲ مورد دیگر

Francesco Silvestri is an Associate Professor in Computer Engineering at the Department of Information Engineering , University of Padova . He has held previous roles as Assistant Professor (2016-2019), Post-Doc (2015-2016 at IT University of Copenhagen; 2009-2014 at University of Padova), and Part-Time Lecturer (2013-2014 at IT University of Copenhagen). He earned a Ph.D. in Computer Engineering from the University of Padova (2009). Research Focus: Algorithms and data structures for big data processing, high-performance computing, similarity search, graph mining, I/O-efficient algorithms, mobility algorithms, and differential privacy applications. Teaching: Courses include Big Data Computing and Algorithm Design (University of Padova), Advanced Algorithms Seminar (IT University of Copenhagen). Scientific Contributions: ICDT Best Paper Award (2023) for subgraph enumeration ACM SIGMOD Research Highlights (2020) for fair similarity search Developed patented MapReduce methods for triangle enumeration Collaborations and Supervision: Current PhD students: Mariafiore Tognon (2024), Linghan Zeng (2023), Fabrizio Boninsegna (2022). Collaborators include Ninh Pham (University of Auckland), Gianfranco Bilardi, and Rasmus Pagh. Personal Interests: Active in Dottor Clown Padova for hospital clowning, family life with wife Elisa Salvagnin and three children, and cycling advocacy in Padova.

• Stochastic Programming
• Mathematical Modeling
• Financial Mathematics
• +۷ مورد دیگر

Prof. habil. dr. Leonidas Sakalauskas is an Affiliated Scientist at the Smart Technologies Research Group within the Institute of Data Science and Digital Technologies at Vilnius University. His extensive research career spans mathematical modeling, stochastic programming, and statistical analysis with significant contributions across multiple disciplines. He maintains an active research profile with recent publications extending into 2025. Professor Sakalauskas' research focuses on stochastic programming, mathematical modeling, financial mathematics, statistical analysis, optimization, and queueing theory. His work demonstrates a consistent pattern of interdisciplinary research, bridging theoretical mathematics with practical applications in finance, social sciences, and computer science. He has developed innovative approaches to agent-based modeling, fractal analysis, and Monte Carlo methods, contributing significantly to both theoretical frameworks and practical implementations. His recent publications reveal a continuing evolution of research interests, with increasing emphasis on data science applications, computational social science, and advanced statistical modeling techniques. The articles demonstrate sophisticated methodological approaches to complex problems across diverse domains including financial modeling, social cohesion analysis, and network performance optimization. Throughout his career, Professor Sakalauskas has maintained a strong publication record in high-impact journals and conference proceedings, reflecting his ongoing engagement with the international research community. His work shows consistent methodological rigor combined with practical relevance across multiple application domains. Professor Sakalauskas has collaborated extensively with researchers across Lithuania and internationally, demonstrating strong teamwork and interdisciplinary engagement. His research has practical applications in financial modeling, social science analysis, and computational systems optimization, contributing to both theoretical advances and real-world implementations.