جست‌وجوی استاد و پژوهشگر

• Information Communication Systems
• Multimedia Information Processing
• Content Distribution Systems
• +۴ مورد دیگر

Wataru Kameyama is a Professor in the Department of Communications and Computer Engineering at Waseda University’s School of Fundamental Science and Engineering. Since 2014 he has held a full-time faculty position at Waseda; he previously served as Professor at GITS (2002–2014) and Associate Professor at GITI (1999–2002), both within Waseda University. He received his M.E. and Ph.D. in Electronics Engineering from Waseda University in 1987 and 1990 respectively. Education: Ph.D. in Engineering, Waseda University, 1990 M.E. in Electronics Engineering, Waseda University, 1987 B.E. in Electronics Communication Engineering, Waseda University, 1985 Research Interests: Prof. Kameyama’s research spans information communication systems, multimedia information processing, content distribution architectures, Named Data Networking, digital rights management, and high-dimensional data mining. His recent work emphasizes proactive content-caching for mobile video, producer mobility in NDN, and outlier-detection algorithms for large-scale datasets. Publications Trend: Across 85 peer-reviewed papers (h-index 9) his recent articles focus on next-generation networking (NDN, ICN), mobile multimedia delivery using transportation infrastructure, and data-mining techniques for high-dimensional outliers, often validated through large-scale field experiments. Awards & Honors: International Cooperation Award, ITU Association of Japan (2012) IEEE/IEICE Distinguished & Outstanding Contribution Awards (2009, 2007) Best Author/Best Paper Awards, Institute of Image Information and Television Engineers (2009, 2006) Professional Memberships: ACM, IEEE, Institute of Image Electronics Engineers of Japan, Information Processing Society of Japan, Institute of Electronics, Information and Communication Engineers, Institute of Image Information and Television Engineers.

Gabriele Kotsisمشاهده پروفایل

استاد

• Artificial Intelligence
• Natural Language Processing
• Machine Learning
• +۶ مورد دیگر

Gabriele Kotsis is a Full Professor at the Institute of Telecooperation, Johannes Kepler University Linz, with extensive contributions to Artificial Intelligence research. Her institutional presence spans multiple departments through interdisciplinary projects while maintaining her primary affiliation with JKU's engineering-focused research units. Her research expertise encompasses: Natural Language Processing and Neural Machine Translation systems Reinforcement Learning applications for Smart Grid optimization Mobile Computing and Multimedia Intelligence frameworks Big Data Analytics and Database Systems innovation Human-centered AI development methodologies Recent publications (2024-2025) reveal a strategic focus on practical AI implementations addressing multilingual communication barriers and energy efficiency challenges. Her work consistently bridges theoretical AI advances with real-world applications across multiple domains. Professor Kotsis maintains an active supervision record with thesis guidance and leads multiple funded research initiatives. Her current portfolio includes: 'Enhancing Neural Machine Translation' project (2025-2026) for Southeast Asian languages 'INTES' regulatory compliance ecosystem (2023-2026) 'Human-centered Artificial Intelligence' initiative (2022-2026) Through her leadership in international conferences (iiWAS, MoMM, DEXA) and editorial roles for major proceedings, she maintains significant influence in the global computer science research community while advancing JKU's research profile.

• Machine Learning
• Deep Learning
• Semantic Web

Dr. Shahin Mehdipour Ataee is an Assistant Professor at the State University of New York at Fredonia with teaching experience since 2007. His office hours are held Tuesdays (12:30-2:00 PM), Wednesdays (3:30-5:00 PM by appointment), and Thursdays (12:30-2:00 PM). Education: Ph.D. in Computer Engineering - Information Systems Track, Eastern Mediterranean University (2018) M.S. in Software Engineering, Qazvin Azad University (2010) B.S. in Computer Engineering - Software Track, Mazandaran University of Science and Technology (2007) His research focuses on Machine Learning , Deep Learning , and Semantic Web technologies, with applications in AI problem-solving, multimedia pattern recognition, and web service frameworks. Recent publications demonstrate expertise in logic-based systems and computational methods. Awards: Teaching Excellence Award 2024-2025 Teaching Excellence Award 2023-2024 Research Excellence Award 2022-2023 He teaches a broad spectrum of computer science courses including core subjects like Algorithms, Operating Systems, and specialized topics in AI/ML. While no active lab or grants are mentioned, his publication record indicates ongoing scholarly activity.

• Data Mining
• Bioinformatics
• Neural Networks
• +۵ مورد دیگر

Dr. Ray R. Hashemi is a Professor in the Department of Computer Science within Georgia Southern University's College of Engineering and Computing. His academic career spans over 14 years of continuous research output from 2003-2017, with significant contributions as co-editor for four International Conferences on Information Technology and Knowledge Engineering (2005, 2010, 2014, 2017). His research focuses on innovative applications of data mining across diverse domains: Bioinformatics: DNA sequence analysis, organ toxicity prediction, and liver cancer predictive systems Medical Informatics: Bone mineral density analysis using DEXA data and dendrograms Financial Systems: Extraction of essential constituents from S&P500 index Environmental Science: Climate prediction using algae sedimentation patterns Computer Vision: Video mining for theatrical analysis and Android-based OCR for non-flat documents Methodologically, Dr. Hashemi specializes in neighborhood systems analysis, association rule mining, and grid-based approaches for sparse data. His work consistently bridges theoretical data mining concepts with practical applications, developing tools for signature-based prediction, record layout discovery, and intent analysis through web behavior. Recent publications (2015-2017) show increased focus on domain-specific applications in finance and toxicology while maintaining core data mining expertise. His collaborative work includes partnerships with international researchers across multiple continents, demonstrated through conference editorial roles and co-authored publications. Dr. Hashemi's research demonstrates sustained scholarly activity with practical implementations in medical diagnostics, financial analysis, and environmental prediction systems.

Marius Călinمشاهده پروفایل

مدرس

• Soft Computing in Agricultural Sciences
• Decision Making under Uncertainty
• Decision Support Systems
• +۲ مورد دیگر

Marius Călin serves as a Lecturer in the Department of Exact Sciences at the Faculty of Horticulture, University of Agricultural Sciences and Veterinary Medicine of Iasi, Romania. His academic profile bridges computational science with agricultural applications through expertise in Information Technology, Applied Mathematics, and E-learning systems development. His research portfolio centers on five core domains: Soft Computing in Agricultural Sciences: Pioneering fuzzy logic applications for plant breeding and greenhouse management Decision Making under Uncertainty: Developing models for agricultural scenarios with incomplete data Decision Support Systems: Creating tools for land suitability assessment and resource optimization E-learning Applications: Designing specialized platforms for agricultural education Graph Databases: Implementing knowledge representation systems for agricultural data Analysis of his publication trajectory reveals an evolution from foundational fuzzy decision models (2000-2007) toward increasingly interdisciplinary work. Recent publications (2015-2022) demonstrate strong convergence between e-learning infrastructure development and agricultural informatics, particularly through Moodle-based systems for remote education during the pandemic and computational models for soil management. His work consistently emphasizes practical implementation of grid computing and soft computing techniques in agricultural contexts. Dr. Călin has secured significant research funding through seven major contracts, including leadership as USAMV Iasi responsible for the CEEX 1801 grid computing project (2006-2008) and contributions to sustainable soil management initiatives (ECOSEUMET, CNCSIS 738). His grant portfolio demonstrates sustained focus on translating computational research into agricultural solutions. Within the university ecosystem, he serves as Moodle platform administrator and coordinates Informatics discipline activities, fostering cross-departmental collaboration. His professional affiliations include ESNA (European Society for New Methods in Agricultural Research) and ROMAI (Romanian Society of Applied and Industrial Mathematics), reflecting his commitment to interdisciplinary scientific exchange.

Zhao Pengمشاهده پروفایل

استادیار

• Data Visualization
• Online Privacy Literacy
• Computational Journalism
• +۴ مورد دیگر

Dr. Zhao Peng is an assistant professor in the Department of Journalism at Emerson College, Boston, specializing in data visualization, online privacy literacy, computational journalism, and topic modeling. Her research addresses critical questions about interactive data presentation, DEI applications in visual communication, audience interactions with algorithmic media, and privacy literacy campaigns for families. She employs mixed-method approaches to bridge theoretical communication frameworks with practical journalistic applications. Education: B.S., Yunnan University of Finance and Economics M.S., Michigan State University Ph.D., Michigan State University Dr. Peng's research integrates technical communication methods with societal challenges, particularly through her work on the Design Justice Framework for DEI-compliant data visualizations and algorithmic journalism. Her sharenting studies examine parental behavior through privacy calculus and multiparty perspectives, revealing tensions between self-presentation and privacy self-efficacy. She actively investigates how news organizations leverage AI for content production while maintaining journalistic integrity, with implications for news gathering, distribution, and audience engagement in digital ecosystems. Her publication trajectory (2017-2024) demonstrates evolving expertise from foundational journalistic interviewing competencies to advanced computational methods. Recent work emphasizes practical DEI applications in data visualization during crises like the COVID-19 pandemic, while her sharenting research identifies cultural and psychological drivers of parental sharing behaviors. The corpus shows consistent focus on communication methodology, with increasing attention to ethical AI implementation and privacy frameworks in digital journalism. Dr. Peng teaches Algorithmic News, Data Journalism, Interactive News, Digital Journalism, and Multimedia Capstone courses, equipping students with technical skills in R (95%), Python (70%), SPSS (90%), and data storytelling. Her research projects include developing DEI data visualization audit protocols and analyzing algorithmic media consumption patterns, aiming to create industry-ready resources for journalists navigating technological transformation.