جست‌وجوی استاد و پژوهشگر

Anna Monrealeمشاهده پروفایل

دانشیار

• Data Analytics
• Data Mining
• Artificial Intelligence
• +۵ مورد دیگر

Anna Monreale is an Associate Professor of Computer Science at the University of Pisa and serves as the Delegate for Masters and Continuing Education. She concurrently holds an Adjunct Professor position at Dalhousie University's Faculty of Computer Science. Her institutional leadership includes vice-coordinating the National Doctorate in Artificial Intelligence for Society and chairing the Internships Committee for the Master's in Data Science & Business Informatics. She earned her Bachelor's and Master's degrees with honors in Computer Science from the University of Pisa in 2007 and completed her PhD there in 2011. Prior to her faculty appointment, she conducted research on European projects from 2011-2014 and has been affiliated with the CNR's Institute of Information Science and Technologies since 2008. Professor Monreale's research centers on ethical AI development, with core expertise in data mining, explainable AI, and privacy-by-design methodologies. Her work bridges technical innovation with societal impact, particularly in healthcare applications, mobility data analysis, and social media systems. She emphasizes transparency and legal compliance in algorithmic decision-making. Her recent publications (2023-2025) reveal a concentrated focus on trustworthy AI frameworks, with significant contributions to federated learning privacy, trajectory data protection, and real-time explainability systems. She actively addresses emerging challenges in content moderation regulation and healthcare AI deployment. As an educator, she teaches Data Mining and Data Science Lab courses while directing major academic initiatives. She leads a PNRR-funded national project and a European research project, with over 90 publications and 3,200+ citations reflecting her scholarly impact. She is a key member of the University of Pisa's KDD Lab and maintains strong ties with CNR research units.

• Machine Learning
• Knowledge Discovery
• Data Mining
• +۶ مورد دیگر

Manuel Filipe Vieira Torres Santos is an Associate Professor with Habilitation at the School of Engineering of the University of Minho, Portugal. He serves as a Senior Researcher at Centro ALGORITMI (Associate Laboratory in Intelligent Systems) where he coordinates the Intelligent Data Systems research lab and the Information Systems and Technologies research group. His academic career combines teaching responsibilities with extensive research activities focused on data science applications in healthcare settings. Dr. Santos' research interests center on applying advanced computational techniques to healthcare challenges. His primary areas include Machine Learning, Knowledge Discovery from Databases, Data Mining, Agent-based Systems, and Intelligent Decision Support Systems. He has pioneered work in Pervasive and Adaptive Business Intelligence specifically tailored for healthcare environments. His research spans both theoretical advancements in data science methodologies and practical implementations in real-world medical settings, particularly in intensive care units, hospital management, and precision medicine applications. Analysis of his recent publications reveals a strong trend toward integrating emerging technologies like blockchain, Internet of Things, and openEHR standards into healthcare information systems. His work demonstrates a progression from foundational data mining techniques to increasingly sophisticated architectures that support real-time decision making, predictive analytics, and personalized patient care. The publications show particular emphasis on standardization efforts, interoperability challenges, and the development of maturity models for digital transformation in healthcare institutions. Coordinator of Intelligent Data Systems research lab Coordinator of Information Systems and Technologies research group Principal Investigator for multiple funded projects including: Intelligent hospitalization management (2021-present) Data Science applied to diabetes (2021-present) Intelligent Decision Support for response times optimization (2020-present) Intelligent Hospital Infection Control (2020-present) Dr. Santos has secured significant research funding for healthcare analytics projects, demonstrating the practical value of his work. His approach combines technical expertise in data science with deep understanding of healthcare workflows and challenges, resulting in solutions that address real clinical needs while advancing the state of the art in health informatics.

• Information Systems
• Business Intelligence
• Data Mining
• +۴ مورد دیگر

Maria Isabel Calapez Cabrita Leal Seruca serves as an Associate Professor at Portucalense University's Science and Technology Department in Porto, Portugal, where she teaches Information Systems and coordinates the Informatics Engineering and Information Systems for Management programs. She additionally holds the role of Department Coordinator for the Erasmus+ Programme. As a Senior Researcher at Universidade do Minho's Centro Algoritmi, she contributes to the IST R&D Group and ISTTOS R&D Lab, bridging academic and research domains across institutions. Her academic foundation includes a degree in Applied Mathematics and Computation from Portucalense University, complemented by an MSc and PhD in Computation from the University of Manchester Institute of Science and Technology (UMIST). This technical background underpins her research trajectory in digital systems and organizational transformation. Her research focuses on Information Systems with specialized expertise in Business Intelligence, Data and Web Mining, Digital Transformation, and Reuse and Patterns. She has pioneered models like E-swim (Enterprise Semantic Web Implementation) and ToOW (Training of Organizations in the Web) to address organizational challenges through technology adoption. Her work consistently integrates practical applications in educational settings and industry contexts, particularly through international Erasmus collaborations. Analysis of her 2019-2025 publications reveals a dominant emphasis on Digital Transformation in educational mobility systems (notably Erasmus+ projects), alongside applied Data Mining in domains including tourism, pharmaceutical distribution, and waste management. Her scholarship demonstrates a trajectory from foundational semantic web research toward contemporary organizational digitalization challenges, with increasing focus on sustainable development frameworks. While no major individual scientific awards are documented in the source material, her research output includes 41 publications with an h-index of 7 and 164 citations, reflecting steady scholarly impact in Information Systems journals and conferences. She actively leads Erasmus+ initiatives including the KA220 project 'Data Science We Teach Data' and has coordinated multiple Erasmus Intensive Programmes on Sustainable Digital Transformation (2022-2023), Business Intelligence (2008-2010), Web Mining (2011-2013), and Data Science (2014). Her grant work consistently targets curriculum development for digital competencies in higher education. Within research infrastructure, she contributes to the Digital Transformation and Innovation in Organisations (DTIO) Group at REMIT (Portucalense University) and the ISTTOS Lab at Centro Algoritmi (Universidade do Minho), focusing on organizational agility through technology adoption and pattern-based solutions.

• Sports Science
• Exercise Physiology
• Environmental Physiology
• +۲ مورد دیگر

Takashi Ichinose is a Professor at Waseda University's Center for Research Strategy and Faculty of Sport Sciences. He has held progressive roles at Waseda since 2004, including his current position in the Center for Research Strategy since 2008, where he manages research strategy while maintaining his academic focus on sports science. His educational background includes: Doctor of Philosophy in Medical Science from Shinshu University Graduate studies in Life Sciences from Waseda University's Graduate School of Human Sciences Undergraduate degree in Sport Sciences from Waseda University's School of Human Sciences Professor Ichinose's research spans exercise physiology, environmental physiology, and sports science, with particular focus on substrate metabolism during exercise, thermoregulation, and plasma volume changes. He has made significant contributions to understanding how nutritional interventions like tea catechins affect fat metabolism. He is also a leading figure in Tee-ball development, a modified baseball sport officially adopted into Japan's elementary school curriculum. His publication record shows an evolution from fundamental exercise physiology research to broader applications in sports education and inclusion. Early work focused on metabolic responses to exercise, while more recent publications address sports education, disability inclusion, and international sports initiatives like the Olympics. Professor Ichinose has received research funding from the Japan Society for the Promotion of Science and collaborated with industry partners including Kao Corporation. His presentations at numerous research meetings demonstrate his active role in promoting research funding opportunities at Waseda University. He has been deeply involved with the Japanese Tee-ball Association, contributing to Tee-ball's adoption in Chinese and Korean educational systems. His work bridges academic research with practical applications in sports education and physical activity promotion across Asia.

• Digital signal processing
• Wireless communications
• Signal processing
• +۳ مورد دیگر

Xiaodan Pang is a Tenure Professor at Riga Technical University (RTU) specializing in cutting-edge photonics and communications research. Her work focuses on developing next-generation optical and wireless technologies for high-speed data transmission systems, with significant contributions to silicon photonics, terahertz communications, and integrated sensing and communication architectures. Her primary research interests include: Digital signal processing Wireless communications Signal processing Fiber optics Optoelectronics Photonics Professor Pang's recent publications demonstrate leadership in overcoming fundamental challenges in high-speed communications. Her 2025 work spans silicon photonics ring-resonator modulators for optical-amplification-free links, photonic terahertz chaos systems for secure ranging, and analog fronthaul solutions for 6G networks. Key themes include neural network equalization for ultra-high baudrate transmission, energy-efficient unamplified optical links, and integrated sensing-communication systems leveraging terahertz frequencies. This research directly addresses critical bottlenecks in data center interconnects, 6G mobile infrastructure, and secure high-precision wireless applications. Her technical profile is documented through ORCID (0000-0003-4906-1704), Scopus (54407301300), and Web of Science (D-5032-2015) identifiers, with active professional engagement via LinkedIn.

• Sports Biomechanics
• Artificial Intelligence in Sports
• Human Movement Science
• +۲ مورد دیگر

Arnold Baca is a Professor at the University of Vienna, Center for Sports Science and University Sports, Institute for Sports and Exercise Science. With over 291 publications and 6 active projects, his work bridges Sports Biomechanics Artificial Intelligence in Human Movement Physiological and Psychological Exercise Analysis Research trends from his 15 most recent articles show focus on Musculoskeletal modeling in elite athletes AI applications for sports performance Adaptive training systems for disabilities Multi-scale biomechanical simulations Team sport pattern recognition Scientific awards include ESMAC Best Paper Award (2023) Projects include AI in Rowing Training (2023-2026) Western Balkan Sports Health Network (2025-2026) Bone Growth Mechanobiology (2022-2025)

• Federated Learning
• Neural Networks
• Data Science
• +۳ مورد دیگر

J.M. Galjaard is a researcher affiliated with the School of Electrical Engineering, Mathematics and Computer Science at an unnamed institution, specializing in the Data-Intensive Systems department. His primary research areas include Federated Learning, Edge Computing, Neural Networks, and Data Science. Research interests derived from his publications and collaborations focus on: Federated Learning and Continual Learning paradigms Edge Computing optimization techniques Multi-DNN/Multi-Model inference scheduling Robustness in meta-learning under label noise Watermarking in Large Language Models Data privacy in distributed systems Article trends highlight consistent contributions to: Federated Learning frameworks (2024-2025) Edge Computing innovations (2020-2021) Meta-Learning and label noise mitigation (2023) Model optimization under resource constraints Collaborative research with international teams Active collaborations include researchers like B. Cox, L.Y. Chen, and R. Birke, with cross-facility projects dominating recent output.

• Blockchain Technology
• Distributed Systems
• Wireless Sensor Networks
• +۳ مورد دیگر

C.U. Ileri is a researcher at Delft University of Technology's Data-Intensive Systems department within the College of Electrical Engineering, Mathematics and Computer Science. His work focuses on blockchain systems, distributed algorithms, and wireless sensor networks. Research Areas: Blockchain, Web3, Sybil attack mitigation, fault-tolerant algorithms, wireless sensor networks Key Contributions: Developed latency-based Sybil defense mechanisms, blockchain interoperability protocols (XChange), and capacitated graph algorithms for WSNs His publications span blockchain-based federated learning, zero-knowledge identity frameworks, and distributed graph optimization techniques. Recent work explores IOTA 2.0's finality guarantees and zkSSI's privacy-preserving capabilities. Collaborations include joint datasets with Q. Stokkink and J. Pouwelse, focusing on latency measurements and reputation system prototypes. Research outputs demonstrate expertise in balancing security, efficiency, and scalability in decentralized systems.

• Data Integration
• Machine Learning
• Schema Matching
• +۵ مورد دیگر

C. Koutras is a researcher at the Data-Intensive Systems group within the School of Electrical Engineering, Mathematics and Computer Science at Delft University of Technology. His work focuses on data integration, schema matching, and machine learning applications in modern data systems. Research Areas: Data Integration, Schema Matching, Machine Learning, Data Lakes, Graph Neural Networks, Biomedical Data Systems Collaborations: Active collaborations with researchers including R. Hai, A. Katsifodimos, and M. Jarke. Recent work includes developing tools like Amalur and Valentine , which address challenges in data lake integration and scalable schema matching. His research leverages large language models and graph-based techniques for biomedical and distributed data environments. Despite significant contributions to data integration and machine learning, no explicit scientific awards or part-time status are documented in the provided materials. His 2024 dissertation at TU Delft highlights expertise in modern data challenges.

• Marketing Analytics
• Data Analysis
• Social Sciences
• +۱ مورد دیگر

Chiara Chiavenna serves as a Research Fellow in the Department of Social and Political Sciences at Bocconi University, actively contributing to academic instruction for the 2025/2026 academic year. Her teaching portfolio includes Strategic Marketing and Analytics (focusing on data-driven decision frameworks) and Foundations of Social Sciences (specializing in empirical research methodologies and analytical techniques). Her research profile centers on quantitative approaches at the intersection of marketing strategy and social science inquiry, with particular emphasis on data analytics applications. This manifests in her dual-course structure: one dedicated to translating marketing analytics into strategic business decisions, the other to rigorous empirical methods for social science research. The integration of computational analysis across both domains suggests a methodological specialization in data-intensive social research. No scientific awards or honors are documented in the available institutional records. While current teaching responsibilities are clearly defined, no information appears regarding graduate student supervision, research grant acquisition, or funded projects. Similarly, institutional documentation does not reference advising activities or mentorship roles beyond classroom instruction. There is no indication of affiliation with specialized research laboratories, interdisciplinary teams, or collaborative research centers within Bocconi University's organizational structure.

Tian Liمشاهده پروفایل

پژوهشگر ارشد

• Glaciology
• Climate Change
• Polar Science
• +۴ مورد دیگر

Tian Li is a Leverhulme Early Career Research Fellow at the Bristol Glaciology Centre, University of Bristol. As a Senior Research Associate, Li focuses on understanding the profound impacts of climate change on polar glaciers and ice sheets using advanced computational methodologies. Li completed their PhD at the Bristol Glaciology Centre, where they specialized in the grounding zone dynamics of the Antarctic Ice Sheet using NASA's ICESat-2 satellite laser altimetry data. During their doctoral research, they developed novel methodologies for mapping the Antarctic grounding zone, leading to the creation of the first ICESat-2-derived Antarctic Ice Sheet grounding zone data product, which was published by the NASA National Snow and Ice Data Center. This research revealed significant retreats in the grounding lines of Moscow University and Totten Glacier ice shelves in East Antarctica over the past two decades. Li's primary research focuses on the calving processes of marine-terminating glaciers in the Arctic, using deep learning and Bayesian statistical models with extensive satellite remote sensing data. Their recent work analyzed millions of satellite images from Svalbard covering the period from 1985 to 2023, examining 149 marine-terminating glaciers at unprecedented scale. This research demonstrated that 91% of these glaciers have been significantly shrinking, with a total loss of more than 800km² of glacier area since 1985. Li's findings established a direct connection between ocean warming and glacier retreat, showing that as ocean temperatures increase in spring, glaciers retreat almost immediately. Leverhulme Early Career Fellowship Li receives funding from the European Commission for their research. Their work on developing AI models to analyze glacier dynamics represents a significant advancement over traditional manual methods, which are labor-intensive and subjective. Li's research contributes to understanding East Antarctic Ice Sheet instability and its potential implications for future sea-level rise, with findings published in Nature Communications. As part of the Bristol Glaciology Centre, Li collaborates with researchers including Professor Jonathan Bamber (Professor of Glaciology and Earth Observation at the University of Bristol) and Konrad Heidler (Chair of Data Science in Earth Observation at the Technical University of Munich). Their interdisciplinary approach combines glaciology, climate science, and advanced computational techniques to address critical questions about polar ice response to climate change.

Steve Wilsonمشاهده پروفایل

استادیار

• Natural Language Processing
• Computational Social Science
• Information Literacy
• +۸ مورد دیگر

Steve Wilson is an Assistant Professor in the Department of Computer Science, Engineering, and Physics at the University of Michigan-Flint, within the College of Innovation and Technology. He holds a PhD in Computer Science & Engineering from the University of Michigan and has previously served as an Assistant Professor at Oakland University and held postdoctoral positions at the University of Edinburgh and the University of Michigan. PhD | 2019 | University of Michigan | Computer Science & Engineering M.S. | 2015 | University of Michigan | Computer Science & Engineering B.Sc. | 2013 | Taylor University | Computer Science/Systems His research focuses on understanding online communication through Natural Language Processing, with emphasis on social context, information literacy, social media narratives, and educational applications. His work bridges computer science, psychology, and social informatics to analyze human behavior in digital environments. The most recent publications highlight a strong trend in using NLP for social good, including detecting misinformation, analyzing educational discourse, improving AI ethics, and understanding social dynamics such as sarcasm, offensive language, and user profiling. His work frequently appears in top venues like ACL, EMNLP, ICWSM, and SemEval, often in collaboration with leading researchers such as Rada Mihalcea and Walid Magdy. MWIN Faculty Innovation Fellow | 2025 | UM-Flint’s Office of Economic Development and EDA University Center for Community and Economic Development UM-Flint Proposal Academy Awardee | 2025 | University of Michigan-Flint Office of Sponsored Research Projects Steve Wilson is the Principal Investigator (PI) on multiple funded research projects, including a National Science Foundation CRII grant on social media framing and an internal UM-Flint grant on AI in STEM education. He co-investigates an NSF REU site on Home Health Technology. He leads the CoCoA Lab at UM-Flint, which focuses on computational analysis of communication and context. He mentors graduate students and is actively involved in developing new courses, including a recent offering in Natural Language Processing.

• Observational black hole astrophysics
• X-ray astronomy
• Active Galactic Nuclei
• +۳ مورد دیگر

Anne Lohfink is an Associate Professor in the Department of Physics at Montana State University's College of Letters & Science, specializing in observational black hole astrophysics and X-ray astronomy. Her educational background includes: Ph.D. in Astronomy from the University of Maryland (2014) M.S. in Astronomy from the University of Maryland (2011) Dr. Lohfink's research centers on high-energy phenomena in black hole systems, with particular emphasis on coronal physics, relativistic reflection, and outflow dynamics in Active Galactic Nuclei. She utilizes data from the NuSTAR observatory to probe extreme gravitational environments, investigating spectral signatures of accretion disks and jet interactions. Her work bridges observational data with theoretical models of matter behavior under intense gravity, contributing to fundamental understanding of black hole astrophysics. Analysis of her publication timeline reveals consistent focus on NuSTAR-based investigations of AGN coronae since 2013, with increasing sophistication in spectral modeling techniques. Her research demonstrates strong collaboration within the high-energy astrophysics community, frequently appearing in leading journals like The Astrophysical Journal and Monthly Notices of the Royal Astronomical Society. Dr. Lohfink actively contributes to the field through service as a member of the NuSTAR Users' Committee (2017-2024), providing scientific oversight for mission operations. She teaches core physics courses including PHSX 331 (Methods of Computational Physics) and ASTR 476 (Theoretical Astrophysics) through academic year 2025. She is affiliated with Montana State University's eXTreme Gravity Institute, which focuses on theoretical and observational studies of gravitational phenomena in extreme cosmic environments.

Lei Buمشاهده پروفایل

استاد

• Software Verification
• Software Testing
• Software Analysis
• +۸ مورد دیگر

Lei Bu is a Professor and Vice Dean of the Software Institute at Nanjing University, China. He has been with Nanjing University since 2010, progressing from Assistant Professor to his current position. His academic career includes a visiting position at Microsoft Research Asia through their StarTrack Program from 2014-2015. Professor Bu received his B.Sc. and Ph.D. degrees in Computer Science from Nanjing University, with additional research experience at Carnegie Mellon University and University of Texas at Dallas during his doctoral studies. His academic journey shows steady progression through the ranks at his alma mater. Lei Bu's research primarily focuses on software verification, testing, and analysis, with particular expertise in model checking, bounded model checking, formal methods, and cyber-physical systems. His work spans theoretical foundations to practical applications in safety-critical systems. Recent publications demonstrate growing interest in integrating machine learning techniques with traditional formal methods, as seen in works like SpecGen that leverage large language models for program specification generation. His research outputs show a consistent focus on verification techniques for complex systems, particularly addressing challenges in hybrid systems, cyber-physical systems, and IoT applications. Professor Bu has developed several verification tools including BACH (Bounded Reachability Checker for Linear Hybrid Automata) and BRICK (Bounded Reachability Checker of Numerically-Intensive C Code), which have been used in international verification competitions. 2023: Zhongchuang Software Talent Award 2022: CCF-IEEE CS Young Computer Scientist Award 2020: The Outstanding Teachers of Computing in Higher Education Award Program 2019: NASAC Young Software Innovation Award 2016: Young Talent Development Program 2014: StarTrack Program Visiting Young Faculty 2007: Full Scholarship under the State Scholarship Fund Professor Bu serves as a Principal Investigator on multiple significant research projects funded by the Natural Science Foundation of China, including a Key Program grant (2023-2027) on dynamic adjustment-control-fault tolerance theory. He has also advised numerous students and taught courses including Formal Languages and Automata, Theoretical Foundation of Software Engineering, and Preliminary Introduction to the Theory of Computation. His laboratory work focuses on developing practical verification tools for industrial applications in cyber-physical and IoT systems.

Erika Vargaمشاهده پروفایل

دانشیار

• Data Mining
• Database Systems
• Information Systems
• +۳ مورد دیگر

Dr. Erika Varga serves as an Associate Professor in the Department of Information Technology at the University of Miskolc's Faculty of Mechanical Engineering and Informatics, maintaining an office in room 109 of the IT building with contact via erika.b.varga@uni-miskolc.hu. Her research expertise centers on data-intensive computational systems, evidenced by teaching core courses in Data Mining, Database Systems, and Information Systems. She integrates theoretical foundations with practical applications across Programming Fundamentals, Object-Oriented Programming, and specialized domains like Vehicle Informatics and Enterprise Information Systems, reflecting a strong emphasis on software engineering methodologies and data analytics. While her scholarly output is cataloged in the MTMT database, the provided materials lack specific publication details. Her instructional patterns indicate sustained focus on evolving data technologies within engineering contexts since at least the 2014/2015 academic year. Dr. Varga actively supervises student research through thesis projects and course-based advising in her technical domains, though individual student names remain unlisted in available records. Her contributions support key degree programs including Software Engineering (BSc), Business Informatics (BSc), and Computer Science Engineering (MSc).