جست‌وجوی استاد و پژوهشگر

Jaylan Azerمشاهده پروفایل

مدرس ارشد

• Services Marketing
• Customer Engagement
• Digital Marketing
• +۴ مورد دیگر

Dr. Jaylan Azer is a Senior Lecturer in Marketing at the Adam Smith Business School, University of Glasgow, serving as Postgraduate Research Convenor and Quality Enhancement Officer. She holds a PhD from the University of Strathclyde and has over 15 years of professional experience in Retail Banking. Her research focuses on Services Marketing, AI, and digital engagement, published in journals like Journal of Service Research and Journal of Business Research . She leads projects with VisitScotland and the Outer Hebrides Tourism Community, emphasizing practical impact. Research interests include customer engagement in digital contexts, visual modality engagement (VME), and human-machine interaction (HME). She is an Associate Editor of the International Journal of Quality and Service Sciences and actively involved in editorial boards of top marketing journals. Awards include the 2025 Excellence Award in Culinary Preservation and multiple reviewer recognitions. Teaching focuses on marketing modules aligned with her research, recognized through a 2019 Above & Beyond Award nomination. She co-designed the School’s first fully online MSc in Marketing and contributed to AACSB reaccreditation efforts. Professional roles include External Examiner at UCL (2023–2027) and Senate membership at the University of Glasgow.

• Artificial Intelligence
• Deep Learning
• Image Analysis
• +۹ مورد دیگر

Lia Morra is a fixed-term Researcher at the Department of Control and Computer Science (DAUIN) at the Polytechnic University of Turin, where she is also a member of the SmartData@PoliTO – Big Data and Data Science Laboratory. She is actively involved in research, teaching, and editorial roles, focusing on artificial intelligence and its applications in vision and data science. She teaches in the College of Computer, Film and Mechatronics Engineering and the College of Management and Production Engineering. Research Interests: Her work centers on artificial intelligence, deep learning, image analysis, and neural-symbolic integration. She applies these techniques to medical imaging, satellite data, autonomous systems, and human-computer interaction. Her research bridges symbolic reasoning with neural networks, emphasizing explainability, robustness, and real-world deployment. Publication Trends: Her recent publications (2023–2025) show a strong focus on neuro-symbolic AI, explainable models, medical imaging, and efficient deep learning. She frequently publishes in top-tier journals and conferences such as Medical Image Analysis , IEEE ACCESS , and IJCNN, with applications in healthcare, autonomous driving, and privacy-preserving AI. Scientific Awards and Recognition: Open badge RIMINI SCHOOL 2025 “SUCCESSFULLY PARTICIPATE IN CALLS FOR RESEARCH FUNDING” Learning to Teach (L2T) Advising and Grants: She supervises multiple PhD students in Computer and Systems Engineering, including Pietro Basci, Rosario Milazzo, Alessandro Sebastian Russo, and Luca Piano. She is the Scientific Responsible for several research projects, including EU-funded CAMOUFLAGE and multiple commercial contracts on AI for medical imaging, satellite data, and digital transformation. She also serves on editorial boards for Frontiers in Medical Engineering and IEEE Consumer Electronics Magazine . Labs and Teams: She is a core member of the GRAINS (GRAphics and INtelligent Systems) research group at DAUIN, focusing on intelligent systems, computer vision, and data science.

Michael Battyمشاهده پروفایل

استاد

• Urban Planning
• Complexity Science
• Smart Cities
• +۷ مورد دیگر

Michael Batty is the Bartlett Professor of Planning at University College London (UCL), where he chairs the Centre for Advanced Spatial Analysis (CASA). He is also External Faculty at the Complexity Science Hub (CSH) since February 2020. His work bridges urban planning, complexity science, and advanced spatial technologies. His research focuses on the science of cities, using computational models, big data, and visualization techniques to understand urban dynamics. Key areas include smart cities, urban complexity, digital twins, and sustainable urban development. He actively shares insights through his blogs at www.complexcity.info and www.spatialcomplexity.info . His recent work emphasizes the integration of complexity science into digital replicas of cities, enabling more adaptive and resilient urban systems. This is reflected in his 2023 publication in Nature Computational Science on digital twins. Scientific Awards: Fellow of the British Academy (FBA) Fellow of the Royal Society (FRS) CBE, Queen’s Birthday Honours (2004) Lauréat Prix International de Géographie Vautrin Lud (2013) Gold Medal, Royal Geographical Society (2015) Senior Scholar Award, Complex Systems Society (2016) Gold Medal, Royal Town Planning Institute (2016) Alonso Prize (for two major books) Michael Batty has supervised numerous students and led major research initiatives in urban analytics and spatial modeling. He has secured significant grants related to smart cities and complexity science, though specific grant names are not listed. He founded and leads the Centre for Advanced Spatial Analysis (CASA) at UCL, a leading interdisciplinary research lab focused on the future of cities. The CASA research group is a hub for innovation in urban data science, bringing together computer scientists, planners, physicists, and geographers to develop new tools for urban analysis and visualization.

• Social Informatics
• Information Science
• Software Engineering
• +۷ مورد دیگر

Alistair Lawson is an Associate Professor at the School of Computing Engineering and the Built Environment , Edinburgh Napier University. He supervises Honours MSc PhD students and serves as Programme Leader for MSc Data Sciences and School Academic Lead for Quality. His research interests span Software Engineering Machine Learning eHealth Cyber-security Information Society Social Informatics with a focus on digital solutions for healthcare, social media analysis, and cloud computing applications. Recent publications highlight trends in AI-powered freelance advisory systems Digital readiness of care homes Deep learning for pathogen detection Reputation dynamics on social platforms Evolutionary design methodologies Healthcare data analytics across interdisciplinary contexts. He leads projects such as Freelance Advisor App (£9,998) FLYSWX AI Feasibility (£23,728) Next Generation Trust Architecture (£149,959) Recovery from SCAD (£184,716) and participates in initiatives like the CyberAcademy and Data Science Special Interest Group .

Andrea Cerioliمشاهده پروفایل

استاد

• Robust Statistical Methodologies
• Multivariate Data Analysis
• Outlier Detection
• +۳ مورد دیگر

Andrea Cerioli is a full Professor of Statistics at the Department of Economics and Management, University of Parma, Italy, since 2002. He specializes in robust statistical methodologies, including the Forward Search approach, multivariate outlier detection, Benford’s Law, and tempered stable distributions, with applications to economic data and international trade fraud detection. Ph.D. in Statistics, University of Padua (1992) Editor-in-Chief of Statistical Methods and Applications (2011-2015) Current Editor of Advances in Data Analysis and Classification President of CLADAG (2009-2011) His research spans robust classification, clustering, and spatial data analysis. He has co-authored over 100 peer-reviewed works, including two Springer books, and has served on numerous conference committees. Recent work focuses on Benford’s Law for fraud detection and tempered stable distributions for modeling heavy-tailed data. He teaches courses like Data Science for Marketing and Statistics for Management at the University of Parma, contributing to graduate programs in trade and consumer marketing. His editorial and academic leadership roles highlight his influence in statistical methodology development.

Helena Dudyczمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Analysis
• Business Intelligence
• +۳ مورد دیگر

Prof. Helena Dudycz is a Professor in the Department of Information Technology at Wrocław University of Economics (UEW). Her research focuses on Artificial Intelligence applications in logistics, data analysis methodologies, business intelligence systems, and semantic technologies. She actively contributes to advancing decision support systems in financial management and has explored the usability of public and corporate websites through heuristic evaluation frameworks. Her work bridges academic research and practical implementation, addressing challenges in multi-site enterprise data integration, smart mobility solutions, and quality of service in web services. Key areas of interest include big data analytics, cognitive technology for investment decisions, and semantic visualization in business intelligence. Recent publications emphasize AI-driven automation in customs processes, Microsoft 365 adoption strategies, and the development of intelligent management systems for higher education institutions. Her research consistently integrates technology with organizational efficiency, aiming to enhance decision-making through innovative IT solutions. Prof. Dudycz maintains an open collaboration stance with socio-economic entities and offers consultations on MS Teams during specified hours. Her academic contributions reflect a commitment to both theoretical advancements and real-world applicability.

Silvia Mikschمشاهده پروفایل

استاد

• Visual Analytics
• Information Visualization
• Time-Oriented Data Analysis
• +۳ مورد دیگر

Prof. Silvia Miksch is a full professor in the Department of Visual Analytics at TU Wien. Her research focuses on visual analytics, time-oriented data visualization, and human-computer interaction. She leads projects in cultural heritage analysis, fraud detection, and pandemic data visualization. Her work bridges computer science with digital humanities, emphasizing user-centric design and uncertainty modeling. Key contributions include guidance systems for VA environments and network visualization frameworks for art history. Affiliations: TU Wien (since 2000+) Research Labs: Network Lab, Visual Analytics Research Group Recent projects explore temporal patterns in artist exhibitions, parameter space exploration, and pandemic data communication. She has advised over 20 PhD/Master’s students, many contributing to VA systems like COVIS and NEVA. Awarded the VGTC Visualization Technical Achievement Award (2024) for foundational work in temporal visualization. Active in IEEE VAST and serves on journal editorial boards. Her labs develop open-source tools for interactive data exploration.

Yifan Huمشاهده پروفایل

استاد

• AI
• Machine Learning
• Natural Language Processing
• +۴ مورد دیگر

Yifan Hu is a Professor of Practice at Northeastern University, specializing in AI/ML/NLP and Information Visualization. He held senior roles at Amazon (Senior Manager of Applied Science, 2023-2024) and Yahoo! Research (Senior Director, 2014-2023), leading teams in AI-driven projects. Earlier, he contributed to AT&T Labs, Wolfram Research, and Daresbury Laboratory. His research focuses on graph visualization, machine learning, and data science applications. Research interests include advanced visualization techniques (e.g., SmartGD framework), AI moderation systems, graph algorithms, and federated learning fairness. He has pioneered methods for graph layout optimization, dynamic data visualization, and adversarial NLP defenses. Notable achievements include winning the 2017 IEEE ICDM 10-Year Highest-Impact Paper Award for work on collaborative filtering. He currently teaches Data Mining (CS 6220) and Machine Learning (CS 6140) at Northeastern, emphasizing practical applications of AI. Labs/Projects: Actively develops visualization tools like SmartGD and DeepGD , exploring interdisciplinary applications in healthcare, cybersecurity, and large-scale data analysis. His work bridges theoretical advancements with industrial-scale implementations.

Jack van Wijkمشاهده پروفایل

استاد

• Information Visualization
• Visual Analytics
• Mathematical Visualization
• +۳ مورد دیگر

Jack van Wijk is a Full Professor in Visualization at Eindhoven University of Technology (TU/e). He holds a MSc (1982) and PhD (1986) in Computer Science from Delft University of Technology. His research focuses on information visualization, visual analytics, and mathematical visualization, with notable contributions to flow visualization and cartographic projections. He has co-authored over 160 papers and served as program co-chair for multiple IEEE Visualization conferences (2003–2023). Awards include the IEEE Visualization Technical Achievement Award (2007), Eurographics 2013 Outstanding Technical Contributions Award, and the VGTC Lifetime Achievement Award (2021). In 2023, he was honored with the Order of the Netherlands Lion. He co-founded MagnaView BV and SynerScope BV, and served as Scientific Director of the Data Science Center Eindhoven (2018–2021). Notable projects include SequoiaView (disk visualization), myriahedral projections, and IBFV (Image-Based Flow Visualization).

Knarik Tunyanمشاهده پروفایل

استادیار مهمان

• Linear Algebra
• Numerical Analysis
• Linear Programming
• +۱۱ مورد دیگر

Knarik Tunyan is a Visiting Assistant Professor in the Department of Mathematics/Computer Science at Purchase College, State University of New York, within the School of Natural and Social Sciences. She contributes to undergraduate education through courses in Linear Algebra, Calculus, Differential Equations, and advanced topics in mathematics. Education: PhD in Technology, Tampere University of Technology, Finland MS in Mathematics, Yerevan State University, Armenia Her research integrates applied mathematics with computational science, focusing on Linear Algebra, Numerical Analysis, Linear Programming, and Computational Inverse Problems . She explores mathematical methods in signal and image processing, optimization, network theory, economics, and biostatistics. Recently, she has been investigating the use of technology in mathematics education, including 3D printing and software tools like Mathematica and OpenSCAD. The 15 most recent publications highlight a consistent trajectory in matrix computations, optimization algorithms, and interdisciplinary applications . Early work centered on pivot rules and interior-point methods, particularly their behavior on challenging problems like the Klee-Minty cube. Later contributions expanded into image compression using QkRk factorization, Bayesian metabolic modeling, and educational innovations involving STEAM and 3D visualization. The articles reflect a blend of theoretical rigor and real-world application, spanning fields from computational biology to educational technology. Scientific Contributions and Recognition: Active presenter at international conferences including ICTCM and WCGO Published in reputable journals such as SIAM Journal on Matrix Analysis and Applications , Journal of Theoretical Biology , and Inverse Problems and Imaging Contributions to algorithmic efficiency in least squares and optimization She advises on curriculum development and student learning, particularly in integrating technology into mathematical instruction. Though no formal students are listed, she mentors through senior seminars and research-oriented courses. She has not received explicitly mentioned awards or grants in the provided text. Her work is supported by institutional resources and collaborative networks in computational mathematics and STEM education. Dr. Tunyan is involved in academic outreach and technological experimentation, especially through her presentations on 3D printing and mathematical software. She maintains regular office hours and engages students through digital platforms like Starfish, emphasizing accessibility and active learning.

Dustin Minkمشاهده پروفایل

استاد پژوهشی

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Dustin Mink is a Research Professor in the Department of Cybersecurity and Information Technology at the University of West Florida, part of the Hal Marcus College of Science and Engineering. He is also affiliated with the National Intelligence University and the Joint Cyber Analysis Course, contributing to military and intelligence cybersecurity education. His work bridges academic research, government collaboration, and practical defense applications. Education: Ph.D. in Computing, University of South Alabama M.S. in Computer Science, University of West Florida B.S. in Computer Science, University of West Florida A.A., Daytona State College Dustin Mink's research focuses on cybersecurity, artificial intelligence, and machine learning , particularly in detecting rare cyber attacks using resampling techniques, visualizing MITRE ATT&CK tactics, and securing critical infrastructure like the FAA's NextGen ADS-B system. His work leverages big data frameworks and Spark-based machine learning for real-time intrusion detection. He has developed the UWF-ZeekData22 dataset to support reproducible cybersecurity research. His recent publications (2021–2023) reflect a strong trend in AI-driven cybersecurity , with emphasis on imbalanced data handling, threat visualization, and aviation system protection. These works appear in journals such as Computers , Future Internet , Sensors , and Big Data and Cognitive Computing . Dr. Mink has secured and contributed to major research grants, including a $7.1M DoD Strategic Capabilities Office grant and a $0.5M NCAE-C grant, where he served as Co-PI on multiple projects related to risk detection, industrial control system security, and cybersecurity workforce development. He mentors students and professionals through teaching and training programs. He teaches courses such as Ethical Hacking, Cyber War Gaming, and Secure Software Development at UWF, and contributes to national cybersecurity education through GenCyber and FCC-funded workforce initiatives. Dr. Mink leads and collaborates on interdisciplinary research teams focused on creating technical solutions through IRADs, contract proposals, and TTOs. His lab-like efforts involve building datasets, detection systems, and AI models for cyber defense, often in collaboration with government and defense agencies.

Nathaniel Vacantiمشاهده پروفایل

استادیار

• Molecular Nutrition
• Metabolic Engineering
• Systems Biology
• +۷ مورد دیگر

Nathaniel Vacanti is an Assistant Professor of Molecular Nutrition in the Division of Nutritional Sciences at Cornell University's College of Agriculture and Life Sciences. He joined the faculty in autumn 2018 after completing his postdoctoral work at Karolinska Institutet. His research sits at the interface of molecular nutrition, metabolic engineering, systems biology, and bioinformatics, focusing on understanding metabolic regulation in health and disease. Dr. Vacanti's educational background includes: BS in Chemical Engineering from the University of Connecticut (2008) MS in Chemical Engineering from MIT (2010) PhD in Bioengineering from UC San Diego (2015) Postdoctoral Training in Department of Oncology-Pathology at Karolinska Institute His research program combines high-throughput proteomics, metabolomics, and bioinformatic analyses with targeted metabolic investigative methods including stable-isotope tracing and respirometry measurements. Current projects focus on identifying targetable mechanisms regulating differential metabolism across breast cancer cell lines, computationally predicting drug targets or nutrient interventions to exploit or correct metabolic dysfunctions, and mapping the proteomic response to nutrient availability in developing skeletal muscle cells. His lab trains PhD students in Nutrition and Biomedical Engineering and offers the course 'Big Data in Molecular Nutrition: Proteins, Transcripts, and Metabolism' in the College of Human Ecology. Dr. Vacanti's publications demonstrate his expertise in metabolic analysis across diverse biological contexts, from cancer metabolism to stem cell biology and tissue regeneration. His work in Nature Communications (2019) on breast cancer proteomics and his development of computational tools like PolyMID for stable-isotope tracing experiments highlight his contributions to methodological advancements in the field. Through the Vacanti Lab, he mentors PhD students and contributes to the academic community by making large-scale molecular data analysis more accessible to traditional approaches in molecular nutrition.

• Statistical Modeling
• Mixed-Effects Models
• Data Analysis
• +۷ مورد دیگر

Jose Antonio Sanchez Espigares is a Senior Lecturer in the Department of Statistics and Operations Research at the Universitat Politècnica de Catalunya (UPC), affiliated with the Faculty of Mathematics and Statistics. He is a member of the ADBD research group (Analysis of Complex Data for Business Decisions) and has a long-standing record of research and teaching in applied statistics and data science. His research interests include: Statistical Modeling and Mixed-Effects Models Computer-Intensive Methods and Bootstrapping Environmental and Public Health Applications Urban Mobility and Transportation Analytics Business Decision Support Systems Food and Agricultural Engineering Statistics His recent publications reflect a strong trend in applying advanced statistical techniques to real-world problems, particularly in urban sustainability, environmental monitoring, and public health. He frequently collaborates on interdisciplinary projects involving soil science, epidemiology, and transportation systems. Scientific awards and recognitions include: Ajuts per al finançament de projectes per a la millora de la qualitat docent a les universitats de Catalunya (2002) Mejora del rendimiento académico de la asignatura Estadística 2 en la Facultad de Informática de Barcelona He has advised doctoral students, including Alexandra Piedad Cortez Ordoñez, and has participated in multiple competitive R&D+i projects. His work often involves collaboration with researchers in environmental engineering, computer science, and public health. While no dedicated lab is explicitly mentioned, his involvement in research groups like ADBD and LIAM indicates active participation in collaborative data science and modeling teams.

Gao Niuمشاهده پروفایل

دانشیار

• Actuarial Science
• Financial Risk Management
• Big Data Analytics
• +۲ مورد دیگر

Gao Niu serves as Associate Professor in Actuarial Science and Professor of Mathematics and Economics at Bryant University, concurrently holding the position of Department Chair for the Department of Mathematics and Economics. His academic leadership spans interdisciplinary domains including quantitative finance, data science, and earth systems research. Dr. Niu's educational foundation comprises: Ph.D., University of Connecticut M.S., Western Illinois University B.S., Iowa Wesleyan University His research program exhibits extraordinary interdisciplinary range, anchored in Actuarial Science and Financial Risk Management while extending into Big Data Analytics, Historical Linguistics, and Paleoclimatology. Core contributions include pension plan analysis, financial protection frameworks for families with special needs children, and innovative applications of statistical modeling to reconstruct ancient climate conditions through plant fossil analysis. Methodologically, he integrates computational techniques from fog computing and machine learning to address complex problems across finance and earth sciences. Analysis of Dr. Niu's publication trajectory (2017-2024) reveals a distinctive cross-disciplinary evolution: initial work focused on actuarial applications in insurance and financial planning progressively incorporated big data visualization techniques, culminating in significant contributions to paleoclimatology since 2020. His earth science collaborations demonstrate how statistical models originally developed for financial risk assessment can be repurposed to analyze stomatal parameters in fossilized plant material, creating novel pathways for ancient CO2 reconstruction. This fusion of quantitative finance with geological data processing represents a unique scholarly signature.

Yannis Velegrakisمشاهده پروفایل

استاد

• Big Data Management & Analytics
• Knowledge Discovery
• Graph Management
• +۱۵ مورد دیگر

Yannis Velegrakis is a Professor in the Department of Information and Computing Sciences at Utrecht University, where he holds the chair of Very Large Data Management. He leads the Data Intensive Systems research group and is the program leader for the Master’s in Data Science . He is also a part-time faculty member at the University of Trento and a Principal Investigator at the Archimedes AI and Data Science hub, Athena Research Center . Education: PhD in Computer Science, University of Toronto MSc in Computer Science, University of Crete BSc in Computer Science, University of Crete His research focuses on Big Data Management & Analytics , Knowledge Discovery , Graph Management , Data Integration , and Data Quality . His work combines theoretical rigor with practical system development, emphasizing human-centered approaches to data exploration and analysis. He has pioneered methods in example-based search, entity resolution, and dynamic graph analysis. His recent publications reflect a strong trend in knowledge graph exploration , data quality assessment , and dynamic network mining . These works often integrate machine learning with database systems to support interactive and intelligent data discovery. His research bridges foundational data management with applied AI, particularly in the context of real-world data challenges. Scientific Leadership and Awards: PC Chair, ICDE 2024 General Chair, VLDB 2013 PC Chair, EDBT 2021 He has advised numerous researchers and contributed to major projects in data integration and semantic search. His secondary activities include visiting positions at IBM Almaden , AT&T Research Labs , UC Santa Cruz , and University of Paris-Saclay . He has secured funding through collaborative research initiatives and has served on program committees of top international conferences. He leads the Data Intensive Systems group, which develops innovative tools for managing and exploring large-scale, heterogeneous datasets. The group emphasizes systems that support intuitive, example-driven interaction with complex data, aligning with the principles of human-centered AI.