جست‌وجوی استاد و پژوهشگر

Benjamin Winterمشاهده پروفایل

پژوهشگر

• Reinforcement Learning
• Imitation Learning
• Autonomous Driving
• +۴ مورد دیگر

Dr. Benjamin Winter is a Researcher at the Department of Computer Science at Beuth University of Applied Sciences Berlin. He holds a PhD in Computer Science (2025) and a Master's in Media Informatics (2018) from the same institution. His research focuses on Reinforcement Learning, Imitation Learning, and Natural Language Processing, particularly in healthcare applications and transformer models. He contributed to the FashionBrain project analyzing European fashion through Big Data techniques and co-developed KIMERA and VisBERT tools for transformer analysis. Key research interests include autonomous driving, healthcare NLP, and domain adaptation of language models. His work spans from foundational research in transformer networks to applied projects in clinical decision-making and media bias analysis. Dr. Winter has published extensively on transformer architectures, including layer-wise analyses and visualization techniques. His work on reinforcement learning in competitive scenarios, such as StarCraft strategies, demonstrated early success in AI surpassing human experts.

Paulo Alexandre Vara Alvesمشاهده پروفایل

استاد

• Intelligent Systems
• Big Data Analytics
• E-learning
• +۳ مورد دیگر

Paulo Alexandre Vara Alves is a Professor in the Informatics and Communications Department at the Polytechnic Institute of Bragança since 1997. He serves as IPB e-learning Coordinator and Vice-Dean of the School of Technology and Management. PhD in Technology and Information Systems (University of Minho) MSc in Multimedia Technology (University of Porto) His research focuses on intelligent systems , big data analytics , and their applications to e-learning , web development , and multimedia . Recent publications highlight applications of machine learning in cosmetic dermatology, nutritional health, and biomedical monitoring. Key themes include neural networks, data engineering for education analytics, and biofeedback systems. He currently holds no listed scientific awards but has an extensive publication record (49 total) and participates in collaborative research projects involving teams like CeDRI (Research Centre in Digitalization and Intelligent Robotics).

Liping Yangمشاهده پروفایل

استادیار

• GIScience
• Geospatial Artificial Intelligence (GeoAI)
• Computer Vision
• +۶ مورد دیگر

Liping Yang is an Assistant Professor in the Department of Geography and Environmental Studies and the Department of Computer Science at the University of New Mexico (UNM). She also holds an affiliate faculty position at the Center for the Advancement of Spatial Informatics Research and Education (ASPIRE). Her research focuses on geospatial artificial intelligence (GeoAI), computer vision, and machine learning applied to geospatial big data analytics, disaster management, and indoor navigation. She directs the GeoAIR Lab, which develops innovative methods for GeoAI and geovisualization. Education: Ph.D. in Spatial Information Science and Engineering from the University of Maine (2015), M.S. in Cartography and GIS from Fujian Normal University (2009), and B.S. in GIS from Yunnan Normal University (2006). Postdoctoral positions at Los Alamos National Laboratory (LANL) and Penn State University. Research interests include GIScience, GeoAI, visual big data analytics, geovisualization, and indoor navigation. Her work emphasizes developing AI-driven solutions for smart cities, disaster management, and infrastructure monitoring. Awards: National Scholarship (China, 2004), Yunnan Province Government Scholarship (2005), Michael J. Eckardt Dissertation Fellowship (2014–2015), and Graduate Research Assistantship at University of Maine. Teaching includes courses on advanced GIScience, internet mapping, and geospatial statistics. Her students have developed interactive web maps for topics like water quality in the Navajo Nation and worldwide shark attacks. Labs/Teams: Directs the GeoAIR Lab, collaborating with institutions like LANL and Penn State. Active in organizing workshops on diagram image retrieval (DIRA) at CVPR.

Abdou Illiaمشاهده پروفایل

استاد

• IT Adoption Factors
• Cyber Security
• E-Business
• +۳ مورد دیگر

Abdou Illia is a Professor in the Department of Business Analytics & Information Systems at Eastern Illinois University's School of Business. He has held academic positions since 2003, progressing from Assistant Professor (2003) to Associate Professor (2008) and Full Professor (2012). His expertise spans IT adoption factors, cybersecurity, e-business, and data visualization. Prior to academia, he worked as a senior system analyst, IT consultant, and deputy chief of staff. Education: Ph.D. in Business Administration (Information Systems), Laval University, Canada M.S. in Business Administration (Computer Information Systems), Université de Sherbrooke B.A. in Economics (Business Management), Niamey University Research Focus: Dr. Illia's work addresses critical areas such as cybersecurity in social media, big data analytics for developing countries, and the impact of natural language processing on CRM. His recent studies emphasize digital technology's role in supply chain resilience during crises and cryptocurrency adoption determinants. Articles Trends: His publications highlight interdisciplinary approaches to IT adoption challenges, cybersecurity solutions, and leveraging data analytics for business innovation. Themes include trust mechanisms in digital platforms, pandemic-driven supply chain adaptations, and conceptual models for mobile financial services. Advising & Grants: No formal student advisees are listed, but his research collaborations span international institutions. Grants and funding details are not specified in the provided data.

Hao Xiongمشاهده پروفایل

پژوهشگر ارشد

• Machine Learning
• Health Informatics
• Medical Imaging
• +۳ مورد دیگر

Hao Xiong is a Research Fellow at the Centre for Health Informatics, Australian Institute of Health Innovation, Macquarie University. He specializes in applying machine learning and artificial intelligence to healthcare challenges, including medical imaging, signal processing, and clinical diagnostics. His work bridges computer science and health informatics, focusing on applications like cough classification, medical prognosis modeling, and automated analysis of clinical interactions. Key research areas include neural networks, acoustic signal analysis, and deep learning for healthcare applications. His systematic reviews and models address issues such as wet/dry cough differentiation, esophageal fistula prognosis, and delirium-dementia correlations. He has contributed to advancements in brain extraction algorithms, logo detection frameworks, and multimodal clinical data analysis. Dr. Xiong has been recognized with prestigious awards, including the IBM Rational Prize in Software Engineering (2010) and Best Student Paper Award (2016). His work emphasizes interdisciplinary collaboration, evidenced by projects involving robotics, epidemiology, and wearable technology for health monitoring. Awards: IBM Rational Prize (2010), Best Student Paper (2016), First Place in Algorithms (2012), Mentoring in Visual Computing (2017/2018) Grants/Projects: Visual Computing and Big Data Analytics (2017-2018), multiple collaborations in healthcare AI His research portfolio includes over 40 peer-reviewed publications, showcasing his impact in health informatics, medical imaging, and machine learning applications.

• Transportation Planning
• Urban Design
• Agent-Based Modeling
• +۴ مورد دیگر

Dr. Alexander Erath Rusterholtz is a Lecturer at ETH Zurich's Department of Mechanical and Process Engineering. He leads the 'Engaging Mobility' research project at the Future Cities Laboratory (FCL) in Singapore, focusing on mobility solutions for dense urban areas. His work integrates agent-based modeling, Big Data, and urban design to analyze travel behavior, particularly cycling and pedestrian mobility. He also directs projects in Switzerland funded by the Swiss Federal Roads Office (FEDRO) to quantify pedestrian potential in agglomerations. Education: PhD in Transport Infrastructure Vulnerability (2011, ETH Zurich) Research Interests: Erath’s research emphasizes multi-agent transport demand modeling, activity-based simulations, and the interplay between transport infrastructure and urban design. His work leverages immersive virtual reality (VR) for behavioral studies, Big Data for modeling urban mobility, and agent-based systems for pandemic spread analysis. Key areas include cycling safety, walkability indicators, and policy impacts on transport systems. Grants & Awards: His projects are funded by the National Research Foundation (NRF) Singapore, Swiss federal agencies, and collaborations with institutions like FCL and Urban Redevelopment Authority (URA). Notable outputs include the MATSim Singapore transport model and the first behaviorally-based walkability metric for Singapore. Advising & Labs: Erath oversees interdisciplinary teams at FCL and ETH Zurich, focusing on mobility futures and urban analytics. His lab develops tools like the Pedestrian Accessibility Tool (PAT) and VR cycling simulators for experimental research.

• Artificial Intelligence
• Machine Learning
• Edge Computing
• +۷ مورد دیگر

Huan Liu is a Professor at Arizona State University's School of Computing, Informatics, and Decision Systems Engineering, with research spanning artificial intelligence, edge computing, and interdisciplinary applications. His work bridges theoretical advances with real-world implementations across healthcare, transportation, and environmental systems. His primary research interests include: Artificial Intelligence and Machine Learning for complex system optimization Edge-Cloud Computing architectures for IoT and vehicular networks Medical image analysis and clinical decision support systems Graph neural networks and multimodal learning Privacy-preserving federated learning frameworks Recent publications demonstrate strong emphasis on applied AI, with 15+ 2025 papers addressing UAV swarm intelligence, medical diagnostics, and sustainable transportation through novel algorithmic approaches. Publication trends reveal consistent leadership in: Developing efficient edge computing solutions for dynamic environments Creating medical AI tools with clinical validation Innovating in graph-based and multimodal learning Applying optimization techniques to logistics and environmental challenges His work frequently appears in top venues including IEEE Transactions, Bioinformatics, and AAAI.

• Internet Technologies
• Mobile and Wireless Networks
• Next Generation Networks (NGN)
• +۷ مورد دیگر

Prof. Toni Janevski is a Professor at the Faculty of Electrical Engineering and Information Technologies, Ss. Cyril and Methodius University in Skopje, Macedonia, and currently serves as Head of the Institute of Telecommunications (2023-2026). He holds a Ph.D. in Electrical Engineering from the same university (2001) and has extensive industry experience, including research at IBM T.J. Watson Research Center (2001) and roles in the Macedonian mobile sector (Mobimak/T-Mobile, 1996-1999). His academic career began in 1999 at his alma mater. Education: Diploma in Electrical Engineering (1996) M.Sc. in Electrical Engineering (1999) Ph.D. in Electrical Engineering (2001) His research focuses on Internet Technologies, Mobile/Wireless Networks, QoS, Cybersecurity , and Next-Gen Networks . He authored influential books like "NGN Architectures, Protocols and Services" (Wiley, 2014) and "QoS for Fixed and Mobile Ultra-Broadband" (Wiley-IEEE, 2019). He has won Macedonia’s highest science award, the Goce Delchev Award (2012) , and the university’s Best Scientist Award (2013) . Awards & Recognition: Senior Member, IEEE (2005) Elected Member of ITU’s GCBI (2019-2026) Established Macedonia’s ITU Centre of Excellence (2009) His recent work explores 6G networks, AI in telecom, IoT, and cloud-edge integration . He leads projects on energy-efficient 6G, future broadband services, and regulatory frameworks for AI-driven networks. Publications: Over 200 articles and 7 books, focusing on NGN, QoS, 5G/6G, and cloud telecom services. His 2024 work includes surveys on AI in telecom and comparative analyses of IoT/network technologies.

Mihail Konchevمشاهده پروفایل

دانشیار

• Mathematical Modeling
• Probabilistic Methods in Sports Analysis
• Artificial Intelligence Applications in Sports
• +۳ مورد دیگر

Prof. Mihail Konchev is an Associate Professor at the Department of Theory of Sport in the National Sports Academy 'Vasil Levski', Sofia. He concurrently serves as Deputy Chairman of the National Statistical Institute since July 2022. His academic roles include teaching advanced statistical courses in sports management, data analysis, and game theory applications at the Master's and PhD levels. Education: PhD in Economics (University of National and World Economy), dual Master's degrees in Software Technologies and Information Technologies. Research focuses on mathematical modeling for sports performance, probabilistic analysis of athletic form, AI applications in sports analytics, and statistical methodologies for sports management. He has published monographs and peer-reviewed articles on topics like predictive analytics in sports and injury profiles in weightlifters. Pedagogical roles include adjunct positions at multiple institutions, including the University of Sofia and New Bulgarian University, where he taught ICT disciplines until 2018. He also serves as a pedagogical expert at the Center for Distance and E-learning at NSA 'Vasil Levski'.

Laurie Garrowمشاهده پروفایل

استاد

• Aviation
• Urban Air Mobility
• Transportation Systems
• +۴ مورد دیگر

Dr. Laurie Garrow is a prominent aviation expert and researcher at Georgia Institute of Technology, directing the ATL@GT Center. Her work focuses on emerging air transportation systems, including electric vertical takeoff and landing (eVTOL) aircraft and urban air mobility (UAM). She leads research on demand modeling for air taxi services, competitive market dynamics, and the integration of new technologies like New Distribution Capability (NDC) in airlines. Her studies analyze commuter behavior, city-specific feasibility of air taxi operations, and policy implications for rural transit funding. Dr. Garrow’s research combines geospatial analysis, discrete choice modeling, and large-scale data to evaluate transportation systems. Notable projects include mapping potential commuter air taxi routes in 40 US cities, comparing UAM with autonomous ground vehicles, and assessing barriers to AAM implementation in Germany and the US. Her work also explores post-COVID-19 airline industry transformations and innovative teaching methods in flipped classrooms. Her publications span transportation engineering, policy, and operations research, with a focus on advancing sustainable mobility solutions. Collaborations with industry partners like Uber Elevate and NASA highlight her role in bridging academic research with real-world applications.

Nuhi Besimiمشاهده پروفایل

استادیار

• Machine Learning
• Artificial Intelligence
• Data Mining
• +۶ مورد دیگر

Nuhi Besimi is an Assistant Professor at the Faculty of Contemporary Sciences and Technologies, South East European University (SEEU). His research focuses on machine learning, data mining, AI applications in education and music, and disaster response systems. He has published extensively in conferences like MECO and IEEE, addressing topics ranging from ethno-fusion music datasets to low-cost machine learning architectures. His work includes creating benchmark datasets for ethno-fusion music, developing energy-efficient ML systems using Raspberry Pi clusters, and analyzing media bias through data mining. Besimi collaborates with institutions in Montenegro, Croatia, and Albania on interdisciplinary projects. Recent trends in his publications emphasize AI-driven solutions for education, music technology, and disaster management. He has contributed to fake news detection surveys and automatic literature review systems leveraging OpenAI tools. His research bridges theoretical data science with practical applications in diverse cultural and technological domains.

• Statistics
• Data Science
• Biostatistics
• +۳ مورد دیگر

Prof. Katrijn Van Deun is a Full Professor in the Department of Methodology at the Tilburg School of Social and Behavioral Sciences, Tilburg University. Her research focuses on statistical methodology, data science, and their applications in health and behavioral sciences. She leads projects such as the WaTCh study, exploring determinants of quality of life in thyroid cancer patients. Her work bridges methodological innovation with practical applications in fields like HR analytics, rheumatoid arthritis treatment, and biostatistics. Key research interests include multivariate analysis, sparse PCA, meta-analyses, and health outcomes research. She has published extensively on topics such as structural equation modeling, infection risks in rheumatoid arthritis, and predictive analytics in HRM. Her contributions address complex data challenges, integrating high-dimensional data with traditional methodologies to uncover meaningful patterns. Prof. Van Deun collaborates across disciplines, involving teams in psychology, medicine, and computer science. Her work emphasizes methodological rigor and translational research, aiming to improve clinical decision-making and patient outcomes. She is actively involved in developing statistical tools and frameworks to handle big data and interdisciplinary research challenges.

Jorn Mehnenمشاهده پروفایل

استاد

• Advanced Digital Manufacturing
• Industry 4.0
• Cyber-Physical Systems
• +۱۰ مورد دیگر

Professor Jorn Mehnen is a leading academic in Advanced Digital Manufacturing at the University of Strathclyde, affiliated with the School of Design, Manufacturing and Engineering Management and the Advanced Manufacturing and Materials Design Research Group. His work focuses on Industry 4.0 technologies, Cyber-Physical Systems, and Smart Manufacturing solutions. He holds a Doctor of Engineering and Master's in Computational Mathematics. Previously, he served as Reader at Cranfield University (2007–2017) and Privatdozent at Dortmund University of Technology (2004–2006). Research interests include Additive Manufacturing, Data Analytics, and IoT-enabled systems. He has supervised 8 students and led 33 projects, including EPSRC HVM Catapult Fellowships and EU-funded initiatives. Awards include the 2025 AI & Robotics Best Research Paper and the 2025 CeeD Industry Award. Key contributions span Cloud Manufacturing frameworks, defect analysis via thermography, and secure IoT integration for legacy machinery. His work addresses sustainability (UN SDG 9) through high-value manufacturing innovations and Through-Life Engineering Optimization.

• Computational Geometry
• Algorithms
• Visualization
• +۳ مورد دیگر

Bettina Speckmann is a Professor at Eindhoven University of Technology in the Netherlands, specializing in computational geometry and algorithms. Her work spans theoretical computer science, visualization, and geographic information systems. She is actively involved in academic conferences such as SoCG, SODA, and GIScience, and serves as a co-author and editor for multiple journals including Computational Geometry and IEEE Transactions on Visualization and Computer Graphics . Key roles: Conference program committee member, journal reviewer. Research interests: Algorithm design, geometric optimization, topological data analysis, and visual abstraction techniques. Her research focuses on advancing computational methods for spatial data analysis, including Fréchet distance algorithms, modular robotics reconfiguration, and visualizing categorical patterns. Recent work includes optimizing symbol placement in maps and analyzing trajectories in mobility data. She collaborates extensively with researchers in computer science and geography, producing impactful contributions to both theoretical and applied domains.

Yusuf Ozbekمشاهده پروفایل

استاد آموزشی

• Machine Learning
• Deep Learning
• Neural Networks
• +۴ مورد دیگر

Yusuf Ozbek is a Teaching Professor in the Information Systems Program at Northeastern University's College of Engineering. He has been teaching since 2007, blending industry experience (Web Programmer, CAD Engineer) with academic roles. Dr. Ozbek holds a Ph.D. in Interdisciplinary Engineering (2012) and MS degrees in Engineering Management (1997) and Computer Systems Engineering (2004), all from Northeastern University, alongside a BS in Mechanical Engineering from Yildiz Technical University (1994). Research Interests : Machine learning, deep learning, neural networks, big data systems, web technologies, and information security. His work spans brain-computer interfaces, distributed applications, and manufacturing optimization strategies. Education : PhD (2012): Interdisciplinary Engineering, Northeastern MS (2004): Computer Systems Engineering, Northeastern MS (1997): Engineering Management, Northeastern BS (1994): Mechanical Engineering, Yildiz Technical University Publications focus on real-time brain interfaces using Hadoop, Q-learning for manufacturing maintenance, and big data applications. His 2017 Martin W. Essigman Outstanding Teaching Award reflects his dedication to pedagogy. Awards : 2017 Martin W. Essigman Outstanding Teaching Award Teaching : Courses include Web Programming, Database Design, Algorithms, and Big Data Systems. His work bridges industry practice and academic rigor.