جستجوی استاد راهنمای دانشگاهی

Won-Jae Yiمشاهده پروفایل

دانشگاهی

• Internet of Things (IoT)
• Wireless Body Sensor Networks
• Wearable Devices for Health Monitoring
• +۷ مورد دیگر

Won-Jae Yi is an Associate Teaching Professor of Electrical and Computer Engineering at the Illinois Institute of Technology (Illinois Tech), part of the Armour College of Engineering. He holds a B.S., M.S., and Ph.D. in Computer Engineering from Illinois Tech, completed in 2010, 2012, and 2017, respectively. In addition to his teaching role, Yi serves as the Undergraduate Laboratory and Computing Facilities Supervisor in the Department of Electrical and Computer Engineering. He has over 13 years of experience in research focusing on IoT, wireless sensor networks, wearable devices for health monitoring, FPGAs, cyber-physical systems, and edge computing. Education: B.S. in Computer Engineering (cum laude, 2010), Illinois Tech M.S. in Computer Engineering (2012), Illinois Tech Ph.D. in Computer Engineering (2017), Illinois Tech Research Interests: Yi’s work spans IoT-enabled systems, AI-driven smart devices, cybersecurity for embedded systems, and assistive technologies for visually impaired individuals. His research emphasizes practical applications such as smart home automation, wearable health monitoring, and real-time data fusion for IoT systems. He consistently integrates his expertise in hardware-software co-design, including FPGAs and SoCs, to create robust, low-power solutions. Teaching Contributions: Yi teaches undergraduate and graduate courses in Software Engineering, Internet of Things, Cybersecurity, and Artificial Intelligence. His teaching excellence was recognized with the 2023 Bauer Family Excellence in Undergraduate Teaching Award. Labs and Facilities: As Laboratory Supervisor, Yi manages computing facilities and oversees the development of experimental setups for student projects in IoT, embedded systems, and AI. His lab focuses on prototyping smart systems that bridge theoretical concepts with real-world applications.

• Semantic Computing
• Big Data Integration
• STEM Education
• +۳ مورد دیگر

Srividya Bansal is an Associate Professor and Program Chair of the Software Engineering program at Arizona State University's School of Computing and Augmented Intelligence. She joined ASU in 2010 and previously held a visiting assistant professorship at Georgetown University. Her research focuses on semantics-based approaches for Big Data integration, knowledge graphs, and outcome-based STEM education design. She currently co-leads the NSF-funded 'SUDOKN: Supply and Demand Open Knowledge Network' project under the Proto-OKN program. Education: B.Tech in Computer Science, National Institute of Technology Warangal (1999) MS in Computer Science, Texas Tech University (2002) PhD in Computer Science, University of Texas at Dallas (2007) Industry experience: 5 years as a software engineer at SAP Labs India and Tyler Technologies Research interests include semantic computing, web service composition, and educational technology. She has over 75 publications and actively teaches courses like Semantic Web Engineering and Software Enterprise . Current NSF grants include IUSE-funded work on step-based tutoring systems for circuit analysis and Proto-OKN's open knowledge networks. Professional affiliations include IEEE Computer Society and ACM. She leads the Instructional Module Development (IMOD) System project to enhance outcome-based curriculum design in STEM fields.

Axel Polleresمشاهده پروفایل

استاد

• Semantic Web
• Knowledge Engineering
• Data Management
• +۱۲ مورد دیگر

Axel Polleres is a full professor in the area of 'Data and Knowledge Engineering' at the Vienna University of Economics and Business (WU Wien) and a faculty member at the Complexity Science Hub (CSH) in Vienna, a position he has held since January 2017. His work bridges academic research and real-world applications in semantic technologies, knowledge graphs, and data governance. His research interests lie at the intersection of semantic web technologies, knowledge engineering, and data management. Focused on querying and reasoning over ontologies, logic programming, and rule-based systems, he explores how structured data can be effectively managed, validated, and integrated—especially through standards like SPARQL and SHACL. His work extends to applications in open data, legal data, biomedical informatics, and socio-environmental challenges such as climate and public health. The most recent publications highlight a growing trend toward practical and societal applications of knowledge graphs, including climate risk assessment, crisis response, healthcare accessibility, and open data governance. These works combine technical rigor in semantic modeling with impactful real-world use cases, particularly in European and Austrian contexts. Axel Polleres has been actively involved in international standardization, notably as co-chair of the W3C SPARQL Working Group, and serves on the editorial boards of the Semantic Web Journal and Journal of Web Semantics . He has contributed to numerous European and national research projects and has published over 100 articles in top-tier journals and conferences. He advises and collaborates with researchers across disciplines and institutions, contributing to interdisciplinary teams working on complex societal challenges. His work is supported by significant research grants, though specific funding sources are not detailed in the provided text. He leads and participates in research groups focused on semantic technologies, knowledge graphs, and data-driven policy.

• Computer Vision
• Intelligent Robot Control
• 3D Computer Graphics
• +۱۲ مورد دیگر

Dr Jiacheng Tan is a Senior Lecturer in the School of Computing at the University of Portsmouth , where he has been a faculty member since 2002. He is actively involved in research and PhD supervision, with a strong focus on intelligent systems and robotics. Dr Tan earned his BEng in Mechanical Engineering from Jilin University of Technology (1983), an MSc in Mechatronics from Xidian University (1989), and a PhD in Computer Graphics from De Montfort University (2001). Prior to joining Portsmouth, he served as a Visiting Researcher in robotics at the University of Salford (1996–1997) and a Research Fellow in artificial intelligence at the Open University (2000–2002). His research centers on computer vision, intelligent robot control, 3D graphics, and human-robot interaction . He investigates how robots can understand and act upon natural language commands by grounding spatial relations, recognizing objects, and reasoning about tasks in unstructured environments. His work integrates fuzzy logic, knowledge engineering, and machine learning to enable robots to learn from demonstrations and interact meaningfully with humans. The trends in his publications reveal a consistent focus on robotics and intelligent systems , evolving from early work in virtual environments and telerobotics to recent contributions in cloud-based scientific visualization and machine learning applications in astrophysics. His interdisciplinary research spans computer science, control theory, and cognitive systems. Dr Tan has not received any explicitly mentioned scientific awards in the provided text. He serves as a PhD supervisor and has contributed to multiple research projects, including the development of integrated AI frameworks for robotic manipulation. While specific grant details are not listed, his collaborations and publications suggest active involvement in funded research initiatives. He has worked with researchers across institutions on topics ranging from scientific visualization to intelligent interfaces. Dr Tan is affiliated with the Computational Intelligence Research Group at the University of Portsmouth. His lab work involves developing virtual environments, symbolic representations of 3D scenes, and natural language interfaces for robot control, supporting both academic research and practical applications in automation.

• Wireless Communications
• Telecommunications Engineering
• Video Quality Optimization
• +۷ مورد دیگر

Berend Willem Martijn Kuipers is affiliated with Universidade Lusófona in Lisbon, Portugal, where he contributes to research in telecommunications and network systems. He holds a doctoral degree from Aalborg University of Technology, Denmark, a Master's from Delft University of Technology, Netherlands, and a Bachelor's from Rijswijk Institute of Technology. Bachelor of Science, Rijswijk Institute of Technology, The Netherlands Master of Science, Delft University of Technology, Delft, The Netherlands Doctor of Philosophy, Aalborg University of Technology, Aalborg, Denmark His research focuses on wireless communication technologies, particularly in the areas of MIMO systems, video quality optimization over IP networks, and robust communication for emergency services. His work bridges theoretical modeling and practical implementation in real-world network environments. Key interests include channel modeling, signal processing, and AI applications in networking. The publication timeline from 2002 to 2022 shows a consistent research trajectory centered on enhancing wireless network performance. Early work focused on Bluetooth and MIMO channel modeling, while later contributions address video quality, substation network delays, and the integration of artificial intelligence in communication systems. These works reflect expertise in both fundamental wireless principles and modern network challenges. No scientific awards were mentioned in the provided text. No information is available regarding student supervision, research grants, or funding sources. No specific laboratory or research team affiliations are mentioned in the provided content.

Anna Bryniarskaمشاهده پروفایل

استادیار

• Knowledge Granulation
• Fuzzy Logic
• Fuzzy Descriptive Logic (Fuzzy DL)
• +۴ مورد دیگر

Anna Bryniarska is an Assistant Professor at the Institute of Computer Science, Opole University of Technology, Poland. She holds a PhD in Automation and Robotics (2014) from the same institution, alongside BSc and MSc degrees in Computer Science (2010). Prior to her academic role since 2015, she gained practical experience in IT companies between 2010–2015. Education: BSc and MSc in Computer Science (2010), Opole University of Technology PhD in Automation and Robotics (2014), Opole University of Technology Her research focuses on knowledge granulation, fuzzy logic systems, semantic web applications, and AI-driven technical diagnostics. Recent work emphasizes machine learning applications in biological signal analysis, particularly EEG data. She has authored one book and over 30 scientific articles. No specific grants or awards are listed in the provided text. Professional networks include ORCID, ResearchGate, and SCOPUS profiles. No lab affiliations or team leadership roles are explicitly mentioned.

Reza Arghaمشاهده پروفایل

مدرس

• Biomedical Engineering
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Dr. Reza Argha is a Lecturer at the Graduate School of Biomedical Engineering, UNSW, Sydney. His research focuses on AI-driven biomedical signal processing, machine learning, and digital health. He holds a PhD from UTS (2017), with earlier degrees from Shiraz University, Iran. His work includes developing algorithms for fall detection, blood pressure estimation, and ECG-based cardiovascular diagnostics. Education: BSc and MSc in Electrical Engineering, Shiraz University (Iran) PhD in Biomedical Engineering, University of Technology Sydney (2017) Research Interests: AI in healthcare, biomedical signal/image processing, telehealth systems, fall detection using mmWave radar, non-invasive blood pressure monitoring, and arrhythmia detection via ECG. Grants & Awards: ARC Research Hub for Connected Sensors for Health ($5M) ARC Industrial Transformation Research Hubs Multiple CVMM Collaborative Grants (2023-2024) Awarded 2017 UTS Chancellor’s Best PhD Thesis UTS FEIT Research Publication Awards (2014-2015) Teaching & Supervision: Teaches Biological Signal Analysis (BIOM9621) and Engineering Projects (ENGG2600). Supervises 6 current PhD students, with 1 successful completion. Seeks new students in digital health, AI clinical decision support, and radar-based fall prediction. Labs & Projects: Leads projects in mmWave radar for unobtrusive elderly monitoring, AI for blood pressure estimation, and deep learning for medical image analysis. Collaborates with industry partners on translational health tech solutions.

Jaafar Alghazoمشاهده پروفایل

دانشیار

• Machine Learning
• Deep Learning
• Medical Image Analysis
• +۷ مورد دیگر

Dr. Jaafar Alghazo is an Associate Professor in the Math, Science and Technology Department at the University of Minnesota. He currently teaches courses in Information Technology Management and Software Engineering, including ITM 3900-E90: Internship, SE 2200-001: Intro to Software Engineering, and SE 4500-001: Senior Project I. Research Focus: Machine Learning, Deep Learning, Medical Image Analysis, Cybersecurity, IoT, UAV Communications Key Publication Trends: 15 most recent works span 2025–2023, emphasizing neural network optimization, Arabic sign language recognition, flood detection, and secure medical imaging techniques

• Crowdsourcing
• IS Project Management
• Predictive Modeling
• +۱ مورد دیگر

Abhishek Tripathi is an Associate Professor of Information Systems in the Department of Accounting and Information Systems at the School of Business, The College of New Jersey. With over ten years of teaching experience in IT-related graduate and undergraduate courses, Dr. Tripathi brings both academic expertise and professional experience from four years working in the software and telecom domain. Dr. Tripathi earned his Ph.D. from the University of Nebraska in 2016. His academic journey reflects a strong foundation in information systems theory and practice, preparing him for his current role shaping future business technology leaders. Dr. Tripathi's research focuses on several cutting-edge areas within information systems. His primary interests include Crowdsourcing, IS Project Management, Predictive Modeling, and IS Theory. His work bridges theoretical frameworks with practical applications, particularly in how organizations can leverage emerging technologies for improved decision-making and operational efficiency. His recent publications demonstrate a growing interest in the intersection of artificial intelligence with traditional information systems challenges, including applications in metaverse technologies, emotion classification, and nano-scale computing environments. Analysis of Dr. Tripathi's publication record over the past decade reveals an evolving research trajectory from foundational work in crowdsourcing and information systems theory toward more specialized applications of artificial intelligence and machine learning in business contexts. His work spans multiple domains including education technology, healthcare informatics, social media analysis, and project management. The consistent thread through his research is examining how information systems can solve complex organizational problems through innovative technological approaches. SIGITPM Membership Excellence Award 2021 Dr. Tripathi has been actively involved in service to the academic community through multiple committee roles. He has served on the Career Development Committee (2018-2020), Support of Scholarly Activities Committee (2019-2022), School of Business Curriculum Committee (2021-2022), and currently serves on the Committee on Faculty Affairs (2024-present) and Faculty Senate (2024-2025). His professional memberships include the Association of Information Systems and the New Jersey Big Data Alliance. While specific grant information isn't detailed in the provided text, his extensive publication record suggests successful research funding to support his scholarly activities. Dr. Tripathi teaches a variety of courses including Business Analytics for Decision Making, Database Management for Business, Foundations of Business Analytics, Introduction to Data and Information Science, Machine Learning, and Web Design and Development, reflecting his broad expertise across the information systems curriculum.

Matthias Wolffمشاهده پروفایل

استاد

• Text and Semantics Processing
• Behavior Control of Cognitive Machines
• Quantum-Inspired AI Methods
• +۷ مورد دیگر

Prof. Dr.-Ing. habil. Matthias Wolff is a Full Professor of Communications Engineering at Brandenburg University of Technology Cottbus-Senftenberg, Germany. He leads the Cognitive Systems research group and serves as Speaker of the Lusatia Center for Artificial Intelligence and the WiR!-alliance Co-Innovation Platform Industrial Automation. His teaching focuses on systems theory, communications engineering, speech/language technology, and cognitive systems. Born in Görlitz, Germany Educated at Technische Universität Dresden (Dipl.-Ing. 1997, Dr.-Ing. 2004, Habilitation 2011) Head of Examination Board for Electrical Engineering (university program) Research Focus: His work spans text and semantics processing , behavior control of cognitive machines , and quantum-inspired AI methods , with interdisciplinary collaboration in scientific software development and acoustic pattern recognition. Current Projects: Includes research on cognitive material diagnostics in cooperation with Fraunhofer IKTS, focusing on AI applications in communication systems and cognitive engineering. Contact: Located in Building 3A, Room 246 | Phone: +49 (0)355 69 2128 | Email: matthias.wolff@b-tu.de

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

Luis Antonio Belanche Muñozمشاهده پروفایل

استاد

• Machine Learning
• Kernel Methods
• Neural Networks

Luis Antonio Belanche Muñoz is a Professor at the Department of Computer Science , Faculty of Informatics of Barcelona (FIB) , Universitat Politècnica de Catalunya (UPC) . He is affiliated with research groups SOCO - Soft Computing and IDEAI-UPC - Intelligent Data Science and Artificial Intelligence Research Group . His career spans over 25 years, with 216 documented activities. His research focuses on Machine Learning , Kernel Methods , and Neural Networks . He has pioneered techniques in feature selection, similarity measures, and hybrid models connecting deep learning with kernel methods. His work applies to diverse domains including finance, microbiology, cancer diagnostics, and environmental engineering. Recent publications highlight trends in kernel matrix analysis using entropy, microbiome data integration , and drug resistance prediction in HIV. Earlier work includes knowledge-based systems for wastewater treatment diagnostics and educational technologies for MOOC environments. He has collaborated with 75+ researchers across UPC's research network, contributing to projects funded under Spain's State Research Plans and Catalonia's RIS3CAT strategy. His 2011 thesis on Feature selection in brain tumor MRS data demonstrates interdisciplinary applications.

• Knowledge representation and reasoning
• Planning
• Nonmonotonic reasoning
• +۲ مورد دیگر

Yannis Dimopoulos is a Professor in the Department of Computer Science at the University of Cyprus. He has previously held research positions at the Max-Planck Institute for Computer Science in Saarbrücken and the University of Freiburg in Germany. He earned his B.Sc. and Ph.D. in Computer Science from the Athens University of Economics and Business. His primary research interests include: Knowledge representation and reasoning Planning Nonmonotonic reasoning Constraint satisfaction Machine learning His recent research, based on publications from 2017 to 2024, focuses on the theoretical and computational aspects of abstract argumentation, particularly control argumentation frameworks, probabilistic extensions, and the integration of argumentation with Boolean networks and negotiation under incomplete information. He has also contributed significantly to Answer Set Programming (ASP) for planning, developing the plasp 3 framework for effective ASP-based planning solutions. His scholarly work appears in top-tier venues such as AAAI, IJCAI, ECAI, KR, AAMAS, and journals like Artificial Intelligence and Autonomous Agents and Multi-Agent Systems. His most frequent collaborators include Pavlos Moraitis, Jean-Guy Mailly, Antonis C. Kakas, and Wolfgang Dvorák. No scientific awards or honors are mentioned in the provided text. Yannis Dimopoulos has advised or collaborated with several researchers, including Jean-Guy Mailly, Pavlos Moraitis, Nabila Hadidi, and Muhammad Adnan Hashmi, though formal student-advisor relationships are not explicitly detailed. His work often involves theoretical and computational modeling, and while specific grants or funding sources are not listed, his sustained publication record suggests active research support. He is actively involved in research teams and collaborations focused on argumentation, multi-agent systems, and automated reasoning, as evidenced by his extensive co-authorship network.

• Computer Vision
• Artificial Intelligence
• Machine Learning
• +۵ مورد دیگر

Fahad Khan is an Associate Professor (Docent) at Linköping University, affiliated with the Department of Electrical Engineering (ISY) and the Computer Vision Laboratory (CVL). He is a core faculty member within the Faculty of Science and Engineering, contributing to research and academic activities in computer vision and related domains. His research interests lie primarily in computer vision , machine learning , and image processing , with applications likely spanning robotics, automation, and intelligent systems. As a member of CVL, his work aligns with cutting-edge developments in visual data analysis and AI-driven perception. While specific scientific awards and publications are not detailed in the provided text, his position as Associate Professor and Docent indicates a strong research track record and academic recognition. He advises students and contributes to the academic community through research supervision and collaboration. Fahad Khan is actively involved in the research ecosystem at Linköping University, particularly in the field of computer vision. His work benefits from institutional support and infrastructure, including access to advanced computing resources and interdisciplinary networks. He collaborates within the Computer Vision Laboratory and contributes to the broader mission of the Department of Electrical Engineering in advancing intelligent systems and technologies.

• Control Engineering
• Biomedical Systems
• Iterative Learning Control
• +۷ مورد دیگر

Dr.-Ing. Maike Stemmler is a researcher and group leader at the Institute of Automatic Control, RWTH Aachen University, where she heads the Biomedical Systems research group. She is currently on parental leave but remains an active contributor to control engineering research with a focus on biomedical applications. Her work bridges advanced control theory and medical technology development. Research Interests: Iterative Learning Control in medical systems Physiological modeling and control of cardiovascular dynamics Robot-assisted rehabilitation and human-robot interaction Deep learning and AI-based control strategies Biomedical cyber-physical systems Ventricular assist device (VAD) control algorithms Her recent publications (2018–2023) demonstrate a strong trend toward integrating machine learning and data-driven modeling with classical control theory, particularly in medical and automotive domains. She frequently applies iterative learning and model predictive control to complex physiological systems, aiming to improve patient outcomes through adaptive, intelligent devices. Her work in robot-assisted training highlights interdisciplinary collaboration between engineering and clinical sciences. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: She completed her PhD in 2023 on iterative learning control in medical applications, indicating mentorship under Prof. Dirk Abel. She has advised or co-supervised junior researchers, as seen in collaborative publications with students and early-career authors (e.g., Xu Chen, Maximilian Basler). No specific grants are listed, but her research is likely supported by institutional and national funding given the scope and publication volume. Labs and Teams: Head of the Biomedical Systems research group at the Institute of Automatic Control, RWTH Aachen. Key collaborator in the IRT (Institute of Automatic Control) and IBF (Institute of Forming Technology) research groups, contributing to interdisciplinary projects involving robotics, AI, and medical devices.