جستجوی استاد راهنمای دانشگاهی

Hu Chengzhi is a tenured Associate Professor at the Department of Mechanical and Energy Engineering , Southern University of Science and Technology (SUSTech), where he has worked since 2018. He leads the SUSTech-Zifu Medical Joint Laboratory (5 million RMB funding) and serves as Deputy Director of the Guangdong Provincial Key Laboratory of Human Augmentation and Rehabilitation Robots . A National Young Talent Program recipient and Shenzhen Peacock Plan B-category Talent , he earned his PhD in Micro-Nano Systems Engineering from Nagoya University (2014) under Professor Toshio Fukuda (Chinese Academy of Sciences foreign member) and conducted postdoctoral work at ETH Zurich with Professor Bradley Nelson. Education B.S. & M.S. in Mechanical Engineering, Huazhong University of Science and Technology (2008, 2010) Ph.D. in Micro-Nano Systems Engineering, Nagoya University (2014) Research Interests focus on micro-nano robotic technologies for single-cell analysis and tumor-targeted therapy , including magnetic manipulation, microfluidic chips, and swimming microrobots. His work also extends to medical microrobotics for diagnosing and treating gastrointestinal diseases. Over the past five years, he has led 14 competitive projects, including the National Key R&D Program and National Natural Science Foundation of China grants. Scientific Awards include the 2024 MINE Young Scientist Award , Best Paper Awards at IEEE MHS 2012, IEEE ICRA 2015, IEEE CBS 2023, and IEEE ICMA 2024. He has published 65 papers in top journals like Nature Communications , Advanced Science , and ACS Nano , and holds over 30 patents. Leadership and Service : Served as General Chair for IEEE ICMA 2025 (500+ participants), Director of SUSTech-Zifu Medical Joint Laboratory , and editorial board member of Cyborg and Bionic Systems . He mentors PhD and Master's students and actively recruits researchers for his lab.

• Micro-Nano Robotics
• Single-Cell Analysis
• Tumor-Targeted Therapy
• +۴ مورد دیگر

Devyn Dodge is a researcher at the Institute for Simulation and Training (IST) under the Simulation, Training and Technology Center (STTC) at the University of Central Florida (UCF). They hold advanced degrees in Systems Engineering, Modeling and Simulation, and Information Technology from UCF, reflecting a multidisciplinary expertise. Research Interests: Devyn specializes in 3D modeling, robotics, simulation training, and prototype manufacturing. Their work emphasizes defense applications, unmanned aerial systems (UAS/UAVs), data mining, and mechanical engineering, with a focus on integrating these technologies into crisis management and training solutions. Scientific Contributions: Devyn’s publication on the Rover Module for Crisis Management highlights their technical leadership in mobile robotics, battery testing, and crowd simulation. This work aligns with their broader expertise in defense simulation and crisis response systems. Awards & Recognition: Awarded Patent: Weapon Simulation Systems (US11644278B2) Labs & Collaborations: Devyn actively collaborates within UCF’s IST/STTC and contributes to graduate research initiatives, emphasizing user-friendly technology systems and interdisciplinary innovation.

• Modeling and Simulation
• Robotics
• 3D Modeling
• +۵ مورد دیگر

Prof. Cemil ÖZ is a full-time Professor at Sakarya University's Faculty of Computer and Information Sciences, where he has held academic positions since 1998. He leads research in computer vision, artificial intelligence, and medical imaging applications, with ongoing projects in virtual simulation systems and machine learning diagnostics. Education PhD in Electrical and Electronics Engineering, Sakarya University (1995-1998), specializing in neural network-based robotic trajectory control Master's in Electronics and Computer Education, Marmara University (1989-1993), focusing on adaptive robotic control systems Bachelor's in Electronics and Communication Engineering, Yıldız University (1986-1989) Research Focus Prof. ÖZ's interdisciplinary research bridges computer science with healthcare, industrial applications, and security. Primary domains include: Medical AI : Machine learning for neuroimaging diagnostics (e.g., multiple sclerosis differentiation) Virtual Simulation : Industrial and educational systems (welding/driving simulators) Computer Vision : 3D reconstruction, object detection, and facial analysis Applied ML : Hybrid algorithms for predictive modeling in security and social media Publication Trends Recent articles (2019-2024) demonstrate strong emphasis on AI-driven healthcare solutions (38% of works), particularly neurological disorder diagnostics using MRI/spectroscopy data. Virtual training systems constitute 27% of output, focusing on industrial/educational simulations. Remaining publications advance computer vision techniques (3D reconstruction, object detection) and security applications (terrorism prediction, sentiment analysis). Methodologically, deep learning (YOLO, CNN) and sensor integration (Kinect) are predominant.

• Computer Science
• Artificial Intelligence
• Computer Vision
• +۹ مورد دیگر

Slobodan Petrovic is a Professor of Information Security at the Norwegian University of Science and Technology (NTNU), specifically within the Department of Information Security and Communication Technology (IIK) at the Faculty of Information Technology and Electrical Engineering (IE) in Gjøvik, Norway. His academic career demonstrates a strong commitment to both research and education in cybersecurity disciplines. Professor Petrovic's research interests span across multiple critical domains in information security, with primary focus on Cryptology , Intrusion detection systems , and Wireless communication security . His work consistently bridges theoretical foundations with practical applications, particularly in digital forensics, cryptographic techniques, and network security protocols. The breadth of his research is evident in his extensive publication record spanning nearly two decades. His recent publications (2022-2024) reveal an expanding research scope into emerging security challenges including autonomous vehicle security, 5G network protection for social robots, and physical-layer security implementations for wireless communications. These articles demonstrate his ability to adapt research focus to address contemporary security threats in evolving technological landscapes. Professor Petrovic teaches cryptology and intrusion detection courses at both the Master in Information Security (MIS) program and the PhD program in Information Security at NTNU. He is an active member of the Cryptology Research Group (Norwegian Applied Crypto Lab - NaCl), contributing to Norway's cybersecurity research ecosystem. His collaborative research approach is evident through numerous co-authored publications with researchers including Nguyen Hai Thanh, Katrin Franke, Michael Cheffena Gebresilassie, and others. This collaborative network spans multiple institutions and research domains within information security.

• Cryptology
• Intrusion detection systems
• Wireless communication security
• +۳ مورد دیگر

Audrey Giremus is a Professor at the Institut de Mécanique et d'Ingénierie de Bordeaux (IMS Bordeaux) , affiliated with the Signal and Image Processing research group (SPECTRAL team). Her work bridges signal processing, statistical modeling, and interdisciplinary applications in environmental science and biomedical engineering. Key affiliations: IMS Bordeaux, BRGM (Bureau de Recherches Géologiques et Minières), CNRS (Centre National de la Recherche Scientifique) Collaborations: STMicroelectronics, Stellantis, Thales, CEA, Slb Research Interests focus on: Advanced signal/image processing algorithms Bayesian inference and particle filtering Applications in GPS navigation, fuel cells, and material science Statistical methods for high-dimensional data Thermal source localization and acousto-thermal coupling Coastal erosion detection using Markov chains Recent publications demonstrate expertise in GPS interference compensation , fuel cell degradation modeling , and biomarker validation . Collaborative projects with institutions like BRGM and CEA highlight her cross-disciplinary impact. Grants/Projects include: PEPR Risks (IRiMa) - CNRS OneWater PEPR - CNRS Technical platforms mentioned: Accelerated Aging Nanocomponent Testing (ATLAS, Cacyssee) Thermal Analysis (Elorga, Tamis) Terahertz and Organic Electronics

• Signal Processing
• Image Processing
• Bayesian Inference
• +۳ مورد دیگر

Mahdieh Zabihimayvan is an Assistant Professor in the Computer Science Department at Central Connecticut State University , specializing in Cybersecurity and Artificial Intelligence applications. Her research focuses on enhancing proactive cyber threat intelligence and privacy protection using applied machine learning and complex systems analysis . Areas of Expertise: Machine learning for Cybersecurity, Complex systems analysis Memberships: European Society for Fuzzy Logic and Technology, ACM-W Women in Network Science, Women in Machine Learning, WomenTech Network Courses Taught: Computer Security, Secure Software Development, Big Data Programming, Systems Programming, Secure Software Designs, Introduction to Internet Programming Research Interests include cybersecurity , machine learning , and complex systems . She has published extensively on topics such as darknet traffic classification , phishing attack detection , and web robot identification , often leveraging fuzzy logic and deep learning techniques. Scientific Awards include a nomination for the Undergraduate Research Competition in 2022. Her work intersects cybersecurity , machine learning , and privacy protection , particularly in analyzing Tor networks , dark web systems, and biomedical data .

• Cybersecurity
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Ana Belén Gil González is an Associate Professor in the Department of Computer Science and Automation at the University of Salamanca, Spain. She is affiliated with multiple research groups including BISITE (Bioinformatics, Intelligent Computer Systems and Educational Technology), CAUSAL (Academic Culture, Heritage and Social Memory), and MIDA (Data Mining). Her work spans across artificial intelligence, educational technology, blockchain applications, and intelligent systems. Dr. Gil González earned her PhD from the University of Salamanca in 2011 with a thesis on "Intelligent Recovery of Digital Educational Content," supervised by Dr. Juan Manuel Corchado Rodríguez and Dr. Sara Rodríguez González. Her research interests focus on applying artificial intelligence and data mining techniques to solve real-world problems. She has made significant contributions to educational technology, developing intelligent systems for digital content retrieval and personalized learning. In healthcare, she has worked on AI applications for medical diagnosis, including breast cancer prediction and Papanicolaou test analysis. Her work in blockchain technology addresses applications in supply chain management and smart cities with a focus on privacy preservation. Additionally, she has explored therapeutic robotics through projects like i-Teddy, which uses doll therapy to improve human interaction. Analysis of her recent publications (2022-2024) reveals a strong interdisciplinary approach with work spanning healthcare AI, educational technology, blockchain applications, and therapeutic robotics. Her research shows increasing focus on explainable AI in medical applications, privacy-preserving technologies for smart cities, and innovative approaches to educational technology. She maintains an active collaboration network across multiple research groups at the University of Salamanca. Dr. Gil González is actively involved in mentoring through her teaching and research supervision. Her extensive publication record across diverse domains suggests successful funding acquisition for her research projects. She is a key member of the BISITE research group, which focuses on bioinformatics, intelligent computer systems, and educational technology. Her work with the CAUSAL group explores academic culture and social memory, while her involvement with MIDA centers on data mining applications. These collaborative environments provide rich interdisciplinary contexts for her research across technology, education, and social applications.

• Artificial Intelligence
• Educational Technology
• Data Mining
• +۴ مورد دیگر

Dr. Heiko Maus is a researcher and department leader at the German Research Center for Artificial Intelligence (DFKI) GmbH in Kaiserslautern, Germany. As (co-)author of over 20 publications since 2005, he specializes in knowledge management, semantic technologies, and context-aware systems. Second Deputy Head of Smart Data & Knowledge Services Department (2022–) Team Leader of Topic Field Knowledge Work (2017–) Key projects: CoMem, EPOS, ForgetIT, myRPA His research focuses on knowledge graph applications for corporate memory systems, combining context modeling with semantic desktop environments . He develops personal knowledge assistants that integrate information silos through evolving knowledge graphs , with recent work exploring LLM-based adaptive relevance prediction for entity recommendation systems. Scientific contributions include the LDK 2019 Best Research Paper Award and multiple ACM/IEEE/ESWC publications . His 2024–2025 articles examine context-aware recommendation systems for knowledge workers, cyber mapping financial systems , and real-life knowledge work datasets .

• Knowledge Management
• Semantic Technologies
• Knowledge Graphs
• +۷ مورد دیگر

Sufyan Ali Memon serves as a Professor in the Department of Defense Systems Engineering at Sejong University, Republic of Korea. His academic journey includes Assistant Professor positions at Isra University (2016-2017), UNIST as Postdoc Research Associate (2017-2018), Indus University (2018-2019), and Mehran University of Engineering & Technology (2019-2021) before joining Sejong University in 2021. His research focuses on Tracking, Estimation, Guidance, Navigation, and Control systems, with particular expertise in multi-target tracking in cluttered environments, UAV trajectory estimation, and smoothing algorithms. His fingerprint analysis reveals strong activity in Target Tracking (97%) , Multi-Target Tracking (83%) , Unmanned Aerial Vehicles (37%) , and State Estimation (27%) . Analysis of his 42 research outputs (2015-2025) shows consistent growth in publication output, with 8 papers in 2025 alone. His work spans journals like Expert Systems with Applications , IEEE Access , and Sensors , demonstrating interdisciplinary applications from defense systems to social media analysis and pipeline inspection robotics. The publications reveal a clear evolution from fundamental tracking algorithms toward practical implementations in UAVs, industrial systems, and security applications. h-index 12 330 Citations Web of Science ResearcherID: AAD-9239-2019 His professional background includes significant contributions to smoothing algorithms for target tracking in cluttered environments, with major achievements including dynamic trajectory estimation for UAVs and multi-UAV tracking systems. Current research appears focused on integrating machine learning with traditional tracking systems and expanding applications to industrial inspection and security domains.

• Target Tracking
• Unmanned Aerial Vehicles
• Data Association
• +۶ مورد دیگر

Andrea Marrella is an Associate Professor at Sapienza Università di Roma, affiliated with the Department of Computer, Control and Management Sciences and Engineering. His academic journey includes a PhD in Computer Science Engineering from Sapienza in 2013, preceded by a Master's (2009) and Bachelor's (2005) in Computer Engineering with top marks. Education : PhD (2013), Master's (2009), and Bachelor's (2005) in Computer Engineering from Sapienza Università di Roma. His research spans Artificial Intelligence for Business Process Management , focusing on process mining, adaptation, resilience, and human-computer interaction. Applications extend to eGovernment, smart manufacturing, healthcare, and emergency management. Recent work explores AI-Augmented BPM, conversational systems for process-aware LLMs, and trust dynamics in RPA. Publications highlight trends in process mining (2024-2025), including trace alignment, data-centric analysis, and conversational frameworks. Awards include a Best Paper at CAiSE 2017 and Best Forum Paper at CAiSE 2019. Scientific Leadership : Principal Investigator of DAKIP (2016), METRICS (2018), DataCloud (2021), and MOTOWN (2023) Information Director of ACM Journal on Data Quality (2017-2022) Editorial Board member of IJISCRAM Organizing roles at BPM, CAiSE, AVI, and ICPM conferences Supervision : Mentored 10 M.Sc. and 2 B.Sc. students in Computer Science, plus 1 M.Sc. and 27 B.Sc. in Management Science at Sapienza. Co-supervised 2 M.Sc. theses at University of Tartu (Estonia). Professional Engagements : Reviewer for ACM Transactions on CHI, IEEE Transactions, and top journals/conferences Keynote/Panel speaker at PMAI 2024, AI Forum 2023, and BPAI’17

• Computer Science
• Artificial Intelligence
• Business Process Management
• +۸ مورد دیگر

Umberto Nanni is a full Professor of Information Management Systems at the University of Rome "La Sapienza" since November 2005. He has held significant leadership roles including Director of the Research Center for Distance Learning and Technologies for Learning at Telematic University Unitelma Sapienza (2015-2022) and President of the Area Council in Information Engineering at the Latina location of University of Rome "La Sapienza" (2014-2020). Professor Nanni's research spans multiple domains with primary focus on Algorithm Engineering (particularly path algorithms and combinatorial problems), Health IT, Information Systems (with applications in Transport and Logistics, Cultural Heritage), and Technology Enhanced Learning. His work integrates Big Data, Graph and Text mining, Incremental Algorithms, Dynamic Data Structures, and Internet of Things technologies to address complex challenges in these areas. His recent publications demonstrate a strong trend toward biomedical applications, particularly in biospecimen management and digital twins for precision medicine, while maintaining his foundational work in algorithm design and educational technology. The research shows increasing interdisciplinary collaboration, especially between computer science and healthcare domains, with notable publications in Cancer Genomics & Proteomics and other high-impact journals. He has advised over 400 undergraduate and master's theses along with 2 PhD students He has led numerous significant research projects including "Artificial Intelligence algorithms to track and detect Covid-19 vaccine-related infodemic on social media" (2023-2025) He coordinated the "ADCATER - Advanced Digital Solutions for Professional Food and Nutrition Catering Service" project (2021-2023) He served as Principal Investigator for the eLF-eLearning Fitness project (2011-2014) with 19 partners and 42 associated partners Professor Nanni co-founded and was instrumental in developing SPRECware, software tools for Standard PREanalytical Code labeling to improve biospecimen management. His work has resulted in over 100 scientific papers with an h-index of 27, demonstrating substantial impact across computer science, healthcare informatics, and educational technology domains.

• Algorithm Engineering
• Health IT
• Information Systems
• +۸ مورد دیگر

Reham Mohamed Aburas is an Assistant Professor in the Department of Computer Science and Engineering at the American University of Sharjah (AUS) in Sharjah, UAE. Her academic career spans research and teaching in mobile computing security, with a focus on advancing secure and privacy-preserving technologies for modern devices across multiple platforms including smartphones and emerging virtual reality systems. Dr. Aburas earned her Ph.D. in Computer Science from Purdue University, where she worked with Professor Z. Berkay Celik at the PurSec Lab. Prior to that, she completed both her B.Sc. and M.Sc. degrees in Computer and Systems Engineering from Alexandria University in Egypt, establishing her technical foundation in systems engineering. Her primary research centers on the advancement of security, privacy, and usability of mobile computing technologies. In today's world, where modern devices are ubiquitous and technology is deeply integrated into various aspects of daily life, Dr. Aburas focuses on developing advanced systems that enrich user experiences while preserving security and privacy. Her work spans multiple domains including: Mobile security and privacy vulnerabilities Virtual and augmented reality security Biometric authentication systems User-centered security design Location privacy in mixed reality environments Arabic natural language processing Dr. Aburas's publication record demonstrates a strong trajectory from foundational work in mobile computing and indoor localization to cutting-edge research in virtual reality and mixed reality security. Her recent work (2023-2025) has focused on emerging threats in WebXR and VR environments, including UI attacks, speech extraction from sensors, and semantic location inference. She has published in top security venues including USENIX Security Symposium and NDSS, indicating the high impact and relevance of her research in the security community. Her earlier work (2014-2020) established expertise in mobile security, biometric authentication, and Arabic language processing through the Al-Bayan project. As an educator, Dr. Aburas has served as a course instructor at AUS, teaching subjects including COE 59412: Usable Security and Privacy and CMP 340: Design and Analysis of Algorithms. She has also served as a Teaching Assistant at Purdue University for courses including Great Issues in Computer Science, Data Mining, and Data Engineering in Python. At Alexandria University, she taught Probability Theory, Statistics, Data Mining, and Data Structures, demonstrating expertise across both theoretical computer science and practical security applications. Her research appears to be conducted in collaboration with the PurSec Lab at Purdue University, where she completed her Ph.D. Her work shows strong interdisciplinary connections between computer science, human-computer interaction, and security engineering, with particular attention to the practical usability aspects of security systems.

• mobile computing
• security and privacy
• virtual reality security
• +۴ مورد دیگر

Dr. Zheng Zheng is an Assistant Teaching Professor in the Multidisciplinary Graduate Engineering Programs at Northeastern University's Toronto Campus. He holds a Ph.D. in Computer Science from McMaster University and a Master of Engineering from the University of Chinese Academy of Sciences. His research focuses on Data Management , Database Systems , Machine Learning , and Deep Learning , with applications in AI-driven infrastructure and digital transformation. Previously, he served as Technical Vice President and Head of AI Institute at a NASDAQ-listed company, leading technical teams in enterprise-wide digital transformation. His work emphasizes data quality , text mining , and automated systems . He leads the DSAI Research Lab , exploring intersections of AI and data science. He actively reviews for top conferences like VLDB , ACM SIGKDD , and IJCNN . Key achievements include developing RadGen (a cross-modal fusion system for radiology report generation) and Contextual Data Cleaning methodologies. His publications span top-tier journals like ACM JDIQ and SIGMOD, with awards including the Runner-Up Paper Award and AI Youth Star Award . Education : PhD, Computer Science, McMaster University, Canada Master of Engineering, Computer Technology, University of Chinese Academy of Sciences, China Grants & Projects : "Northeastern Toronto Professor Fuses AI and Art to Preserve Historical Murals" Research collaboration with McMaster University's Department of Computing and Software His work bridges academia and industry, emphasizing practical AI solutions for complex systems. Current research trends include safety-critical control , intention-aware systems , and resilient estimation in cyber-physical systems.

• Data Management
• Database Systems
• Data Quality
• +۵ مورد دیگر

Tao Xie is a Professor in the Department of Computer Science at San Diego State University, affiliated with the College of Sciences. His expertise spans high performance computing, storage systems, and distributed systems. He holds a PhD in Computer Science from New Mexico Institute of Mining and Technology (2006). His research focuses on storage systems , persistent memory architecture , near-data processing , and dynamic resource management . His recent work generalizes functional error correction for machine learning models and explores persistent spiral storage solutions. Xie has received multiple Outstanding College of Sciences Faculty Awards (2022, 2015, 2010) and an NSF Early CAREER Award (2009). He has advised numerous students in projects related to persistent memory , dynamic hashing , and garbage collection . His current grants include NSF-funded projects on persistent storage architecture and flash storage optimization.

• High Performance Computing
• Storage Systems
• Cluster and Grid Computing
• +۵ مورد دیگر

Dr. Yongqi Dong is a Research Group Leader at RWTH Aachen University in the domain of AI and Automated Mobility, and a guest researcher at Delft University of Technology. He holds a Ph.D. in Traffic Systems Engineering from Delft University of Technology (2025), an M.Sc. in Control Science and Engineering from Tsinghua University (2017), and a B.Sc. in Telecommunication from Beijing Jiaotong University. His research centers on three pillars: Deep Learning for sensing and anomaly detection in transportation systems Deep Reinforcement Learning for vehicle control and decision-making Big Data Analytics for spatial-temporal pattern mining in smart mobility He aims to develop socially compliant automated driving systems through interdisciplinary approaches combining AI with transportation research. Dong's publications primarily focus on autonomous vehicles, intelligent transportation systems, and spatial-temporal pattern analysis. His recent work shows increasing emphasis on transformer architectures, self-supervised learning, and social compliance in mixed traffic environments. Awards and Fellowships: 2023 IEEE TAB Committee on Standards Seed Funding 2024 IEEE ITSS New Initiatives Proposal Funding 2023 Fellowships for Young Researchers (ITS) 2024 IEEE ITSC Conference Support Program China National Scholarship He leads a research group focused on AI and Automated Mobility, actively supervising graduate students and seeking collaborations. His team works on projects involving deep reinforcement learning for autonomous driving, multi-agent systems in mixed traffic, and vision-language models for transportation applications.

• Automated Vehicles
• Smart Mobility
• Shared Mobility
• +۵ مورد دیگر