جست‌وجوی استاد و پژوهشگر

• Mobile Software Engineering
• Intelligent Software Engineering
• Software Security
• +۶ مورد دیگر

Xiaoyu Sun is a Lecturer in the School of Computing at Australian National University (ANU), specializing in Software Engineering with a focus on Mobile Software Engineering and Intelligent Software Engineering. She holds a PhD from Monash University (2023) and a Bachelor's degree in Computer Science from Beijing Normal University (2016). Her research emphasizes applying static code analysis, dynamic testing, and NLP techniques to enhance software security and reliability, particularly in Android systems. Current projects include tools for detecting compatibility issues and privacy leaks in mobile apps. Her research interests span code generation frameworks (e.g., A^3-CodGen), security management in open-source projects, and AI-enhanced software development practices. Collaborations with tech giants like Bytedance and Alibaba highlight her industry engagement. Xiaoyu is Co-Investigator in the Tech4HSE project (2025-2027), developing AI-based monitoring systems for workspace safety. She has published in top venues including ICSE, ASE, and IEEE Transactions on Software Engineering. Her work bridges academia and industry, addressing challenges in mobile app security, code reuse efficiency, and developer toolchain innovation. Ongoing efforts focus on AI-driven solutions for software engineering tasks and fostering transparent privacy practices in open-source AI applications.

Stephen K. Robinsonمشاهده پروفایل

استاد

• Turbulence flow physics and modeling
• Experimental techniques
• Instrumentation
• +۷ مورد دیگر

Stephen K. Robinson is a Professor in the Department of Mechanical and Aerospace Engineering at the University of California Davis. His work focuses on turbulence flow physics, experimental techniques, and aerospace applications, with recent emphasis on human-robot interaction, space systems, and autonomous technologies. Research interests include turbulence modeling, advanced instrumentation, 3D visualization, and safety engineering. His work integrates applied aerodynamics with cognitive psychology to address challenges in space exploration, such as haptic interfaces, VR training, and human performance under extreme conditions. Recent publications highlight innovations in spacecraft maintenance, robotic systems, and autonomous glider design. His projects often combine hardware development with simulation tools, such as digital twins and real-time performance metrics for manual spacecraft operations. Robinson collaborates on NASA-related initiatives including EVA training systems, habitat technology, and lunar construction dynamics. He has pioneered solutions for CO2 removal systems, spacesuit glove ergonomics, and radiation-resistant materials for extraterrestrial missions.

Xiaolin Wuمشاهده پروفایل

استاد

• Image Processing
• Multimedia Coding and Communication
• Computer Vision
• +۷ مورد دیگر

Dr. Xiaolin Wu is a Professor in the Department of Electrical & Computer Engineering at McMaster University's Faculty of Engineering. With expertise in image processing, multimedia coding, computer vision, and artificial intelligence, he has made groundbreaking contributions to visual/multimedia computing and communication. His research has resulted in over 350 publications and four patents, including the renowned CALIC algorithm for lossless image coding and L3 codec for digital cinema. B.Sc. from Wuhan University (1982) Ph.D. from University of Calgary (1988) Dr. Wu's research spans critical areas such as image restoration (demosaicing, denoising, superresolution), multimedia coding (JPEG, wavelet transforms), and information display (Temporal Psychovisual Modulation). His work on TPVM, featured in MIT Technology Review, redefines display technology by leveraging human vision properties for multi-user VR/AR experiences. Recent publications focus on deep learning applications for image/video restoration, including multi-modality approaches and l∞-constrained compression. His algorithms, such as fast color quantizers and compressive sensing recovery methods, have been widely adopted in industries like medical imaging and digital cinema. Dr. Wu also holds an NSERC Senior Industrial Research Chair and has collaborated with global tech leaders including Microsoft, Nokia, and Huawei. IS&T/SPIE Best Paper Award UWO Distinguished Research Professor Award McMaster Distinguished Engineering Professor Award IEEE Fellow As an educator, Dr. Wu teaches courses in image processing, discrete methods, numerical analysis, and parallel computing, emphasizing algorithm design, data structures, and real-time systems. His work bridges academic research with industrial applications, ensuring practical impact across biomedical imaging, digital security, and smart display technologies.

Adelina Rogowska-Wrzesinskaمشاهده پروفایل

دانشیار

• Proteomics
• Mass Spectrometry
• Oxidative Stress
• +۳ مورد دیگر

Adelina Rogowska-Wrzesinska is an Associate Professor at the Department of Biochemistry and Molecular Biology, University of Southern Denmark. Specializing in Biomedical Mass Spectrometry and Systems Biology , her research spans proteomics, oxidative stress, protein oxidation, and 3D cell culture models. Affiliation : Department of Biochemistry and Molecular Biology, University of Southern Denmark Academic Role : Teaching and research leadership Her research interests focus on: Redox biology and protein oxidative modifications 3D spheroid models for disease and toxicity studies Mass spectrometry-based proteomics and lipidomics Oxidative stress in cellular senescence and disease Plant stress responses and proteomics Scientific Trends : Recent publications highlight advancements in 3D culture modeling (NAFLD, genotoxicity), redox proteomics (glutathionylation, disulfide bonds), and multi-omics integration (chromatin proteomics, lipidome mapping). Teaching and Supervision : She has supervised multiple PhD students (e.g., Christina Erika Hagensen, Helle Frandsen) and taught courses like Basic Biochemistry (BMB530) and Fundamental Molecular Biology (BMB504) .

• Network Security
• Cloud Security
• Software Defined Networking Security
• +۶ مورد دیگر

Udaya Kiran Tupakula is an Associate Professor in the Department of Computing within the School of Computer, Data and Mathematical Sciences at Western Sydney University. With over two decades of research experience, his work focuses on cybersecurity with particular emphasis on network security, cloud infrastructure protection, and emerging technologies. He has established himself as a leading researcher through extensive collaborations, particularly with Professor Vijay Varadharajan, and has contributed significantly to securing virtualized environments and next-generation networks. Dr. Tupakula's research spans multiple critical areas in cybersecurity. His early work focused on fundamental security challenges like DDoS mitigation and TCP SYN flood attacks, evolving into more complex domains including Software Defined Networking security, cloud security architectures, and IoT protection. Recent research directions include AI security, privacy-preserving machine learning, and securing 5G infrastructure. His approach combines theoretical frameworks with practical implementations, often developing novel security architectures that address specific vulnerabilities in emerging technologies. Analysis of his publication trends shows a clear progression from traditional network security toward more sophisticated systems. His work has increasingly incorporated machine learning techniques for intrusion detection, expanded into industrial control systems security, and recently embraced AI safety challenges. The breadth of his research demonstrates adaptability to evolving security landscapes while maintaining focus on practical, implementable solutions rather than purely theoretical approaches. Dr. Tupakula has mentored numerous researchers who have become active contributors in the cybersecurity field. His collaborative approach is evident in the extensive co-authorship network spanning multiple institutions and countries. His research has been supported by various grants focusing on critical infrastructure protection, cloud security, and next-generation network security. His laboratory work focuses on practical security testing environments for SDN, cloud infrastructure, and IoT systems. Current research directions include securing AI systems from malicious use, enhancing privacy in federated learning environments, and developing more robust security frameworks for industrial control systems. The applied nature of his research ensures close alignment with real-world security challenges faced by industry and government organizations.

Shamsad Parvinمشاهده پروفایل

استادیار

• Computer Science Education
• Wireless Communications
• Cognitive Radio Networks
• +۸ مورد دیگر

Dr. Shamsad Parvin is an Assistant Professor of Teaching in the Department of Computer Science and Engineering at the University at Buffalo (SUNY), School of Engineering and Applied Sciences. Previously, she held positions as Assistant Professor at SUNY Morrisville and Postdoctoral Researcher at the University of New Mexico and New Mexico State University. She completed her PhD at the University of Electro-Communications in Tokyo, Japan, with MSc and BSc degrees from the University of Rajshahi, Bangladesh. Education: PhD, Electrical and Computer Engineering, University of Electro-Communications, Tokyo (2013) MSc, Information and Computer Engineering, University of Rajshahi (2006) BSc, Information and Computer Engineering, University of Rajshahi (2004) Research Interests: Dr. Parvin specializes in wireless communications, cognitive radio networks, routing protocols, machine learning applications in networking, and computer science education. Her work emphasizes energy efficiency, network stability, and security in mobile and sensor networks. Publications: Her 15 most recent articles focus on routing optimization, cognitive radio networks, and wireless sensor systems, with consistent themes of stability, energy efficiency, and adaptive protocols across dynamic network environments. Awards & Honors: Best Paper Presenter (IEEE UEMCON 2021) Best Research Paper Award (TriSaI2012) JT Scholarship (2008-2011) Multiple academic scholarships from Rajshahi University Advising & Grants: She currently advises nine MS students on projects involving machine learning, data science, and AI applications. Secured funding from the U.S. Department of Defense (2016-2018) and Japanese Ministry of International Affairs (2009-2013). Research Teams: Leads multiple MS project teams in areas like automated ML pipelines, crime prediction, accident analysis, and educational tools, demonstrating applied research in data-intensive computing.

Shannon Robertsمشاهده پروفایل

دانشیار

• Human Factors in Transportation Safety
• Driver-Vehicle Interaction
• Automated Vehicles
• +۵ مورد دیگر

Shannon Roberts is an Associate Professor in the Mechanical and Industrial Engineering Department at the University of Massachusetts Amherst, leading research in Human Factors and Transportation Safety. Her work spans driver-vehicle interaction, automated systems, and equity-focused transportation design, with notable grants from NSF, Sloan Foundation, and UMass ADVANCE. Education: PhD and MS in Industrial Engineering (University of Wisconsin-Madison), BS in Mechanical Engineering (MIT) Research focuses on: Teen Driver Safety and Training Effectiveness Human Factors in Automated Vehicles Cybersecurity Response in Transportation Feedback Systems for Driver Performance Community-Centered Traffic Mitigation Recent publications analyze distraction patterns, automation impacts, and VR-based driver assessment. Her team has received multiple awards including the Bentzi Karsh Early Career Service Award and Stephanie Binder Young Professional Award . Students: Mentored 15+ undergraduate and graduate researchers including Meng Wang (PhD candidate), Jaji Pamarthi (PhD student), and alumni like Yalda Ebadi (New Relic) and Fangda Zhang (Ohio State University) Contact: scroberts@umass.edu | LinkedIn | Google Scholar

Junpei Komiyamaمشاهده پروفایل

استادیار

• Machine Learning Methodology
• Decision-making Models
• Algorithmic Bias
• +۷ مورد دیگر

Junpei Komiyama is an Assistant Professor in the Department of Technology, Operations, and Statistics at the Leonard N. Stern School of Business, New York University, where he has been a faculty member since September 2019. His research bridges machine learning methodology and business applications, focusing on decision-making, fairness, and reproducibility in data-driven systems. His educational background includes a PhD in Information Science, an MS in Applied Physics, and a BTech, all from the University of Tokyo. Professor Komiyama's research interests center on machine learning with a strong emphasis on ethical and robust methodologies. He investigates decision-making models such as multi-armed bandits, algorithmic fairness , experimental design , and the reproducibility of data science findings. His work aims to redefine how we validate and trust results generated by machine learning systems. The recent articles reflect a strong trend in fairness-aware machine learning , online decision-making , and reproducible research practices . They span applications in business analytics, recommender systems, pricing, and cloud computing, demonstrating both theoretical depth and practical relevance. Presented work at top venues including NeurIPS and KDD Focus on ethical AI and robust scientific methodology He has advised students in machine learning and data science, though specific names are not listed. His research has been supported through academic grants related to AI ethics and data science innovation, though specific grant details are not provided. Professor Komiyama teaches courses in programming and machine learning, contributing to the technological education of business students at Stern. He is actively involved in advancing the integration of rigorous machine learning methods into business decision-making frameworks.

• Security and Reliability of VR/AR Systems
• Internet of Things and Mobile Sensing
• Blockchain and Distributed Systems
• +۷ مورد دیگر

Dr. Henry Hong-Ning Dai is an Associate Professor in the Department of Computer Science at the Faculty of Science, Hong Kong Baptist University (HKBU). He previously held academic positions at Lingnan University and Macau University of Science and Technology, where he advanced from Assistant to Associate Professor. He holds a Ph.D. from the Chinese University of Hong Kong and a D.Eng. from Shanghai Jiao Tong University. Education: Ph.D. in Computer Science and Engineering, Chinese University of Hong Kong (2008) D.Eng. in Computer Technology Application, Shanghai Jiao Tong University (2012) M.Eng. in Computer Science and Engineering, South China University of Technology (2003) B.Eng. in Computer Science and Engineering, South China University of Technology (2000) Dr. Dai's research focuses on security and reliability of VR/AR systems , Internet of Things , blockchain and distributed systems , federated learning , and cyber-physical systems . His work integrates AI, networking, and software engineering to address real-world security and performance challenges in emerging technologies. He has published over 300 papers in top journals and conferences such as IEEE JSAC, TMC, ICSE, INFOCOM, and AAAI, accumulating more than 24,000 citations. The 15 most recent publications (2023–2025) highlight his leadership in VR/AR security (e.g., AcouListener, Meta VR study), blockchain scalability and fairness (e.g., Porygon, Auncel, Justitia), federated and robust learning (e.g., EBS-CFL, FedDP), and edge-AI and wireless security (e.g., HARBOR, Smart Shield). His recent work also explores AI-generated art evaluation and LLM-driven manufacturing systems , showcasing interdisciplinary innovation. Scientific Awards and Recognition: Holder of 1 U.S. patent and 1 Australia innovation patent Winner of more than 17 awards Senior Member of ACM, IEEE, and EAI Dr. Dai has been Principal or Co-Investigator on over 12 research projects totaling HK$16 million, funded by UGC, NSFC, FDCT, and HKBU. He serves as an Associate Editor for IEEE Communications Surveys & Tutorials , IEEE Transactions on Intelligent Transportation Systems , and several other IEEE journals. He has chaired program committees and served on the PC of top conferences including ICSE, KDD, and INFOCOM. He is actively recruiting Ph.D. students and RAs in security, blockchain, and AI. Laboratories and Research Teams: While not explicitly named, Dr. Dai leads a research group focused on secure and intelligent distributed systems, with active projects in blockchain, VR security, and edge AI. His team has developed open-source tools such as VR-SP Detector , PrettySmart , and RLF for smart contract analysis and security assessment.

Mai Vuمشاهده پروفایل

استاد

• Machine Learning
• Wireless Communications
• 5G/6G Systems
• +۳ مورد دیگر

Dr. Mai Vu is a Professor in the Department of Electrical and Computer Engineering at Tufts University, with a secondary appointment in Computer Science. She leads the Laboratory for Intelligent NetworKed Systems (LiNKS), focusing on machine learning and wireless communication systems, particularly for 5G/6G networks. Her research addresses challenges in beamforming, user association, and network optimization using advanced optimization and reinforcement learning techniques. Education: Ph.D. in Electrical Engineering from Stanford University (2006), MS from Stanford and University of Melbourne, and B.E. from RMIT University. Research Interests: Machine learning applications in wireless networks, optimization algorithms, 5G/6G systems, and reconfigurable intelligent surfaces (RIS). Her group has published extensively on topics like hybrid beamforming, user association in dense networks, and interference mitigation. Awards: Senior Member of IEEE, Editor roles for IEEE Transactions on Wireless Communications (2013-2016) and IEEE Transactions on Communications (since 2022). Active in conference organization, including Technical Program Co-Chair for IEEE Communication Theory Workshop 2023. Grants: Multiple NSF-funded projects on terahertz networks, edge computing, and RIS beamforming. Collaborates on latency-aware VR/AR edge computing and 6G system design. Labs/Teams: LiNKS lab actively involves PhD students and postdocs in projects like GNN-based beamforming and satellite-terrestrial network coexistence.

Maria Kyrariniمشاهده پروفایل

استادیار

• Robot Learning from Human Demonstrations
• Human-Robot Interaction
• Assistive Robotics
• +۳ مورد دیگر

Maria Kyrarini is an Assistant Professor at Santa Clara University's School of Engineering and leads the Human-Machine Interaction & Innovation (HMI 2 ) research group. Her work is supported by federal (NSF) and internal SCU grants. Education: Ph.D. in Electrical Engineering (University of Bremen, 2019), M.Sc. in Automation Systems (NTUA, 2014), M.Eng. in Electrical & Computer Engineering (NTUA, 2012) Her research focuses on Robot Learning from Human Demonstrations , Human-Robot Interaction , and Assistive Robotics , with emphasis on enhancing human performance and cognitive fatigue assessment. Her publications span healthcare robotics, accessible design, and machine learning applications in rehabilitation. Recent articles highlight advancements in robotic nursing assistance , cognitive load assessment , gesture recognition , and multimodal systems for child development studies . Key keywords include Robotics, Machine Learning, and Human-Centered Design. Scientific Awards: NSF DARE Award (PI), David Packard Jr. Faculty Fellowship, multiple SCU Kuehler Undergraduate Research Grants She has secured significant grants for projects like iRCSA (NSF-funded assistive robotics) and Collaborative Assembly (NSF CHS). Her lab explores safe human-robot cooperation, fatigue detection, and personalized assistive systems. She also contributes to educational initiatives in STEM access and K-12 robotics programs.

• Data Mining
• Social Network Analysis
• Machine Learning
• +۳ مورد دیگر

Jaideep Srivastava is a Professor affiliated with Qatar Foundation (Doha, Qatar), University of Minnesota (Minneapolis, USA), and holds a PhD from University of California Berkeley. His research spans data mining, social network analysis, time series modeling, and health informatics. Recent work focuses on clinical deterioration prediction, sleep research, and computational analysis of pandemic behaviors. Key contributions in clustering algorithms and graph neural networks Active in multimodal learning and misinformation detection His publications from 2024-2022 demonstrate expertise in hierarchical clustering , large language model applications , and health data analytics . Articles often integrate machine learning , social network dynamics , and clinical monitoring systems . Current projects involve Covid-19 in-hospital mortality prediction , virtual influencer analysis , and low-light imaging techniques . Collaborations span institutions in Qatar, USA, and India with applications in urban mobility and precision medicine.

Fazleena Badurdeenمشاهده پروفایل

استاد

• Sustainable Manufacturing
• Lean Manufacturing
• Manufacturing Systems Modeling
• +۲ مورد دیگر

Fazleena Badurdeen is a Professor of Mechanical Engineering and Director of Graduate Studies for the Manufacturing Systems Engineering Program at the University of Kentucky , where she leads research in sustainable manufacturing and supply chain systems. She holds a Ph.D. and M.S. in Industrial Engineering from Ohio University, an M.B.A. from the Post Graduate Institute of Management, and a B.Sc. in Engineering from the University of Peradeniya. Research Interests: sustainable manufacturing, lean systems, supply chain modeling, and sustainable product design. Awards: Earl Parker Robinson Chair Professor in Mechanical Engineering. Notable Contributions: development of frameworks for product sustainability, circular economy integration, and risk assessment tools using Bayesian networks and simulation. Her recent Google Scholar publications focus on additive manufacturing , digital product passports , and real-time defect detection , with keywords spanning sustainable manufacturing, systems engineering, and environmental metrics. She has also contributed to education through curriculum development in systems thinking and graduate program leadership.

• Building Automation
• Energy Management
• Query Optimization
• +۷ مورد دیگر

Paulo Carreira is an Associate Professor at Instituto Superior Técnico , Universidade de Lisboa. His research focuses on Building Automation , Energy Management , and Data Integration , with a particular emphasis on Smart Urban Environments and Cyber-Physical Systems . Fields: Building Automation, Energy Management, Data Privacy, Query Optimization, ETL, Streaming Query Processing Research Trends : His recent work spans from SQL Injection Attacks in LLM-integrated systems to Multi-Paradigm Modeling for Cyber-Physical Systems. Publications highlight his expertise in Energy Efficiency in buildings and BIM Integration with real-time data. Scientific Awards : Best Student Paper Award Nomination for Best Paper Award

İBRAHİM RIZA HALLAÇمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Parallel and Distributed Systems
• +۳ مورد دیگر

İbrahim Rıza Hallaç is an Assistant Professor at RAFET KAYIŞ FACULTY OF ENGINEERING, Department of Computer Engineering. He holds a PhD (2021), Master's (2014), and Bachelor's (2011) in Computer Engineering from Firat University, with doctoral research on social media analysis using deep learning methods under advisor Galip Aydın. His research focuses on: Artificial Intelligence and language models Machine learning for distributed systems Big data analytics and cloud computing Natural language processing applications Social media sentiment analysis Radar-based object detection systems His publications (2015-2024) demonstrate consistent focus on machine learning applications across diverse domains including social network analysis, distributed computing, Turkish NLP, and sensor data processing. Recent works emphasize radar-based multi-target detection (2024), credit scoring models (2023), and wind speed estimation (2023), reflecting expertise in scaling AI solutions for real-world problems. He has led four national R&D projects: Deep Learning-Based Face Recognition Authorization System (2017-2019) DEĞİRMEN Big Data Analysis Platform for Turkish Armed Forces (2017-2021) KOSGEB Innovation Program for Face Recognition Systems (2017-2019) Cloud-Based Social Media Reputation Analysis System (2015-2017) No students, awards, or contact emails are documented in available records.