جست‌وجوی استاد و پژوهشگر

• Software Engineering
• IT Security
• Cloud Systems Engineering
• +۳ مورد دیگر

Diomidis Spinellis is a Professor at the Department of Management Science and Technology, Athens University of Economics and Business. He is a leading researcher in software engineering, IT security, and cloud systems engineering, with over 300 publications and 10,000 citations. He has authored award-winning books including Code Reading , Code Quality: The Open Source Perspective , and Effective Debugging: 66 Specific Ways to Debug Software and Systems (2016). As a Senior Member of ACM and IEEE, he served as Editor-in-Chief of IEEE Software (2015–2018) and contributed to open-source tools like CScout, UMLGraph, and dgsh. Award-winning author in software engineering Developer of critical open-source tools Editorial leadership in IEEE Software Contributor to macOS and BSD Unix His research spans code quality, software evolution, security, and developer productivity. Articles highlight Unix modernization, AI-assisted coding, dependency analysis, and incident management. He has served on the IEEE Computer Society Board of Governors and holds degrees from Imperial College London (MEng, PhD). Scientific contributions include open-source datasets (e.g., VulinOSS, Alexandria3k) and innovative tools for software analysis. His work bridges academic research and industrial practice, with case studies on Eclipse, Android APIs, and ING’s incident management.

V. K. Cody Bumgardnerمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Digital Pathology
• +۷ مورد دیگر

V. K. Cody Bumgardner serves as Associate Professor of Pathology and Laboratory Medicine , Assistant Dean for Artificial Intelligence and Data Science , and Division Chief of Pathology Informatics at the University of Kentucky College of Medicine . He also directs the IBI Center for Applied AI , focusing on AI integration in healthcare. Academic Rank: Associate Professor Leadership Roles: Assistant Dean, Division Chief, Director Research Focus: AI/ML in pathology, medical imaging, and clinical data Education: PhD in Computer Science (University of Kentucky, 2017). Research Themes combine artificial intelligence with clinical applications, including: Digital pathology automation Edge computing for healthcare Large language models in structured medical tasks Opioid crisis forecasting systems Medical imaging enhancement techniques Recent Publications demonstrate cross-disciplinary innovation in AI-driven clinical diagnostics , secure healthcare infrastructure , medical imaging enhancement , and addiction epidemiology .

• Software Engineering Education
• Computational Technology in Education
• Machine Learning Applications
• +۵ مورد دیگر

Saheed Popoola, PhD, is an Assistant Professor at the University of Cincinnati's CECH - School of Information Technology. His research spans software engineering education, computational technology in K-12 classrooms, and model-driven engineering. He actively explores strategies to enhance collaborative learning environments through student-driven software projects and investigates challenges in open-source development and cybersecurity incident analysis. His work bridges empirical software engineering practices with educational technology, focusing on topics such as static analysis tool efficacy, user sentiment analysis in software communities, and ensemble learning techniques for network security. He has contributed to tools like Loupe for model analysis and has examined LabVIEW/Simulink model evolution patterns. Recent studies include the impact of computational thinking in K-12 education, early heart disease detection via machine learning, and strategies for managing diverse classroom environments. His research emphasizes practical applications in both academic and industrial software development contexts. Notably absent from the provided data are specific details about grants, awards, or direct student advising. However, his involvement in initiatives like the student software solutions center highlights a focus on experiential learning and community-driven development practices.

Vassil Vassilevمشاهده پروفایل

استاد

• Artificial Intelligence
• Cyber Security
• Machine Learning
• +۶ مورد دیگر

Prof. Vassil Vassilev is a Professor of AI and Intelligent Systems and Head of the Cyber Security Research Centre at London Metropolitan University. His work spans artificial intelligence, cyber security, and data integration with a focus on applications in urban systems, malware analysis, and cyber-physical systems. He leads initiatives in secure transaction risk assessment (CyDRA) and smart city technologies. Research interests include reinforcement learning for cyber incident response, AI policy validation frameworks, and adaptive security systems. He explores hybrid AI models, IoT-based environmental monitoring, and semantic frameworks for vulnerability detection. His work bridges theoretical AI advancements with practical implementations in cyber defense, urban data spaces, and financial security. Grants and funding support his projects on cyber security analytics, threat intelligence, and low-cost data platforms. He collaborates on initiatives like the first urban data space in Bulgaria and innovative authentication techniques using audio steganography. His leadership at the Cyber Security Research Centre drives applied research in both academic and enterprise contexts.

• Artificial Intelligence
• Deep Learning
• Machine Learning
• +۴ مورد دیگر

Mostafa Rahimi Azghadi is a Professor and Head of the Electrical and Electronic Engineering Discipline at James Cook University (JCU). He specializes in neural-inspired computing, machine learning, and AI applications in agriculture, aquaculture, medicine, and biosecurity. He has secured over $20M in research funding and holds leadership roles in several ARC research hubs and centers, including Deputy Director of the ARC Training Centre in Plant Biosecurity and AI Leader of the ARC Industrial Transformation Research Hub for Aquaculture. Education: PhD in Electrical and Electronic Engineering (University of Adelaide, 2014), with prior degrees from Iranian National University. Affiliations: Acting Director of JCU's Agriculture Technology and Adoption Centre, IEEE Northern Australia Section Chair, and Associate Editor of Frontiers in Neuroscience and IEEE Access. Research Interests: Focus on AI, deep learning, neuromorphic engineering, and hardware acceleration. His work spans applications in sustainable agriculture, biosecurity, and healthcare, including projects on fish phenotyping, stress monitoring via wearables, and memristive neuromorphic systems. Publications & Impact: Over 100 journal articles and conference papers. Recent work includes advances in memristive neural networks, underwater fish segmentation, and stress prediction models. His research bridges theory and practice, emphasizing real-world problem-solving. Awards: Top 2% global citation rank in EEE (2020), QLD Young Tall Poppy Science Award (2017), and multiple best paper awards. Grants & Funding: Secured major grants for aquaculture genomics, sugarcane health monitoring, and robotic weed control. Teaching: Coordinates and lectures on embedded systems and sensors. Emphasizes hands-on learning and industry relevance, with a focus on connecting theory to practical engineering challenges.

Banani Royمشاهده پروفایل

استادیار

• Software Maintenance and Renovation
• Software Analytics
• Empirical Software Engineering
• +۴ مورد دیگر

Dr. Banani Roy is an Assistant Professor in the Department of Computer Science at the University of Saskatchewan. Her research focuses on software maintenance, empirical software engineering, program comprehension, and scientific workflow management systems. She explores challenges in quantum computing applications for software engineering, AI-driven code tools, and reproducibility in computational experiments. Her work includes developing frameworks like VizSciFlow for scientific workflow visualization and Nutrient App for environmental monitoring via smartphone applications. Her research interests span topics such as code quality analysis, gender disparities in software systems, and XAI (Explainable AI) challenges. She has contributed to tools like CloneCognition for code clone validation and FSECAM for feature-to-architecture linkage. Her recent studies address developer challenges with large language models and federated learning approaches for real-time bug prediction. Dr. Roy's publications emphasize interdisciplinary applications of software engineering principles in scientific computing, quantum algorithms, and community-driven problem-solving platforms. She advocates for reproducible workflows and FAIR (Findable, Accessible, Interoperable, Reusable) data practices in collaborative scientific research. While no awards are explicitly listed, her extensive contributions to open-source tool development and empirical studies highlight her impact in advancing software engineering methodologies. She collaborates on projects involving legacy system reengineering, cloud-based code clone validation, and asynchronous collaboration frameworks for scientific teams.

Maria Chliمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Multi-agent Systems
• +۳ مورد دیگر

Maria Chli is Reader (Associate Professor) in Computer Science at Aston University, specializing in multi-agent systems and machine learning applications for smart cities. As Director of Aston's Computer Science Industry Club, she bridges academic research with industrial applications. Her lab develops deep reinforcement learning approaches for urban traffic optimization, including vision-based traffic signal control systems deployable in real-world settings. Recent work creates synthetic traffic datasets through graph neural networks and designs auction mechanisms for efficient resource allocation. Teaching includes modules on Operating Systems (CS2230) and Multi-agent Systems (CS3340). Research has applications in sustainable transportation and adaptive infrastructure management.

Russell Zaretzkiمشاهده پروفایل

پژوهشگر ارشد

• Applied Statistics
• Big Data Analytics
• Computational Statistics
• +۳ مورد دیگر

Russell Zaretzki serves as a Heath Faculty Fellow in the Department of Business Analytics & Statistics at the Haslam College of Business, University of Tennessee, Knoxville. His research bridges industry and academia through predictive analytics and business intelligence applications, with expertise spanning applied statistics, big data, and healthcare analytics. His educational foundation includes: Ph.D. in Statistics from Cornell University (2004) M.S. in Statistics from Cornell University (1999) B.S. in Physics from the University of Michigan (1995) Zaretzki's research integrates computational statistics and data mining across diverse domains. His work demonstrates exceptional versatility in applying statistical methodologies to software engineering ecosystems (e.g., developer behavior analysis), genomic evolution (e.g., codon usage modeling), financial risk assessment, and healthcare outcomes. This interdisciplinary approach emphasizes real-world problem solving through predictive modeling and large-scale data analysis. Recent publications reveal a pronounced trend toward methodological innovation in open-source software analytics and biological data science, with increasing focus on ensemble learning techniques and mutation-selection modeling. His work consistently addresses data quality challenges in version control systems while expanding into healthcare applications like oral mucositis dynamics. Recognition includes the prestigious Heath Faculty Fellowship. While specific advising activities remain undocumented, his publication record indicates active mentorship through collaborative research projects. His work on infrastructure initiatives like the World of Code project suggests leadership in developing research frameworks for software ecosystem analysis. Zaretzki maintains significant involvement with the World of Code research initiative, which provides infrastructure for mining open-source version control data at scale, indicating leadership in computational research infrastructure development.

Ritu Chaturvediمشاهده پروفایل

دانشیار

• Data Mining
• Predictive Modeling
• Educational Data Mining
• +۵ مورد دیگر

Ritu Chaturvedi is an Associate Professor at the School of Computer Science, University of Guelph. Her research focuses on data mining and predictive modeling with applications in educational technology, e-commerce, and social media analysis. She holds a faculty position emphasizing interdisciplinary approaches to computational challenges in learning systems and digital environments. Her research interests span Intelligent Tutoring Systems (ITS) Educational Data Mining Sentiment Analysis Recommendation Systems Natural Language Processing Machine Learning Methodologies Recent work includes developing algorithms for personalized learning systems, analyzing social media sentiment during global health crises, and enhancing cross-platform e-commerce recommendations. Her publications demonstrate expertise in data-driven solutions for instructional design, consumer behavior modeling, and privacy policy optimization. Advising focuses on graduate students exploring adaptive learning technologies and data integration strategies. Her lab's projects often involve open-source implementations of recommendation engines and educational analytics frameworks.

Stephen P. Robertsمشاهده پروفایل

استاد

• Comparative Physiology
• Insect Flight Aerodynamics
• Stress Tolerance
• +۵ مورد دیگر

Dr. Stephen P. Roberts is a **Professor of Biological Sciences** at Missouri University of Science and Technology and currently serves as the **Vice Chancellor of Strategic Initiatives**, advising leadership on strategic planning to achieve institutional goals such as Carnegie R1 status, Top-100 USN&WR ranking, and enrollment growth. His research focuses on **comparative and integrative physiology of insects**, exploring flight aerodynamics, stress tolerance, aging, and locomotion biomechanics across global biomes. He has conducted fieldwork on all continents except Australia and has authored a book on ecological and environmental physiology of insects. **Education**: He earned a B.S. (1990) and M.S. (1992) in Biology from Illinois State University, followed by a Ph.D. in Zoology (1998) from Arizona State University. His postdoctoral training was at the University of Chicago in the 1990s. **Research Interests**: Roberts’ work spans **insect physiology**, **machine learning applications** in environmental science, and **climate modeling**. His recent projects include developing the **RTide** software package for tidal analysis and storm surge prediction, as well as studying the effects of environmental stressors on insect behavior and aging. **Awards and Grants**: While specific awards are not listed, his extensive publication record and leadership roles suggest significant recognition in his field. His grants include major projects on insect physiology and environmental modeling. **Advising and Labs**: Though students are not explicitly listed, his research involves collaborative projects in entomology, bioenergetics, and computational ecology. He leads strategic initiatives that likely involve interdisciplinary teams focused on institutional growth and research excellence.

Emily Jane McTavishمشاهده پروفایل

استادیار

• Evolution
• Phylogenetics
• Genomics
• +۱ مورد دیگر

Emily Jane McTavish is an Assistant Professor at the University of California, Merced, within the School of Life & Environmental Sciences. Her research focuses on evolutionary biology, phylogenetics, and computational genomics. She leads the McTavish Lab, which develops tools for large-scale evolutionary inference and integrates genomic, phylogenetic, and biodiversity data. She holds a B.S. from McGill University (2006) and a Ph.D. from the University of Texas (2013). Her research interests center on leveraging computational methods to address evolutionary questions using vast datasets. This includes developing pipelines for phylogenetic analysis and exploring the dynamics of diversification and introgression. She has contributed to open-source tools such as Physcraper and OpenTree, which facilitate the updating and analysis of phylogenetic trees. While Emily’s own scientific awards are not explicitly listed, her students and collaborators have achieved notable recognition, including NSF GRFP Honorable Mentions and grants for research projects. The McTavish Lab has received support through the Intelligent Adaptive Systems National Research Training Grant and collaborations with institutions like JGI. Emily advises several graduate students, including Lesly Lopez Fang, Jasper Toscani Field, and James Rosindell, among others. Her lab’s work has been published in journals like Systematic Biology and PLoS Genetics, and they maintain active collaborations on biodiversity informatics and phylogenetic tools. The McTavish Lab is based at UC Merced’s SE1 282, and their projects include creating dynamic phylogenetic trees for birds and developing methods to map biodiversity using environmental DNA and Earth observation data.

• Artificial Intelligence
• Data Science
• Machine Learning
• +۷ مورد دیگر

Wang Zhaoxia is an Associate Professor of Computer Science (Practice) at the School of Computing and Information Systems, Singapore Management University. Her expertise lies in Artificial Intelligence, Data Science, and Machine Learning with a focus on Natural Language Processing (NLP), Sentiment Analysis, and Knowledge Graphs. She holds a PhD from Nankai University (2004). Research Interests: Her work spans AI-driven solutions for sentiment analysis, maritime risk management, audio-to-music conversion, and social media analytics. Recent projects include leveraging Large Language Models (LLMs) for tasks like logical reasoning, translation optimization, and risk assessment. Publications: Recent work highlights include developing metrics for LLM performance (RAP), emotion analysis in images, and automated music transcription. Her research demonstrates cross-disciplinary applications of AI in fields like finance (ICO success prediction) and health (disease surveillance). Awards: No awards explicitly listed Advising: Supervises 3 PhD/Master students including HUANG Donghao and Nicole Anne Hui Ying TEO Contact: zxwang@smu.edu.sg | +65 68261347

Pei Wangمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Artificial General Intelligence
• Non-Axiomatic Reasoning
• +۳ مورد دیگر

Pei Wang is an Associate Professor in the Department of Computer and Information Sciences at Temple University's College of Science & Technology. His research focuses on Artificial General Intelligence (AGI), Non-Axiomatic Reasoning System (NARS), and cognitive systems. He holds a Ph.D. in Computer Science and Cognitive Science from Indiana University (1995), and has contributed to foundational AI theories through works like Rigid Flexibility: The Logic of Intelligence (2006) and Non-Axiomatic Logic: A Model of Intelligent Reasoning (2013). Education: Ph.D., Indiana University (1995); M.S./B.S., Peking University (1986/1983) Research interests include AGI architecture design, formal logic for reasoning under uncertainty, and the integration of perception and cognition. His work emphasizes general-purpose intelligence systems capable of adaptive learning and decision-making in dynamic environments. Recent publications explore neurosymbolic systems, consciousness modeling, and causal inference frameworks. Notable contributions include co-editing the Journal of Artificial General Intelligence and leading the OpenNARS project, an open-source AGI implementation. Awards include the Jet Propulsion Laboratory Summer Faculty Research Program (2013–2014) and the Outstanding Dissertation Award from Indiana University (1996).

Iris Xiaohong Quanمشاهده پروفایل

استاد

• Entrepreneurship
• Technology Management
• Innovation Management
• +۵ مورد دیگر

Dr. Iris Xiaohong Quan is a Full Professor of entrepreneurship at the Lucas College and Graduate School of Business, San José State University. She holds a PhD in Regional Planning from the University of California - Berkeley and serves as Director for the Center for Global R&D and Innovation (GLORAD) Silicon Valley. Dr. Quan's research focuses on entrepreneurship, technology and innovation management, with particular expertise in global R&D strategies, business model innovation, and digital health. Her work examines how firms navigate innovation ecosystems across different cultural and regulatory contexts, with a special focus on Chinese companies and their internationalization strategies. Her recent publications demonstrate a strong trajectory in artificial intelligence business ecosystems, open innovation models, and healthcare technology innovation. She has contributed significantly to understanding how organizations leverage external knowledge sources and develop context-specific innovation approaches in global markets. Best Paper Publication Award from IEEE TEM in 2020 for "Cross-National Complementarity of Technology Push, Demand Pull, and Manufacturing Push Policies: The Case of Photovoltaics" Dr. Quan serves on the editorial boards of Technovation and IEEE Transactions on Engineering Management. She is actively involved with the Silicon Valley Center for Entrepreneurship as a steering committee member and has judged numerous business plan competitions. Additionally, she brings practical experience as a part-time investment partner at TSVC, an early-stage deep tech-focused venture capital firm. Her leadership extends to directing the Center for Global R&D and Innovation (GLORAD) Silicon Valley, which serves as a hub for research on global innovation practices and cross-border technology transfer.

Andrew P. Michelsonمشاهده پروفایل

استادیار

• Machine Learning in Medicine
• Artificial Intelligence in Healthcare
• Lung Transplantation
• +۳ مورد دیگر

Andrew P. Michelson, MD is an Assistant Professor of Medicine in the Division of Pulmonary and Critical Care Medicine at Washington University in St. Louis School of Medicine. He holds affiliations with the Institute of Clinical and Translational Sciences (ICTS), Institute for Informatics, Data Science and Biostatistics (I2DB), and the Center for Applied Health Informatics (CAHI). His research focuses on machine learning and artificial intelligence applications in medicine, particularly in lung transplantation, critical care, and clinical informatics. He has contributed to over 36 peer-reviewed publications since 2011, emphasizing predictive modeling for primary graft dysfunction, AI-driven diagnostics for cochlear implants, and sepsis prediction systems. His work bridges clinical practice and advanced computational methods to improve patient outcomes in respiratory and transplant medicine. Affiliations: ICTS, I2DB, CAHI Key Research Areas: Machine learning in healthcare, lung transplantation outcomes, clinical informatics Notable Projects: Development of machine learning models for predicting primary graft dysfunction post-lung transplantation, AI applications in otology and neurotology Michelson's research integrates clinical data with advanced analytics to address critical questions in pulmonology and critical care. His recent work highlights the use of CT imaging and multimodal data to enhance transplant outcomes and improve diagnostic accuracy in otolaryngology. He collaborates widely, contributing to translational research initiatives like the CRITICAL resource for intensive care analytics.