جست‌وجوی استاد و پژوهشگر

ZHUANG Yizhouمشاهده پروفایل

استادیار

• Weather and climate extremes
• Climate change and event attribution
• Atmospheric circulation and thermodynamics
• +۳ مورد دیگر

Prof. ZHUANG Yizhou is an Assistant Professor in the Department of Geography at Hong Kong Baptist University. His research focuses on weather and climate extremes, climate change attribution, and land-atmosphere coupling. With a Ph.D. in Meteorology from Peking University and extensive postdoctoral experience at UCLA, he brings significant expertise in atmospheric sciences to his academic role. Dr. Zhuang's educational background includes: 2019-2024: Postdoctoral Scholar, University of California, Los Angeles (UCLA), USA 2017: Visiting Graduate Researcher, University of California, Los Angeles (UCLA), USA 2015-2017: Visiting Research Scholar, University of Texas at Austin, USA 2013-2019: Ph.D., Meteorology, Peking University, China 2009-2013: B.S., Atmospheric Sciences (Remote Sensing Focus), Nanjing University of Information Science and Technology, China Dr. Zhuang's research spans multiple critical areas in climate science. His work on weather and climate extremes examines phenomena like wildfires, droughts, and floods. In climate change attribution , he investigates the human influence on extreme weather events, with several publications in PNAS demonstrating how anthropogenic warming has altered drought mechanisms and fire risks. His research on land-atmosphere coupling explores the complex feedback mechanisms between Earth's surface and the atmosphere. Additionally, he applies machine learning techniques and remote sensing technologies to analyze cloud formations and precipitation patterns. Analysis of Dr. Zhuang's recent publications reveals a consistent focus on drought mechanisms and fire weather risk in western North America. His work frequently employs advanced statistical methods like self-organizing maps and canonical correlation analysis to understand complex climate phenomena. A notable trend is his investigation of how anthropogenic climate change is fundamentally altering the nature of droughts, shifting from precipitation-deficit dominated to temperature-driven events, with significant implications for water resource management. Dr. Zhuang has received several prestigious awards for his research contributions: JIFRESSE Outstanding Leadership/Service Award, UCLA, 2023 Richard P. and Linda S. Turco Exceptional Research Publication Award, UCLA, 2023 China Scholarship Council (CSC) Joint Ph.D. Scholarship, 2015-2017 As an academic mentor, Dr. Zhuang supervises graduate students, with evidence of at least one student (G. Wang) whose work has been published under his supervision. He serves as a reviewer for numerous high-impact journals including Proceedings of the National Academy of Sciences (PNAS), Earth's Future, and Geophysical Research Letters. Additionally, he has mentored students in the UCLA Joint Institute for Regional Earth System Science and Engineering (JIFRESSE) Summer Internship Program, with his mentee Annie Rosen winning the 2024 Best JSIP Presentation Award. Dr. Zhuang maintains an active research group, as indicated by his personal website www.zhuangyz.org. His team focuses on climate extremes, attribution studies, and land-atmosphere interactions, with ongoing projects examining drought mechanisms, fire weather risks, and precipitation variability across different regions of the United States, particularly the western states and Great Plains.

Syed Ahmar Shahمشاهده پروفایل

دانشیار

• Signal Processing
• Machine Learning
• Biomedical Engineering
• +۵ مورد دیگر

Syed Ahmar Shah is a Senior Research Fellow (Associate Professor) and the Director of Innovation at the Usher Institute within the College of Medicine and Veterinary Medicine at the University of Edinburgh. He holds a tenured academic position and leads the DIME group (Data-driven Innovation in MEdicine). His work bridges biomedical engineering, data science, and clinical medicine, with a focus on improving healthcare through technological innovation. Dr. Shah completed his educational journey with a BEng in Electronics Engineering from GIK Institute of Engineering Sciences and Technology in Pakistan, followed by an MSc and DPhil (PhD) in Biomedical Engineering and Biomedical Signal Processing and Machine Learning, respectively, from the University of Oxford. His academic credentials reflect his interdisciplinary expertise spanning engineering, data science, and medicine. His research interests center around the application of advanced data analytics to healthcare challenges. Specifically, he focuses on signal processing for time-series analysis and filtering, machine learning for classification, regression, and clustering tasks, and the development of digital health systems for chronic disease management. His work particularly targets chronic respiratory conditions like COPD and asthma, where he applies data mining techniques to electronic health records to identify patterns and develop predictive models. Dr. Shah's publication portfolio includes over 60 peer-reviewed articles in prestigious journals such as The Lancet, Brain, BMJ Open, Thorax, IEEE Transactions, JMIR, and JACI. His recent work demonstrates a strong trajectory in applying artificial intelligence to predict asthma attacks, analyze long COVID outcomes, and develop tools for personalized COPD care, particularly for women. His research often involves large-scale data analysis from national healthcare databases across the UK, Brazil, and Scotland, enabling cross-country comparisons of disease patterns and healthcare system responses. Florence Nightingale Award for Excellence in Healthcare Data Analytics (2023) As an active supervisor, Dr. Shah is open to PhD supervision enquiries and has contributed to training the next generation of researchers at the intersection of data science and healthcare. His DIME research group serves as a hub for innovative projects that combine engineering approaches with clinical medicine to address pressing healthcare challenges. Dr. Shah also engages with industry through data science consulting, offering expertise in developing intelligent algorithms for businesses with large datasets, particularly in healthcare but extending to other domains as well.

• Human-Computer Interaction
• Auditory & Speech-Based Interfaces
• Accessibility Technologies
• +۳ مورد دیگر

Ian Pitt is a Lecturer in Usability Engineering and Interactive Media at University College Cork (UCC). He leads the Interaction Design, E-Learning and Speech (IDEAS) Research Group, focusing on multimodal human-computer interaction, auditory interfaces, and accessibility solutions for visually impaired users. Pitt holds a D.Phil from the University of York, followed by research fellowships at Otto-von-Guericke University in Germany before joining UCC in 1997. His research interests include speech-based interfaces, e-learning systems, and accessibility technologies for blind users. Key projects include the EU-funded ENABLE Network (2011–2014) and prototype development for UniWink. He has secured significant grants, including €72,009 from IRCSET for voice analysis research and €19,478 from the EU for ICT-supported learning initiatives. Pitt has advised numerous PhD students, including Flaithri Neff (2011), Emma-Kate Crowley (2014), and current candidates Aine Kearns and Patrick Egan. His publications span journals like International Journal of Game-Based Learning and conferences such as ICCHP and ACM SIGACCESS. He has contributed to committees for conferences like CHI and the Irish HCI conference. Teaching modules include Usability Engineering, Human-Computer Interaction, and Digital Media Development. His work emphasizes inclusive design principles, with projects addressing navigation systems for blind students and adaptive e-learning frameworks. Recent research trends focus on ICT-delivered aphasia rehabilitation, emotional BCI interfaces, and multimodal learning systems. Collaborations include international partners through EU grants, reflecting his global impact in accessibility and educational technology.

• Machine Learning
• Data Mining
• Bioinformatics
• +۱۱ مورد دیگر

Andrea Passerini is a Full Professor in the Department of Information Engineering and Computer Science at the University of Trento, Italy, where he also serves as Coordinator of the PhD programme in Information Engineering and Computer Science (Ministerial Decree 45/2013). His academic footprint spans multiple departments including Mathematics, Sociology, Cellular Biology, and Industrial Engineering, reflecting deep interdisciplinary engagement across computational sciences and life sciences. His research centers on Machine Learning and Data Mining with specialized expertise in Neuro-Symbolic AI , Probabilistic Reasoning , and Statistical Relational Learning . He pioneers methods for graph-based learning, medical AI applications, and explainable systems, with significant contributions to bioinformatics (particularly RNA-protein interactions) and healthcare diagnostics. His work bridges theoretical rigor with practical implementations in critical domains. Analysis of his 2025 publications reveals dominant trends in neuro-symbolic integration for graph data, human-AI collaboration in medical decision-making, and robust recommender systems. His research increasingly focuses on interpretable AI for high-stakes applications like surgical planning and physician support, while advancing foundational techniques in graph neural networks and concept-based modeling. As PhD programme Coordinator, Professor Passerini mentors doctoral candidates across AI and computer science disciplines. His collaborative network extends to medical researchers at CIBIO (Cellular, Computational and Integrative Biology department) and industrial partners, though specific lab structures aren't documented in available materials. Current projects emphasize medical AI validation, temporal network modeling, and LLM integration with structured reasoning frameworks.

• Human Activity Recognition
• Wearable Sensors
• Machine Learning
• +۴ مورد دیگر

Thomas Ploetz is an Adjunct Professor in the School of Electrical and Computer Engineering at Georgia Institute of Technology. His work focuses on sensor-based human activity recognition, wearable computing, and computational behavior analysis with applications in healthcare, smart homes, and education. He leads interdisciplinary research integrating machine learning, IoT systems, and human-centered design. His research interests include improving activity recognition robustness through synthetic data generation, developing explainable AI for time-series analysis, and advancing healthcare technologies like diabetic foot ulcer monitoring systems. He explores ethical implications of wearable sensing in diverse populations, including underrepresented groups. Key contributions include IMUTube (virtual sensor data generation), ProxiCycle (cyclist safety monitoring), and DISCOVER (smart home activity recognition framework). His work addresses challenges in data scarcity, domain adaptation, and long-term system maintenance in pervasive computing environments. He has been awarded grants such as the GVU/IPaT Research and Engagement Grant (2020), and his research appears in top venues like UbiComp and ISWC. He actively contributes to the wearable computing community through conference organization and editorial work.

Wout Josephمشاهده پروفایل

استاد

• electromagnetic field exposure assessment
• in-body electromagnetic field modelling
• electromagnetic medical applications
• +۱۲ مورد دیگر

Wout Joseph is a Professor in the domain of Experimental Characterization of wireless communication systems at Ghent University (Belgium), where he has been working since October 2009. He is also an IMEC Principal Investigator since 2017. His research is conducted within the wireless, acoustics, environment & expert systems (WAVES) research unit at the Department of Information Technology (INTEC). Dr. Joseph was born in Ostend, Belgium on October 21, 1977. He received his M.Sc. degree in electrical engineering from Ghent University in July 2000. From September 2000 to March 2005 he was a research assistant at the Department of Information Technology (INTEC), where his scientific work focused on electromagnetic exposure assessment around base stations for mobile communications related to health effects. This work led to his Ph.D. degree in March 2005. Professor Joseph's research expertise spans multiple domains within wireless communications and bioelectromagnetics. His primary research interests include electromagnetic field exposure assessment, in-body electromagnetic field modeling, electromagnetic medical applications, propagation for wireless communication systems, IoT, antennas and calibration. He also specializes in wireless performance analysis, industry 4.0 applications, wireless localization, and Quality of Experience metrics. His work is particularly notable for its focus on dosimetric studies in the radiofrequency range, where his research is ranked first in number of peer-reviewed studies. His research has practical applications in wireless network planning, occupational safety, and public health policy related to electromagnetic fields. His extensive publication record (over 886 publications with an h-index of 45 in ISI Web of Science and 66 in Google Scholar) demonstrates a clear trajectory from fundamental electromagnetic field measurements to applied research in industrial wireless networks and bioelectromagnetic applications. Recent work shows a strong emphasis on 5G exposure assessment across multiple European countries, millimeter-wave channel modeling, and the application of machine learning techniques to exposure assessment and wireless localization. EBEA council board member (2015-2018) EBEA board member at large (2019) Bioelectromagnetics Society board member (2022) Bioelectromagnetics Society board member (2024) 24 research awards Professor Joseph leads significant research efforts in electromagnetic field exposure assessment, with particular emphasis on developing measurement methodologies and computational models for real-world exposure scenarios. His work bridges theoretical electromagnetic modeling with practical applications in wireless communications and bioelectromagnetics. His research group within the WAVES unit is highly active in both theoretical and experimental aspects of wireless communications and bioelectromagnetics, with current projects focusing on 5G exposure assessment across Europe, millimeter-wave channel modeling for data centers and industrial environments, and the development of novel exposure assessment methodologies using advanced signal processing and machine learning techniques.

Andreas Beyerمشاهده پروفایل

استاد

• Aging Research
• Proteomics
• Systems Biology
• +۵ مورد دیگر

Prof. Dr. Andreas Beyer holds a faculty position at the University of Cologne, affiliated with the Cluster of Excellence Cellular Stress Responses in Aging-Associated Diseases (CECAD) and the Cologne Excellence Cluster for Cellular Mechanisms in Cancer (CMMC). His research focuses on systems-level analysis of aging processes in humans and model organisms, integrating genomic, proteomic, and computational approaches. Key interests include understanding how genetic variation influences protein networks, developing algorithms for big data analysis, and exploring epigenetic mechanisms related to longevity. Research projects include studying age-associated changes in transcriptional elongation, molecular networks in kidney disease, and the impact of dietary restriction on aging. His group develops tools for proteomics and systems biology, such as methods for analyzing limited proteolysis data and single-cell resolution imaging. Collaborative efforts emphasize translational research in aging-related diseases and drug discovery. Prof. Beyer’s work spans computational biology, molecular genetics, and translational medicine. Notable contributions include identifying epigenetic changes linked to longevity and developing predictive models for age-related disease progression. His lab’s projects often involve multi-omics integration and network-based analyses to uncover disease mechanisms. His research has implications for personalized medicine, cancer biology, and interventions to extend healthspan. Current efforts include optimizing drug combinations targeting aging processes and advancing proteomic technologies for clinical applications.

Lin Caiمشاهده پروفایل

استاد

• Wireless Communications
• Networking
• Mobile Computing
• +۷ مورد دیگر

Lin Cai is a Professor in the Department of Electrical & Computer Engineering at the University of Victoria, Canada. She holds prestigious fellowships including NSERC Steacie, IEEE, CAE, and Royal Society of Canada. Her research focuses on wireless communications, networking, and mobile computing, with emphasis on protocols for multimedia and IoT systems. She has led projects in vehicular networks, UAV-assisted systems, and federated learning for edge intelligence. Dr. Cai has advised over 20 students, many of whom have received awards and prominent roles in academia and industry. She has authored numerous high-impact papers, secured grants from NSERC, CFI, and industry partners, and serves in leadership roles at IEEE and educational institutions. Notable contributions include work on congestion control, network security, and autonomous systems. Education: BEng (Nanjing U. of Sci. & Tech.), MASc/PhD (University of Waterloo) Affiliations: IEEE Vehicular Technology Society Board of Governors, IEEE ComSoc Distinguished Lecturer Awards: 2020 IEEE N2Women 'Star in Networking', RSC Fellow 2024, Best Paper Awards (ICC 2008, WCNC 2011) Research Labs: Connected Autonomous Vehicles (CAV) Lab, AI-driven Networking Group Her work integrates networking, AI, and control theory to address challenges in 6G, IoT, and smart transportation. She actively promotes diversity through initiatives like the 'Riko-chan' STEM manga series.

Chris Leeمشاهده پروفایل

استاد

• Transportation Engineering
• Traffic Safety
• Driver Behavior Analysis
• +۵ مورد دیگر

Professor Chris Lee is a faculty member in the Department of Transportation Science and Engineering at the University of Windsor's Faculty of Engineering. His research focuses on advancing transportation safety through the analysis of driver behavior, traffic flow dynamics, and the integration of emerging technologies like autonomous vehicles and machine learning. Key areas include collision risk prediction, driver vigilance assessment, and the development of advanced car-following models. He has contributed to initiatives such as the Transportation Science and Engineering scholarship program, supporting student research in innovative technologies like driving simulators for lane change behavior studies. His work bridges engineering and human factors, addressing challenges such as driver response to autonomous systems, heavy vehicle traffic management, and cross-cultural automotive design. Lee's interdisciplinary approach leverages data analytics, physiological signals, and machine learning to solve real-world transportation problems. His research has implications for policy-making, infrastructure design, and vehicle safety standards. Lee has collaborated extensively on projects analyzing crash precursors, variable speed limits, and the impact of ITS (Intelligent Transportation Systems) on safety. His publications span over two decades, demonstrating a commitment to both academic rigor and practical applications in transportation engineering. Notable contributions include refining car-following models, studying driver aggression, and evaluating the effectiveness of traffic management strategies.

Peter Bellمشاهده پروفایل

استاد

• Automatic Speech Recognition (ASR)
• Cross-domain and Cross-lingual Adaptation
• Speech Representation Learning
• +۴ مورد دیگر

Roles: Prof Peter Bell holds a personal chair in speech technology at the University of Edinburgh's School of Informatics and is a core member of the Centre for Speech Technology Research (CSTR). His primary research focus is automatic speech recognition (ASR), particularly in cross-domain adaptation, lightly supervised training, and minority language systems. He teaches the Automatic Speech Recognition course and advises multiple PhD students. Research Interests: Prof Bell's work spans ASR system development for diverse domains, audio-visual integration, end-to-end models, and under-resourced languages. His projects include the CoG-MHEAR healthcare initiative and the Unmute project addressing language marginalization. He has pioneered techniques for speaker adaptation, raw-waveform modeling, and multi-task learning. Commercial Activities: He advises industry on speech tech adoption, co-founded Quorate Technology (acquired by LSEG), and provides consultancy to firms developing speech solutions. His work bridges academic research with commercial impact through projects like the BBC's MGB Challenge and EU-funded SUMMA platform. Grants & Projects: Leads EPSRC-funded CoG-MHEAR and Unmute initiatives, collaborates on IARPA MATERIAL for low-resource ASR, and contributed to the SpeechWave waveform-based ASR project. His research has been supported by Bloomberg, Ericsson, Samsung, and Toshiba. Labs & Teams: Active in CSTR, leading teams in speech representation learning, adaptation techniques, and multi-modal ASR. His lab supports interdisciplinary work with NLP, HCI, and biomedical engineering groups. Personal: A passionate hillwalker, he explores Scottish Highlands and Corbetts. Previously active in Edinburgh University Hillwalking Club, his outdoor pursuits reflect his disciplined approach to research exploration.

Fernando Sánchez-Figueroaمشاهده پروفایل

استاد

• Software Engineering
• Big Data
• Business Intelligence
• +۳ مورد دیگر

Fernando Sánchez-Figueroa is a Full Professor at the University of Extremadura's Department of Computer Systems Engineering and Telematics. He is a co-founder of Homeria Open Solutions, a spin-off engaged in R&D projects under EU frameworks. His research focuses on Software Engineering, Machine Learning, Data Visualization, and Ambient Intelligence. He has authored over 50 scientific articles and led numerous R&D contracts with public and private entities. Key roles include: Academic: Full Professor at University of Extremadura Entrepreneur: Co-founder of Homeria Open Solutions Research: Participation in EU-funded projects and development of AI-driven solutions for healthcare, smart cities, and education Research Interests: Machine Learning applications in healthcare, predictive analytics for education, and sustainable smart city technologies. His work bridges theoretical advancements with practical implementations, such as medical image segmentation using SAM models and cost-efficient UAV systems. Publications: Recent works include decision support systems for employability analysis, zero-shot learning in medical imaging, and recommender systems for education. He emphasizes data-driven approaches and model-driven engineering in software development. Impact: Developed tools like CompareML for preliminary data analysis and LiveSankey for advanced web visualization. His contributions span academia and industry, addressing challenges in healthcare, urban sustainability, and educational technology.

Santiago Ontañónمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Game AI
• +۱۰ مورد دیگر

Santiago Ontañón is an Associate Professor in the Department of Computer Science at Drexel University's College of Computing and Informatics. He is also a Senior Research Scientist at Google DeepMind, reflecting a strong dual affiliation in both academic and industrial AI research. His work bridges theoretical AI with practical applications in gaming and machine learning. PhD in Computer Science (Artificial Intelligence), cum laude, Autonomous University of Barcelona Postdoctoral Researcher, Georgia Institute of Technology Researcher, Artificial Intelligence Research Institute (IIIA), Barcelona, Spain Dr. Ontañón's research focuses on artificial intelligence, machine learning, and robotics, with a particular emphasis on game AI. His interests span case-based reasoning, reinforcement learning, Monte Carlo tree search, player modeling, and procedural content generation. He has made significant contributions to AI in real-time strategy games and explainable AI systems. His recent publications reflect a consistent trend in AI for games, hierarchical planning, and learning from demonstration. The articles span topics such as reproducible deep reinforcement learning, adaptive player modeling, and integrating domain knowledge into search algorithms, indicating a mature and impactful research trajectory in AI and game technologies. Senior Research Scientist, Google DeepMind Organizer, microRTS AI Competition Advising multiple PhD students in AI and game-related topics He has advised numerous PhD students, many of whom have completed their theses on advanced AI topics in games and reasoning. His research is supported by access to substantial computational resources and collaborative networks in both academia and industry. He actively promotes open science by releasing software, data, and teaching materials. He leads research efforts in AI for games and maintains an active lab focused on game AI, with projects like microRTS, FTL, and Darmok. His team develops systems for reinforcement learning, planning, and natural language understanding in game environments.

Alberto Monge Roffarelloمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Digital Wellbeing
• End-User Development
• +۷ مورد دیگر

Alberto Monge Roffarello is an Assistant Professor (RTDb) at the Department of Control and Computer Engineering (DAUIN), Politecnico di Torino, where he is a member of the e-Lite Research Group. He teaches courses including Human-AI Interaction, Digital Wellbeing, and Web Applications across multiple degree programs such as Computer Science and Systems Engineering, Computer Engineering, and Film and Media Engineering. His research centers on Human-Computer Interaction, with a strong focus on Digital Wellbeing and End-User Development in the Internet of Things . He investigates how users can personalize smart environments through trigger-action programming and how design patterns in digital interfaces—such as Infinite Scroll and Never-Ending Autoplay—exploit psychological vulnerabilities to capture attention. His work aims to empower users with tools for self-control, meaningful interaction, and habit mitigation. The recent publications highlight a consistent trajectory in digital self-control tools , attention-aware design , and semantic recommendation systems for IoT. His research bridges theoretical analysis with practical tools, including conversational agents (HeyTAP), debugging platforms (EUDebug, My IoT Puzzle), and ontologies (EUPont) to lower the barrier for non-technical users. Scientific contributions include: Systematic review of digital self-control tools (TOCHI 2022) Typology of Attention-Capture Damaging Patterns (CHI 2023) Design frameworks for meaningful interactions (AVI 2022) Behavioral interventions for smartphone habits (TiiS 2021) He actively supervises PhD students—Francesca Russo, Robert Everett Schwartz, and Luca Scibetta—on topics ranging from AI for education to digital wellbeing in high-schools. His work is supported by competitive research projects such as EMPATHY - Empathetic Mobility Platform . He has contributed to multiple national and international collaborations, notably with Santa Clara University (USA). Alberto leads and participates in research initiatives within the e-Lite - Intelligent and Interactive Systems group, focusing on intelligent interfaces, user empowerment, and ethical design. His lab develops tools that translate high-level user intentions into executable IoT rules, promote digital literacy, and support conscious technology use.

Sabine Grafمشاهده پروفایل

استاد

• Educational Technology
• User Adaptive Systems
• Learning Analytics
• +۷ مورد دیگر

Dr. Sabine Graf is a Full Professor at the School of Computing and Information Systems, Athabasca University. She holds a PhD in Computing and Information Systems from Vienna University of Technology (2007) and has been a faculty member since 2009. Her research focuses on user adaptive systems, learning/academic analytics, personalization, and game-based learning, with over $2.3M in external funding and 130+ peer-reviewed publications (cited 8,200+ times). She leads the OMEGA+ educational game project and the User Adaptive Systems (UAS) research cluster. Education: PhD in Computing and Information Systems, Vienna University of Technology, 2007 MSc in Computing and Information Systems, University of Vienna, 2003 Research Interests: Dr. Graf specializes in making learning systems more intelligent through adaptive interfaces, AI-driven recommendations, and data analytics. Her work bridges educational technology, artificial intelligence, and collaborative learning. Recent projects include developing OMEGA+, analyzing student behavior in online courses, and enhancing adaptive learning systems with context-aware features. Grants & Funding: NSERC Discovery Grant ($205,000), 2020 CFI John R. Evans Leaders Fund ($164,573), 2020 AU IDEA Lab Grant ($5,000), 2021 Multiple Mitacs Globalink Internships, NSERC awards, and AU-funded projects. Advising & Team: Dr. Graf has mentored over 30 students, including PhD, MSc, and postdoctoral fellows. Notable advisees include Moustafa Mahmoud (NSERC Scholar) and Mohammad Belghis-Zadeh (3MT Competition winner). The research team collaborates globally, with members from Brazil, Taiwan, Spain, and beyond. Labs & Initiatives: The Academic Analytics Tool (AAT) project and the OMEGA+ game platform are central to her work. The UAS cluster connects researchers worldwide to advance adaptive systems research.

Mark NK Saundersمشاهده پروفایل

استاد

• Research Methods
• Business Research Methods
• Human Resource Management
• +۱۰ مورد دیگر

Professor Mark NK Saunders is a distinguished academic at the University of Birmingham, serving as Professor of Business Research Methods within the Department of Management at Birmingham Business School. He holds visiting professorships at the Universities of Pretoria, Mälardalen, Surrey, and Worcester, reflecting his international academic standing. Education: PhD in Geography (1986) PGCE (1982) MSc in Urban Studies (1981) BA in Geography and Soft Systems (1980) Mark Saunders' research is centered on research methodology, particularly participant selection and methods for understanding organizational relationships. His work extends to human resource aspects of organizational change, with a focus on trust and organizational learning, as well as the success factors of small and medium-sized enterprises (SMEs). He is also deeply engaged in pedagogic practice, textbook writing, and the supervision of doctoral students. His extensive publication record includes over 150 research articles and book chapters, with recent works exploring themes such as social entrepreneurial identities in later life, trust dynamics in consulting, and the impact of generative AI on human resource management. His research is frequently published in top-tier journals like the British Journal of Management , Human Relations , and the International Small Business Journal . Scientific Awards and Fellowships: Fellow of the Academy of Social Sciences (2019) Fellow of the British Academy of Management (2014) British Academy of Management Medal for Leadership Fellow of the First International Network for Trust Fellow of the Chartered Institute of Personnel and Development (2006) Fellow of the Higher Education Academy (2007) Mark Saunders is a dedicated mentor and advisor, actively supervising doctoral students and contributing to doctoral capacity building. He has co-authored and edited over 30 books, including the highly influential Research Methods for Business Students , which is currently in its 9th edition. He holds significant editorial responsibilities as a consulting editor for the International Journal of Management Reviews and as an associate editor (Methodology) for the Human Resource Management Journal . His work is supported by his active involvement in key academic networks, including the British Academy of Management’s Research Methodology Special Interest Group, where he serves as Co-Chair, and the European Foundation for Management Development Doctoral Programmes Conference Committee.