جست‌وجوی استاد و پژوهشگر

Kunal Mankodiyaمشاهده پروفایل

استاد

• Wearable Sensors
• Smart Textiles
• Body Sensor Networks
• +۳ مورد دیگر

Professor Kunal Mankodiya is a faculty member in the Department of Electrical, Computer and Biomedical Engineering at the University of Rhode Island. He holds the rank of Professor and is affiliated with the College of Engineering. His research focuses on wearable technologies, smart textiles, and medical IoT systems, with notable contributions in neural engineering and body sensor networks. Education Ph.D., University of Luebeck, Germany (2010) M.S., Biomedical Engineering, University of Luebeck, Germany (2007) B.S., Biomedical Engineering, Saurashtra University & C.U. Shah College (2003) Postdoctoral Researcher, Carnegie Mellon University (2011-2014) Postdoctoral Researcher, University of Pittsburgh (2011) Research Interests Mankodiya’s work emphasizes innovative wearable sensor systems for healthcare applications, including smart textiles for medical monitoring and IoT-enabled telemedicine solutions. His research integrates electrical engineering, biomedical engineering, and computer science to address challenges in neurological disorders, neonatal care, and movement disorders. Grants & Awards 2017 NSF CAREER Award 2017 40 under 40 Award, Providence Business News Lead PI on grants totaling over $5M, including NSF CAREER and NIH-funded projects Labs & Teams Mankodiya leads the Smart Wearable and IoT Lab at URI, focusing on interdisciplinary research in healthcare technology. His team collaborates with clinical partners on projects like neonatal monitoring systems and Parkinson’s disease tele-assessment tools.

Alicia Orea Ginerمشاهده پروفایل

استاد

• Sustainable Tourism
• Food Tourism
• Tourism and Social Justice
• +۵ مورد دیگر

Alicia Orea Giner is a Professor at the Faculty of Economics and Business Sciences of Rey Juan Carlos University, specializing in sustainable tourism and social justice. Her research focuses on Generation Z's sustainable consumption practices in food tourism, AI integration in hospitality, and ethical dimensions of tourism. She co-authored the award-winning article The way we live, the way we travel: generation Z and sustainable consumption in food tourism experiences , recognized with the 2024 Emerald Literati Outstanding Paper Award. Her work bridges sustainability, technology, and cultural studies. Key themes include feminist methodologies in tourism research, transformative travel through ecofeminism, and post-pandemic food tourism trends. She explores AI applications in service robots, wellness tourism's mental health connections, and marginalized communities' roles in culinary tourism. Recent studies address LGBTQ+ tourism challenges, stakeholder participation in World Heritage sites, and ghost tours' cultural narratives. Awards highlight her contributions to sustainability and social justice in tourism. Awards: 2024 Emerald Literati Outstanding Paper Award Research Foci: Food Tourism, AI Ethics, Feminist Tourism Studies, Cultural Heritage Management Labs/Teams: No specific lab mentioned; collaborates across interdisciplinary tourism research networks.

Dennis Herhausenمشاهده پروفایل

استاد

• Digital Communication
• Customer Journeys and Experience
• Multichannel Management
• +۷ مورد دیگر

Prof. Dr. Dennis Herhausen is a Full Professor of Marketing and Head of the Marketing Department at Vrije Universiteit Amsterdam’s School of Business and Economics. Previously, he held positions as Associate Professor at KEDGE Business School, Visiting Professor at the University of St. Gallen, and Assistant Professor at the University of St. Gallen. His research focuses on digital communication, customer journeys, multichannel management, and social media strategies, with publications in top-tier journals like the Journal of Marketing and Journal of Marketing Research. His work addresses critical issues such as online firestorm mitigation, complaint de-escalation, and gig economy communication. He has been awarded multiple prestigious prizes, including the 2021 CBSIG Consumer Research Award and the 2019 William R. Davidson Award. Education: PhD in Marketing from University of St. Gallen (2011) Teaching: Courses on Customer Experience Management, Survey Research Methods, and Thesis Guidance Research Interests: His work explores digital marketing strategies, customer-centric innovations, and the dynamics of online platforms. Recent studies address topics like business-to-investor marketing signals, privacy orientation measurement, and machine learning biases in marketing. He actively contributes to editorial boards of journals including Journal of Marketing and Journal of Interactive Marketing. Awards: 2021 CBSIG Consumer Research in Practice Award 2021 Retail & Pricing SIG Best Paper Award 2021 SERVSIG Best Services Article Award 2020 William R. Davidson Award Grants & Editorial Work: His research has been funded by national/international grants, and he serves on multiple editorial boards. Notable datasets include studies on online retailer strategies and virtual brand sabotage responses.

Zheng Yuanمشاهده پروفایل

دانشیار

• Machine Learning
• Natural Language Processing
• Educational Technology
• +۳ مورد دیگر

Dr. Zheng Yuan is an Associate Professor (Senior Lecturer) in the School of Computer Science at the University of Sheffield. Previously, they held roles as an Assistant Professor at King's College London and a Research Associate at the University of Cambridge's Department of Computer Science and Technology. Their primary research focuses on machine learning and deep learning applications in natural language processing (NLP), particularly in educational technology, healthcare, creativity, and multilingual contexts. Key projects include computer-assisted language learning (CALL), human-centered NLP in education, computational code-switching, and creative AI. Education includes a PhD and MPhil in Natural Language Processing from the University of Cambridge, and a BSc(Eng) from Queen Mary University of London. They hold affiliated positions at the University of Cambridge, King's College London, and are a Fellow of Trinity College, Cambridge. They contribute to The Alan Turing Institute's Data-Centric Engineering Programme and hold FHEA status (2024-). Research interests span educational NLP, multilingual systems, transfer learning, and explainable AI. They actively organize workshops and serve on editorial boards (e.g., PeerJ Computer Science) and conference committees (ACL/EMNLP). Recent activities include co-organizing NLP workshops at ACL 2025 and NAACL 2024, alongside roles in professional societies like the ACL Professional Conduct Committee. Awards include Fellowship of the Higher Education Academy (2024-) and ASEFClassNet18 Faculty Collaboration (2025-). They welcome PhD applications in NLP and machine learning, emphasizing interdisciplinary applications.

• Machine Learning
• Data Mining
• Neural Networks
• +۳ مورد دیگر

Professor Irena Koprinska is a faculty member at the School of Computer Science, University of Sydney, specializing in Machine Learning, Data Mining, and Neural Networks. Her research focuses on practical applications in education, health, and energy sectors. She has received multiple awards, including the Dean’s Award for Outstanding Teaching (2017, 2008) and Best Paper Awards at CHI 2019 and other conferences. Koprinska has supervised 11 PhD and over 60 Honours students, many of whom have won prestigious scholarships like the Google Fellowship. Education: PhD and MSc in Computer Science, MEd in Higher Education. Research Interests: Develops algorithms for pattern extraction and predictive modeling in healthcare (e.g., sleep apnea prediction), education (student behavior analysis), and energy (solar power forecasting). Her work bridges algorithmic innovation with multidisciplinary collaboration. Publications: Over 100 articles in top journals/conferences, emphasizing applications of machine learning in health, energy, and education. Recent works include deep learning for sleep apnea and ensemble methods for solar forecasting. Awards: Highlighted awards include the Dean’s Teaching Awards, Best Paper recognitions, and the Thompson Research Fellowship (2018). Advising & Grants: Currently supervises Hanxue Yao. Previously led initiatives like the Data Science for Social Good workshop at ECML PKDD. Served as Associate Head for Research Education and Sub-Dean for Teaching & Learning. Labs/Teams: Leads the Computer Human Adapted Interaction Research Group, focusing on human-centric technology solutions.

• Machine Learning and Deep Learning
• Big Data and Artificial Intelligence in Accounting
• Accounting Fraud
• +۳ مورد دیگر

Nurul Alam is a Lecturer in Accounting at the University of Sydney Business School. His research focuses on machine learning applications in finance and accounting, corporate distress prediction, and financial reporting. He holds a PhD from the University of Sydney, where he investigated machine learning models for US corporate bankruptcies. His teaching excellence has earned over 15 awards, including multiple Dean’s Citations and nominations for the Wayne Lonergan Award for Teaching Excellence. Education Master of Commerce (Finance & Accounting), University of Sydney Bachelor of Business Administration (Finance & Banking), University of Rajshahi, Bangladesh Research Interests Machine Learning and Deep Learning in accounting and finance Big Data analytics for corporate financial decision-making Accounting fraud detection mechanisms Financial reporting standards and regulations Current Projects Corporate bankruptcy prediction using survival models and AI Deep Learning applications in panel data analysis for firm distress Machine learning variable selection in corporate finance Accounting fraud patterns among lower/mid-level employees Teaching Contributions Financial Accounting B (ACCT3011) Quantitative Methods for Accounting (QBUS5002) Quantitative Business Analysis (BUSS1020)

Emily Diane Gottfriedمشاهده پروفایل

دانشیار

• Clinical Forensic Psychology
• Sex Offender Risk Assessment
• Physiological Assessment of Sexual Arousal
• +۷ مورد دیگر

Emily D. Gottfried, PhD, is an Associate Professor at the Medical University of South Carolina (MUSC) in the Department of Psychiatry and Behavioral Sciences, College of Medicine. She serves as Director of the MUSC Sexual Behaviors Clinic and Lab (SBCL) and CPSPD Student Education & Research, conducting forensic evaluations, physiological sexual arousal assessments, and mentoring students. She is licensed in South Carolina and Georgia, and a National Register Health Service Psychologist. Bachelor’s in Psychology, San Diego State University Master’s in Psychology, Teachers College, Columbia University PhD in Clinical Psychology, Florida State University Her research focuses on sex offender risk assessment , malingering detection (PPG/VPP technology), female offender psychology , and forensic evaluation validity . Recent work includes dimensional personality models and QAnon-related threat analysis . She has published extensively on psychopathy , penile/vaginal plethysmography , and mental health court outcomes . Notable publication trends include Forensic validity of symptom detection tools Psychopathy in female offenders Technology's role in sexual behavior assessment Telehealth adaptation during pandemics MMPI-2-RF applications in correctional settings Psychological factors in civil commitment She leads ongoing funded studies on female sexual arousal and police officer behavioral outcomes , operating at the intersection of forensic science and clinical psychology .

Willemijn van Dolenمشاهده پروفایل

استاد

• Consumer Behavior
• Marketing Strategy
• Social Media Analysis
• +۵ مورد دیگر

Prof. Willemijn van Dolen is a Professor in the Section of Marketing at the Faculty of Economics and Business, University of Amsterdam. Her research focuses on consumer behavior, marketing strategies in digital environments, and the psychological impacts of communication in service encounters. She has extensively studied topics such as visual influence in consumer decisions, corporate greenwashing detection, and the role of humor in service interactions. Her academic career includes notable contributions to understanding online consumer behavior through multimodal datasets and AI frameworks. She has published widely on social media analytics, CSR communication, and child helpline effectiveness, bridging psychological insights with practical marketing applications. Prof. van Dolen’s work emphasizes empirical investigations into customer engagement, ethical consumption, and the interplay between digital platforms and human decision-making. Her research demonstrates a consistent focus on real-world applications, from optimizing brand posts on Instagram to analyzing CEO communication during global crises. Her studies often employ interdisciplinary methods, combining marketing theory with data science and behavioral economics. Despite her prolific output (over 50+ publications), no specific scientific awards are highlighted in the provided texts.

Elisa B Schweigerمشاهده پروفایل

مدرس ارشد

• Retailing
• Marketing Analytics
• AI in Retail
• +۵ مورد دیگر

Elisa B Schweiger is a Senior Lecturer (equivalent to Associate Professor) in Marketing at King’s Business School, King’s College London. She specializes in retailing, AI applications in marketing, multi-sensory marketing, and pricing strategies. Her research employs behavioral experiments, field studies, and text mining. Education: PhD in Consumer Psychology from the University of Bath. She also holds a background in psychology and marketing. Research Interests: Elisa’s work focuses on retail innovation, AI-driven consumer interactions, luxury branding, and the psychological underpinnings of consumer decisions. She explores how technology (e.g., voice assistants, mixed reality) impacts customer journeys and brand relationships. Her research bridges theory and practice, addressing topics like AI ethics, retail atmospherics, and consumer risk perceptions in energy technologies. Publications: Her work appears in top journals such as the Journal of the Academy of Marketing Science , Journal of Retailing , and Journal of Business Research . Recent topics include voice assistant evaluations, AI in lateral markets, and digital retailing trends. Awards: Recipient of the Dean’s Award for Early Career Researcher of the Year (2022), Journal of Retailing Davidson Best Paper Award Runner-up, and MMA Teaching Excellence Award. Teaching & Engagement: Teaches Marketing Analytics, Retailing, and research methods in graduate and executive programs. She serves on the editorial boards of Journal of Retailing and Journal of Business Research , and reviews for top marketing journals. Labs/Teams: Active in King’s Business School’s marketing research teams, focusing on innovation in retail and AI applications.

• Spoken Language Processing
• Natural Language Processing (NLP)
• Paralinguistics
• +۶ مورد دیگر

Sarah Ita Levitan is an Assistant Professor in the Department of Computer Science at Hunter College, CUNY, and a member of the doctoral faculty in both Computer Science and Linguistics PhD programs at the CUNY Graduate Center. She previously served as a Postdoctoral Research Scientist at Columbia University, where she completed her PhD in Computer Science in 2019 under Dr. Julia Hirschberg. Research Focus: Spoken Language Processing Natural Language Processing Paralinguistic Analysis Trustworthiness and Deception Detection Acoustic-Procedic and Lexical Feature Extraction Online Radicalization and Misinformation Recent Publications demonstrate expertise in analyzing speech and text for trust cues, deception detection, and mental health prediction. Her awards include grants from NSF, Google, and Columbia University fellowships. She leads the Hunter Speech Lab , mentoring PhD, MS, and undergraduate students in computational linguistics research. Scientific Awards and Grants: NSF EAGER Grant (2023) Google Cyber NYC Grant (2023) NSF AI Institute Grant (2023) Air Force Office of Scientific Research Grant (2020) Brown Institute Seed Grant (2020) Knight News Innovation Fellowship (2018) Teaching: Courses include Natural Language Processing (undergraduate/graduate), Computational Linguistics, Computer Theory, and advanced topics in spoken language processing at both Hunter College and Columbia University.

Vicki L. Plano Clarkمشاهده پروفایل

استاد

• Mixed Methods Research
• Research Methodology
• Qualitative Research
• +۳ مورد دیگر

Vicki L. Plano Clark is a Professor in the Research Methods area of the School of Education at the University of Cincinnati, where she advises students in the Quantitative and Mixed Methods Research Methodologies (QMRM) concentration of the Educational Studies doctoral program and the Applied Research Methods (ARM) track of the Educational Studies master's program. She joined the University of Cincinnati in 2012 after serving as the director of the Office of Qualitative and Mixed Methods Research at the University of Nebraska-Lincoln. Dr. Plano Clark earned her Ph.D. in Quantitative and Qualitative Methods in Education from the University of Nebraska-Lincoln (2005), M.S. in Physics from Michigan State University (1993), and B.A. in Physics from Kalamazoo College (1990). Her academic journey transitioned from physics education to research methodology, bringing a unique interdisciplinary perspective to her work. As a leading methodologist specializing in mixed methods research, Dr. Plano Clark's scholarship focuses on delineating useful designs for conducting mixed methods research, examining procedural issues associated with these designs, and exploring the contexts for the adoption and use of mixed methods. Her research spans diverse application areas including cancer pain management, STEM graduate student identity development, teacher professional development, and the well-being of rural low-income families. Her work demonstrates how mixed methods approaches can effectively address complex research questions across multiple disciplines. Dr. Plano Clark has made significant contributions to the field through her editorial leadership and publications. She was the founding Managing Editor for the Journal of Mixed Methods Research and currently serves as an Associate Editor. In 2011, she co-led the development of Best Practices for Mixed Methods in the Health Sciences for NIH's Office of Behavioral and Social Sciences Research. In 2012, she became a founding co-editor of the Mixed Methods Research Series with Sage Publications. She has authored numerous influential books including 'Designing and Conducting Mixed Methods Research' (now in its 3rd edition) and 'Mixed Methods Research: A Guide to the Field.' Founding Managing Editor for the Journal of Mixed Methods Research Co-developer of NIH's Best Practices for Mixed Methods in the Health Sciences (2011) Founding co-editor of the Mixed Methods Research Series with Sage Publications (2012) Chair of the Mixed Methods Research Special Interest Group of AERA As an active researcher, Dr. Plano Clark has secured multiple grants including a Department of Education grant evaluating Ohio Network of Education Transformation (ONET) Schools (as Principal Investigator) and a UC University Research Council grant on reducing mass incarceration by improving public defense (as Collaborator). Her recent publications continue to advance methodological understanding in mixed methods research, with a focus on integration techniques, terminology challenges, and applications across health sciences and education. Dr. Plano Clark maintains an active role in the research community through invited presentations and workshops worldwide, helping to train the next generation of researchers in mixed methods approaches and contributing to the ongoing development of methodological standards and practices.

• Distributed Systems
• High Performance Computing
• Fault Tolerance
• +۴ مورد دیگر

Aishwarya Ganesan is an Assistant Professor at the Siebel School of Computing and Data Science, University of Illinois Urbana-Champaign. Her research focuses on distributed systems, storage systems, and fault tolerance mechanisms, with emphasis on high-performance computing and datacenter infrastructure. She leads projects addressing challenges in replicated storage, consensus protocols, and system resilience. Her work explores fault tolerance in disaggregated datacenters, log abstractions for low-latency applications, and novel replication strategies for modern storage systems. She has developed frameworks like LazyLog and IONIA to improve system efficiency and reliability. Her research also extends to automatic reliability testing for cluster management controllers and analyzing distributed storage vulnerabilities. Key contributions include demonstrating how redundancy alone does not guarantee fault tolerance, and proposing consistency-aware durability mechanisms for storage systems. She received the NSF CAREER Award in 2024 for her research on storage-aware fault tolerance. Her work spans 21 peer-reviewed publications, with notable contributions in conferences like SOSP, EuroSys, and FAST. Current projects investigate fault tolerance in emerging memory technologies and system recovery protocols for consensus-based storage.

Susanne Iwarssonمشاهده پروفایل

استاد

• Gerontology
• Occupational Therapy
• Public Health
• +۶ مورد دیگر

Susanne Iwarsson is a Professor in Gerontology and Care for Older People at Lund University's Faculty of Medicine, holding the Mary and Seved Ribbing Professorship since 2005. She also serves as Director of the Centre for Ageing and Supportive Environments (CASE) and Coordinator of the Swedish National Graduate School for Competitive Science on Ageing and Health (SWEAH). Her academic journey includes a PhD in Medical Science (1997) and promotions to Associate Professor (1999) and Professor (2003). She has held leadership roles such as Pro-dean of the Faculty of Medicine (2006-2011) and Coordinator of the strategic research area MultiPark (2012-2014). Her research focuses on environmental gerontology, housing accessibility, and proactive ageing strategies. Key contributions include the Housing Enabler assessment tool and studies on ageing populations' participation in society. She has supervised 18 PhD students and 6 licentiates, with ongoing supervision of 4 current PhD candidates. Major projects include the Home, Health and Disability longitudinal study (2002-present) and the UserAge program exploring user participation in ageing research. Awards include the Excellent Teaching Practitioner title (2010) and Doctor Honoris Causa. Her work addresses UN Sustainable Development Goals related to health, sustainable cities, and reduced inequalities. Collaborations span universities in Sweden, Finland, Latvia, and the U.S., with partnerships in healthcare and municipal sectors.

Guanglei Hongمشاهده پروفایل

استاد

• Causal Inference
• Social Policy Evaluation
• Child and Youth Development
• +۳ مورد دیگر

Guanglei Hong is a Professor at the University of Chicago, holding tenure in the Comparative Human Development Department and the Committee on Education. She chairs the University-wide Committee on Quantitative Methods in Social, Behavioral, and Health Sciences and the Committee on Education. Her research focuses on causal inference methodologies for evaluating educational and social policies, particularly mediation and moderation effects in multi-level longitudinal studies. She developed the RMPW and MMWS methods, widely used in causal mediation analysis. Hong holds a Master’s in Applied Statistics and a Ph.D. in Education from the University of Michigan. Education: Ph.D. in Education, University of Michigan, 2004 Master's in Applied Statistics, University of Michigan, 2002 Research Interests: Hong’s work centers on causal moderation and mediation, spillover effects, and sensitivity analysis in policy evaluation. She applies these methods to assess impacts of educational programs, contextual changes, and socioeconomic factors on child/youth development. Her monograph *Causality in a Social World* (2015) is a foundational text in the field. Awards: John Simon Guggenheim Fellowship (2021–2022) William T. Grant Scholar Award (2009–2014) NAE/Spencer Postdoctoral Fellowship (2006–2007) AERA Mary Catherine Ellwein Dissertation Award (2005) Teaching & Training: Hong teaches advanced quantitative methods courses, including causal inference and mediation analysis. She leads the NSF-funded SIARM for STEM institute, training researchers in computational methods for education research. Notable courses include *Advanced Topics in Causal Inference* and *Mediation, Moderation, and Spillover Effects*. Grants & Leadership: Hong leads major grants from NSF, IES, and private foundations. Her current projects include methodological advancements for multisite trials and sensitivity analysis in mediation. She has co-authored over 60 peer-reviewed articles and edited volumes, and serves on editorial boards of leading journals. Labs/Teams: Hong directs the Quantitative Methods Group at the University of Chicago, fostering interdisciplinary collaborations in causal inference and policy evaluation. Her work integrates statistical innovation with real-world applications in education and health sciences.

Zaiqiao Mengمشاهده پروفایل

مدرس

• Natural Language Processing
• Information Retrieval
• Large Language Models
• +۳ مورد دیگر

Zaiqiao Meng is a Lecturer (Assistant Professor) at the University of Glasgow's School of Computing Science, affiliated with the Information Retrieval Group and IDA section. He also holds an Affiliated Lecturer position at the University of Cambridge's Language Technology Lab. His research focuses on the intersection of machine learning, knowledge graphs, and NLP, particularly in biomedical applications. Key areas include AI agents, large language models, and healthcare informatics. Current roles include co-leading the Glasgow AI4BioMed Lab, which develops AI solutions for biomedical knowledge extraction. He has extensive postdoctoral and visiting research experience, including at KAUST's MINE lab. Meng has published widely in top conferences like ACL and EMNLP, with over 40 publications since 2019. His work spans topics such as drug-target interaction prediction, clinical summarization, and knowledge graph construction. Teaching includes courses on Recommender Systems and Data Science at both undergraduate and graduate levels. He advises multiple PhD students on projects involving LLMs, biomedical entity representation, and conversational agents.