جست‌وجوی استاد و پژوهشگر

• Accessibility
• Deaf Studies
• Human-Computer Interaction
• +۴ مورد دیگر

Abraham Glasser serves as an Assistant Professor in the Department of Accessible Human-Centered Computing within Gallaudet University's School of Science, Technology, Accessibility, Mathematics, and Public Health. His teaching portfolio includes graduate courses such as AHC-604 Applied Information Accessibility, AHC-691 Thesis Accessibility Project Planning, and specialized topics in accessibility standards and data visualization, reflecting his commitment to advancing accessible technology education. Professor Glasser's research centers on dismantling digital barriers for deaf and hard of hearing communities through innovative human-computer interaction solutions. His work pioneers sign language interfaces for personal assistants, investigates captioning system optimization, and explores AI-driven accessibility enhancements. Methodologically, he combines empirical user studies with technical development to create genuinely inclusive technologies that address real-world communication challenges faced by deaf users in digital environments. Analysis of his recent publications reveals a strong trajectory toward integrating artificial intelligence with accessibility needs, particularly through large language models for caption customization and robust sign language recognition systems. His research consistently prioritizes deaf user perspectives in technology design, with increasing focus on ethical AI implementation and workplace accessibility solutions as evidenced by his 2025 publications on employment barriers and AI action planning. Professor Glasser actively mentors graduate students, currently advising five master's candidates in accessibility-focused research. His professional service includes leadership roles as Chair for the ASSETS 2023 Student Research Competition and Associate Chair for ACM CHI 2024, demonstrating significant contributions to the accessibility research community. He has secured internal funding through Gallaudet's Faculty Development Committee grant to advance his accessibility initiatives.

• Computer Science
• Human-Computer Interaction
• Information and Communication Technologies for Development (ICTD)
• +۳ مورد دیگر

Richard J. Anderson is a Professor at the University of Washington, with a PhD from Stanford University (1985). His career spans computer science, human-computer interaction, and information technology for development (ICTD). Research Focus: His work integrates mobile technologies for health and education in low-resource settings parallel algorithms and data structures security and privacy challenges in developing regions gender-inclusive digital systems context-aware educational tools Article Trends: Recent publications (2022-2025) emphasize mHealth , mobile financial services , and privacy frameworks in Sub-Saharan Africa and South Asia. Earlier works (1985-1999) focused on theoretical computer science, including parallel list ranking and N-body simulation algorithms.

T. M. Muraliمشاهده پروفایل

استاد

• Computational Biology
• Systems Biology
• Algorithm Design
• +۵ مورد دیگر

T. M. Murali is a Professor in the Department of Computer Science at Virginia Tech's College of Engineering. He serves as Associate Department Head for Research and directs the NSF-funded Center for Community Empowering Pandemic Prediction and Prevention from Atoms to Societies, as well as the Pandemic Prediction and Prevention Destination Area. His research focuses on computational system biology, algorithm design, and applying computational methods to experimental biology. Education: PhD in Computer Science from Brown University and B.Tech in Computer Science from Indian Institute of Technology Madras. Research emphasizes biological network analysis, including signaling pathways, gene regulatory networks, and host-pathogen interactions. His work bridges algorithm development with applications in virology, cancer biology, and drug discovery. Key contributions include frameworks like GraphSpace for network visualization, PathLinker for protein interaction networks, and tools like HAVEN for viral host inference. Recent articles highlight advancements in long COVID predictive modeling, drug synergy prediction, and effective sequence computation. Collaborations involve NIH-funded programs and interdisciplinary teams in systems biology. Grants include NSF support for pandemic prediction and computational biology initiatives. Active in mentoring through labs like GraphSpace and teaching advanced courses in systems biology and data science.

• Information Literacy
• Data Literacy
• Digital Literacy
• +۷ مورد دیگر

Sirje Virkus is a distinguished Professor of Information Science at Tallinn University's School of Digital Technologies, where she serves as Head of the Study Area of Information Sciences. With a career spanning over four decades, she has established herself as a leading expert in information literacy, digital competencies, and the application of technology in education. Her academic journey includes significant leadership roles such as Coordinator of the joint master's program 'Digital Library Learning' (2007-2020) and Head of the Information and Communication Sciences PhD program (2015-2017). Doctor of Philosophy, Manchester Metropolitan University (2011) Master in Information Science (cum laude), Tallinn Pedagogical University (1997) BA of Library and Information Science, Tallinn Pedagogical University (1979) Professor Virkus's research interests have evolved from traditional library science to cutting-edge digital education technologies, with recent focus on telepresence robotics in education, micro-credentials, and digital transformation. Her scholarly work demonstrates remarkable adaptability and foresight, consistently addressing emerging challenges in information science and education. She has published extensively in top journals and contributed to numerous edited volumes across her career. Her most recent publications (2023-2025) reveal a strong emphasis on telepresence technology in educational settings, micro-credential quality assurance, and the intersection of digital transformation with teaching practices. These works span multiple disciplines including educational technology, library science, human-computer interaction, and public health information behavior, demonstrating the interdisciplinary nature of her current research. State Decoration with the Order of the White Star (4th Class) of the Estonian President (2021) Member of the European Database of Outstanding Women Academics (2021) Alumni of the Century of Tallinn University (2019) Tallinn University Medal for outstanding contribution (2009) UNESCO Certificate of Appreciation (2009) Emerald LiteratiNetwork Highly Commended Award (2008) Professor Virkus has served on numerous editorial boards and research councils, demonstrating significant influence in her field. She currently serves on the editorial boards of Sage Open journal, PriMera Scientific Engineering, and several other prestigious publications. Her administrative leadership extends to the Research Council of the School of Digital Technologies and various national committees. While specific grant information isn't detailed in the provided text, her extensive publication record and leadership roles suggest successful grant acquisition throughout her career. Her work with telepresence robots represents a significant research thrust, exploring how this technology can bridge divides in educational and cultural settings. This work connects with the STEAM4EDU cluster mentioned in her profile, indicating active participation in interdisciplinary educational innovation projects at Tallinn University.

• Communication Sciences and Disorders
• Acoustics
• Speech Analysis
• +۳ مورد دیگر

Eric J. Hunter is a Professor in the Department of Communication Sciences and Disorders at the University of Iowa's College of Liberal Arts and Sciences. He also holds an adjunct professorship at Michigan State University's College of Education. His research focuses on voice acoustics, speech analysis, and vocal health in occupational contexts such as teaching and broadcasting. Dr. Hunter has been recognized with prestigious awards including the Harriet B. and Harold S. Brady Chair (2023) and the William J. Beal Outstanding Faculty Award (2022). Education: Bachelor of Science in Physics and Astronomy, Brigham Young University (1988–1995) Master of Science in Physics and Astronomy, Brigham Young University (1996–1997) PhD in Speech and Hearing Sciences (Voice Modeling, Voice Acoustics), University of Iowa (1997–2001) Research: Specializes in vocal effort, room acoustics' impact on voice production, bilingual speech patterns, and clinical voice disorder screening. His work integrates engineering principles with healthcare applications. Affiliations: University of Iowa: Communication Sciences and Disorders Michigan State University: College of Education Grants & Collaborations: Active in longitudinal studies on aging voices and vocal fatigue mitigation strategies for teachers. Collaborates with architectural acoustics experts and speech therapists. His recent studies address vocal health in educators, machine learning applications for voice disorder detection, and cross-cultural clinical practices in voice assessment. Dr. Hunter also leads initiatives to standardize voice measurement techniques in reverberant environments.

Ashique KhudaBukhshمشاهده پروفایل

استادیار

• Natural Language Processing (NLP)
• AI for Social Impact
• Computational Social Science
• +۴ مورد دیگر

Ashique KhudaBukhsh is an Assistant Professor in the Department of Software Engineering at the Golisano College of Computing and Information Sciences, Rochester Institute of Technology (RIT). He directs the Social Insight Lab and is affiliated with the ESL Global Cybersecurity Institute. His research focuses on NLP and AI for Social Impact, addressing challenges in multilingual social media analysis, political polarization, and auditing AI systems for unintended harms. His work has been featured in top AI conferences and media outlets like The New York Times and BBC. Education: PhD in Artificial Intelligence from Carnegie Mellon University (2017). He joined RIT in 2021. Notable awards include the RIT GGCIS Emerging Scholar Award (2023) and selection for the AAAI 2024 New Faculty Highlight program. Research Interests: Machine Learning and NLP applications for social issues Political polarization analysis through AI Audit of generative AI for harmful content Multilingual and noisy social media text processing Teaching: Offers courses like 'Tracking Political Sentiments Using Machine Learning' (co-taught with political scientists) and advanced data science seminars. Current students include Sujan Dutta, Arka Dutta, and Soumyajit Datta. Awards and Media: Over 100 media mentions including coverage on BBC and Wired. His team exposed toxic content in Google's PaLM2 model, leading to model improvements. Labs and Collaborations: Leads the Social Insight Lab, collaborating on projects like analyzing gender bias in Bollywood and AI-based responses to global crises.

Aaron Shawمشاهده پروفایل

دانشیار

• Data Science
• Digital Media
• Human-Machine Interaction
• +۳ مورد دیگر

Aaron Shaw is an Associate Professor in the Department of Communication Studies at Northwestern University's School of Communication. He holds a courtesy appointment in Sociology and is affiliated with the Center for Human-Computer Interaction + Design, Institute for Policy Research, and Buffett Institute for Global Affairs. Shaw's research focuses on collaboration, governance, and inclusion in online communities like Wikipedia, with expertise in Data Science, Digital Media, and Human-Machine Interaction. Shaw earned his PhD and MA in Sociology from UC Berkeley, and MA in Humanities from Stanford University, alongside a BA in Humanities from Stanford. His work has been recognized through awards including the Lenore Annenberg Fellowship at CASBS, multiple NSF grants, and inclusion on Northwestern's Faculty Honor Roll. His research explores how organizational structures and technologies shape collective action, particularly in open collaboration platforms. Key themes include participation inequality, rule-making dynamics, and the societal impacts of digital systems. Recent projects address generative AI ethics, misinformation, and governance in decentralized networks. Awards: CASBS Fellowship, NSF funding, King County Residency Grants: Co-PI on NSF IIS:CHS grants (2016, 2019) Affiliations: Berkman-Klein Center (Harvard), Community Data Science Collective Shaw advises on digital policy and teaches courses on data science methodologies and online collaboration. His work bridges sociology, computer science, and design to advance understanding of human-computer interaction in collective systems.

Romit Roy Choudhuryمشاهده پروفایل

استاد

• Machine Learning
• Signal Processing
• Generative Models
• +۶ مورد دیگر

Romit Roy Choudhury is the Gilmore Family Endowed Professor of Electrical and Computer Engineering (ECE) at the University of Illinois at Urbana-Champaign (UIUC) and an Amazon Scholar since 2022. He earned his Ph.D. in Computer Science from UIUC in 2006 and previously held positions at Duke University, including Associate Professor (2011–2013) and Assistant Professor (2006–2011). His research spans generative models, signal processing, and wearable computing, with a focus on audio denoising, source separation, and spatial inference. Education: Ph.D. in Computer Science, University of Illinois at Urbana-Champaign, 2006. Research Interests: Generative Models, Machine Learning, Sensing, Inverse Problems, Wearable Devices, Localization, and Acoustic Sensing. His lab, Signals and Inference Research Group (SiNRG), explores innovations in audio and sensor-based systems. Publications: Over 50+ peer-reviewed articles, including impactful work on inaudible sound attacks (MobiSys 2017 Best Paper), noise cancellation via IoT (SIGCOMM 2018), and earable computing (MobiCom 2020). Awards: ACM Sigmobile Rockstar Award (2015), IEEE Fellow (2019), NSF CAREER Award (2007), and multiple best-paper recognitions. Teaching: Courses include ECE 438 (Communication Networks), ECE 434 (Real-World Algorithms for IoT), and ECE 101 (Intro to Computing). Labs/Teams: SiNRG at UIUC, collaborations with Amazon, Samsung AI, Intel, and Microsoft Research.

Gianluigi Viscusiمشاهده پروفایل

دانشیار

• e-Government
• Information Systems Strategy
• Data-Driven Business Models
• +۶ مورد دیگر

Gianluigi Viscusi is an Associate Professor at Linköping University's Department of Management and Engineering (IEI), specializing in information systems and digitalization. His research focuses on digital innovation's societal and organizational impacts, particularly in e-Government, media industries, and creative sectors. Key areas include crowd-driven innovation, AI in public services, and data-driven business models. Current projects include the Emerging Technology in the Media initiative at Germany's CAIS, examining generative AI's media coverage across three countries. He leads interdisciplinary collaborations involving institutions like HES-SO and Digital Content Next. His work bridges theory and practice through design science approaches, emphasizing human-AI collaboration and policy implications. Publications span journals like Technovation and conferences such as Hawaii International Conference on System Sciences (HICSS), addressing topics from smart meter adoption to blockchain discourse analysis. Research methodologies include interpretive studies, fsQCA analysis, and case-based investigations. Viscusi actively contributes to Linköping University's Information Systems and Digitalization (INDIG) division, advancing strategic insights for digital transformation.

Matthew Bietzمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Sociotechnical Systems
• Distributed Collaborative Work
• +۳ مورد دیگر

Matthew Bietz is an Associate Professor of Teaching in the Department of Informatics at the University of California, Irvine. His research focuses on sociotechnical systems, data sharing, and distributed collaborative work. He earned his Ph.D. in Information from the University of Michigan, following degrees in musicology and performance. His work bridges Human-Computer Interaction (HCI) with ethical considerations in technology design. Education: 2008 Ph.D., Information (University of Michigan); 2000 M.S., Information (University of Michigan); 1997 M.A., Historical Musicology (SUNY Stony Brook); 1995 B.A., English and B.Mus., Cello Performance (Lawrence University). Research Themes: Data management, distributed collaboration, large-scale infrastructures, and ethical design. He emphasizes empathy in design processes and the societal impacts of pervasive data systems through projects like PERVADE (Pervasive Data Ethics). Grants & Projects: PI of NSF-funded PERVADE (2017-2023), co-investigator in NIH-funded privacy studies, and leader in health data exploration initiatives. His work spans cyberinfrastructure development, trust in virtual teams, and genomic data standards. Labs/Teams: Active in the Department of Informatics, collaborating with interdisciplinary teams on projects like Health Data Exploration and Virtual Standards Development Organizations. Teaches courses on HCI, organizational systems, and project-based learning.

Yue Wangمشاهده پروفایل

استادیار

• Text Data Mining
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Yue (Ray) Wang is an Assistant Professor at the School of Information and Library Science, University of North Carolina at Chapel Hill. He holds a PhD in Computer Science and Engineering from the University of Michigan, and prior degrees from Shanghai Jiao Tong University and Georgia Institute of Technology. His research focuses on text data mining, machine learning, and health informatics, with an emphasis on developing interactive and interpretable algorithms to reduce human effort in data analysis. He has contributed to clinical NLP, high-recall information retrieval, and computer-assisted qualitative analysis, winning awards such as the WSDM Best Paper Award (2016) and the Deborah Barreau Award for Teaching Excellence (2021). Dr. Wang’s work bridges computational methods with real-world applications, including environmental policymaking through extreme systematic reviews and NeuroBridge—a platform for neuroimaging data discovery. He teaches courses like INLS 509 (Information Retrieval) and INLS 690-270 (Data Mining). His recent research explores user-centered explainability in AI, leveraging crowdsourced experiments and in-lab studies to understand how users interact with machine predictions. He collaborates with organizations like the EPA on machine-assisted literature screening for environmental policy. Education: BS/BA in Information Security & English (Shanghai Jiao Tong University) MS in Computer Applied Technology (Shanghai Jiao Tong University) MS in Electrical and Computer Engineering (Georgia Tech) PhD in Computer Science and Engineering (University of Michigan) Awards: UNC Junior Faculty Development Award (2022) Outstanding Reviewer Award (WSDM 2022) Deborah Barreau Teaching Excellence Award (2021) WSDM Best Paper Award & Outstanding Reviewer (2016) Dow Distinguished Award (2015) Key Research Themes: User-centric AI interpretability Health informatics applications Large-scale data screening for policy Explainable retrieval models

Nic Badcockمشاهده پروفایل

دانشیار

• Developmental Cognitive Neuroscience
• Reading and Reading Disabilities
• Dyslexia
• +۵ مورد دیگر

Nic Badcock is an Associate Professor in the School of Psychological Science at The University of Western Australia. His research focuses on developmental cognitive neuroscience, investigating how perceptual and attentional capacities underpin complex behaviors such as reading development and cognitive control. He employs neuroimaging techniques like functional transcranial Doppler ultrasound (fTCD), EEG, and fMRI to study learning difficulties, exceptional development, and neurophysiological mechanisms. Education: PhD (Applied Developmental Psychology) from The University of Western Australia (2008), BSc (Honours First Class) in Psychology (2003). Previous roles include Senior Researcher at Macquarie University (2018–2019), and postdoctoral positions at the ARC Centre of Excellence in Cognition and its Disorders (2011–2017) and the University of Oxford (2008–2011). Research Interests: Reading disabilities (dyslexia), cerebral lateralization, attention mechanisms, neuroimaging methodologies, and translational research for educational interventions. His work contributes to UN Sustainable Development Goals related to quality education and improved mental health. Recent research highlights include studies on cerebral lateralization during language tasks, neurotechnology applications (e.g., consumer EEG devices), and cultural responsiveness in university curricula. He has secured grants such as the ARC-funded 'Listen & Learn' project (2017–2021), exploring audio-based learning in education. Labs/Teams: Collaborates with multidisciplinary teams in cognitive neuroscience, including the ARC Centre of Excellence and international networks. Active in developing novel methods for assessing neurovascular coupling and lateralization in children and clinical populations.

Waleed Iqbalمشاهده پروفایل

مدرس

• Social Network Analysis
• Fairness in Content Moderation
• Socio-Economic Inequality
• +۳ مورد دیگر

Waleed Iqbal is a Lecturer in Data Science at Northeastern University London and Teaching Fellow in Computer Science at Queen Mary University of London's School of Electronic Engineering and Computer Science. He holds a PhD from Queen Mary University of London, focusing on online manifestations of socio-economic inequality. His academic journey includes roles at Arden University and IHSAN Lab, Information Technology University Lahore. Education: PhD (2020–Present), MSc in Computer Science (2018, ITU Lahore with Dean’s Roll of Honor), BSc in Electrical Engineering (2014, University of the Punjab). Research focuses on fairness in content moderation, socio-economic inequality analysis in social networks, and data-driven AI applications in education. Research Interests: Online inequality patterns, digital ethics, and leveraging social data science for societal insights. Notable work includes analyzing income disparities via Nextdoor platform data and studying pandemic-era communication on WhatsApp and Reddit. Key Achievements: Recognized as UK Global Talent (Exceptional Promise) in Research, secured travel grants for IMC conferences, and published in top venues like ICWSM, IMC, and IEEE journals. His work spans 10+ peer-reviewed articles, with contributions to ACM CCR, IEEE Transactions on AI, and Scientometrics. Teaching Roles: Leads courses in Data Mining, Web Technologies, and Foundations of Computer Science. Active in pedagogical innovation and student mentorship.

Chow Ming Faiمشاهده پروفایل

دانشیار

• Sustainable Stormwater Management
• Hydrological Modeling
• Flood Forecasting
• +۳ مورد دیگر

Dr. Chow Ming Fai is an Associate Professor in Civil Engineering at Monash University Malaysia's School of Engineering. He holds a PhD in Civil Engineering from Universiti Teknologi Malaysia (2012) and has previously worked at Academia Sinica Taiwan and Universiti Tenaga Nasional. His research focuses on sustainable stormwater management, hydrological and water quality modeling, flood forecasting, and hydropower systems. Dr. Chow has received multiple awards including the Green Talent Award from the German Federal Ministry of Education and Research (2015), Outstanding Young Academician Award (2016), Excellent Teaching Award (2019), and most recently the Certificate of Merit for MWA Malaysia Water Award for Research (2024). He is recognized as a Professional Engineer (Ir.), Chartered Engineer (CEng.), Professional Technologist (Ts), and Professional Geospatialist. His research addresses urban water challenges through innovative modeling approaches and nature-based solutions. His recent publications demonstrate increasing focus on climate change adaptation, sustainable mining practices, and circular economy applications in water resources management.

Chenyi Huمشاهده پروفایل

استاد

• Interval Computing
• Crowdsourcing Analysis
• Text Mining
• +۴ مورد دیگر

Chenyi Hu is a Professor in the Department of Computer Science and Engineering at the University of Central Arkansas (UCA). Their research focuses on interval computing and its applications in areas such as crowdsourcing, text mining, and machine learning. Dr. Hu holds a PhD in Scientific Computing from the University of Louisiana and has been actively contributing to advancements in data analysis, privacy-preserving techniques, and neural network architectures. Education: PhD in Scientific Computing, University of Louisiana Research Interests: Interval computing and applications in statistical analysis Crowdsourcing reliability and anomaly detection Privacy-preserving text representation and natural language processing Graph neural networks for comment summarization Entropy-based methods in data mining Recent publications highlight contributions to improving data quality in crowdsourcing systems, developing explainable AI techniques for text summarization, and advancing interval-based statistical methodologies. Dr. Hu's work bridges theoretical foundations of interval computing with practical applications in computer science and finance.