جست‌وجوی استاد و پژوهشگر

• Artificial Intelligence
• Machine Learning
• Graph Neural Networks
• +۵ مورد دیگر

Gustavo Borges Moreno e Mello is an Associate Professor in the Department of Computer Science at Oslo Metropolitan University's Faculty of Technology, Art and Design. Based in office SG209 at Stensberggata 29, Oslo, he actively contributes to the Applied Artificial Intelligence research group with focus on cutting-edge computational methodologies. His research spans artificial intelligence with emphases on graph neural networks, multilingual NLP, reinforcement learning, and medical imaging. Key interests include developing propagation schemes for GNNs, cross-lingual BERT adaptations, rodent fMRI analysis, and empathy assessment in human-computer interaction systems. His work bridges theoretical AI with practical healthcare and communication applications. Analysis of his 2020-2024 publications reveals strong thematic continuity in neural architecture design, particularly HITS-GNN variants for graph analytics and time-representation models. Recent work increasingly integrates neuroscience principles with reinforcement learning while maintaining Nordic language processing applications, notably in English-Norwegian BERT alignment. No information regarding graduate student supervision or research grants was found in the provided materials. He maintains active collaboration within OsloMet's Applied Artificial Intelligence research group, focusing on interdisciplinary projects that connect computer vision, natural language processing, and neuro-inspired computing with real-world healthcare and communication challenges.

Dan Johnsonمشاهده پروفایل

استاد

• Cognitive Science
• Data Science
• Creativity Assessment
• +۳ مورد دیگر

Dan Johnson is the David G. Elmes Term Professor of Cognitive and Behavioral Science at Washington and Lee University. He has worked there for 14 years as of 2023, teaching courses like Introduction to Data Science: Mind Analytics and Cognition . His research focuses on computational creativity assessment, leveraging machine learning to evaluate creativity in narratives, STEM education, and interdisciplinary contexts. He developed the Creativity Assessment Platform (CAP) for automated scoring of creative outputs across languages and disciplines. Johnson’s work bridges cognitive science and education technology, with a focus on measuring creativity through semantic analysis, multilingual computing, and educational design. His projects include evaluating poetic creativity, analyzing thematic influences on creative ideas, and understanding how engineering prompts affect student innovation. He emphasizes collaborative learning, stating his courses bring together diverse perspectives to solve complex problems. His research trends highlight interdisciplinary approaches to creativity assessment, with recent work emphasizing AI applications, cultural context analysis, and cross-linguistic methodologies. While no specific awards are listed, his contributions to computational creativity metrics are notable. Johnson also co-founded initiatives like the CAP platform, reflecting his commitment to advancing tools for creativity measurement in academia and industry. His advising and grants history is not detailed here, though his courses suggest active engagement with student research projects. He is affiliated with the Department of Cognitive and Behavioral Science within The College, and his work extends beyond academia through collaborations with tech and educational institutions.

Mingxia Liuمشاهده پروفایل

استادیار

• Machine Learning in Medical Imaging
• Neuroimaging Analysis
• Alzheimer’s Disease Research
• +۵ مورد دیگر

Mingxia Liu, PhD, is an Assistant Professor in the Department of Radiology at the University of North Carolina School of Medicine and Co-Director of Radiological Sciences Faculty Development. She specializes in advancing machine learning technologies for automated brain disease analysis, particularly using MRI/PET modalities. Her work focuses on clinical applications of neuroimaging analysis, including Alzheimer’s disease, MCI, and autism. Dr. Liu leads the UNC Imaging Analysis Group and has developed groundbreaking frameworks for disease diagnosis and prognosis. Dr. Liu holds a PhD from Nanjing University of Aeronautics & Astronautics and completed a postdoctoral fellowship at UNC. Her research integrates computational methods with clinical neuroimaging, emphasizing multimodal data fusion and domain adaptation techniques to address challenges in heterogeneous datasets. Her research interests include: Machine learning for brain disorder classification Functional connectivity analysis using fMRI Domain adaptation in medical imaging Federated learning for multisite studies AI-driven clinical decision support systems Her recent publications highlight advancements in self-supervised learning, graph neural networks, and hybrid multimodal frameworks, with applications to cognitive decline prediction and neurodegenerative disease modeling. These innovations aim to improve early diagnosis and personalized treatment strategies. Dr. Liu has been recognized with prestigious awards including the MICCAI Young Scientist Award (Honorable Mention) and multiple doctoral dissertation honors. Her work bridges cutting-edge AI techniques with translational neuroimaging research, fostering interdisciplinary collaboration in healthcare technology.

David A. Robbمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Human-Robot Interaction
• Image-based emotion feedback
• +۵ مورد دیگر

David A. Robb is a Research Fellow in the School of Mathematical and Computer Sciences at Heriot-Watt University, where he has been actively contributing to academic research since completing his PhD in 2015. His career progression shows a clear trajectory from PhD student (2011-2015) to Research Associate (2015-2020) to his current Research Fellow position. Robb is currently leading research efforts across three major EPSRC/UKRI projects: DeMILO (studying expert laser aligners' thinking and strategies), @tas_trust (investigating trust in autonomous systems), and HUME (focusing on human-machine teaming for AUVs). Robb's research interests span multiple interconnected domains within human-centered computing. His primary focus areas include Human-Computer Interaction (HCI) and Human-Robot Interaction (HRI), where he investigates how humans interact with and trust autonomous systems. Additional interests include image-based emotion feedback mechanisms, image summarization techniques, cognitive aspects of human-computer interaction, computer-supported cooperative work (CSCW), and visualization of complex data. His work often bridges theoretical understanding with practical applications in industrial and service environments. Analysis of Robb's publication record reveals a strong trajectory of scholarly contribution with 54 publications to date. His recent work demonstrates a clear focus on practical applications of human-robot interaction in real-world settings, particularly in industrial automation (laser alignment systems) and service robotics (robo-barista studies). A notable trend is his increasing focus on understanding human factors in automation adoption, trust dynamics in human-machine teams, and the practical implementation challenges of deploying autonomous systems in complex environments. His publications span top-tier venues in HCI, HRI, and robotics, demonstrating both breadth and depth in his scholarly contributions. ACM ICMI2021 Best Reviewer Awards (Top 5%) ACM ICMI 2023 Outstanding Reviewer Award ACM ICMI 2024 Outstanding Reviewer Award Honourable Mention ACM CHI 2017 Research Papers and Notes Honourable Mention ACM DIS 2017 Research Papers and Notes Robb has been instrumental in securing and executing multiple significant research grants, particularly through EPSRC/UKRI funding mechanisms. His current role as Experimental Lead for the HRI theme of the ORCA Hub project (a major £5.7 million EPSRC-funded initiative) demonstrates his capability in managing substantial research programs. While specific details about student supervision aren't prominently featured in the provided materials, his active research program and teaching responsibilities (Experimental Design and Web Design and Databases) suggest engagement with graduate students. His work extends beyond pure research to practical implementation, as evidenced by projects like the MIRIAM multimodal interface for autonomous systems and the robo-barista field studies. Robb is affiliated with the Strategic Futures Lab at Heriot-Watt University and has been involved with the ORCA Hub (Offshore Robotics for Certification of Assets), a major UK Robotics and Artificial Intelligence Hub. His collaborative network is extensive, with co-authors from multiple departments at Heriot-Watt as well as international collaborators. Recent projects like the DeMILO study of laser alignment expertise and the @tas_trust project on human trust in autonomous systems indicate his work is increasingly focused on translating fundamental HRI research into practical industrial applications.

Mladen Nikolićمشاهده پروفایل

دانشیار

• Machine Learning
• Automated Reasoning
• Earthquake Damage Assessment
• +۲ مورد دیگر

Mladen Nikolić is an Associate Professor at the Faculty of Mathematics, University of Belgrade, specializing in machine learning and automated reasoning. He actively contributes to the Machine Learning and Applications Group (MLA@MATF) and the Automated Reasoning GrOup (ARGO) . His research spans earthquake damage assessment , deep learning object tracking , LLM integration with tabular data , and astronomical data analysis . Recent work focuses on the RELAR Project for rapid earthquake loss assessment, CNN architectures for structural damage classification, and novel approaches to incorporating large language model priors into machine learning systems. His publications demonstrate strong interdisciplinary applications connecting computer science with seismology, transportation engineering, and astrophysics. Developed frameworks for earthquake loss assessment and recovery Advanced deep learning filters for non-linear motion tracking Integrated LLMs with tabular data processing systems Contributed to LSST astronomical data challenges Nikolić maintains active research collaborations through MLA@MATF and ARGO groups, with publications spanning machine learning theory, automated reasoning systems, and practical applications across multiple scientific domains. His work shows increasing focus on real-world deployment of AI systems for disaster response and scientific data analysis.

• Building Information Modelling (BIM)
• ICT in the built environment
• Child-friendly outdoor environments
• +۴ مورد دیگر

Professor Lamine Mahdjoubi is Professor of the Built Environment at the University of the West of England (UWE Bristol), Faculty of Environment and Technology, Department of Architecture and the Built Environment. An architect and urban designer by training, he has gained international recognition for integrating advanced ICT solutions into the design, management, and visualisation of the built environment. Education & Qualifications: Qualified in Architecture and Urban Design (specific institutions and dates not provided in the text). Research Interests: Professor Mahdjoubi’s research focuses on the intersection of digital technologies and sustainable urban environments. He develops innovative methodologies and tools for analysing human–environment interactions, with particular emphasis on: Building Information Modelling (BIM) for design, construction, and operations; Virtual and immersive environments for collaborative design and stakeholder engagement; Child-friendly outdoor environments, linking urban design to public health outcomes; Heritage Building Information Modelling (HBIM) for retrofitting and conservation; Blockchain and machine-learning applications for decentralised energy management; Decision-support systems for brownfield redevelopment and risk assessment. Publication Trends: Across >100 peer-reviewed outputs, recent work (2020–2025) demonstrates a clear pivot toward AI and data-driven approaches in the built environment. A dominant strand couples BIM with machine-learning and VR/AR technologies to enhance design coordination, safety, and sustainability. Energy research increasingly integrates blockchain and large-language-model systems for smart grids. A second major strand addresses socio-environmental equity, producing evidence-based guidelines for healthy outdoor spaces for children and refugee communities. Scientific Awards & Recognition: While specific named awards are not listed, Professor Mahdjoubi’s international recognition is evidenced by: Membership of the Advisory Board, International Making Cities Livable (USA); Invited keynote speaker at numerous national and international conferences; Chief/External Examiner roles for universities in the UK and overseas; Founding Director of the pioneering Virtual Reality Enterprise Centre (VREC) at UWE Bristol. Research Funding & Teams: He has secured and directed major projects funded by: UK Engineering & Physical Sciences Research Council (EPSRC); British Academy; European Union Framework Programmes; Qatar National Research Fund (QNRF); Industry partners and local authorities. These initiatives have supported multidisciplinary teams spanning architecture, computer science, public health, and social policy. Laboratories & Centres: Professor Mahdjoubi founded and directed the Virtual Reality Enterprise Centre (VREC) , the first UK centre dedicated to knowledge exchange in visualisation and analysis of the built environment. The centre provides state-of-the-art VR/AR facilities for teaching, research, and industry collaboration.

• Artificial Intelligence
• Machine Learning
• Information Systems
• +۵ مورد دیگر

Dr. Daniel Beck is a Senior Lecturer in the School of Computing Technologies at RMIT University. His research focuses on interdisciplinary areas including Artificial Intelligence, Machine Learning, Information Systems, and their applications in Health Services and Linguistics. He actively supervises projects exploring topics like Large Language Models in design empathy, Alzheimer’s disease progression modeling, and multilingual NLP tasks. His work bridges computational methods with real-world challenges in healthcare, education, and technology ethics. Research Interests: His key areas include AI ethics, graph-based biomedical discovery, Bayesian modeling for disease trajectories, and NLP applications in clinical documentation. He contributes to advancing methodologies such as graph embeddings, state-space emotion modeling, and quality estimation in machine translation. Teaching & Supervision: Beck coordinates courses in Computing Technologies and mentors projects like 'Between activism and literacy: The mediatization of data journalism in Indonesia' and 'Can large language models provide investment advice like humans?' Publications: His 2023-2025 works emphasize AI’s role in healthcare (e.g., Alzheimer’s biomarker analysis), ethical AI autonomy debates, and multimodal NLP innovations. Recent studies highlight contributions to medical record generation via synthetic dialogue and empathic accuracy prediction in design contexts.

Subhasish Dasمشاهده پروفایل

استادیار

• Bayesian Nonparametrics
• Machine Learning
• Predictive Analytics
• +۳ مورد دیگر

Subhasish Das is an Assistant Professor of Instruction in the Department of Computer Science at the University of Texas at San Antonio (UTSA). He holds a Ph.D. in Computer Engineering from Arizona State University and a B.Tech. in Computer Science and Engineering from the West Bengal University of Technology. His research focuses on Bayesian Nonparametrics, Machine Learning, Predictive Analytics, and Time-Series Analysis, with applications spanning healthcare monitoring, planetary science, and bioinformatics. Dr. Das's work emphasizes interdisciplinary approaches, such as combining machine learning with physical systems modeling and developing multimodal reasoning frameworks. His recent publications explore topics like action recognition in visual-linguistic modalities, predictive analytics for physical systems, and image prioritization for space missions. He is actively involved in teaching and advancing computational methodologies in academia. Leveraging tools like kernel density estimation and deep learning, his research bridges theoretical foundations with practical applications in biomedicine, robotics, and space technology. His academic contributions highlight the integration of computational techniques with domain-specific challenges to drive innovation.

Tom Williamsمشاهده پروفایل

دانشیار

• Human-Robot Interaction
• Natural Language Processing
• Robot Ethics
• +۱ مورد دیگر

Dr. Tom Williams is Associate Professor of Computer Science and Director of the MIRRORLab at Colorado School of Mines. His research program centers on natural language-based human-robot interaction with emphasis on ethical, social, and contextual dimensions of communication. Williams investigates how robots can understand and generate context-sensitive language, particularly in morally complex situations. Current projects explore role-based moral reasoning, politeness strategies, and cultural variations in human-robot interaction. His work integrates cognitive science, artificial intelligence, and human-computer interaction methodologies. He has received significant recognition including NSF CAREER, NASA Early Career Faculty, and AFOSR Young Investigator awards. Williams teaches courses in Human-Robot Interaction, Robot Ethics, and Computer Vision, maintaining international collaborations on socially intelligent robotic systems.

Sandeep K Neemaمشاهده پروفایل

استاد

• Embedded Systems
• Design-Space Exploration
• Model Integrated Computing
• +۳ مورد دیگر

Sandeep K Neema is a Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He serves as Chair of the Executive Council of the Institute of Software Integrated Systems. His research focuses on embedded systems, design-space exploration, and model integrated computing. He holds a Ph.D. from Vanderbilt University, an M.S. from Utah State University, and a B.Tech. from the Indian Institute of Technology. Dr. Neema's educational background includes: Ph.D., Electrical Engineering & Computer Engineering, Vanderbilt University M.S., Electrical Engineering & Computer Engineering, Utah State University B.Tech., Electrical Engineering, Indian Institute of Technology His research interests encompass embedded systems, design-space exploration, and model integrated computing, with a strong emphasis on cyber-physical systems. He explores formal methods, model-based design, and their applications in automotive systems, smart cities, and reliability engineering. His work integrates theoretical frameworks with practical tools to address challenges in system reliability and adaptive software design. Recent publications highlight advancements in artificial general intelligence evaluation, cybersecurity defense mechanisms, and formal methods applied to neural networks and language models. His work also addresses societal-scale control systems and adaptive software frameworks for resource management. These contributions underscore his interdisciplinary approach to solving complex engineering problems. Scientific Awards include: Best Paper Award (2013) In addition to his academic roles, Dr. Neema leads initiatives in the Institute for Software-Integrated Systems. His work often involves collaborative projects and the development of innovative tools for cyber-physical systems design. He is a key member of the Institute for Software-Integrated Systems, contributing to research in model integration, tool development, and cyber-physical systems engineering.

Corwin Smidtمشاهده پروفایل

دانشیار

• American Electoral Politics
• Presidential Primaries
• Political News Media
• +۳ مورد دیگر

Corwin Smidt is an Associate Professor in the Department of Political Science at Michigan State University. He joined MSU in 2008 following his Ph.D. from Ohio State University. His research focuses on American electoral politics, presidential primaries, political news media, and campaign dynamics, with methodological expertise in state space models and natural language processing. Smidt's work examines interactions between political campaigns, media, and voters, including studies on poll worker retention, election logistics, and partisan polarization. He has published extensively on topics ranging from polling place quality to the psychological underpinnings of political trust. His methodological contributions include advanced statistical techniques for analyzing repeated events and dynamic processes in political behavior. Smidt's research highlights include analyses of the 2016 and 2020 U.S. elections, Super PAC influence in campaign finance, and the decline of floating voters in polarized environments. His scholarship bridges empirical political science with innovative methodological approaches, emphasizing real-world electoral systems and institutional challenges. He maintains an active research agenda through his website and Google Scholar profile, focusing on advancing understanding of electoral processes, media effects, and the evolving nature of American political behavior.

Akram Khaterمشاهده پروفایل

استاد

• Middle East Studies
• Migration and Diaspora
• Gender Studies
• +۳ مورد دیگر

Dr. Akram Khater is a Professor of History and holds the Khayrallah Chair in Diaspora Studies at North Carolina State University, where he also directs the Khayrallah Center for Lebanese Diaspora Studies. A native of Lebanon (born 1960), he earned a B.S. in Electrical Engineering from California Polytechnic State University, followed by M.A. and Ph.D. degrees in History from the University of California, Santa Cruz and Berkeley, respectively. His expertise spans Middle East Studies, migration and diaspora, gender, nationalism, religious history, and digital humanities. He has authored influential books like Inventing Home: Emigration, Gender and the Making of a Lebanese Middle Class and edited the International Journal of Middle East Studies . His research often bridges academic scholarship with public engagement, including a PBS documentary on Lebanese communities in North Carolina and major museum exhibitions like The Lebanese in America . Khater has received notable accolades for teaching and research, including awards from NC State and fellowships from the National Humanities Center and Fulbright Foundation. His work critically examines how migration reshapes identity, and how religious and cultural practices evolve in diasporic contexts. Education: B.S., Electrical Engineering, California Polytechnic State University M.A., History, UC Santa Cruz Ph.D., History, UC Berkeley Key Roles: Director, Khayrallah Center for Lebanese Diaspora Studies Editor, International Journal of Middle East Studies Grants & Fellowships: National Humanities Center Fellowship (20XX) American Philosophical Society Grant (20XX) Council of American Overseas Research Centers Award Public Engagement: Over 300 public lectures on Middle Eastern history Curator of widely toured exhibitions

Michael Kearnsمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Algorithmic Game Theory
• +۵ مورد دیگر

Michael Kearns is a Professor and National Center Chair in the Department of Computer and Information Science at the University of Pennsylvania. He serves as the Founding Director of the Warren Center for Network and Data Sciences and founded the Networked and Social Systems Engineering (NETS) program. He holds secondary appointments in Economics, Statistics and Data Science (Wharton), and Operations, Information and Decisions (Wharton). Since 2020, he has been an Amazon Scholar at Amazon Web Services, focusing on responsible AI. He received his undergraduate degree in mathematics and computer science from the University of California, Berkeley (1985) and a Ph.D. in computer science from Harvard University (1989). Kearns' research integrates theoretical computer science with empirical applications in: Machine Learning & AI : Algorithmic fairness, privacy, and reinforcement learning. Algorithmic Game Theory : Microeconomic modeling and strategic behavior in networks. Computational Social Science : Social network dynamics and information aggregation. Quantitative Finance : Algorithmic trading and market microstructure. His work bridges mathematical rigor with real-world impact, including human-subject experiments and financial applications. His recent publications emphasize algorithmic ethics , with themes like fairness in machine learning, privacy attacks (e.g., reconstruction and membership inference), and model disgorgement. Trends show a focus on reinforcement learning scalability, ensemble methods, and mitigating biases in generative AI. Awards & Honors: National Academy of Sciences (2020) ACM Fellow (2014) Best Paper Awards (Journal of Trading, UAI, COLT) George H. Heilmeier Faculty Award (2005) Elected to American Academy of Arts and Sciences, AAAI, and Economic Theory Society Advising & Grants: He mentors doctoral and master's students, often collaborating with Aaron Roth. His group investigates fairness, privacy, and game theory, supported by institutional and industry partnerships (e.g., Amazon, NSF). Labs & Teams: He directs the Warren Center for Network and Data Sciences , fostering interdisciplinary research. His lab includes doctoral students and postdocs working on algorithmic accountability, network science, and AI ethics.

Joseph Navelskiمشاهده پروفایل

مدرس پاره‌وقت

• Microeconomics
• Game Theory
• International Trade and Finance
• +۵ مورد دیگر

Joseph Navelski is a Part-Time Lecturer at Washington State University's School of Economic Sciences within the Department of Economic Sciences. He has been actively teaching undergraduate economics courses while completing his Ph.D. in Economics, which was expected to be completed in 2023. His educational background includes a Ph.D. in Economics (expected 2023), M.S. in Statistics (2022), and M.S. in Agriculture and Resource Economics (2018) from Washington State University and the University of Arizona, respectively. His research spans multiple interdisciplinary areas connecting economics with data science and network analysis. Navelski's research interests focus on applying advanced statistical and machine learning techniques to economic problems, particularly in network formation, sentiment analysis, and corporate social responsibility modeling. His work bridges traditional economic theory with modern data science approaches, especially utilizing social media data for network analysis and opinion formation studies. His publication record shows a strong emphasis on methodological innovation, particularly in network modeling and statistical reliability analysis, with applications ranging from media industry analysis to genome editing opinions on social media platforms. 2021 WSU SES Excellence in Teaching Award 2018 University of Arizona AREC Meritorious Graduate Teaching Assistant Navelski has successfully taught multiple economics courses with consistently high student evaluations, demonstrating his effectiveness as an educator. He has also contributed to the academic community through presentations at the WSU Python Working Group on topics including A/B testing, API usage, and network analysis in Python. His technical expertise spans multiple programming languages and statistical tools including R, Python, GitHub, STATA, SAS, RSTAN, LaTeX, and Tableau, making him well-equipped for both teaching and research in quantitative economics.

• Cognitive engineering
• Interface design
• Trust in automation
• +۴ مورد دیگر

John D. Lee serves as the Emerson Electric Professor in the Department of Industrial & Systems Engineering at the University of Wisconsin-Madison College of Engineering, with additional affiliation in Civil & Environmental Engineering. His pioneering research examines human-automation interaction and trust dynamics across domains including UAVs, maritime operations, automated vehicles, and deep space exploration. He co-authored the foundational textbook Designing for People: An Introduction to Human Factors Engineering and edited the Oxford Handbook of Cognitive Engineering , with research funded by NSF, ONR, NHTSA, NASA, and major automotive manufacturers. His educational background includes: PhD, University of Illinois, Urbana-Champaign (1992) MS, University of Illinois, Urbana-Champaign (1989) BS, Lehigh University (1988) BA, Lehigh University (1987) Year Abroad Program, University of York (1985) Dr. Lee's research centers on cognitive engineering and trust calibration in human-AI systems. He investigates how interface design, automation transparency, and human adaptation mechanisms affect team performance in safety-critical environments. His work develops frameworks for appropriate reliance on automation through modeling human behavior and designing responsive systems that maintain operator situational awareness. Recent publications (2023-2025) reveal expanding focus on conversational trust metrics, prosocial automation design, and explainable AI interfaces. The research increasingly integrates machine learning for dialogue analysis while maintaining core human factors principles, with growing applications in healthcare automation and environmental AI systems alongside traditional transportation domains. His major recognitions include: Byron Bird Research Publication Award (2022) Paul M. Fitts Education Award (2014) Human Factors and Ergonomics Society Fellow (2011) Emerson Electric Professorship (2009) Dr. Lee mentors graduate researchers through I SY E 699, 790, 890, and 990 courses while leading funded projects from: Federal Agencies: NSF, ONR, NHTSA, NASA Industry Partners: Nissan, Toyota, GM His laboratory develops experimental methodologies for measuring trust dynamics in real-world settings, focusing on driver-automation collaboration and AI explainability challenges in complex operational environments.