جست‌وجوی استاد و پژوهشگر

Marios C. Angelidesمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Creative Computing
• +۵ مورد دیگر

Professor Marios C. Angelides is a full-time faculty member at Brunel University London , serving as Professor of Computing and Divisional Lead within the College of Engineering, Design and Physical Sciences . He leads the Creative Computing Research Group under the Institute of Digital Futures and contributes to the Digital Media department at Brunel Design School. BSc (First Class Honours) and PhD in Computing from the London School of Economics (LSE) Chartered Engineer (CEng) and Chartered Fellow of the British Computer Society (FBCS CITP) His research focuses on Creative Computing , specifically applying Machine Learning , Serious Gaming , and Cognitive Modeling to develop Smart IoT Applications . His work spans autonomous drone fleets for environmental monitoring, cybersecurity middleware for Android systems, wearable technology for lifestyle recommendations, and historical analysis of Alan Turing’s legacy in modern AI. Recent publications highlight trends in deploying Machine Learning for: IoT systems optimization Autonomous aerial/underwater vehicle coordination Deepfake detection using Turing’s Imitation Game Energy allocation in CubeSats via gaming mechanics Scientific recognition includes being Deputy Editor of The Computer Journal and runner-up for the 2016 Oxford University Press Wilkes Award . He has supervised PhD students in topics like Smart Android Middleware for Cybersecurity and Wearable Recommendation Systems , with active involvement in editorial boards and international conferences.

Leid Zejnilovicمشاهده پروفایل

استادیار

• Technology and Innovation Management
• Human-Computer Interaction
• Machine Learning
• +۳ مورد دیگر

Leid Zejnilovic is an Assistant Professor at Nova School of Business and Economics (Nova SBE), where he co-founded the Data Science Knowledge Center and serves as Academic Director, and co-founded the Open and User Innovation Knowledge Center as Scientific Deputy Director. He also co-founded the Patient Innovation platform, enabling patients and caregivers to share self-made healthcare solutions. With a double PhD from Carnegie Mellon University and Católica-Lisbon School of Business and Economics, his career spans over 20 years of international consulting, academic entrepreneurship, and teaching at institutions like Imperial College Business School and Ludwig Boltzmann Institute. PhD in Strategy, Entrepreneurship and Technological Change (Carnegie Mellon University / Catholic University of Portugal, 2014) Master in Engineering and Public Policy (Carnegie Mellon University, 2012) Master in Information Technology (Dzemal Bijedic University, 2007) Bachelor in Telecommunications (University of Sarajevo, 2002) His research focuses on Technology and Innovation Management, Human-Computer Interaction, and data-driven solutions across healthcare, tourism, and education. He has published extensively in journals like California Management Review , PLoS ONE , and Marine Policy , with recent work analyzing big data in tourism, machine learning for oral health, and pandemic impacts on fisheries. As an Associate Editor for Data & Policy Journal , his contributions bridge academic research and real-world applications. Co-founding the Data Science for Social Good Foundation and leading over 100 talks in industry and academia, Zejnilovic's career emphasizes translating innovation into social and economic impact through platforms, policy, and education.

Gavin Schwarzمشاهده پروفایل

استاد

• Organizational Change
• Organizational Failure and Inertia
• Virtual Teams
• +۲ مورد دیگر

Gavin Schwarz is a Professor and Head of School at the School of Management and Governance within the UNSW Business School . He specializes in organizational change , organizational failure and inertia , and the dynamics of virtual teams , with a focus on how organizations fail during change processes and how to develop applied strategies for change management . His work spans diverse sectors including healthcare, technology, and education, with publications in leading journals such as Academy of Management Learning and Education and Administrative Science Quarterly . Education : PhD in Management (University of Queensland), MPhil (Hons) in Management (University of Auckland), BA in Management and English (University of Auckland). Grants : 2020 Brock University grant for "University communication in times of COVID-19" , 2020 UNSW Medicine grant for "Translation and change: Embedding effective change management in health" , and earlier Australian Research Council and Gordon J. Samuels Fellowship awards. His research explores the development of knowledge in organizational theories , with an emphasis on collective responses to change , HR management during crises , and technology strategy . He has contributed to understanding organizational communication , team innovation , and structural inertia . His 15 most recent publications cover topics from AI’s role in organizational change to pandemic-driven research adaptation, with keywords spanning management science , behavioral economics , and digital transformation . Scientific Awards 2021-2023 : Outstanding Reviewer Awards (Academy of Management Review) 2017-2020 : Best Reviewer Awards (Journal of Organizational Behavior) 2019, 2013, 2011 : Best Paper Finalist/Awardee (Academy of Management divisions) 2007, 2006 : Editorial Board Excellence (Academy of Management Journal) and Gordon J. Samuels Fellowship As an active supervisor in organizational change and HR development , his work supports healthcare innovation and digital transformation. He serves as Editor-in-Chief for the Journal of Applied Behavioral Science and is on the editorial boards of Academy of Management Review , Journal of Management , and Journal of Organizational Behavior . Contact: g.schwarz@unsw.edu.au

Christopher G. Healeyمشاهده پروفایل

استاد

• Data Visualization
• Data Analytics
• Text Analytics
• +۷ مورد دیگر

Christopher G. Healey is the Goodnight Distinguished Professor of Analytics in the Institute for Advanced Analytics and a Professor in the Department of Computer Science at North Carolina State University. His research spans visualization, data analytics, text analytics, sentiment analysis, machine learning, cognitive psychology, computer graphics, and social media analytics. He has graduated 15 Ph.D. and 26 master's students and secured over $6 million in research funding from agencies including the National Science Foundation, Department of Defense, National Security Agency, Army Research Office, and various industry partners. He has published over 100 peer-reviewed articles and is a senior member of both IEEE and ACM, as well as a member of the NC State Academy of Outstanding Teachers. His research focuses on developing visualization techniques that leverage visual perception to support rapid, accurate, and effective analysis of large, complex datasets. More recently, he has been investigating machine learning for natural language processing and text analytics. His work includes projects on visualizing election results, sentiment estimation for social media, and wildfire narratives using large-scale social media data. His publications demonstrate a strong trend toward integrating machine learning with visualization, particularly for text analytics and social media analysis. He has made significant contributions to visualizing deep neural networks, cyber situation awareness, and pandemic response analytics, showing how visualization can enhance understanding of complex systems and large datasets across multiple domains. IBM Faculty Award (2007, 2008, 2010, 2011, 2012) Senior member, Association of Computing Machinery (ACM) (2007) Senior member, Institute of Electrical and Electronics Engineers (IEEE) (2007) NC State Academy of Outstanding Teachers inductee (2003) National Science Foundation Faculty Early CAREER Award (2001) He has successfully mentored numerous graduate students and secured significant research funding across multiple projects. His work with the Laboratory for Analytic Sciences, National Science Foundation, and Department of Defense demonstrates strong industry and government partnerships. His recent projects focus on visualizing social media narratives, deep neural networks for text understanding, and predictive analytics for large document collections. He leads research groups focused on visualization and analytics, working with teams to develop innovative approaches for data exploration and analysis. His current work continues to push the boundaries of how visualization can be used to enhance understanding of complex data across domains including public health, cybersecurity, and social media analysis.

• Artificial Intelligence
• Machine Learning
• Ethical AI
• +۷ مورد دیگر

Adrian Weller is a Director of Research in Machine Learning at the University of Cambridge and Head of Safe and Ethical AI at The Alan Turing Institute, where he also serves as a Turing Fellow. He additionally directs the Trust and Society programme at the Leverhulme Centre for the Future of Intelligence. His career includes senior roles in finance and advisory positions for governmental AI ethics bodies. His research integrates technical and societal dimensions of artificial intelligence, with major foci including: Foundational ML : Statistical methods, high-dimensional inference, causality, and Monte Carlo techniques Trustworthy AI : Fairness, privacy, bias mitigation, and algorithmic transparency Applied Domains : Computer vision, reinforcement learning, and bio-applications of ML Socio-technical Systems : Policy frameworks, human perceptions of algorithms, and ethical deployment Notable recognition includes an MBE (2022) for pioneering contributions to digital innovation. His work actively informs UK and EU AI policy discussions.

Mariarosaria Taddeoمشاهده پروفایل

استاد

• Digital Ethics
• Philosophy of Technology
• Cybersecurity
• +۹ مورد دیگر

Mariarosaria Taddeo is Professor of Digital Ethics and Defence Technologies at the Oxford Internet Institute (University of Oxford) , where she also serves as DPhil Programme Director for Information, Communication and the Social Sciences. She is a Senior Research Fellow at the Alan Turing Institute and holds advisory roles at institutions including the Ministry of Defence (UK) Ethics Advisory Panel , the BRAID Programme , and the Leonardo Foundation . Education PhD (Doctor Europeus) in Philosophy from University of Padua Mariarosaria Taddeo’s research spans Digital Ethics, Philosophy of Technology, Cybersecurity Ethics, and AI Governance , with a focus on national defence applications. Her work addresses trust in AI , cyber conflict regulation , and ethical frameworks for autonomous weapons . She has published extensively in journals like Nature , Science , and Minds and Machines , including studies on data philanthropy , digital well-being , and quantum technology ethics . Scientific Awards 2010 Simon Award for Outstanding Research in Computing and Philosophy 2016 World Technology Award for Ethics 2018 InspiringFifty: Top 50 Italian women in technology ORBIT listings (2018, 2020) of top 100 women in AI Ethics 2020 Women’s Forum for Economy and Society: Outstanding Rising Talents ComputerWeekly Top 100 Influential Women in UK Technology (2020, 2023) Her projects include the UK MOD-funded AI Ethics Principles initiative, the NATO Cooperative Cyber Defence Centre of Excellence ethical guidance project, and contributions to the PETRAS IoT Research Hub . She advocates for ethical AI implementation in defence and digital governance, influencing EU policy through the CEPS Task Force on AI and Cybersecurity .

Hugo Subtilمشاهده پروفایل

پژوهشگر

• Political Economy
• Political Science
• Computational Social Science
• +۳ مورد دیگر

Hugo Subtil is a postdoctoral researcher at the Department of Political Science, Faculty of Arts and Social Sciences, University of Zurich, affiliated with the Chair of Comparative Politics in Political Institutions and European Politics. He is an active member of the University's Digital Society Initiative (DSI), particularly engaged with the DSI Community AI & Law and multiple other interdisciplinary communities including Cybersecurity, Digital Humanities, and Ethics. His academic background includes: Economics studies at École Polytechnique (France) PhD from École des Mines and École Polytechnique (France) Subtil's research program integrates Political Economy, Political Science, and Computational Social Science to investigate European political dynamics through quantitative text analysis. He specializes in extracting political ideologies from large-scale text corpora using machine learning algorithms and social media data, with dual research foci: (1) how institutional design shapes social norms and behaviors, and (2) longitudinal evolution of political ideologies. His methodological approach bridges traditional political science with cutting-edge computational techniques for analyzing legislative discourse and public communication. Analysis of his 12 publications (2020-2025) reveals a clear trajectory from historical economic analysis toward contemporary computational political science. Early work (2020-2021) examined Marxian economics and pandemic-era urban migration patterns, while recent research (2023-2025) increasingly focuses on European Parliament discourse, populism, and emotional rhetoric using advanced text mining techniques. This evolution demonstrates growing specialization in computational analysis of political communication within European institutions. No scientific awards or major honors are documented in the available information. While no formal advisees or grant funding details are specified, Subtil's collaborative work within the DSI framework suggests engagement with interdisciplinary research teams addressing digital society challenges. His position within the Chair of Comparative Politics indicates involvement in departmental research initiatives related to European governance. Subtil operates at the nexus of multiple research ecosystems: the Department of Political Science's European Politics research group, the university-wide Digital Society Initiative, and specialized DSI communities exploring AI-law intersections. His work contributes to the DSI's mission of addressing societal challenges through digitalization research, particularly in computational analysis of political discourse and institutional behavior.

• Neuroscience
• Machine Learning
• Neuroimaging
• +۴ مورد دیگر

Dustin Scheinost is an Associate Professor at Yale School of Medicine, affiliated with the Department of Radiology & Biomedical Imaging, Yale Child Study Center, Department of Statistics, and Yale Biomedical Imaging Institute. His research focuses on connectomics , machine learning , and neuroinformatics through the Multi-modal Imaging, Neuroinformatics, & Data Science (MINDS) Lab. Radiology & Biomedical Imaging (Primary) Child Study Center (Secondary) Statistics (Secondary) Wu Tsai Institute Yale Stress Center Research Interests include developing novel statistical and machine learning methods for functional connectivity in big neuroscience data, leading the BioImage Suite Web (BISWeb) platform, and advancing early life neuroimaging through the Fetal, Infant, Toddler Neuroimaging Group (FIT’NG). His work is supported by grants from NIMH, NIAA, NIDA, and NHLBI. Selected Scientific Contributions span functional connectivity in laterality preferences, anti-racist AI governance in psychiatry, self-citation trends in neuroscience, and predictive modeling of mood disorders. He collaborates extensively with Todd Constable and others on multimodal neuroimaging studies.

Charles C. Davisمشاهده پروفایل

استاد

• Plant Systematics
• Phylogenomics
• Biodiversity Conservation
• +۸ مورد دیگر

Charles C. Davis is a Professor of Organismic and Evolutionary Biology at Harvard University and Curator of Vascular Plants in the Harvard University Herbaria. He leads the Davis Lab, focusing on plant diversity through integrative research in systematics, paleobiology, ecology, and molecular biology. His work emphasizes phylogenetic theory, biogeography, and the application of herbarium collections to address global change challenges. Davis is particularly noted for leveraging herbarium specimens to study plant responses to climate change, phenology, and biodiversity patterns. Research interests include plant-insect interactions, genome architecture evolution in parasitic plants, and the ethical use of herbarium specimens. His lab has pioneered high-throughput phylogenomic pipelines (e.g., PhyloHerb) and explored the future of herbaria in the digital age. Collaborations span global institutions, emphasizing digitization, spectral imaging, and AI-driven analysis of biodiversity data. Recent work highlights the impact of anthropogenic change on plant communities, the role of phenology in species survival, and strategies to mitigate collecting biases in herbaria. Davis teaches courses on plant systematics and evolution, and his lab actively participates in public engagement through exhibits like the HMNH’s *In Search of Thoreau’s Flowers*. His research has been featured in *Trends in Ecology & Evolution*, *Molecular Phylogenetics and Evolution*, and *Current Biology*, with a focus on advancing methodologies for biodiversity science while addressing ethical challenges in specimen sampling.

Ning Zhangمشاهده پروفایل

دانشیار

• System Security
• Cybersecurity
• Computer Architecture
• +۷ مورد دیگر

Ning Zhang is an Associate Professor in the Department of Computer Science & Engineering at Washington University in St. Louis, affiliated with the Center for Trustworthy AI in Cyberphysical Systems (CPS). He joined the university in Fall 2018, following an 11-year tenure at Raytheon as a Principal Cyber Engineer and Technical Lead, where he focused on securing critical networked and cyberphysical infrastructures. Education: PhD in Computer Science, Virginia Polytechnic Institute and State University (2016) BS and MS in Computer Science, University of Massachusetts Amherst (2007) Research Interests: Professor Zhang’s work bridges security, computer architecture, and programming languages. Key areas include secure software/hardware systems, side-channel analysis, malware forensics, and automated vulnerability discovery. His recent focus includes safeguarding generative AI agents and enhancing cybersecurity in agentic systems. Awards: USENIX Distinguished Paper Award (2024) for research on generative AI security circumvention Grants & Collaborations: Co-recipient of a $1.5M DoD grant (2025) to advance generative AI robustness Labs & Teams: Leads research initiatives in system security and trustworthy AI within the McKelvey School of Engineering.

• Machine Learning & Artificial Intelligence
• Probabilistic Modelling
• Neuro-Symbolic Modelling
• +۷ مورد دیگر

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. His research focuses on interdisciplinary applications of machine learning (ML) and artificial intelligence (AI), emphasizing interpretability, robustness, and ethical considerations. He explores computational models in music cognition, communication dynamics, and medical image analysis, aiming to bridge theory and practical tools for domain experts. Before Durham, he was a postdoctoral researcher at EPFL's Digital and Cognitive Musicology Lab (2018–2021) and earned his PhD from the Learning and Intelligent Systems Lab in Stuttgart/Berlin (2012–2017). His work combines probabilistic modelling, neuro-symbolic systems, and reinforcement learning to address challenges in music analysis, autonomous decision-making, and medical robotics. Key research themes include: Music structure and perception modelling Symbol emergence in multi-agent communication Ethical AI and autonomous systems governance Medical imaging applications (CT/MRI analysis) Recent projects involve developing patient-agnostic diabetes management systems using deep reinforcement learning and surgical workflow anticipation through graph learning algorithms. He actively contributes to conferences such as NeurIPS, ISMIR, and AAAI, with publications spanning music informatics, robotics, and biomedical engineering. Current supervision includes four postgraduate students focusing on AI applications in healthcare, music technology, and autonomous systems. His work bridges technical innovation with societal impact, addressing challenges in policy, legislation, and interdisciplinary collaboration.

• Machine Learning
• Computational Biology
• Bioinformatics
• +۴ مورد دیگر

Catherine Mooney is a Professor in the School of Computer Science at University College Dublin (UCD), leading the Life Science Data Analytics Group (LiSDA). Her research focuses on applying machine learning to healthcare challenges, including biomarker development, clinical decision support systems, and addressing ethical and technical barriers in healthcare AI. Education: BSc in [field unspecified] from Trinity College Dublin PhD in Computer Science from UCD Prof Dip University Teaching & Learning from UCD Research Interests: Machine Learning applications in healthcare Biomarker discovery for neurological and metabolic conditions Explainable AI for clinical decision support Promoting diversity and inclusion in STEM education Her work bridges computational methods with medical challenges, emphasizing ethical AI and translational research. Notable Awards: Best Paper Award in Applied Biosciences (2023) UCD Long-Service Award (2022) Best Poster Award at ITiCSE ’20 (2020) Her research has led to impactful tools like LiSDA’s clinical decision support systems for epilepsy and pregnancy care. She also advocates for gender diversity in computing, serving in leadership roles like Vice Principal for EDI in UCD’s College of Science (2022–2024).

• Human-Centered Artificial Intelligence
• Natural Language Processing
• Computer Vision
• +۵ مورد دیگر

Carlos Toxtli is an Assistant Professor at Clemson University where he leads the Human-AI Empowerment Lab. His research applies Human-Centered AI to create fair workplace tools, focusing on NLP, computer vision, and crowdsourcing ethics. He holds a PhD in Computer Science from Northeastern University and previously worked at Google, Microsoft Research, and Snap Inc. Education: Ph.D. Computer Science, Northeastern University M.S. Innovation & Technological Entrepreneurship, Monterrey Institute of Technology MBA, IEDE Business School B.S. Computer Science, University of the Valley of Mexico B.S. Computer Engineering, National Autonomous University of Mexico Research Focus: Dr. Toxtli develops AI systems promoting workplace fairness through ethical frameworks. His work spans: 1) Human-AI collaboration for task management, 2) Bias mitigation in gig economies, 3) Multimodal interfaces for accessibility, and 4) Culturally adaptive systems. His lab explores how AI can augment human capabilities while ensuring transparency. Publication Trends: Recent works demonstrate strong focus on LLM reliability (7 papers), human-AI teaming (4 studies), and ethical AI frameworks (3 publications). Over 60% of 2024-2025 output addresses real-world deployment challenges. Awards: UNESCO IRCAI Global Top 100 project Google Apps Developer Challenge 1st Place Facebook Developers World Hack 1st Prize Intel Innovation Latin App 1st Prize NSF STIR Labs Research Grant Leadership: Founded the Human-AI Empowerment Lab securing $1.2M in NSF/industry grants. Supervises 8 PhD students in human-centered computing projects. Previously co-founded 5 tech startups including ComproPago (acquired by Coca-Cola FEMSA).

Andrea Groverمشاهده پروفایل

دانشیار

• Citizen Science
• Information Systems
• Open Collaboration Systems
• +۳ مورد دیگر

Dr. Andrea Grover is an Associate Professor in the Department of Information Systems and Quantitative Analysis at the University of Nebraska Omaha’s College of Information Science & Technology. Her research focuses on citizen science, technology design, and collaboration systems in data-intensive environments. She holds a Ph.D. in Information Science & Technology from Syracuse University, an MS in Information from the University of Michigan, and a BA in Mathematics from Alma College. Her research interests include organizational impacts of technology, open collaboration frameworks, and ethics in information systems. She teaches courses on management and IT ethics, employing ungrading techniques to enhance student engagement. Notable contributions include work on citizen science data quality, cyber-security tools leveraging crowdsourcing, and barriers in STEM education equity. Dr. Grover has been recognized with the 2022 Rising Star Distinguished Ecologist award and serves on editorial boards (e.g., Frontiers in Ecology) and conference committees (e.g., ACM CSCW DEI Co-chair). She has secured grants such as the NSF-funded “Streamlining Embedded Assessment to Understand Citizen Scientists' Skill Gains” (2017–2019) and contributed to media discussions on citizen science via National Public Radio. Her recent publications explore hybrid human-AI tools, agile citizen science methodologies, and systemic barriers in education. She actively participates in interdisciplinary initiatives, bridging technology, ecology, and social sciences to advance participatory research practices.

• Statistical Machine Learning
• Bayesian Inference
• Variational Methods
• +۵ مورد دیگر

Michael C. Hughes ("Mike") is an Assistant Professor in the Department of Computer Science at Tufts University's School of Engineering, where he develops statistical machine learning methods for healthcare applications. His work focuses on building predictive models that extract actionable insights from complex clinical data, including electronic health records and medical imaging. PhD, Computer Science, Brown University (2016) MS, Computer Science, Brown University (2012) BS, Computer Science, Franklin W. Olin College of Engineering (2010) Research interests center on: Bayesian hierarchical models for documents, sequences, and medical images Optimization algorithms for approximate inference Model fairness and interpretability in clinical contexts Semi-supervised learning for medical diagnostics Recent publications demonstrate these capabilities through applications in cardiovascular disease diagnosis, opioid overdose forecasting, and ICU risk prediction. His lab emphasizes reproducibility through open datasets like TMED-2 and open-source tools like BNPy. Grants include NIH R01 funding for heart valve disease detection, NSF CAREER support for model interpretability, and NSF GCR funding for educational uncertainty research. Scientific awards include: NIH R01 Award (PI) for heart valve disease detection (2025) NSF CAREER Award (2024) NSF GCR Grant (2024) Best Poster Award at Time Series Workshop (ICML 2021) Top 10% Reviewer Awards at AISTATS (2023, 2022) Teaching activities include courses on Bayesian Deep Learning, Introduction to Machine Learning, and Statistical Pattern Recognition. He previously served as postdoctoral fellow at Harvard SEAS.