جست‌وجوی استاد و پژوهشگر

Marco Tomiettoمشاهده پروفایل

استاد

• Clinical Learning Environments
• Mentorship in Nursing Education
• Organizational Socialization of Healthcare Workers
• +۷ مورد دیگر

Marco Tomietto is a Professor at Northumbria University's Nursing Midwifery and Health Department. He also holds a Professorship at the University of Oulu. His expertise spans clinical learning environments, mentorship, newcomer organizational socialization, and VR in healthcare. He earned a PhD in Work and Organizational Psychology from the University of Verona, a Master's in Nursing Science from the University of Udine, and a Nursing degree from the University of Udine. His research focuses on nursing workforce sustainability, vaccine hesitancy, VR applications, and healthcare organizational dynamics. He has published over 80 articles and secured grants like the MONARCH project for veteran healthcare needs analysis. Awards include the 2010 Editor’s Award in Nursing Science and the 2022 Sigma Global Nursing Research Award. Key projects include developing mentoring frameworks, analyzing vaccine hesitancy among nurses, and leveraging VR for clinical training. He advises PhD students on topics like healthcare workforce retention and VR implementation. Education: PhD in Work and Organizational Psychology (2014), MSc Nursing Science (2008), BSc Nursing (2001) Grants: Partnership for Workforce Sustainability, MONARCH, and RAF Families Federation initiatives Labs/Teams: Collaborates with international networks on healthcare policy, public health, and nursing education

• Business Process Management
• Process Mining
• Explainable AI
• +۵ مورد دیگر

Chun Ouyang is a Professor at Queensland University of Technology (QUT) in the School of Computer Science within the Faculty of Science. With an extensive publication record spanning over two decades from 2002 to 2025, Professor Ouyang has established themselves as a leading researcher in Business Process Management, Process Mining, and Explainable AI. Their work bridges theoretical foundations with practical applications across healthcare, finance, and industrial sectors. Professor Ouyang's research interests primarily focus on Business Process Management systems, Process Mining techniques, Explainable Artificial Intelligence, and Healthcare Process Analysis. Their work has evolved from foundational BPMN/BPEL translation research in the early 2000s to sophisticated process mining approaches in the 2010s, and most recently to cutting-edge Explainable AI applications in clinical and business contexts. They have developed novel methodologies for process querying, predictive process analytics, and XAI evaluation frameworks that have significantly advanced the field. Their research consistently emphasizes practical applicability while maintaining strong theoretical foundations, with publications in top-tier journals and conferences including IEEE Transactions, Springer journals, and major BPM conferences. Analysis of Professor Ouyang's recent publications (2023-2025) reveals a strategic research trajectory that integrates traditional process mining with modern AI techniques, particularly focusing on explainability and trustworthiness. Their work demonstrates a consistent pattern of addressing real-world challenges through rigorous methodological development, with increasing emphasis on healthcare applications, clinical decision support systems, and the ethical implications of AI deployment. The publications show strong interdisciplinary collaboration patterns, particularly with medical researchers and industry partners. Professor Ouyang has mentored numerous PhD students and early-career researchers who have gone on to establish themselves in the BPM and AI communities. Their research group at QUT has secured multiple competitive grants supporting innovative work in process analytics and AI. They maintain active collaborations with leading researchers globally, including Catarina Pinto Moreira, Arthur ter Hofstede, and Moe Wynn. Professor Ouyang leads the Process Analytics Research Group at QUT, which focuses on developing advanced techniques for business process analysis, prediction, and optimization. The group maintains strong industry connections with healthcare providers, financial institutions, and government agencies, ensuring their research has practical impact. Current projects include developing trustworthy AI systems for clinical decision support, cross-organizational process analysis frameworks, and next-generation process mining techniques for complex, distributed systems.

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۵ مورد دیگر

Goran Oreški is an Associate Professor and Head of the Laboratory at the Faculty of Informatics in Pula (University Jurja Dobrile, Croatia), where he has been employed since 2019. He teaches courses on databases, object-oriented programming, data warehousing, and artificial intelligence at both undergraduate and graduate levels. Education: Ph.D. in Informatics (2016), Faculty of Organization and Informatics Industry Experience: 9 years as software architect and programmer in banking sector Research Focus: Artificial Intelligence systems, classical machine learning algorithms, and deep learning architectures. His work bridges theoretical advancements with practical applications in autonomous vehicles, traffic monitoring, and financial risk assessment. Recent Publication Trends: 2023-2025 works emphasize generative AI for synthetic credit data, traffic object segmentation with monocular cameras, and context-aware detection models (YOLO*C). Earlier works focus on genetic algorithms and ensemble learning for imbalanced datasets. Awards: Google RFP Award for autonomous vehicle research Highly Cited Paper (Web of Science, top 1%) Best Paper at CECIIS conference Leadership: Director of FIPU Laboratory since 2022, leading projects like ai.Shuttle (autonomous mini-bus) and CenAI (industry collaboration with Cenosco).

Clinton Fookesمشاهده پروفایل

استاد

• Computer Vision
• Artificial Intelligence
• Human Behavior Analysis
• +۷ مورد دیگر

Professor Clinton Fookes is a faculty member at the Queensland University of Technology (QUT) within the School of Electrical Engineering & Robotics . His research focuses on leveraging computer vision and artificial intelligence to develop automated systems that understand, anticipate, and interact with human behaviors, with applications in medical diagnostics, autonomous vehicles, defense, and industrial efficiency . Research areas include AI adaptability, multimodal biosignal analysis, and human-machine interaction Collaborates with CSIRO Data61, Defence Science and Technology Group, Orica, Airbus, and Sentient Vision Systems Develops systems for human action detection, infrastructure monitoring, and stress response prediction His work addresses critical challenges in AI deployment, such as environmental adaptability and reducing diagnostic errors in medical and autonomous systems. Recent publications highlight trends in self-supervised learning, zero-shot knowledge transfer, multimodal integration , and 3D reconstruction for healthcare , while exploring ethical AI use in sectors like mining and defense . Professor Fookes emphasizes interdisciplinary collaboration, bridging engineering, medicine, and social sciences to advance AI systems capable of real-world impact. His research agenda includes improving AI memory capabilities and explainability for safer, more reliable automation.

Constantine E. Kontokostaمشاهده پروفایل

استاد

• Urban Science
• Urban Informatics
• Urban Planning
• +۶ مورد دیگر

Constantine E. Kontokosta is Professor of Urban Science and Planning at NYU Marron Institute of Urban Management, Director of Civic Analytics and Urban Intelligence Lab, with cross-appointments at Center for Urban Science and Progress (CUSP) and Department of Civil and Urban Engineering. He serves as affiliated faculty at Wagner School of Public Service and previously held leadership roles including inaugural Deputy Director of CUSP. His educational background includes: PhD, Urban Planning (Minor: Econometrics) from Columbia University MPhil, Urban Planning from Columbia University MS, Urban Planning; Quantitative Analytics from Columbia University MS, Real Estate Finance and Economics from New York University BSE, Systems Engineering - Civil from University of Pennsylvania Kontokosta leverages large-scale data and computational methods to advance urban energy/climate policy, neighborhood dynamics, and bias detection in public decision-making. His research integrates urban planning with data science to develop equitable solutions for sustainable development, with recent projects analyzing COVID-19 disparities through mobility data and creating methods to reduce building emissions. The work emphasizes evidence-based policy, information transparency, and uncovering algorithmic discrimination. His honors include the IBM Faculty Award, UN Data for Climate Action Challenge Award, Goddard Junior Faculty Fellowship, and multiple best paper awards. Key recognitions: 2023 Best Paper Award (ICLR Climate Workshop) 2021 Article of the Year (Journal of American Planning Association) 2017 Microsoft Azure Research Award 2014 IBM Faculty Award 2012 Fellow of Royal Institution of Chartered Surveyors Funded by National Science Foundation, MacArthur Foundation, Sloan Foundation, U.S. Department of Transportation, NYC Mayor’s Office of Sustainability, Lincoln Institute, and HUD, Kontokosta has served on UNEP Sustainable Buildings Council, Royal Institution of Chartered Surveyors Americas Board, and Suffolk County Planning Commission. His entrepreneurial ventures translate research into practical urban solutions. He leads the Urban Intelligence Lab focused on data-driven urban methodologies and Civic Analytics program advancing evidence-based policy through transparent knowledge democratization, with research featured in Nature Communications, PNAS, and major media outlets.

Anton Rozhkovمشاهده پروفایل

استادیار

• Geospatial Tools and Analysis
• Urban Planning
• Data Science for Cities
• +۷ مورد دیگر

Anton Rozhkov is an Industry Assistant Professor and Director of the M.S. in Applied Urban Science and Informatics Program at the Center for Urban Science and Progress (CUSP) at New York University (NYU) Tandon School of Engineering. His work focuses on applying geospatial tools, modeling techniques, and data science to address complex challenges in urban environments, with particular emphasis on infrastructure planning and city design. Dr. Rozhkov earned his Ph.D. in Urban Planning and Policy from the University of Illinois Chicago, where his research centered on decentralized and renewable energy systems in urban contexts through a complex systems approach. Prior to his doctoral studies, he received an M.S./B.S. in Engineering in Land Cadaster from the State University of Land Use Planning in Moscow, Russia, and worked as a senior specialist in the Russian power grid sector with "Rosseti" Group of Companies. His research interests span the application of complex systems, data science, and spatial analytics to solve urban challenges, particularly focusing on how data-driven policies and new technologies can transform infrastructure planning and city design. Dr. Rozhkov employs methods including causal loop diagrams, system dynamics, and agent-based modeling to understand how decentralized energy systems interact with existing power grids and contribute to sustainable urban development. He has published extensively on urban transportation, energy systems, and census data analysis, with a notable focus on Chicago's urban landscape and Illinois state initiatives. Dr. Rozhkov has been actively involved in several significant research projects including an empirical investigation into affordable transit-oriented development in California sponsored by the California State University Transportation Consortium, the Sustainable Urban-Regional Modeling Network project funded by the Illinois Innovation Network, and the Census 2020 Map-The-Count project with the Illinois Department of Human Services which developed predictive models for census response rates and a GIS platform for reporting outreach activities. Ph.D. in Urban Planning and Policy, University of Illinois Chicago M.S./B.S. in Engineering in Land Cadaster, State University of Land Use Planning (Moscow, Russia) His teaching portfolio includes courses on geographic information systems (GIS), advanced spatial analysis, decision modeling, and machine learning for cities. Dr. Rozhkov emphasizes not just understanding urban trends but exploring the "why" behind these trends to develop sustainable solutions. His recent publications (2020-2025) demonstrate a consistent research trajectory examining the complex interrelationships between urban infrastructure systems, particularly focusing on energy, transportation, and spatial patterns through sophisticated analytical methods. Outside of his academic work, Dr. Rozhkov is passionate about urban and landscape photography, traveling, running, snowboarding, and playing guitar. He was born and raised in Balashikha, a city in the Moscow suburbs in Russia, and maintains a gallery of his photographic work from various global locations.

Alex Richterمشاهده پروفایل

استاد

• Human-AI Collaboration
• The Future of Work
• Digital Humans
• +۱۰ مورد دیگر

Professor Alex Richter is a Professor of Information Systems at the School of Information Management, Victoria University of Wellington, where he also serves as Director of the Executive MBA program and PhD Director. He leads the Digital Work Lab and edits the Digital Work Diaries. With over 150 refereed publications cited more than 11,500 times, Professor Richter is a leading researcher in the field of Information Systems, recently ranked 285th globally among 18,561 researchers in the Information Systems sub-field on the Top 2% Scientists List. Professor Richter's research focuses on how digital technologies transform work to enhance innovation, productivity, and employee satisfaction. His primary areas of interest include Human-AI Collaboration , Digital Work and Innovation , Value-driven design , and The Future of Work . He explores practical applications of human-AI collaboration with global industry partners, identifying use cases, benefits, risks, and enablers of adoption. His conceptual work emphasizes value-driven, context-aware, and adaptive approaches to create meaningful sociotechnical systems. His recent publications demonstrate a strong trend toward understanding the evolving relationship between humans and AI in workplace settings. Professor Richter's work examines trust in generative AI across organizational departments, the transformation of innovation practices through human-AI collaboration, and the implementation challenges of emerging technologies like augmented reality. His research consistently bridges theoretical frameworks with practical applications, focusing on how organizations can effectively integrate AI while maintaining human-centered values. Top 2% Scientists List (2024), ranking 285th globally among 18,561 researchers in Information Systems 'Innovation in Teaching Award' from the Association for Information Systems (2024) Research Fellow (2022) Multiple best paper awards throughout his career Professor Richter actively supervises numerous PhD students working on human-AI collaboration topics, including Carlos Forero, Chloe Latto, Ghazaleh Moqadam, Hedi Bigham Sohanaki, Jingyu Zheng, Mina Sanabadi, Shafiqul Alam, and Yao Zhang. He has successfully led research projects funded by the European Union, national governments, and organizations across Germany, Switzerland, Denmark, Australia, and the USA. His engaged scholarship approach connects academic research with practical industry applications. As Director of the Digital Work Lab, Professor Richter leads a team exploring the future of work in the digital age. The lab investigates how digital technologies transform work practices, with particular focus on hybrid work environments, visibility in digital workplaces, and human-AI collaboration. Through the Digital Work Diaries initiative, the lab documents and analyzes real-world digital work transformations, providing practical insights for organizations navigating digital change.

Dian Tjondronegoroمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +۷ مورد دیگر

Professor Dian Tjondronegoro is a leading academic at Griffith University's Department of Management within the Griffith Business School. He holds roles such as Acting Head of Department and Deputy Head (Research), and is affiliated with the Centre for Work, Organisation and Wellbeing, Griffith Asia Institute, and the Griffith Inclusive Future Beacon. His research focuses on AI ethics, eHealth systems, and digital governance, with over $9M in grants from bodies like the ARC and NHMRC. He has published 145+ peer-reviewed papers and leads initiatives like the 'Governing in the Digital Age' program. A Fellow of the Australian Computer Society and Senior Member of IEEE/ACM, he has won the Gold Disrupters Award (2019) and multiple teaching accolades. Education: PhD in Information Systems (Deakin University, 2005), BIS (QUT, 2001). Research Interests: AI, machine learning, healthcare innovation, responsible AI, workplace design, and digital economy strategies. His articles emphasize AI applications in healthcare monitoring, workplace productivity, and ethical surveillance. Recent work explores post-COVID workplace trends and AI-driven public health solutions. He advises on government policy and innovation through roles like Gold Coast Health's Digital Innovation Advisory Committee. His teaching includes courses on digital strategy and innovation management.

Ferreira Joao Carlosمشاهده پروفایل

استاد

• Artificial Intelligence
• AI in Health and Medicine
• AI in Energy and Transportation
• +۶ مورد دیگر

Joao Carlos Amaro Ferreira is a Professor at the Faculty of Logistics, Molde University College (HiMolde), Norway. He holds PhDs in Computer Engineering and Industrial Engineering from the Technical University of Lisbon and the University of Minho, respectively. His research focuses on Artificial Intelligence (AI) applications in healthcare, energy, transportation, IoT, blockchain, and smart cities. He has led over 40 projects, including 6 as Principal Investigator, and contributed to international conferences like OAIR and INTSYS. He served as IEEE CIS President (2016-2018) and is an IEEE Senior Member since 2015. His academic contributions span AI-driven solutions for public sector informatics, healthcare data quality, and cybersecurity. He actively participates in European projects such as e-Hospital4Future and explores blockchain applications in supply chains and medical records. Ferreira leads the ABC-AI research group, emphasizing ethical and applied AI. His work bridges academia and industry through projects like gamification systems for eco-driving and AI in fisheries traceability. Recent publications highlight AI's role in cardiovascular disease detection, emergency department optimization, and blockchain-enhanced healthcare interoperability. He collaborates internationally, co-editing journals like Applied Sciences , and has authored patents in edge computing for maritime monitoring.

Chenang Liuمشاهده پروفایل

دانشیار

• Smart Manufacturing
• Data Analytics
• Machine Learning
• +۲ مورد دیگر

Dr. Chenang Liu is an Associate Professor in the Department of Industrial Engineering & Management at Oklahoma State University's College of Engineering, Architecture and Technology (CEAT). Their research focuses on smart manufacturing systems, real-time quality monitoring, and machine learning applications in manufacturing and healthcare. Ph.D., Industrial and Systems Engineering, Virginia Tech, 2019 M.S., Statistics, Virginia Tech, 2017 B.S., Mathematics (Statistics track), Zhejiang University, China, 2014 B.S., Environmental and Resource Sciences, Zhejiang University, China, 2014 Research Interests: Dr. Liu develops advanced sensing and data analytics methodologies for smart manufacturing, statistical frameworks for real-time quality control, and mathematical models integrating machine learning with healthcare applications. Their work bridges industrial engineering principles with cutting-edge data science techniques. Publication Trends: Recent articles demonstrate expertise in diabetic retinopathy prediction via interpretable AI, supply chain coordination mechanisms, EHR analytics for disease progression modeling, and combinatorial optimization algorithms. Key themes include healthcare data science, resilient manufacturing systems, and stochastic resource allocation. Scientific Recognition: Featured Article in ISE Magazine, IISE, 2019 Gilbreth Memorial Fellowship, IISE, 2018-2019 Best Poster Award, INFORMS Annual Meeting, 2018 Best Student Paper Finalist, IISE Annual Conference, 2018 Best Paper Awards at INFORMS (2017) and IISE (2017)

Jason Heinمشاهده پروفایل

دانشیار

• Mechanistic Organic Chemistry
• Automated Reaction Analysis
• Self-Driving Laboratories
• +۷ مورد دیگر

Jason Hein is an Associate Professor in the Department of Chemistry at the University of British Columbia's Faculty of Science. His research focuses on the development of automated reaction analysis technology and self-driving laboratories that integrate robotics with synthetic organic chemistry. Dr. Hein leads the Hein Lab, which pioneers innovative solutions for mechanistic organic chemistry, catalytic reaction mechanisms, and chemical manufacturing processes. His research interests center on creating modular robotic tools and integrated analytical hardware for automated reaction profiling, with applications in pharmaceutical manufacturing, battery materials processing, and sustainable chemistry. The lab's work combines advanced robotics, artificial intelligence, and process analytical technology to develop self-optimizing chemical systems that accelerate discovery and improve manufacturing efficiency. Analysis of Hein's recent publications reveals a strong focus on AI-driven laboratory automation, with particular emphasis on crystallization optimization for battery materials, computer vision for process monitoring, and interoperable software systems for self-driving laboratories. His work bridges fundamental mechanistic understanding with practical industrial applications, particularly in lithium extraction from waste brines and pharmaceutical process development. NSERC Postdoctoral Fellowship Dr. Hein's research program includes significant grant funding supporting the development of self-driving laboratory technologies and their application to challenging chemical problems. His lab actively collaborates with industry partners in pharmaceuticals and clean energy sectors to translate fundamental insights into deployable technologies. Current projects focus on battery-grade lithium carbonate production, continuous manufacturing processes, and AI-optimized chemical synthesis. The Hein Lab operates as a multidisciplinary research environment combining expertise in organic chemistry, robotics engineering, computer science, and data analytics to create the next generation of autonomous chemical discovery systems.

Pedro Oliveiraمشاهده پروفایل

استاد

• Open and User Innovation
• Patient Innovation
• Social and Impact Innovation
• +۳ مورد دیگر

Pedro Oliveira is a leading academic and administrator with diverse roles across institutions. He currently serves as Dean at Nova School of Business and Economics , where he holds the Cascais Chair in Operations, Technology, and Innovation Management . Additionally, he is a Professor MSO at Copenhagen Business School , Academic Fellow at Cornell University , and Adjunct Professor at MIT Sloan School of Management . He also founded the Patient Innovation platform and co-founded PPL Crowdfunding . Education: Ph.D. in Operations, Technology, and Innovation Management (2004), University of North Carolina at Chapel Hill M.Sc. in Operations Research and Systems Engineering (2000), Instituto Superior Técnico Licenciatura in Naval Engineering (1996), Instituto Superior Técnico Research Focus: Pedro Oliveira's work centers on Open and User Innovation , Patient Innovation , and Social and Impact Innovation , with a strong emphasis on Service Operations Management and Artificial Intelligence in Innovation . His studies often bridge Technology Strategy with Healthcare and Financial Services , exploring how users can drive innovation in resource-constrained environments. Publication Trends: His recent works emphasize collaborative innovation in public and semi-public domains , patient-driven healthcare solutions , and user innovation in digital finance . He has also published extensively on knowledge commons and innovation during crises , such as the COVID-19 pandemic . Leadership and Advisory Roles: Pedro has held significant administrative roles, including Senior Associate Dean for Faculty & Research and Director of the Research Unit in Business and Economics . He has advised the Portuguese Ministry of Science, Technology, and Higher Education and served on the Environment & Sustainability Council at EDP Energias de Portugal . He is also the President of the Open and User Innovation Society and a counselor on the National Council for Science, Technology, and Innovation (CNCTI) and Economic and Social Council (CES) in Portugal. Labs and Teams: As founder of Patient Innovation , he has established a global platform for user-driven healthcare solutions. His work also includes collaborative digital health initiatives , such as the Saúde.Come Senior program addressing food insecurity in elderly populations through TV-based eHealth interventions.

• Business Informatics
• Artificial Intelligence
• Business Intelligence
• +۱۰ مورد دیگر

Prof. Dr. Harald Ritz serves as Professor of Practical Computer Science, especially Business Informatics, at the Technical University of Central Hesse (THM) within the Department of Mathematics, Natural Sciences and Computer Science since 2003. He holds leadership roles as Chair of Examination Committees for B.Sc. and M.Sc. Business Information Systems and Spokesperson for the MNI department in the Business Informatics Working Group (AKWI). His educational background includes a Diplom in Business Informatics (Dipl.-Wirtsch.-Inform.) and doctorate (Dr. rer. pol.) from the Technical University of Darmstadt, following professional experience at SAP SI AG and a professorship at Heilbronn University of Applied Sciences. Ritz's research centers on AI-driven digital transformation for data-driven enterprises, with focus on the “Data to Decision” value chain encompassing Framing, Allocation, Analytics, and Preparation phases. His work integrates business intelligence, data warehousing, machine learning, and SAP ecosystems to address challenges in SME digitalization, operational IT management, and educational technology. Current projects emphasize AI applications in higher education, including intelligent tutoring systems and automated feedback mechanisms. Analysis of his 15 most recent publications reveals a consistent trajectory toward applied AI solutions in business contexts, particularly in intelligent chatbots for educational support, financial trading algorithms, and cloud-based data infrastructure. The research demonstrates increasing integration of no-code platforms, real-time analytics, and domain-specific AI applications across logistics, banking, and procurement sectors. No scientific awards were documented in the source materials. Professor Ritz actively supervises academic development through bachelor’s and master’s theses, doctoral research, and collaborative projects. Current initiatives include the “Winfy” AI chatbot (v4.0, 2025), AI-based feedback systems for educational content (Freiraum 2025 grant), the frits intelligent tutoring project with Prof. Kammer, and doctoral research on AI adoption in SMEs. His work bridges theoretical research with practical implementation in SAP environments and cloud platforms. He operates within THM’s MNI department infrastructure, collaborating through the Business Informatics Working Group (AKWI) and contributing to the Digital Classroom communication platform for online education.

• Digital platforms
• Social media
• Public communication
• +۶ مورد دیگر

Thomas Poell is Senior Lecturer in the Department of Media Studies at the Faculty of Humanities, University of Amsterdam. His scholarly work sits at the intersection of digital media studies, platform studies, and political communication, with a focus on how digital platforms reshape public communication and cultural production. Poell's research interests primarily center on platformization processes and their societal implications. His influential work examines how digital platforms mediate public communication, with particular attention to protest movements, multilingual communication, and the transformation of cultural industries. He has made significant contributions to understanding the political economy of platforms, platform power dynamics, and the evolving relationship between platforms and public values. His methodological approach combines qualitative analysis of platform architectures with empirical studies of user practices across different cultural and linguistic contexts. Analysis of Poell's recent publications reveals a clear trajectory from studying social media's role in political movements to examining broader platformization processes. His work increasingly focuses on platform power across specialized domains including medical imaging, advertising ecosystems, and news industries. A consistent thread throughout his scholarship is examining how platform architectures shape communication practices and power relations, with growing attention to issues of visibility, governance, and equity in platformized environments. Poell has made significant contributions to the field through his co-authored book The Platform Society (2018), which established foundational concepts for understanding how digital platforms transform various sectors of society. He has also co-edited Global Cultures of Contestation (2017) and The Sage Handbook of Social Media (2018), demonstrating his leadership in synthesizing scholarly knowledge about digital media and platform dynamics. His influential article "Twitter as a multilingual space: The articulation of the Tunisian revolution" exemplifies his approach to studying platform-mediated communication during political upheavals. This work demonstrated how Twitter functioned as a global communication space during the Tunisian revolution, with different language communities articulating distinct accounts of the revolution while remaining interconnected through strategic language use by key activists.

Novi Quadriantoمشاهده پروفایل

استاد

• Machine Learning
• Responsible AI
• Algorithmic Fairness
• +۷ مورد دیگر

Novi Quadrianto is a Professor of Machine Learning at the School of Engineering and Informatics, University of Sussex, where he joined as a Lecturer in February 2014. He is currently a Principal Investigator on three active EU grants: BayesianGDPR (ERC), TANGO (EU Horizon RIA), and Act.AI (ERC Proof of Concept). He also holds an Adjunct Professor position in Data Science at Monash University, Indonesia, and serves as Strategic Lab co-Leader of the BCAM Severo Ochoa Strategic Lab on Trustworthy Machine Learning in Bilbao, Spain. His educational background includes a PhD in Machine Learning from the Australian National University (2012) and a BEng in Electrical and Electronics Engineering from Nanyang Technological University, Singapore. During his PhD, he conducted research at multiple international institutions including HIIT-Finland, Yahoo! Research-US, University of Alberta-Canada, Fraunhofer IAIS-Germany, and IST Austria. From 2012-2014, he was a Newton International Fellow of the Royal Society at the University of Cambridge. Professor Quadrianto directs the Predictive Analytics Lab (PAL) since 2017, which focuses on "Responsible AI" research developing AI models that embed fairness, accountability, transparency, and trustworthiness. His research spans algorithmic fairness, federated learning, and computer vision, with applications in sustainable development, healthcare, and finance. His work has been funded by prestigious organizations including the European Research Council, EPSRC, and HM Treasury. His publications reveal a strong focus on addressing challenges in AI fairness, robustness, and privacy, particularly in dynamic environments and heterogeneous data settings. Recent work explores performative prediction, diversity-driven learning, and efficient vision transformer inference, demonstrating his leadership in cutting-edge machine learning research. European Research Council ERC Proof of Concept Grant (2023) Guarantor Researcher for BCAM Severo Ochoa Excellence Accreditation (2023) European Lab for Learning and Intelligent Systems (ELLIS) Scholar/Fellow (2020) European Research Council ERC Starting Grant (2019) Newton International Fellowship (2012) Microsoft Research Asia Fellowship (2009) Professor Quadrianto currently supervises six PhD students and five postdoctoral researchers. He has served as Action Editor for Transactions on Machine Learning Research since 2022 and as Associate Editor for IEEE Transactions on Pattern Analysis and Machine Intelligence since 2016. He has also been an Area Chair for major conferences including NeurIPS, ICML, and AAAI. His PAL laboratory hosts a team of 15 members focused on inter-disciplinary AI research with domain experts across various sectors. The PAL Lab operates three innovation strands: AI for Sustainable Development (supporting UN SDGs), AI for Healthcare (transforming health outcomes), and AI for Finance (personalized loan decision-making). The lab also leads initiatives in Diversity & Inclusion in AI and offers Pro-Bono Office Hours to organizations seeking guidance on machine learning aspects.