جستجوی استاد راهنمای دانشگاهی

Peter Hendersonمشاهده پروفایل

استادیار

• AI safety
• Foundation models
• Law and AI
• +۷ مورد دیگر

Peter Henderson is an Assistant Professor at Princeton University with joint appointments in the Department of Computer Science and the School of Public and International Affairs. He is affiliated with the Center for Information Technology Policy (CITP), Princeton Language and Intelligence Initiative (PLI), Center for Statistics and Machine Learning (CSML), and Program in Law & Public Policy (PLAW). J.D./Ph.D., Stanford University, 2023 M.Sc., McGill University and Montréal Institute for Learning Algorithms Henderson's research focuses on the critical intersection of artificial intelligence and law, with particular emphasis on AI safety, methods to improve reasoning in foundation models, interdisciplinary approaches to law and AI, and AI governance. His work spans language-grounded reinforcement learning, alignment techniques, strategic decision-making in legal contexts, and public interest artificial intelligence. He investigates how legal frameworks can guide the development of AI systems that benefit society while addressing potential harms. Henderson's recent publications reveal a strong trajectory toward addressing the safety, governance, and legal implications of foundation models. His work combines rigorous technical AI research with deep legal analysis, particularly examining how intellectual property law interacts with AI development, regulatory pathways that balance innovation with harm prevention, and the role of law in shaping responsible AI deployment. A significant thread throughout his research is the development of better evaluation methodologies for AI systems, especially in critical domains like law. SEAS Excellence in Teaching Award (2025) Henderson leads the Princeton Law+Language, AI, & Society (POLARIS) Lab, where he advises students and researchers working at the intersection of AI and legal studies. His research has been supported through collaborations with government agencies, including work with the IRS on audit selection algorithms. His findings have informed practical applications in government efficiency and equity, as well as legal system improvements. Henderson runs the POLARIS Lab at Princeton, which focuses on developing AI systems that work for the public interest, particularly in legal contexts. His team develops sequential decision-making systems for government efficiency, foundation models capable of reasoning about law, and improved safety evaluation frameworks for AI systems. The lab maintains strong connections with legal practitioners and policymakers to ensure research has real-world impact.

• Artificial Intelligence
• Machine Learning
• Knowledge Representation
• +۷ مورد دیگر

Dr. George Stamou is a Professor at the School of Electrical and Computer Engineering of the National Technical University of Athens (NTUA), serving as Director of the Artificial Intelligence and Learning Systems Laboratory (AILS). His expertise spans knowledge representation, machine learning, neural networks, and semantic technologies. He leads interdisciplinary initiatives such as the postgraduate program 'Data Science and Machine Learning' (2018–2022). Research Interests: Focuses on knowledge graphs, interpretable AI, semantic web applications, and multimodal learning. His work integrates formal logic systems (e.g., description logics) with modern deep learning techniques, addressing challenges in explainability, bias detection, and ethical AI applications. Publications: Over 150 articles in AI journals/conferences with an h-index of 34 (Google Scholar). Notable contributions include datasets like CHORDONOMICON (music analysis), GOSt-MT (gender bias in MT), and methodologies for counterfactual explanations in machine learning. Awards & Committees: Active in W3C and RuleML standardization bodies. Co-organized major AI conferences. Recognized for contributions to semantic interoperability and knowledge-based systems. Labs & Teams: Directs AILS-NTUA lab and collaborates with CISRI (Computer & Information Systems Research Institute). Engages in EU projects like CultureLabs (cultural heritage digitalization) andsmarty4covid (health data analysis).

Md Manjurul Ahsanمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Deep Learning
• +۷ مورد دیگر

Dr. Md Manjurul Ahsan serves as a Research Assistant Professor in the Department of Industrial & Systems Engineering at the University of Oklahoma, where he develops AI-driven solutions for healthcare diagnostics and advanced manufacturing optimization. His work bridges theoretical AI advancements with practical industrial and medical applications. Education: Ph.D. in Industrial and Systems Engineering, University of Oklahoma M.S. in Industrial Engineering, Lamar University B.S. in Industrial and Production Engineering, Shahjalal University of Science and Technology Research Focus: Dr. Ahsan specializes in Artificial Intelligence with technical depth in Machine Learning , Deep Learning , and Computer Vision to solve critical challenges in healthcare diagnostics and additive manufacturing . His research emphasizes Explainable AI to enhance model trustworthiness and deployment efficiency across Cyber-Physical-Social Systems, with significant contributions to Aerospace and Defense applications. Publication Trends: Recent work (2023-2025) reveals a strategic expansion from core manufacturing applications into medical AI (diffusion models for diagnostics), cultural preservation (NLP for Dravidian languages), and geopolitical AI analysis. His publications consistently address data imbalance challenges while advancing digital twin integration in quality control systems. Scientific Recognition: GCOE Dissertation Excellence Award (2023) International Student Scholarship (2022) Outstanding Academic Achievement in Engineering (2022) IEEE IEMCON Best Paper Award (2020) Netti Vincent Boggs Engineering Excellence Award (2020) Research Leadership: As director of the Sooner Additive Manufacturing Laboratory , Dr. Ahsan leads cross-disciplinary teams developing real-time monitoring systems using FARO arms and CMM metrology. His postdoctoral work at Northwestern University (2023-2024) advanced AI deployment frameworks, resulting in 60+ peer-reviewed publications with multiple papers ranking in engineering's top 1% for citations.

Sungsoo Ray Hongمشاهده پروفایل

استادیار

• Human-Computer Interaction
• Computer-Supported Cooperative Work
• Data Science
• +۵ مورد دیگر

Sungsoo Ray Hong is an Assistant Professor at George Mason University's Department of Information Sciences and Technology, directing the Alignment Lab (A-lab). His research focuses on bridging human mental models with AI systems through Human-Computer Interaction (HCI) and Computer-Supported Cooperative Work (CSCW). Dr. Hong earned his PhD in Human Centered Design and Engineering at the University of Washington. Research Domains: Human-AI Collaboration, Interactive Data Annotation, Steerable Deep Neural Networks, and AI-Driven Applications Key Grants: NSF Future of Work at the Human-Technology Frontier (2021-2024, $160K) Email: shong31@gmu.edu Hong's research explores how humans interact with machine learning systems to improve decision-making and task productivity. His lab develops tools for collaborative data annotation, interpretable model building, and AI-augmented creativity in professional contexts. Recent work includes applications for neurodiverse workers and comic professionals. The 15 most recent publications show strong focus on Human-AI collaboration (7 articles), interactive systems design (11 articles), and accessibility applications (5 articles). Topics span from explainable AI frameworks to collaborative annotation interfaces and creative industry tools. Dr. Hong maintains active collaborations with KAIST researchers, including Dr. Jaegul Choo and Dr. Juho Kim. His lab offers remote internships and accepts PhD students for summer positions.

• Artificial Intelligence
• Reinforcement Learning
• Large Language Models
• +۳ مورد دیگر

Dr. Meng Fang is a researcher specializing in Artificial Intelligence with a focus on Reinforcement Learning, Large Language Models, and their applications in medical QA, game theory, and causal inference. Their work combines technical innovation with practical problem-solving in safety-critical and domain-specific contexts. Key research areas: Social bias in AI, data augmentation, embodied agents, and model-based reinforcement learning Teaching: Coordinated module COMP532 - Machine Learning and BioInspired Optimisation (2024-25). Recent publications address challenges in offline RL robustness, vision-based safe reinforcement learning, and strategic game generalization.

Yu Huangمشاهده پروفایل

استادیار

• Software Engineering
• Human Factors
• AI for Software Engineering
• +۷ مورد دیگر

Dr. Yu Huang is an Assistant Professor in the Department of Computer Science at Vanderbilt University's School of Engineering, with a secondary appointment in the Department of Teaching and Learning at the Peabody School of Education. She is affiliated with the Institute for Software Integrated Systems, the Frist Center for Autism and Innovation, the Vanderbilt Lab for Immersive AI Translation (VALIANT), and the Vanderbilt LIVE Learning Innovation Incubator. Her academic journey began with a BS in Aerospace Engineering from Harbin Institute of Technology in China (2011), followed by an MS in Computer Engineering from the University of Virginia (2015), and culminated with a PhD in Computer Science and Engineering from the University of Michigan in 2021 under Professor Westley Weimer. Dr. Huang's research bridges human cognition and machine intelligence to enhance software development. Her work spans software, hardware, AI, medical imaging (fMRI/fNIRS), eye tracking, and mobile sensing through collaborations with Security, Education, Psychology, and Neuroscience researchers. She leads the MIND Lab (Mixed INtelligence Development for programming lab), investigating programming expertise formation, code comprehension processes, cognitive error patterns, and diversity in programming communities. Her innovative approach combines empirical human studies with AI model development to create more effective programming tools. Her recent publications reveal a growing emphasis on leveraging human attention data to improve code language models, analyzing cognitive biases in security contexts, and examining social factors in technical communication. The research shows strong interdisciplinary connections between neuroscience, psychology, and software engineering, with increasing applications of LLMs in developer tooling. Dr. Huang's work consistently demonstrates how understanding human cognition can inform better AI systems for programming tasks. Dr. Huang has received numerous prestigious recognitions including the 2025 ICPC Vaclav Rajlich Early Career Achievement Award and three ACM SIGSOFT Distinguished Paper Awards (ICSE 2019, FSE 2023, ICSE 2024). Her lab has earned the Best Presentation Award at GI2024, while her students have received the Richard Bennett/Dorothy Danforth Compton Prize scholarship and the C. F. Chen Best Paper award. She actively mentors a diverse team of graduate students (Yifan Zhang, Zach Karas, Zihan Fang, Yueke Zhang, Jiahao Zhang) and undergraduate researchers, with many former students advancing to top institutions (Stanford, Harvard, Duke, UC Berkeley) and organizations (NASA JPL). Her research is supported by a 4-year NSF grant, GitHub Tech for Social Good funding, and the Provost's Faculty Immersion Vanderbilt Grant, enabling comprehensive studies of human-AI collaboration in software engineering. The MIND Lab maintains a strong collaborative culture, frequently working with Professor Kevin Leach's research group and organizing retreats to locations like Radnor State Park and the Great Smoky Mountains. This environment fosters innovation at the intersection of human cognition and software engineering while supporting the professional development of emerging researchers in the field.

• Artificial Intelligence
• Cognitive Science
• Human-Agent Interaction
• +۵ مورد دیگر

Prof. Dr.-Ing. Stefan Kopp is a faculty member at Bielefeld University's Faculty of Engineering and serves as Research Group Leader of the Cognitive Systems and Social Interaction Group . He also holds administrative roles as Vice Dean and Deputy CITEC Coordinator . His work focuses on Artificial Intelligence , Cognitive Systems , and Socio-Technical World research areas. Research Group Leader: Cognitive Systems and Social Interaction Group Vice Dean: Faculty of Engineering Deputy Coordinator: Center for Cognitive Interaction Technology (CITEC) Project Manager: TRR 318 "Constructing Explainability" subprojects His research explores human-agent interaction , multimodal conversational agents , and social AI through projects like 39-Inf-11 Human-Machine Interaction and 39-M-Inf-VKI Virtual Humans and Conversational Agents . Publications address topics including adaptive explanation generation , gesture synthesis , and social cognition in dynamic environments. Current research topics span cooperative AI , explainable decision-making , and sensorimotor grounding in artificial systems.

Charlotte Hanlonمشاهده پروفایل

استاد

• Global Mental Health
• Implementation Science
• Public Mental Health
• +۷ مورد دیگر

Professor Charlotte Hanlon serves as Co-Director of the Centre for Global Mental Health and co-director of the World Health Organization Collaborating Centre for Mental Health Research and Training at King's College London's Institute of Psychiatry, Psychology & Neuroscience. Based in Ethiopia, she has maintained a continuous research and implementation presence in the country since her PhD work from 2004-2006 on perinatal mental health in rural settings, returning permanently in 2009. Her research expertise spans global mental health implementation science, with particular focus on developing, testing and implementing interventions for people with mental health conditions in low- and middle-income countries. Professor Hanlon has significant experience with psychological interventions, social/psychosocial approaches, service models, and system strengthening, with special attention to severe mental health conditions including psychoses and developmental disabilities. Her work consistently incorporates capacity strengthening as an integral component. She coordinates the PhD program for Mental Health Epidemiology at Addis Ababa University and serves on Ethiopia's technical working group for mental health scale-up. Analysis of her recent publications reveals a strong emphasis on context-specific implementation research, particularly in Ethiopia, with growing attention to developmental disabilities, caregiver support, and inclusive education. Her work bridges clinical interventions with health systems strengthening, demonstrating how context shapes effective mental health service delivery in resource-limited settings. The publications show increasing collaboration with African institutions and a focus on sustainable, locally adapted solutions. Commissioner on the Lancet Commission on Ending Stigma and Discrimination in Mental Health Co-lead (with Professor Ashok Malla) of the interventions, services and systems section of the Lancet Psychiatry Commission on Psychoses in Global Context Professor Hanlon leads multiple major research initiatives including HOPE (NIHR global health research group on homelessness and severe mental illness), SCOPE (Wellcome Trust-funded study of psychosis context in Ethiopia), PROMISE (improving detection and care for psychosis in Malawi), PRIZE (peer support intervention for psychosis in South Africa), and SPARK (WHO caregiver skills training for developmental disabilities). Her research approach emphasizes participatory methods, cultural adaptation, and building local research capacity across multiple African countries. She works closely with the Centre for Global Mental Health, which aims to address inequities by closing the care gap and reducing human rights abuses experienced by people living with mental, neurological and substance use conditions in low-resource settings. Her projects often involve collaboration between King's College London, Addis Ababa University, and other African institutions to develop sustainable mental health solutions.

Nikos Aletrasمشاهده پروفایل

استاد

• Natural Language Processing
• Machine Learning
• Computational Social Science
• +۲ مورد دیگر

Nikos Aletras is a Professor of Natural Language Processing at the University of Sheffield's School of Computer Science, where he serves as Head of the Natural Language Processing research group and is co-affiliated with the Machine Learning group. His academic journey began with a Bachelor's degree in Computer Science from the University of Crete, followed by a PhD in Natural Language Processing at the University of Sheffield. Prior to his current position, he worked as a research scientist at Amazon (Core ML and Alexa) and as a research associate at UCL's Department of Computer Science. Aletras' research spans multiple domains within AI, with particular emphasis on Natural Language Processing applications across social science, legal contexts, and data science. His work demonstrates a consistent focus on practical implementations of NLP techniques to solve real-world problems, especially in computational social science and legal technology. He has developed innovative text analysis methods that bridge traditional disciplinary boundaries, creating tools applicable across multiple scientific domains. His recent publications reveal a strong trend toward efficient and responsible AI, with significant work on model compression, hallucination mitigation in language models, and ethical considerations in computational social science research. The publications also show deep engagement with multilingual NLP challenges, explainable AI, and applications of NLP to social media analysis and legal contexts. Area Chair Award: Society and NLP (2023) Aletras has secured substantial research funding as both Principal Investigator and Co-Principal Investigator, including grants from EPSRC, ESRC, Leverhulme, EC Horizon 2020, and industrial partners like Amazon. His current projects focus on efficient deployment of large language models, addressing socio-technical limitations of LLMs for medical and social computing, and developing speech and language technologies. He actively supervises PhD students and collaborates with researchers across multiple disciplines. He leads the Natural Language Processing research group at Sheffield, which focuses on advancing NLP methodologies while applying them to diverse domains including computational social science, legal informatics, and healthcare technologies. The group maintains strong industry connections, particularly with technology companies working on language technologies, and collaborates with legal scholars and social scientists on interdisciplinary projects.

Liang-Yuan 'Leo' Wuمشاهده پروفایل

پژوهشگر

• Human-Computer Interaction
• Artificial Intelligence
• Sound Accessibility
• +۷ مورد دیگر

Liang-Yuan 'Leo' Wu is a Researcher at the University of Michigan's Computer Science and Engineering department, working with Prof. Dhruv 'DJ' Jain in the Soundability Lab at the AI Laboratory. He recently completed his Master's degree in Computer Science & Engineering at the University of Michigan. His educational background includes: Master of Science in Computer Science & Engineering, University of Michigan (2022-Present) University of Edinburgh (2021) Bachelor's degree, National Taiwan University (2017-2021) Wu's research centers on human-centered AI solutions for auditory accessibility, with deep collaboration with the Deaf and Hard of Hearing (DHH) community. He develops technologies that leverage multimodal AI and large language models to interpret soundscapes, generate personalized audio descriptions, and enhance captioning systems—particularly in challenging environments like clinical settings where communication accuracy is critical. His work bridges technical innovation with real-world user needs through mixed-methods UX research. His publication trajectory reveals a strategic focus on applying cutting-edge AI models to solve accessibility gaps in sound interpretation and captioning, with increasing emphasis on healthcare applications and community-driven design principles. This represents a significant shift toward context-aware, deployable accessibility tools rather than theoretical frameworks. Wu's research impact is recognized through: BEST POSTER AWARD at ASSETS 2024 for CARTGPT Google Academic Research Award for 'Audio Scene Understanding' proposal While not yet mentoring formal advisees, Wu secures competitive research funding through awards like Google's Academic Research Award and actively collaborates with interdisciplinary teams across HCI, AI, and accessibility domains. His work in the Soundability Lab emphasizes community co-creation with DHH individuals to ensure technologies address authentic user needs rather than theoretical scenarios. The Soundability Lab serves as Wu's primary research environment, focusing on making sound universally accessible through AI-driven innovation. The lab maintains direct partnerships with the DHH community throughout the research lifecycle—from problem identification to solution validation—ensuring technologies are both technically robust and socially impactful.

Gudrun Kiesmuellerمشاهده پروفایل

استاد

• Inventory Management
• After Sales Service
• Design and Analysis of Manufacturing Systems
• +۴ مورد دیگر

Prof. Dr. Dr. h.c. Gudrun Kiesmüller serves as Full Professor holding the Chair for Operations Management at TUM School of Management, Technical University of Munich, based at Campus Heilbronn since July 2019. She concurrently holds the position of Hedda Andersson Guest Professor at Lund University's Department of Industrial Management & Logistics since January 2021. Previously, she held professorial positions at Otto-von-Guericke-University Magdeburg (2013-2019), Christian-Albrechts-University zu Kiel (2010-2013), and Technical University Eindhoven (2002-2009). Her research program focuses on Operations Management with particular emphasis on the implications of digitization in Industry 4.0, especially in after-sales services. She develops analytical methods to optimize processes across manufacturing and supply chains. Her work spans three interconnected domains: stochastic manufacturing systems design (examining buffer sizing and spare parts planning), safety stock optimization under uncertain demand and supply conditions, and maintenance-reliability integration. She investigates how digitization transforms traditional operations, with growing emphasis on AI applications for supply chain optimization and inventory planning. Prof. Kiesmüller's extensive publication record reveals consistent contributions to operations research methodology with practical business applications. Her work demonstrates increasing integration of data-driven approaches, particularly in the most recent publications which explore AI applications for supply chain optimization. The research shows progression from theoretical inventory models toward more complex, integrated systems that consider multiple uncertainties simultaneously, reflecting the growing complexity of modern supply chains. Her professional recognition includes: 2022 Service Award from the International Society for Inventory Research Multiple Outstanding Reviewer Awards from OR Spectrum (2017, 2020) EURO Best Paper Award (2014) for influential review on lateral transshipments Multiple teaching awards recognizing excellence in both bachelor and master level instruction At TUM, Prof. Kiesmüller teaches a comprehensive curriculum in Operations Management, emphasizing both theoretical foundations and practical applications. Her courses equip students with skills to analyze supply chain planning problems, apply quantitative models, and solve complex operational challenges. She maintains an active research group investigating how digitization transforms operations management practices, particularly in after-sales service contexts where Industry 4.0 technologies enable new optimization possibilities.

Steven P. Dowمشاهده پروفایل

استاد

• Social Computing
• Human-Computer Interaction
• Collective Intelligence
• +۴ مورد دیگر

Steven P. Dow is a Professor at the University of California San Diego leading the ProtoLab, which investigates collective intelligence, creativity, and decision-making through social computing and human-centered design. His work develops systems leveraging LLMs, crowdsourcing, and visualization to scaffold problem-solving for individuals and communities. His research spans Social Computing , Human-Computer Interaction , and Participatory Design , focusing on cognitive/social aspects of collaborative processes. Key areas include AI-augmented creativity tools, value-centered civic engagement frameworks, and metadata-driven collective ideation systems that bridge technical and cultural dimensions of design. 2025 publications reveal a strong trend toward LLM-crowd integration for creative workflows, with work appearing at UIST and Collective Intelligence conferences. Research emphasizes inclusive idea convergence in civic contexts and dynamic exploration in design processes using forward-backward propagation techniques. Prof. Dow actively mentors students, with advisees like Sangho Suh and Nancy Qiu leading publications on AI storyboarding and self-reflective crowds. His ProtoLab drives projects including community-driven design environments and context-aware exploratory search systems. The ProtoLab serves as an interdisciplinary hub developing socio-technical systems for community co-design, leveraging ubiquitous devices and mixed-initiative interfaces to transform how teams solve complex problems through emergent scaffolding and value-centered frameworks.

Remco M. Dijkmanمشاهده پروفایل

استاد

• Business Process Management
• Data-Driven Process Optimization
• Supply Chain Management
• +۳ مورد دیگر

Remco M. Dijkman serves as Full Professor in Information Systems at Eindhoven University of Technology (TU/e), chairing the Information Systems group within the Industrial Engineering and Innovation Sciences school. He additionally holds a Full Professor position at EAISI High Tech Systems and acts as research director for high-tech supply chains at the European Supply Chain Forum—a network of over 50 multinational companies. His research centers on Business Process Management with emphasis on data-driven optimization of business processes. His academic background includes both PhD and Master's degrees in Computer Science from the University of Twente. Publications span Information Systems, Computers in Industry, and Transactions on Software Engineering and Methodology, with over 100 papers and service on the editorial board of Information Systems. He has held visiting positions at New York University, Hasso Plattner Institute, IBM Zurich Research Lab, Humboldt-University Berlin, and Queensland University of Technology. Dijkman's research interests focus on detecting, diagnosing, and predicting optimal execution scenarios in business processes, developing mathematical models for quantitative process analysis , and resource assignment optimization . These are primarily applied in transportation logistics and high-tech supply chains, where he investigates data-driven predictions for transport order assignment and supply chain planning. His work bridges artificial intelligence with practical business applications. Recent publications (2024-2025) reveal concentrated efforts in deep reinforcement learning for resource allocation, process pattern discovery, and software library development (GymPN, SimPN). Key trends include predictive process monitoring for healthcare applications, event data enrichment frameworks, and uncertainty handling in logistics planning—demonstrating strong interdisciplinary integration. Scientific recognition includes: Best Demo Award (2019) Best Reviewer Award (2016) Test of Time Award (2019) He has supervised 150 students, including Lotte Vugs who received the Dow Chemical Best OML Master Thesis Award in 2020. Grant leadership spans eight projects: NXTGEN Smart Industry (2023-2030), CollChain (2023-2029), CERTIF-AI (2020-2025), FENIX (2019-2023), and DynaPlex (2021-2024), focusing on digital twins, federated networks, and AI-driven supply chain solutions. Dijkman directs the Information Systems group at TU/e and leads the European Supply Chain Forum's high-tech supply chain research. His work integrates with semiconductor manufacturing and transportation logistics through collaborations with industry partners, while his 2023 invited talks at Technical University of Munich and Humboldt University Berlin highlight his international engagement.

• K-12 Computing Education
• Social Justice in Computing
• Physical Computing
• +۳ مورد دیگر

Gayithri Jayathirtha is an Assistant Professor in the College of Education at the University of Illinois at Urbana-Champaign, specializing in Curriculum & Instruction. Her research focuses on integrating social justice into K-12 computing education through electronic textiles and pedagogical frameworks. She holds a Ph.D. and Master's in Learning Sciences from the University of Pennsylvania, along with a B.S. in Computer Science Engineering from Bangalore University, India. Research Interests: Jayathirtha explores how electronic textiles can bridge technical and societal dimensions in computing education. Her work emphasizes critical engagement, equity, and teacher roles in reshaping curricula to include sociopolitical implications of technology. Key areas include computational thinking assessment, collaborative debugging, and justice-centered learning. Publications: Since 2017, she has published extensively in ACM and IEEE conferences, focusing on topics like teacher identity in social justice, student problem-solving with e-textiles, and portfolio assessments for computational thinking. Notable works include redesigning introductory computing programs to address colonialism and bias, as well as frameworks for politicized trust in classrooms. Scientific Contributions: Jayathirtha has co-authored studies on bugs as catalysts for peer collaboration, the invisibility of everyday computing systems, and strategies for equitable K-12 computing education. Her research often involves partnerships with educators to develop and test innovative teaching methods. Professional Impact: Through her publications and presentations at RESPECT, SIGCSE, and ICLS conferences, Jayathirtha advocates for expanding computing education beyond technical skills to include societal implications. Her work provides practical tools for teachers to integrate critical perspectives into curricula.

Jan Christof Reckerمشاهده پروفایل

استاد

• Digital Innovation
• Digital Transformation
• Digital Entrepreneurship
• +۳ مورد دیگر

Jan Christof Recker is Nucleus Professor and holder of the chair for Information Systems and Digital Innovation at the University of Hamburg Business School, funded through the Excellence Strategy of the Federal and State Governments. He also holds adjunct professor positions at the University of Agder (Kristiansand, Norway) since 2022 and at the QUT Business School (Brisbane, Australia) since 2018, and has previously served as Professor for Information Systems and Systems Development at the University of Cologne (2018-2021) and Full Professor for Digital Innovation at QUT Business School. Dr. Recker holds bachelor's and master's degrees in information systems from the University of Münster and a PhD in Information Systems from the Queensland University of Technology. His educational background forms the foundation for his expertise in bridging technical and organizational aspects of digital transformation. His research focuses on how organizations deal with digital innovation, digital transformation, and digital entrepreneurship. As a field researcher, he has collaborated with large organizations including Woolworths, SAP, Hilti, Commonwealth Bank, Federal Police, Lufthansa, and Ubisoft, as well as various startups. Dr. Recker employs quantitative, qualitative, and mixed field methods in his research and is also competent in design research. His current research interests include technology analysis and design in the digital age, digital entrepreneurship and new venture creation, digital innovation and transformation in large organizations, digitalization of products, services, and processes, and digital solutions for sustainable development. His work aligns with multiple UN Sustainable Development Goals including Industry, Innovation and Infrastructure (SDG 9), Responsible Consumption and Production (SDG 12), and Reduced Inequalities (SDG 10). Dr. Recker's recent publications demonstrate a sophisticated exploration of the intersection between physical and digital experiences, generative AI development methodologies, societal impacts of crises on business growth patterns, and responsible digital innovation frameworks. His work spans multiple high-impact journals across information systems, management science, and entrepreneurship disciplines, consistently addressing practical organizational challenges with theoretically grounded approaches. His notable awards and honors include: AIS Fellow Award (2018) Outstanding Associate Editor Award by MIS Quarterly (2019) SIGGreen Best Paper Award (2021) Best Paper Award by the Journal of Information Technology Theory and Application (2014) Vice Chancellor's Award for Excellence (2014) Dr. Recker has secured significant research funding for projects related to digital innovation and transformation, including the HIVESOUND start-up scholarship (2024-2025) and research on how digital products are created through hardware-software interactions (2022-2024). As Editor-in-Chief for Communications of the Association for Information Systems (2015-2020) and current Senior Editor for the MIS Quarterly, he has shaped scholarly discourse in the field. His supervision of doctoral students has been recognized with multiple awards, reflecting his commitment to developing the next generation of information systems scholars. His Management Transfer Lab at the University of Hamburg serves as a nexus for academic-industry collaboration, focusing on translating theoretical insights into practical solutions for digital transformation challenges. The lab maintains strong partnerships with major corporations and startups alike, ensuring research relevance while providing students with real-world experience in digital innovation contexts.