جست‌وجوی استاد و پژوهشگر

Qing Changمشاهده پروفایل

استاد

• Smart Manufacturing
• Cyber-Physical Systems
• Real-time Production Control
• +۵ مورد دیگر

Qing (Cindy) Chang is a Professor in the Department of Mechanical Engineering at the University of Virginia. Her research focuses on cyber-physical systems for smart manufacturing, real-time production control, and human-robot collaboration. Prior to academia, she worked at General Motors, earning three Boss Kettering Awards for innovation. She holds an M.S. from the University of Wisconsin-Madison and a Ph.D. in Manufacturing from the University of Michigan. Education: M.S. in Mechanical Engineering, University of Wisconsin - Madison Ph.D. in Manufacturing, University of Michigan – Ann Arbor Research Interests: Cyber-Physical Systems for Smart Manufacturing Real-time Production Control Knowledge-guided Machine Learning-based Control Human-Robot Collaboration in Industrial Settings Intelligent Maintenance and Energy Management Awards: 20 most influential professors in smart manufacturing (2020) NSF CAREER Award (2014) General Motors Boss Kettering Awards (2005, 2006, 2008) GM R&D Charles L. McCuen Special Achievement Awards (2005, 2006, 2008) Leadership & Grants: She serves on the board of NAMRI/SME and holds editorial roles in ASME, IEEE, and SME journals. Her work bridges AI, robotics, and manufacturing systems, with notable grants including the NSF CAREER Award. Labs & Teams: Her Intelligent Systems Lab develops AI-driven solutions for manufacturing efficiency and sustainability, focusing on energy management, predictive analytics, and human-robot collaboration.

• Computer Science Education
• AI in Education
• AI Literacy
• +۵ مورد دیگر

Michael O'Dea is a Senior Lecturer in the Department of Computer Science at the University of York, United Kingdom. He has held previous academic positions at York St John University, Beijing University of Technology, University of Hull, and Waikato Institute of Technology, bringing extensive international experience in computer science education. He is actively engaged in pedagogical scholarship and leadership in higher education innovation. Senior Lecturer, Department of Computer Science, University of York Senior Lecturer in Computer Science, York St John University Lecturer in Software Engineering, Beijing University of Technology, China Lecturer in Computer Science, University of Hull Lecturer in Information Technology, Waikato Institute of Technology, NZ Dr. O'Dea earned his Ed.D. in Computer Based Learning from the University of Leeds. His research centers on the integration of artificial intelligence into educational practices, with a strong emphasis on AI literacy, the effectiveness of generative AI in teaching and learning, and the evolving landscape of technology acceptance in higher education. He investigates how AI tools can enhance student learning, faculty development, and institutional policy. His recent publications span topics such as AI literacy assessment, the future of online and blended learning, the application of machine learning in earthquake prediction, and international study abroad effectiveness. These works reflect a broad interdisciplinary approach, combining computer science, educational theory, and policy analysis. His scholarship is increasingly focused on the transformative potential of generative AI in academic settings, as evidenced by his leadership in special journal issues and funded research projects. Dr. O'Dea holds significant editorial responsibilities as Associate Editor and Lead Guest Editor for the Journal of University Teaching and Learning Practice and as Guest Editor for a special issue in Education Sciences on generative-AI-enhanced learning. He is also an Invited External Academic Affiliate at the King's Institute for Artificial Intelligence, King's College London. Associate Editor - Special Issues, Journal of University Teaching and Learning Practice Lead Guest Editor, Special Issue on Technology Acceptance Models, JUTLP (2024) Guest Editor, Special Issue on Generative-AI-Enhanced Learning, Education Sciences Principal Investigator, QAA Collaborative Enhancement Project on Graduate Attributes in the Era of GenAI (2025) He has delivered numerous invited talks and workshops at institutions such as the University of York, Queen Mary University of London, and international conferences including the Academy of Management and the International Conference on Artificial Intelligence in Education. His work bridges research, practice, and policy in higher education, with a strong commitment to inclusive and innovative teaching methodologies.

Rosella Gennariمشاهده پروفایل

دانشیار

• Human-Computer Interaction
• Interaction Design
• Technology-Enhanced Learning
• +۱۱ مورد دیگر

Rosella Gennari is an Associate Professor in Computer Science at the Faculty of Engineering, Free University of Bozen-Bolzano, where she conducts research and teaches in Human-Computer Interaction (HCI). Her work is centered on designing interactive technologies for children, focusing on physical-digital (phygital) artefacts, Technology-Enhanced Learning (TEL), and inclusive design. She leads the Research Unit Human-Centred Intelligent Systems and is actively involved in institutional leadership, including serving on the Third-Mission Board. Ph.D. : Computer Science, Amsterdam University (2002) Postdoctoral Experience : CWI, Amsterdam (ERCIM Alain Bensoussan Fellow); FBK-irst, Trento Leadership : Scientific & Technological Coordinator of the FP7-EU TERENCE project Her research explores how children interact with and design smart technologies, including IoT and AI, through playful and tangible interfaces. She investigates socio-emotional learning, digital well-being, and responsible design, often employing participatory and action research methods. Her work bridges computer science, education, and social impact, aiming to empower young learners as co-creators of technology. The analysis of her recent publications reveals a strong trend in developing and evaluating toolkits and frameworks for children and pre-teens to engage in designing smart things, IoT systems, and sustainable cities. Her work consistently emphasizes inclusivity, reflection, and responsible design, often in collaboration with teachers and learners. The publications span top HCI venues and journals, demonstrating a focus on practical applications in educational settings and the impact of technology on young users. Scientific Awards and Recognition ERCIM Alain Bensoussan Fellowship for talented young researchers Editorial Board Member, Journal of Child Computer Interaction (Elsevier, Q1) Regular reviewer for top HCI conferences and journals Advising and Grants : While specific advisees are not listed, her leadership role in the FP7-EU TERENCE project and numerous other research initiatives indicates extensive experience in securing and managing competitive grants. She mentors students through her research group and teaching, fostering the next generation of HCI researchers. Her collaborative network is extensive, with frequent co-authorship with researchers such as Alessandra Melonio, Maristella Matera, and Mehdi Rizvi. Labs and Teams : She leads the Human-Centred Intelligent Systems research unit, which serves as her primary lab and team. This group focuses on placing humans at the center of computer science and information engineering research. She is also a core member of the organizing committee for the MIS4TEL international conference series, highlighting her role in building and sustaining a global research community in Technology-Enhanced Learning.

Elina Rönnbergمشاهده پروفایل

استاد

• Discrete Optimisation
• Mathematical Optimisation
• Scheduling
• +۱۸ مورد دیگر

Elina Rönnberg is a Professor and Deputy Head of Department at the Department of Mathematics, Linköping University, where she leads research in discrete optimisation and intelligent decision-making. Her work bridges theoretical method development and real-world applications in sectors such as healthcare, aviation, mining, and transportation. She is actively involved in the Wallenberg AI, Autonomous Systems and Software Program (WASP) and has collaborated with industry leaders like Saab and Scania. Her research focuses on advanced optimisation techniques including Dantzig-Wolfe decomposition, Lagrangian relaxation, column generation, branch-and-price, and logic-based Benders decomposition. She also explores hybrid methods combining mathematical programming with constraint programming and machine learning. Applications span nurse rostering, electric vehicle routing, aircraft arrival scheduling, and underground mine planning. Recent publications highlight a strong trend toward integrating AI and machine learning—particularly graph neural networks—with classical optimisation frameworks to accelerate solution methods. Her work emphasizes practical impact, robustness, and scalability in solving complex scheduling and resource allocation problems. Nurse Rostering with Strategic Planning of Skills for Sick-Leave Robustness (2024) Pricing for the EVRPTW with Piecewise Linear Charging (2024) Speeding Up Logic-Based Benders Decomposition with Graph Neural Networks (2024) Elina supervises several PhD students and has co-supervised doctoral research at international institutions including Makarere University (Uganda) and the University of Exeter (UK). She has contributed to applied projects through student theses in collaboration with Scania and Saab, focusing on electric vehicle routing and search-and-rescue optimisation. She previously served as a Specialist in Optimisation at Saab Aeronautics (2014–2020) and co-founded Schemagi, a scheduling tool aimed at improving quality in healthcare. She teaches courses such as Introduction to Optimization (TAOP07) and Project - Applied Mathematics (TATA62). Her research group, 'Mathematics and algorithms for intelligent decision-making,' operates within the Division of Applied Mathematics (TIMA) at the Department of Mathematics. The team develops decision support tools that enhance efficiency and sustainability in complex systems, particularly under the growing demands of electrification and digitalisation in transport and logistics.

John Josephمشاهده پروفایل

استاد

• Organizational Design
• Artificial Intelligence and Organization
• Strategic Decision Making
• +۷ مورد دیگر

John Joseph is a Professor of Strategy and Entrepreneurship at the Paul Merage School of Business, University of California, Irvine. His research and teaching focus on organizational design, strategic decision-making, innovation, and the integration of artificial intelligence in business strategy. He is actively involved in editorial leadership as Senior Editor at Organization Science and former editor of the Journal of Organization Design . PhD, Kellogg School of Management, Northwestern University MBA, Wharton School, University of Pennsylvania John Joseph's research centers on how organizations can be designed to enhance innovation, strategic planning, and decision-making. His work explores the role of attention, feedback mechanisms, and AI in shaping strategic outcomes. He investigates organizational structures in technology and healthcare sectors, with a focus on platform ecosystems and community-driven innovation. His recent publications and research projects examine AI-enabled organizational transformation, mobile industry innovation, and healthcare system design. The body of work shows a strong trend toward behavioral strategy, integrating cognitive and structural perspectives to understand how firms adapt and grow. 2017 Ralph Gomory Award, Industry Studies Association John Joseph has advised numerous organizations including General Electric, Samsung Electronics, Molina Healthcare, and UC Irvine. He has received multiple teaching awards and has taught in full-time, part-time, and executive education programs at Kellogg, Duke, and UC Irvine. He serves as Chair of the Behavioral Strategy Interest Group of the Strategic Management Society. His research is supported by engagements with centers such as the Center for Health Care Management and Policy and the Beall Center for Innovation and Entrepreneurship at UCI.

• Underwater Acoustics
• Signal Processing
• Synthetic Aperture Sonar
• +۴ مورد دیگر

Alan Hunter is a Professor in Autonomous Systems at the University of Bath's Department of Mechanical Engineering. He serves as Deputy Head of Department for Workload and Wellbeing and is affiliated with the Water Innovation & Research Centre (WIRC) and the UKRI CDT in Accountable, Responsible and Transparent AI. His research focuses on underwater acoustics, signal processing, imaging, and machine intelligence, with applications in sonar-based remote sensing and marine robotics. Education: B.E. (Hons I) in Electrical and Electronic Engineering from the University of Canterbury (2001), PhD in Synthetic Aperture Sonar (SAS) from the same institution (2006). Career highlights include roles at the University of Bristol (2007-2010), TNO Netherlands (2010-2014), and NATO CMRE (2014). He has led projects on sub-sediment imaging, autonomous mine-hunting systems, and precision navigation algorithms. Research Interests: • Underwater Acoustics & Sonar Imaging • Autonomous Underwater Vehicles • Machine Learning for Acoustic Data Analysis • Non-Destructive Inspection via Ultrasound • Sustainable Coastal Protection (via UN SDG contributions) Active Projects (2023-2025+): - Noise Network Plus : Engineering a Quieter Future (EPSRC) - TESSMEX SR 4 : Naval Mine-Hunting Technology (Defence Lab) - Decision-Making with Ambiguities : Legal AI for Robotics (EPSRC) Professional Affiliations: • Senior Member, IEEE • Associate Editor, IEEE Journal of Oceanic Engineering • Collaborations with NATO, TNO, and UK Defence Orgs. Labs & Teams: • Robotics and Autonomous Systems Lab • Centre for Space, Atmospheric and Oceanic Science • WIRC @ Bath (Water Innovation Hub)

Daniel Collinsمشاهده پروفایل

دانشیار

• Composition Studies
• Writing Pedagogy
• Digital Humanities
• +۳ مورد دیگر

Daniel Collins serves as an Associate Professor in the English department at Guttman Community College, where he has taught for two years across the First-Year Program (Reading and Writing, Composition I) and Liberal Arts and Sciences Program of Study (Introduction to Humanistic and Philosophical Thought, Composition II, Capstone). His research focuses on composition studies and digital writing pedagogy , particularly the integration of generative AI tools in writing instruction. This is exemplified by his course Self-Representation in a Digital Age , which foregrounds AI-assisted writing practices and digital identity exploration. Collins actively contributes to CUNY-wide writing initiatives through membership in the Teaching Courses on the Commons group (264 members) and the CUNY Developmental and ESL Writing Collaboration (C-DEWC), where he collaborates on developmental writing best practices across the university system.

• Critical Care Medicine
• Intensive Care
• Patient Deterioration Monitoring
• +۶ مورد دیگر

Professor Peter Watkinson serves as Professor of Intensive Care Medicine at the University of Oxford and is an NHS consultant in intensive care at the Oxford University Hospitals NHS Foundation Trust. He leads the Critical Care Research Group based at the Kadoorie Centre for Critical Care Research & Education at the John Radcliffe Hospital, Oxford. His work bridges clinical practice with academic research in the field of critical care medicine through the Nuffield Department of Clinical Neurosciences. Professor Watkinson's research primarily focuses on the identification of deteriorating patients in hospital settings. His work encompasses: Design and implementation of studies on wearable monitoring devices Exploration of non-contact monitoring technologies Analysis of standard electronically-recorded patient descriptors Pattern recognition in vital signs data to predict clinical deterioration Development of electronic monitoring systems Application of human factors techniques for technology integration in healthcare Assessment of long-term effects of critical illnesses on patient quality of life The Critical Care Research Group maintains a strong collaborative link with the University of Oxford Institute of Biomedical Engineering. Using data collected from thousands of patients' vital signs both in Oxford and elsewhere, the multi-disciplinary team investigates patterns that precede and predict clinical deterioration in hospitalized patients. Recent publications indicate a strong focus on early warning scores, patient monitoring technologies, and the application of machine learning approaches to critical care data. Professor Watkinson's research output demonstrates consistent productivity with numerous 2024-2025 publications spanning systematic reviews of early warning systems, development of novel monitoring technologies, and analytical approaches to predicting patient deterioration. His work frequently employs rigorous methodology including systematic reviews, meta-analyses, and innovative study designs to address critical questions in intensive care medicine. As leader of the Critical Care Research Group, Professor Watkinson oversees a multi-disciplinary team investigating vital sign patterns and developing predictive algorithms that have direct clinical applications. The group's research has significant implications for improving patient safety through earlier recognition of clinical deterioration and more effective resource allocation in hospital settings.

Stuart Reevesمشاهده پروفایل

دانشیار

• Human-computer interaction
• Collaborative computing
• Design research
• +۴ مورد دیگر

Stuart Reeves is an Associate Professor at the School of Computer Science, University of Nottingham, UK. He is a member of the Mixed Reality Lab, Horizon research institute, Centre for Doctoral Training (CDT), and Social Interaction and Technology (SIT) Special Interest Group. Reeves serves as an elected member of the University of Nottingham Senate with his term extending until 2026. His academic career spans over two decades with significant contributions to human-computer interaction, particularly focusing on social and collaborative technologies in real-world contexts. Reeves' research interests primarily focus on human-computer interaction, collaborative computing, design research, and the application of ethnomethodology and conversation analysis (EMCA) to technology studies. His work examines how people interact with diverse interactive devices and systems in real-world situations and places, with particular attention to public spaces, video gaming contexts, and collaborative work environments. Reeves has developed significant expertise in understanding spectatorship within interactive spaces and the 'work' involved in technological engagements. His publication record demonstrates consistent high-impact contributions to the field, with recent research focusing on robots and AI technologies in action, particularly examining how autonomous robots interact with humans in public streets. Reeves has received multiple prestigious awards including the HRI 2024 Best Paper Award for his work on public robot encounters and previous Best Paper Awards at CHI 2005 and CHI 2008. HRI 2024 Best Paper Award for 'Encountering autonomous robots on public streets' CHI 2015 Honourable Mention Award CHI 2008 Best Paper Award CHI 2005 Best Paper Award CHI 2012 Honourable Mention Award CHI 2020 Best Paper Award Reeves has secured substantial research funding including an EPSRC Early Career Fellowship, multiple EPSRC grants, and international collaborations. His teaching responsibilities include undergraduate and postgraduate supervision across various computer science modules, with a focus on sensor-based systems and software design principles. He maintains an active presence in the research community through publications, conference participation, and his Medium page where he shares insights on research methodology and practice.

• Mathematical Optimization
• Algorithmic Intelligence
• Energy Systems Modeling
• +۵ مورد دیگر

Thorsten Koch serves as Head of the Department of Applied Algorithmic Intelligence Methods within the Division of Mathematical Algorithmic Intelligence at Zuse Institute Berlin (ZIB). His research spans mathematical optimization, energy systems modeling, quantum computing applications, and scientometrics. Koch leads significant research projects including FAN (focusing on AI in scholarly communication), UNSEEN (energy scenarios), HPO-NAVI (research software visibility), and Multi-Energy Models for European Energy System Planning. Koch's research interests center on developing advanced optimization algorithms for complex systems, particularly in energy networks and scientific data analysis. His work bridges theoretical mathematics with practical applications in gas network optimization, wind farm design, portfolio management, and quantum computing. He has pioneered methods for large-scale mixed-integer programming, scenario generation, and the integration of machine learning with traditional optimization techniques. His recent publications demonstrate growing emphasis on quantum optimization, scientometrics, and the application of AI to scientific communication infrastructure. His publication trends reveal a strategic expansion from traditional mathematical optimization into quantum computing applications and scientific data infrastructure. Recent work shows increasing collaboration across disciplines - connecting energy systems analysis with financial modeling, integrating machine learning with optimization solvers, and applying computational methods to scientometrics. The 15 most recent articles highlight three major thrusts: quantum optimization (33%), energy systems modeling (27%), and scientific data infrastructure (40%), reflecting his leadership in both theoretical algorithm development and practical implementation for societal challenges. Koch actively contributes to research infrastructure through leadership roles in projects like KOBV (Berlin-Brandenburg Cooperative Library Network), HDC (Humanities Data Centre), and CIB (future library networks). His work on the DeepGreen initiative focuses on establishing legally secure workflows for implementing open-access components in scientific publication licensing agreements, demonstrating his commitment to open science principles and research data management.

Jürgen Bernardمشاهده پروفایل

استادیار

• Visual Analytics
• Information Visualization
• Interactive Machine Learning
• +۱۲ مورد دیگر

Jürgen Bernard is an Assistant Professor of Computer Science at the University of Zurich , leading the Interactive Visual Data Analysis (IVDA) Group . He is associated with the Digital Society Initiative (DSI) and holds a PhD in Computer Science from Technische Universität Darmstadt (2015) with a focus on time-oriented data analysis. His academic journey includes postdoctoral research at TU Darmstadt and the University of British Columbia. Education : Diploma in Computer Science (2009, TU Darmstadt) PhD in Computer Science (2015, TU Darmstadt) Research Interests : Dr. Bernard specializes in interactive visual data analysis , explainable machine learning , and human-centered AI . His work explores time series analysis , multivariate data exploration , and user-driven preference elicitation . He develops visual analytics systems for domains like healthcare , digital humanities , and industrial applications , with a particular focus on responsible AI and transparency in algorithmic systems . Research Trends : His publications emphasize interactive machine learning workflows , visual analytics for healthcare , and time-stamped event sequence analysis . Recent work includes LLM validation frameworks (Human-Data-Model Interaction Canvas) and personalized ranking systems funded by the Swiss National Science Foundation. He integrates temporal data with multivariate analysis across applications from medical manufacturing to chronic disease management . Scientific Recognition : EuroGraphics Young Researcher Award (2022) EuroVis Young Researcher Award (2021) Best Paper Awards at IEEE VIS (2021), EuroVA (2021, 2025) Dirk Bartz Prize (2017), Hugo-Geiger Preis (2016) Teaching & Grants : He teaches Interactive Visual Data Analysis (6 ECTS), Digital Health Seminars , and People-Oriented Computing . Currently leads a SNF Grant on Personalized Visual Analytics for multi-criteria decision support (2024-2028) with ETH Zurich's Prof. M. El-Assady.

Chao Liuمشاهده پروفایل

پژوهشگر

• Surgical Robotics
• Teleoperation
• Haptics
• +۲ مورد دیگر

Chao Liu is a Research Scientist at CNRS (French National Center for Scientific Research) since 2008, affiliated with the DEXTER team and the Department of Robotics, LIRMM at University of Montpellier, France. He earned his Ph.D. in Electrical & Electronic Engineering from Nanyang Technological University, Singapore (2006). Current research focuses on surgical robotics , haptics , teleoperation , and nonlinear control theory with applications in computer vision. His work addresses challenges in robotic-assisted telesurgery, including: Stable and transparent human-robot interaction through wave variable compensators and passivity filters Physiological motion compensation using spatio-temporal LSTM and dual Kalman filters EMG-based motion recognition for surgical skill assessment 3D soft-tissue reconstruction with stereo-endoscopes and deep learning Dr. Liu leads European and French projects like: TS2RT (CNRS-funded): Safer teleoperation with motion compensation ROBACUS (ANR-funded): Needle positioning with MPC control HaTUMoCo (CNRS-funded): Haptic teleoperation with uncertainty handling ARAKNES (EU-funded): Microrobotic systems for endoluminal surgery Scientific honors include Senior Member of IEEE and Member of Sigma Xi . He supervises Ph.D. and Master's students working on topics such as concentric tube robot optimization, haptic teleoperation, and EMG-based force estimation. Dr. Liu serves on IEEE Technical Committees for Telerobotics and Haptics , and as Technical Editor of IEEE/ASME Transactions on Mechatronics.

Matthew Stephensonمشاهده پروفایل

مدرس

• Artificial Intelligence
• Machine Learning
• Data Science
• +۵ مورد دیگر

Matthew Stephenson is a Lecturer at Flinders University's College of Science and Engineering, specializing in Artificial Intelligence applications for games. He leads the Data for Decisions initiative within the Factory of the Future Transdisciplinary Hub, focusing on AI-powered scenario generation for smart digital twins. Additionally, he is a member of IRL CROSSING, an international lab studying human-autonomous agent teaming dynamics. PhD in Computer Science (Australian National University, 2019) B.Sc.(Hons) in Computer Science (University of Canterbury, 2015) His research applies AI, Machine Learning, and Data Science to game domains, including intelligent agent development for physics-based environments, procedural content generation, and game analytics. He also investigates deceptive behaviors in multi-agent systems and leverages games as testbeds for real-world AI solutions. Recent publications focus on large language models for game benchmarking, physical reasoning challenges, and evolutionary game generation. Scientific awards include an honourable mention at Foundations of Digital Games (FDG'18). He supervises students in procedural generation, game AI, and physics-based task creation, with teaching roles in computational intelligence and neural networks courses.

Raymond J. Mooneyمشاهده پروفایل

استاد

• Artificial Intelligence
• Bioinformatics and Computational Biology
• Machine Learning
• +۱۴ مورد دیگر

Raymond J. Mooney is a Professor in the Department of Computer Science at the University of Texas at Austin, where he has been a faculty member since 1987. He is the Director of the UT Artificial Intelligence Laboratory and affiliated with multiple research groups including the Machine Learning Research Group, UT Computational Linguistics Lab, and the UT Center for Computational Biology and Bioinformatics. He holds a B.S., M.S., and Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, where his thesis was supervised by Gerald DeJong. His research spans diverse areas in artificial intelligence, machine learning, and natural language processing: Natural Language Learning Connecting Language and Perception Statistical Relational Learning Information Extraction Transfer and Active Learning Abductive Reasoning Text Mining and Clustering Recommender Systems Knowledge-Base Refinement Recent publications highlight trends in grounded language processing, human-robot interaction, and multimodal reasoning. He has been recognized with prestigious fellowships including ACL (2014), ACM (2010), and AAAI (2005). Scientific awards: Fellow of the Association for Computational Linguistics (2014) Fellow of the Association for Computing Machinery (2010) Fellow of the American Association for Artificial Intelligence (2005) Classic Paper Award (2019) Best Paper Awards (2007, 2004, 1996) He teaches graduate courses like CS 371R: Information Retrieval and Web Search (Fall 2025) and CS 395T: Grounded Natural Language Processing (Spring 2025). His research labs include: UT Artificial Intelligence Laboratory Machine Learning Research Group UT Computational Linguistics Lab UT Center for Computational Biology and Bioinformatics

Alina Landowskaمشاهده پروفایل

استادیار

• Cultural Studies
• Discourse Analysis
• Cultural Evolution
• +۳ مورد دیگر

Alina Landowska serves as Assistant Professor in the Department of Cultural Studies at the Faculty of Humanities, SWPS University of Social Sciences and Humanities in Warsaw. Her academic profile integrates cultural studies, computational linguistics, and ethics, with significant contributions to understanding moral foundations in digital discourse and technological futures. Her educational background includes degrees in Management and Economics from Gdańsk University of Technology and European Integration from the Pontifical University of John Paul II in Krakow, complemented by international scholarship experiences: SDG Academy (United Nations) Tantur Ecumenical Institute, Jerusalem (University of Notre Dame) Baltic University Program, Uppsala Swedish Institute of Environmental Research, Kalmar Royal Danish Student Fund, Copenhagen Landowska's research investigates cultural evolution through computational discourse analysis, focusing on morality-technology intersections. She pioneers text-mining methodologies to examine moral foundations in social media, anticipatory rhetoric in digital communication, and value-based management frameworks. Her work bridges humanities with data science, analyzing polarization mechanisms and proleptic cues in online environments while exploring cooperation ethics in business contexts. Recent publications demonstrate methodological innovation in mapping technological futures through sentiment/emotion analysis and moral-value detection systems. Her article corpus reveals consistent thematic threads: digital rhetoric analysis (particularly prolepsis functions), moral psychology applications in AI/social media, and cultural evolution studies linking cooperation theory with business ethics. This interdisciplinary approach positions her at the nexus of computational social science and humanistic inquiry. No scientific awards or prizes are documented in her current professional profile. As an educator, Landowska mentors students in discourse analysis methodologies and socio-economic media studies while serving as executive coach with EMCC Poland. Her research leadership extends to co-founding the Institute for Development think tank and representing Employers of Poland at the OECD's Business and Industry Advisory Committee (2016-2018). She previously held vice-presidential roles in the Polish Association of Businesswomen. Landowska maintains active research affiliations with the Humanistic Management Center (University of Lucerne), International Council for Small Business (George Washington University), and ArgDiaP association. Her 2022-2024 tenure with New Ethos Lab advanced dialogue studies in persuasion frameworks, complementing her current work on digital rhetoric's ethical dimensions.