جستجوی استاد راهنمای دانشگاهی

Maura R. Grossmanمشاهده پروفایل

استاد پژوهش

• High-Recall Information Retrieval
• AI Ethics
• Electronic Discovery
• +۳ مورد دیگر

Maura R. Grossman is a Research Professor at the University of Waterloo, specializing in High-Recall Information Retrieval, AI ethics, and legal technology. Her work focuses on ensuring comprehensive information retrieval in high-stakes contexts like electronic discovery in law, healthcare data curation, and medical evidence synthesis. She explores the intersection of artificial intelligence and legal systems, particularly addressing challenges posed by AI-generated evidence and deepfakes in judicial processes. Education: J.D. (Georgetown University Law Center, 1999), Ph.D. (Adelphi University, 1984), M.A. (Adelphi University, 1982), A.B. (Brown University, 1980). Research interests include AI accountability in courts, responsible data science practices, and improving electronic discovery methodologies. Recent work analyzes AI’s role in legal proceedings, ethical AI frameworks, and healthcare data governance. Her publications emphasize validating technology-assisted review (TAR) systems and evaluating generative AI impacts on marginalized communities. Her articles highlight trends in AI’s legal implications, healthcare data sharing protocols, and the need for transparent algorithmic systems in justice contexts. She contributes to TREC tracks, advancing high-recall retrieval techniques for legal and medical document analysis. Notable projects include developing frameworks for unbiased health data sharing and analyzing AI’s effects on marginalized writers. Her work underscores interdisciplinary collaboration between law, computer science, and healthcare to address emerging technological challenges.

Stephan Schlöglمشاهده پروفایل

استاد

• Human-Computer Interaction (HCI)
• Artificial Intelligence
• Interaction Design
• +۶ مورد دیگر

Stephan Schlögl is a Full Professor at MCI - The Entrepreneurial School in Austria, leading research and teaching in Human-Computer Interaction (HCI), Artificial Intelligence (AI), and Information Systems. He holds editorial roles at journals like the Springer Discover Artificial Intelligence Journal and MDPI Multimodal Technologies and Interaction Journal. His work spans over two decades, with key roles including Postdoctoral Research Fellow at Télécom ParisTech (2012–2013) and PhD Researcher at Trinity College Dublin (2008–2012). Schlögl’s research focuses on HCI, AI-driven conversational systems, and assistive technologies for aging populations. Education includes a PhD in Computer Science from Trinity College Dublin, an MSc in Human-Computer Interaction from University College London, and a Mag.(FH) in Applied Informatics & Management from MCI. His teaching spans Software Engineering, Business Intelligence, and Research Methods. Research interests emphasize natural language interfaces, AI ethics, and technology’s societal impact. Notable projects include the EU-funded EMPATHIC initiative (2017–2021), developing an empathic virtual coach for elderly care, and the CRYSTAL project (2024–present) on conversational systems for emotional support. Schlögl has supervised numerous theses on AI applications, chatbots, and UX design. He co-organized major conferences like CUI (Conversational User Interfaces) and received awards for best papers in AI-HCI and CHIRA. His work bridges academia and industry, addressing challenges in AI adoption, digital well-being, and ethical technology design.

Naeha Sharifمشاهده پروفایل

مدرس

• Computer Vision
• Natural Language Processing
• Medical Image Analysis
• +۵ مورد دیگر

Dr. Naeha Sharif is a Lecturer in Computer Science & Software Engineering at The University of Western Australia (UWA), specializing in Computer Vision (CV) and Natural Language Processing (NLP). She holds a PhD from UWA (2021) focused on AI-driven image captioning and received the prestigious ACS 1962 Medal for outstanding IT/CS research. Her work bridges AI with healthcare, including automated medical assessment via spine scans (DXA) for abdominal aortic calcification analysis and AI-driven medical report generation for fundus imaging. Education: PhD in Computer Science (UWA 2021), Master of Engineering in Biomedical Engineering (Kyung Hee University, 2013). Previous roles include Postdoctoral Fellow at Edith Cowan University (2021–2022). Research Interests: Vision-language models, medical image analysis, captioning metrics, multimodal learning, and AI applications in healthcare. Active in UN SDGs related to health and innovation. Teaching: Unit Coordinator for Computer Graphics & Animation (CITS3003, 2022–2023) Computational Thinking with Python (CITS1401, 2023) Grants: Lead investigator on the 'Generative AI for Screening of Depressive Disorders' project (2024). Awards: Recognized for student learning contributions (2025), best paper awards (2024/2020), and academic excellence honors (2021/2020). Labs/Teams: Member of UWA Natural & Technical Language Processing Group and IEEE Signal Processing Society.

• Natural Language Processing (NLP)
• Text Generation
• Computational Linguistics
• +۳ مورد دیگر

Robert Dale is an Honorary Professor at the School of Computing, Macquarie University. His research focuses on Natural Language Processing (NLP), with significant contributions to automated writing assistance, legal technology applications, and NLP commercialization. He has led projects such as 'Beyond the Grammar Checker: Automated Copy-Editing Assistance' (2013–2019) and 'Natural Language Generation for Aboriginal Languages' (2010–2016). His work spans computational linguistics, text generation, and legal sector applications of NLP. Key research interests include NLP systems, legal tech integration, and industry adoption trends. Recent publications analyze the landscape of automated writing tools, NLP APIs, and commercialization strategies. His projects emphasize interdisciplinary collaboration, addressing challenges in language technology and real-world applications. Robert has advised on multiple research initiatives, including ARC-funded projects, and collaborates internationally on NLP applications. He has contributed to over 159 research outputs since 1990, with a focus on advancing NLP's practical utility across sectors.

Peter Buxmannمشاهده پروفایل

استاد

• Artificial Intelligence
• Digital Transformation
• Business Informatics
• +۴ مورد دیگر

Prof. Dr. Peter Buxmann is a full professor of business informatics at the Technical University of Darmstadt, holding the Chair in this field. He is an internationally renowned speaker, senior business advisor, and podcaster for the Frankfurter Allgemeine Zeitung. His roles include advisory board memberships for institutions like the Weizenbaum Institute for the Networked Society and Eckelmann AG. His research focuses on AI applications, digital transformation, and future work paradigms, with over 300 publications in top journals and conferences. Education: PhD and habilitation from University of Frankfurt, including a research stay at UC Berkeley's Haas School of Business. Previous positions: Professor at Technical University of Freiberg (2000-2004). Research Interests: Artificial Intelligence (applications in business, healthcare, and ethics), Digital Transformation (organizational and societal impacts), Data-Driven Business Models (platform economics, innovation ecosystems), and Future of Work (human-robot collaboration, agile principles). Keynote Topics: Generative AI and its industry applications Strategic digital transformation frameworks Ethical AI governance Labs/Teams: Leads the HIGHEST innovation center at TU Darmstadt and collaborates with TechQuartier Frankfurt. Founded multiple startups focusing on AI solutions.

Simon Fosterمشاهده پروفایل

مدرس ارشد

• Verification
• Theorem Proving (Isabelle/HOL, Agda)
• Formal Semantics
• +۴ مورد دیگر

Simon Foster is a Senior Lecturer in the Department of Computer Science at the University of York. His research focuses on formal methods, theorem proving (using tools like Isabelle/HOL and Agda), and the verification of cyber-physical systems. He holds a PhD and MComp from the University of Sheffield. Research Interests: Foster specializes in formal semantics, unifying theories of programming, and functional programming. His work addresses challenges in verifying complex systems, including robotic control software and safety-critical applications. He has contributed to projects like CyPhyAssure and RoboCalc, emphasizing assurance case generation and probabilistic modeling. Recent Work Trends: His recent publications (2022–2025) emphasize scalable verification techniques for cyber-physical systems, probabilistic modeling, and formal verification of robotic systems using Isabelle/HOL. Key themes include hybrid systems theorem proving, assurance case automation, and the integration of formal methods with robotic state machines. Grants & Projects: He led the CyPhyAssure project (2018–2021) and contributed to the H2020 INTO-CPS initiative. His roles include Research Fellowships in safety-critical systems and model-driven architectures. Labs & Teams: Active in the High Integrity Systems group at York, focusing on formal methods for safety-critical systems and collaborative tool development for systems engineering.

Barry Smithمشاهده پروفایل

استاد

• Applied Ontology
• Artificial Intelligence
• Biomedical Informatics
• +۵ مورد دیگر

Barry Smith is a SUNY Distinguished Professor of Philosophy at the University at Buffalo, holding the Julian Park Chair in the Department of Philosophy. He also serves as Director of the National Center for Ontological Research (NCOR) and maintains affiliate appointments in Biomedical Informatics and Computer Science & Engineering. With joint appointments across multiple departments, Smith is a leading figure in the field of applied ontology and its applications across disciplines. Smith received his PhD in Philosophy from the University of Manchester in 1976, following MA and BA degrees in Mathematics and Philosophy from Oxford University. His educational background reflects his interdisciplinary approach that bridges philosophy, mathematics, and computer science. Smith's research focuses primarily on applied ontology, with significant contributions to biomedical informatics, artificial intelligence, and philosophy of science. His work has established foundational frameworks that enable data integration and knowledge representation across multiple domains. He has pioneered the development of Basic Formal Ontology (BFO), which has become the most widely adopted top-level ontology framework and was approved as an ISO standard (ISO/IEC:21838-2). His research has significantly influenced the Open Biomedical Ontologies (OBO) Foundry, a suite of interoperable ontology modules supporting biology and biomedicine research. Smith's publications over the past five years demonstrate a consistent focus on the applications of ontology to pressing scientific challenges, particularly in biomedical domains (including COVID-19 research), artificial intelligence limitations, and knowledge representation. His work spans theoretical developments in formal ontology while maintaining strong practical applications in real-world information systems. Wolfgang Paul Award of the Alexander von Humboldt Foundation (2002) Paolo Bozzi Prize in Ontology (2011) Fellow of the American College of Medical Informatics (FACMI) (2013) Smith has supervised numerous graduate students and postdoctoral researchers, contributing to the development of a new generation of ontologists. His research has been supported by major funding agencies including the National Institutes of Health, National Science Foundations of the US, Switzerland and Austria, the Volkswagen Foundation, the European Union, and the US Department of Defense. Since 2000, he has served as a consultant to Hernando de Soto on projects related to property and business rights among the poor in developing countries. As Director of the National Center for Ontological Research, Smith leads a team focused on advancing ontological methods and applying them to diverse domains including biomedical informatics, defense intelligence, and industrial engineering. His work continues to shape how we represent and integrate knowledge across disciplinary boundaries.

Bowen Xuمشاهده پروفایل

استادیار

• Software Engineering
• Machine Learning
• Program Analysis
• +۱۲ مورد دیگر

Bowen Xu is an Assistant Professor in the Department of Computer Science at North Carolina State University (NC State), College of Engineering. His research focuses on software engineering, machine learning, and program analysis, particularly in securing AI models and improving code quality. He holds a PhD from Singapore Management University (SMU), where he also conducted postdoctoral research. Education: PhD in Computer Science, Singapore Management University (SMU) Postdoctoral Researcher, SMU School of Computing and Information Systems Research Interests: AI for Code, Backdoor Attacks on Code Models, Vulnerability Detection Code Representation Learning, Model Compression, Safety of AI Systems Chatbot Development for Developers, Automatic Code Review Key Contributions: Developed PTM4Tag+, a Stack Overflow tag recommendation system using pre-trained models Explored stealthy backdoor attacks in code and reinforcement learning systems Pioneered work on automatic vulnerability repair using LLMs and broader input analysis Awards: 2022: Honorable Mention Award (ACSAC) 2018: Highly Commended Full Paper Award (ESEM) Service Roles: Editorial Board Member, Empirical Software Engineering Journal Program Committee Co-chair for ICSE/FSE Research Tracks Organized workshops like FORGE, MaLTeSQuE, and SEA4DQ Labs & Teams: Leads the Softmax Lab at NC State, advising 12+ students across PhD, Master's, and undergraduate levels. Alumni include industry professionals at Microsoft, Barclays, and Marvell Semiconductor.

Fred Popowichمشاهده پروفایل

استاد

• Natural Language Processing
• Artificial Intelligence
• Big Data
• +۴ مورد دیگر

Fred Popowich is a Professor in the School of Computing Science at Simon Fraser University (SFU), Canada. He holds adjunct positions at Dalhousie University's Faculty of Graduate Studies and is an Associate Member of SFU's Department of Linguistics and Cognitive Science Program. His academic career began post-PhD (Cognitive Science/Artificial Intelligence, University of Edinburgh, 1989) and has spanned over three decades at SFU. Education: PhD in Cognitive Science/Artificial Intelligence (University of Edinburgh, 1989); M.Sc. and B.Sc. in Computing Science (Simon Fraser University and University of Alberta, 1985/1982). Research focuses on natural language processing (NLP), machine translation, intelligent systems, and big data applications. He directs SFU’s Big Data Initiative and leads the Natural Language Laboratory, supervising MSc/PhD students in computing science. His work includes developing systems for smart homes, toxic language detection in social media, and energy grid analysis. Industry roles include co-founding Axonwave Software (as CTO/President) and contributing to technology commercialization. Current projects address EV charging impacts, personalized learning systems, and real-time load monitoring. Publications span machine translation, sentiment analysis, and NLP applications in education and energy systems. His work bridges theoretical computer science with practical applications in healthcare, smart cities, and education.

Peter Butcherمشاهده پروفایل

مدرس

• Immersive Analytics
• Human-Computer Interaction
• Data Visualization
• +۴ مورد دیگر

Peter Butcher is a Lecturer in Human-Computer Interaction at Bangor University's School of Computer Science and Engineering. With a PhD in Computer Science from the University of Chester, he specializes in visualization techniques and immersive analytics, particularly focusing on Virtual Reality, Augmented Reality, and WebXR platforms. 2015-2020: PhD, Computer Science, University of Chester 2014-2015: MSc, Computer Science, Bangor University 2011-2014: BSc, Computer Science, Bangor University His research interests center on Immersive Analytics , Human-Computer Interaction , and Data Visualization . He explores how contextual awareness and multivocality in visualization can enhance user experience and data interpretation. His work spans from technical frameworks like WebXR to applications in e-lexicography and flood risk management. Recent publications highlight his contributions to collaborative platforms ( DashSpace ), attention-aware systems ( Attention-Aware Visualization ), and magic-inspired tools ( Wizualization ). He also investigates educational applications of visualization, particularly in critical thinking development. Scientific achievements include the Best Full Papers Honourable Mention at IEEE VIS 2021 and recognition as a Fellow of the Higher Education Academy (FHEA) via Bangor University's CPD Recognition Scheme. He actively supervises doctoral students in areas like AI for Legal Technologies and Multivocality in Visualization. Peter contributes to teaching modules on Web Technologies , Game Production , and High Performance Computing . He also co-founded Sim Broadcasts and serves as Chairman of Rethink Racing , demonstrating his engagement beyond pure academia.

Ricardo A. Calixمشاهده پروفایل

استاد

• Machine Learning
• Natural Language Processing
• Deep Learning
• +۴ مورد دیگر

Ricardo A. Calix is a Professor of Computer Information Technology at Purdue University Northwest. His research focuses on Machine Learning, Natural Language Processing, AI applications in Cyber Security and Healthcare, and Deep Learning methodologies. He holds a Ph.D. in Engineering Science from Louisiana State University. Key research areas include ethical AI model analysis, reinforcement learning for aerospace control systems, and industrial automation through machine learning. His work spans cybersecurity tools (e.g., CyberSecTK) and healthcare data mining from social media. Calix has secured grants as PI/Co-PI for projects like 'Detection of Potential Drug Effects from Twitter Data' (NIH) and 'A Smart and Fast IDS' (Northrop Grumman). He authored Getting Started with Deep Learning: Programming and Methodologies Using Python (2017). Recent publications (2023-2024) address biases in AI models, blast furnace automation, and autonomous aircraft control via reinforcement learning. His work bridges theoretical AI with practical applications in industry and security.

Oliver Schaerمشاهده پروفایل

استادیار

• Business Analytics
• Predictive Analytics
• Time Series Forecasting
• +۱۳ مورد دیگر

Oliver Schaer is an Assistant Professor in the Department of Decision Sciences & MIS at LeBow College of Business, Drexel University. He holds a PhD in Management Science and an MSc in Marketing Analytics from Lancaster University, UK, and brings extensive industry experience from his role as a project manager at a global health technology company in Switzerland. His research centers on predictive analytics in business operations and marketing, with a focus on improving decision-making through data-driven forecasting. Key areas include new product forecasting, supply chain forecasting, and leveraging online data such as user-generated content and pre-release search traffic for competitive intelligence. He has developed the R package diffusion to support forecasting of product life-cycle data, demonstrating strong technical and applied expertise. Oliver's recent publications analyze trends in forecasting software and explore advanced methods for integrating digital signals into forecasting models. His work appears in journals such as Production and Operations Management , International Journal of Forecasting , and Foresight , as well as ORMS-Today survey reports. His research trend shows a consistent focus on practical forecasting tools, software evaluation, and data integration for operational and marketing decisions. Member, INFORMS Member, International Institute of Forecasting Visiting Research Fellow, Centre for Marketing Analytics and Forecasting, Lancaster University Oliver collaborates closely with industry, translating academic insights into practical tools for managers. He has not advertised any formal PhD or Master’s students, but actively mentors through research collaboration and software development. His work is supported by his academic affiliations and industry engagement. He is involved in the development and maintenance of the open-source R package diffusion , which serves as a platform for forecasting with diffusion models, indicating leadership in computational tools for analytics.

Muhammad Humayounمشاهده پروفایل

مدرس ارشد

• Machine Learning
• Computational Linguistics
• Natural Language Processing
• +۵ مورد دیگر

Dr. Muhammad Humayoun is a Senior Lecturer at Karlstad University, Sweden, specializing in informatics education and computational linguistics research. He holds a Ph.D. from University of Grenoble Alpes (2012) and M.Sc. from Chalmers University (2006). His academic career spans over 15 years across Pakistan and Sweden, including teaching roles at COMSATS University, University of Central Punjab, and Higher Colleges of Technology. Research Focus: His work centers on NLP applications for under-resourced languages like Urdu/Punjabi, including text summarization, abusive language detection, and formalization of mathematical texts. He has developed linguistic resources (corpora, lexicons) and contributed to plagiarism detection systems in programming courses. Teaching Contributions: Designed and taught over 15 courses across multiple universities, including advanced programming, AI, and cloud computing modules. Currently teaches graduate-level courses like 'Legal Tech, AI and Rules-as-Code' and undergraduate software engineering courses at Karlstad University. Awards: Recognized for Urdu threat detection system (3rd place, 2021) and top rankings in Urdu fake news competitions. His work on Urdu summarization corpora has been widely cited in computational linguistics research.

• Stochastic Simulation
• Optimization
• Machine Learning
• +۵ مورد دیگر

Giulia Pedrielli is an Associate Professor at Arizona State University's School of Computing and Augmented Intelligence, with affiliations as a Senior Global Futures Scientist. She holds a Ph.D., M.Sc., and B.S. in Mechanical and Industrial Engineering from Politecnico di Milano, Italy. Her research focuses on stochastic simulation, optimization, and machine learning applications in biomanufacturing, digital twins, and cyber-physical systems. She has contributed to frameworks for pandemic modeling (PySIRTEM), RNA design, and high-dimensional optimization. Pedrielli has collaborated with institutions like NUS and UC Berkeley, and her work spans biomedical engineering, supply chain resilience, and security of critical infrastructure. Recent articles emphasize co-simulation tools, AI-driven logic translation, and falsification methods for CPS security. Her teaching includes advanced stochastic simulation courses and thesis supervision across multiple disciplines. Education: Ph.D. Mechanical Engineering, Politecnico di Milano, Italy M.Sc. Industrial Engineering, Politecnico di Milano, Italy B.S. Industrial Engineering, Politecnico di Milano, Italy Research interests integrate simulation-based optimization with machine learning, particularly in safety-critical systems like healthcare and smart infrastructure. She develops digital twin methodologies for real-time control under uncertainty, leveraging Gaussian processes and Bayesian approaches. Pedrielli's work bridges theoretical advancements (e.g., BASSO optimization algorithm) with practical applications in biomanufacturing and pandemic response.

Isak Samstenمشاهده پروفایل

مدرس ارشد

• Data Science
• Machine Learning
• Time Series Analysis
• +۳ مورد دیگر

Isak Samsten is a Senior Lecturer at Stockholm University's Department of Computer and Systems Sciences (DSV), specializing in data science and machine learning. He leads research in temporal machine learning, counterfactual explanations, and interdisciplinary applications in healthcare and environmental science. His work includes developing the wildboar Python module for time series analysis. Current research projects focus on AI for insurance fraud detection and environmental remediation. Samsten is affiliated with the Data Science Research Group, which bridges algorithmic innovation with practical decision-making. He holds an ORCID identifier (0000-0002-3056-6801) and is active in publishing influential papers on topics like time series classification, ESG performance prediction, and clinical decision support systems. Education: Unspecified in text (assumed doctoral degree given academic rank) Affiliations: DSV, Stockholm University; Data Science Research Group Research Interests: Time series analysis, interpretable machine learning, healthcare informatics, environmental sustainability metrics, and AI ethics. Key contributions include shapelet-based classification methods (e.g., Castor algorithm) and counterfactual explanation frameworks (e.g., Glacier system). Grants & Awards: None explicitly listed in provided text. Labs/Teams: Leads the Data Science Research Group, collaborating on projects like AI to detect unclear insurance claims and Toxicity guided inverse design of materials .