جست‌وجوی استاد و پژوهشگر

Alexander Bondarenkoمشاهده پروفایل

پژوهشگر

• Natural Language Understanding
• Question Answering Systems
• Information Retrieval
• +۲ مورد دیگر

Alexander Bondarenko is a postdoctoral researcher at the Databases and Information Systems group, Friedrich-Schiller-Universität Jena, where he works on natural language understanding, argument retrieval, and causality extraction since October 2022. He completed his PhD in June 2023 with a dissertation titled Understanding Comparative Questions and Retrieving Argumentative Answers . Research Focus Primary research areas: natural language processing, question answering systems, information retrieval, argumentation modeling, and causal inference. Key projects: developing frameworks for comparative QA, stance-aware ranking, and misinformation-resistant biomedical search systems. Recent Article Trends His publications (2020–2025) emphasize: Argument retrieval systems and axiomatic re-ranking Biomedical question answering robustness Comparative question modeling across languages Causal inference in neural models Search quality impact on decision-making AI applications in medical education Teaching & Advising Supervised 8 theses including Marvin Vogel ( Axiomatic Re-ranking ) and Lukas Zeit-Altpeter ( Health QA with Trusted Sources ) Contributed to Touché shared tasks and Webis research group Contact Phone: +49 3641 9-46506 | Room 3237, Ernst-Abbe-Platz 2, 07743 Jena

• Artificial Intelligence
• Natural Language Processing
• Deep Learning
• +۳ مورد دیگر

Reza Haffari is a Professor in the Department of Data Science & AI at Monash University, within the Faculty of Information Technology. His research focuses on Artificial Intelligence, particularly in low-level perception and high-level reasoning under uncertainty from text and other modalities like vision and speech. Key areas include natural language understanding, semi-supervised learning, and model explainability for critical applications such as healthcare. Education: Doctor of Philosophy in Computer Science, Simon Fraser University (2010) Master of Science in Artificial Intelligence, Sharif University of Technology (2002) Bachelor of Science in Software Engineering, Sharif University of Technology (2000) Teaching Commitments: Chief Examiner for courses: FIT1045, FIT1053, FIT2004, FIT5201 Lecturer for units: FIT1045, FIT2004, FIT3080, FIT4005/FIT5185, FIT4009, FIT5201 Research Projects (2023–2027): Principal Investigator for Aligning Large Language Models with Human Intention Chief Investigator in Trustworthy Generative AI , Hierarchical Abstractions and Reasoning for Neuro-Symbolic Systems , and CSIRO Mental Health AI Research Trends in Articles: Recent work emphasizes continual learning , multimodal models , and ethical AI alignment . Key themes include adapting models to dynamic environments, ensuring safety in large language systems, and integrating causal reasoning with machine learning. Labs & Teams: Active in the Data Science & AI Group at Monash, collaborating on projects involving generative AI, neuro-symbolic systems, and healthcare applications.

• General Purpose AI
• AI Adoption
• Information Systems
• +۳ مورد دیگر

Jordan Abras is affiliated with the Namur Digital Institute at the University of Namur, focusing on research in General Purpose AI, AI Adoption, and Information Systems. Their work explores topics such as strategic decision-making with generative AI, long-term AI integration in organizations, and automation in data management. Research interests include the dynamics of AI adoption, decision support systems leveraging Retrieval-augmented Generation (RAG), and the exploration of technology interactions in business contexts. Recent contributions analyze how AI mitigates decision complexity and examines the endurance of AI systems over time. Publications highlight interdisciplinary approaches to AI applications in business and digital transformation. No scientific awards are explicitly listed, though their active conference participation indicates academic recognition. Advising roles or grants are not detailed in available texts. Jordan contributes to the Namur Digital Institute’s initiatives, advancing theoretical and practical insights in digital innovation.

• Medical Imaging
• Computer Vision
• Machine Learning
• +۴ مورد دیگر

Professor Yalin Wang is a faculty member in the School of Computing and Augmented Intelligence (SCAI) at Arizona State University (ASU), where he has held roles since 2010. He was promoted to full Professor in 2023, following his tenure as Associate Professor starting in 2017. His research focuses on medical imaging, computer vision, machine learning, and neuroimaging, with an emphasis on Alzheimer’s disease biomarker discovery and geometric modeling. He leads the Geometric & Scientific Computing Lab (GSL), hosting a research website at http://gsl.lab.asu.edu . Education: PhD in Electrical Engineering (University of Washington, 2002), MS and BS in Computer Science (Tsinghua University, 1996 and 1994). Research interests include surface-based morphometry, optimal transport applications in neuroimaging, and deep learning for medical image analysis. He has published over 200 peer-reviewed papers and received the 2016 ASU CIDSE Best Junior Faculty Researcher Award, among other accolades. His grants include NIH-funded projects on Alzheimer’s biomarkers, retinotopic mapping, and pediatric brain development. Collaborations span institutions like Mayo Clinic and ADNI. Notable contributions include developing neurodegeneration biomarkers using low-rank/sparse subspace decomposition and predictive models for cognitive decline via hyperbolic stochastic coding.

Hakan Emekciمشاهده پروفایل

استادیار

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

Hakan Emekci is an Assistant Professor at TED University's Graduate School of Applied Data Science. He teaches courses such as Statistical Learning, Data Analytics, and Information Retrieval in the Applied Data Science Master Program. His research focuses on AI applications in healthcare, education technology, econometrics, and data-driven methodologies. Emekci has published works on specialized language models for medical contexts, RAG systems optimization, and the integration of AI in educational frameworks. His recent studies emphasize domain-specific AI solutions and their practical implementations across diverse sectors. Emekci's academic contributions include analyzing central bank policies' impact on financial markets and developing computational frameworks for data mining. He actively teaches advanced courses at both undergraduate and graduate levels, including Data Structures and Algorithms. His work bridges theoretical data science concepts with real-world applications in healthcare, education, and finance. Notable research areas include: Healthcare informatics & AI-driven diagnostic systems Educational technology innovations Machine learning model optimization Financial econometric analysis Domain-specific large language models His publications from 2025 highlight advancements in multi-agent medical frameworks and comparative studies of retrieval-augmented generation techniques. Earlier work (2017-2018) established foundational research in econometric policy analysis and R-based data mining methodologies.

• Information Retrieval
• Machine Learning
• Natural Language Processing
• +۳ مورد دیگر

Andrew Parry is a PhD Student and Tutor in the School of Computing Science at the University of Glasgow. His research focuses on advancing information retrieval (IR) systems through machine learning and large language models (LLMs), with particular expertise in ranking models, interpretability frameworks, and evaluation methodologies. He has contributed to frameworks like PyTerrier and SimIIR, and his work bridges theoretical IR advancements with practical applications in generative search and relevance assessment. Education: Pursuing PhD in Computing Science at the University of Glasgow. Research Interests: Parry explores mechanistic interpretability of IR systems, the role of LLMs in relevance assessment, and the ethical implications of neural retrieval models. His work emphasizes transparency in search algorithms and the development of robust evaluation techniques for large-scale systems. Key Publications: He has published at top venues including SIGIR, WSDM, and ECIR, with notable contributions on declarative RAG pipelines, LLM-based relevance assessment discrepancies, and corpus subsampling strategies. His 2025 work on MechIR proposes a novel interpretability framework, while recent collaborations address contamination in ranking distillation and interactive search simulations. Advising & Grants: As a Tutor, Parry supports teaching and research supervision within the School of Computing Science. His research is aligned with broader efforts in the Glasgow Information Retrieval Group, though specific grant details are not disclosed in available texts. Labs/Teams: Active member of the University of Glasgow's Terrier Team, contributing to TREC evaluations and collaborative workshops like ECIR's Collab-a-thon.

Premtim Sahitajمشاهده پروفایل

پژوهشگر

• Knowledge-based reasoning
• Multimodal Large Language Models
• Retrieval-Augmented Generation (RAG)
• +۵ مورد دیگر

Premtim Sahitaj is a researcher and doctoral student affiliated with the Q&U Lab of the XplaiNLP group, focusing on computational approaches to fake news detection under the BMBF-funded news-polygraph project. He holds a B.Sc. in Business Information Systems and an M.Sc. in Data Science from the University of Trier, where he previously worked as a research assistant on the ReCAP project (DFG priority program RATIO) addressing natural language processing in argumentation domains. His research interests span knowledge-based reasoning (multimodal information extraction, RAG with LLMs, data synthesis), language modeling (multimodal LLM architectures, pre-training optimization), and applications in low-resource languages (e.g., Albanian). He actively supervises student theses and advanced projects through the Q&U Lab. Prior work includes integrating background knowledge into LLMs' argumentation processes and evaluating information representation strategies. His doctoral research aims to develop computational frameworks supporting investigative journalism through fake news detection platforms.

• Artificial Intelligence
• Explainable AI
• Case-Based Reasoning
• +۴ مورد دیگر

Professor Nirmalie Wiratunga is a leading academic at Robert Gordon University, focusing on artificial intelligence and its applications across diverse domains. Her work bridges theoretical advancements with practical implementations in areas requiring high explainability and domain-specific adaptation. Research Pillars : Explainable AI (XAI), Case-Based Reasoning (CBR), Legal AI, Healthcare Informatics, and Machine Learning. Methodological Focus : Retrieval-Augmented Generation (RAG), domain adaptation, dialogue understanding, and systematic review automation. Recent publications highlight her contributions to neonatal pain assessment transparency, legal question answering systems (SCaLe-QA and CBR-RAG), and multi-shot explanation platforms (iSee). Her work on hallucination detection and federated learning emphasizes robustness and privacy preservation. Collaborations span biomedical, legal, and energy sectors, including mini-grid optimization for rural communities. Her research integrates context-aware systems into data-to-text generation and dual-task dialogue understanding, pushing boundaries in human-AI collaboration. Projects like Agentic CBR demonstrate practical implementations for financial decision-making with counterfactual explanations.

Stephane Faulknerمشاهده پروفایل

پژوهشگر

• Computer Science
• Artificial Intelligence
• Requirement Engineering
• +۷ مورد دیگر

Stephane Faulkner is a researcher at the Namur Digital Institute , University of Namur. Holding a Doctor of Economics degree, his work bridges Multi-Agent Systems , Requirement Engineering , and Blockchain applications in Information Systems. He supervises PhD candidates like Vermaut, B. and DEHOUSSE, S. and contributes to projects such as the Data-Intensive Recommendation Framework for Player Selection in Sport Management . Research Trends : Recent work includes Generative AI in decision-support systems (2025) Bibliometric analysis of soccer performance (2024) Blockchain-based requirements engineering (2021) User interface modeling from informal designs (2005) Affiliations : He collaborates with researchers across Europe, with projects spanning software engineering, sports analytics, and decentralized applications. His expertise aligns with UN SDGs in education and digital transformation.

Maciej Pondelمشاهده پروفایل

استاد

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

Dr. Maciej Pondel is a Professor and Head of the Department of Business Intelligence in Management at Wrocław University of Economics (Wrocław, Poland). He holds a PhD in Economics and has over 25 years of experience in IT, business strategy, and academic research. His work bridges artificial intelligence (AI), machine learning (ML), and business management, with a focus on digital transformation, data-driven decision-making, and healthcare analytics. Educations: Bachelor's Degree in Computer Science from Wrocław University of Science and Technology PhD in Economics from Wrocław University of Economics Research Interests: Applications of AI/ML in healthcare (e.g., kidney transplantology, heart failure management) Business intelligence systems, data warehouses, and big data analytics Digital twin technology and smart city solutions Marketing analytics, customer segmentation, and churn prediction IT project management and enterprise systems optimization Research Trends: Pondel's recent work emphasizes AI-driven solutions for both medical diagnostics and business strategy. He has explored predictive models in healthcare, financial market analysis via multi-agent systems, and urban sustainability applications like parking optimization. His interdisciplinary approach combines technical expertise with real-world business challenges. Grants & Projects: Developed the Upsaily system for sales data analysis using AI Led numerous commercial and R&D projects since 2000 Contributed to knowledge portals for local governments and bioscience initiatives Labs/Teams: Heads the Department of Business Intelligence in Management, fostering collaboration between academia and industry through applied research.

ZHANG Jiahengمشاهده پروفایل

استادیار

• Cryptography
• Security
• Artificial Intelligence
• +۷ مورد دیگر

ZHANG Jiaheng is an Assistant Professor in the Department of Computer Science at the National University of Singapore (NUS), School of Computing. His work bridges cryptography, artificial intelligence, and system security, with a focus on scalable and privacy-preserving technologies. He teaches CS3235 – Computer Security and leads research in zero-knowledge proofs, LLM safety, and trustworthy AI. Research Interests: His research spans Cryptography , Security , Machine Learning & AI , Privacy , and Algorithms & Theory . He specializes in making zero-knowledge proofs practical at scale and securing large language models against jailbreaking, backdoors, and privacy leaks. His recent projects include zkGPT, BatchZK, and Guardreasoner, highlighting his dual focus on theoretical foundations and real-world applications. The recent publications show a strong trend toward scalable zero-knowledge systems and AI security , particularly in verifying and protecting LLMs. These works integrate cryptographic rigor with modern AI challenges, reflecting a cohesive research vision at the frontier of trustworthy computing. Scientific Contributions: Developed scalable collaborative zk-SNARKs for efficient proof generation. Pioneered techniques for secure LLM inference and jailbreak detection. Advanced GPU-accelerated and distributed zero-knowledge proof systems. Advising & Grants: While specific students and grants are not listed, his active publication record in top-tier venues suggests ongoing research supervision and external funding in cybersecurity and AI. He is likely involved in advising PhD and Master’s students in cryptography and AI security. Labs & Teams: He is part of the NUS School of Computing research ecosystem, potentially affiliated with cybersecurity or AI labs, contributing to Singapore’s leadership in privacy-preserving technologies.

• Artificial Intelligence
• Machine Learning
• Natural Language Processing
• +۹ مورد دیگر

Dr. Nasrin Dehbozorgi is an Assistant Professor of Software Engineering at the College of Computing and Software Engineering, Kennesaw State University (KSU), where she directs the AIET Lab. With a Ph.D. in Computer Science from the University of North Carolina at Charlotte and prior industry experience as a Software Engineer and IT Manager, she bridges academic research and practical application. Affiliation: College of Computing and Software Engineering, KSU Academic Rank: Assistant Professor Email: dnasrin@kennesaw.edu Lab: AIET Lab (https://aiet.kennesaw.edu) Research Focus: Dr. Dehbozorgi pioneers the intersection of AI and educational innovation, leveraging Machine Learning (ML), Natural Language Processing (NLP), and Large Language Models (LLMs) to develop adaptive educational systems. Her work emphasizes Computational Thinking, Recommender Systems, and Affective Computing, aiming to enhance student success through soft skills training and curriculum modernization. Key Projects: As Principal Investigator (PI) for the 'Student Success' initiative at KSU, she integrates technological skills into curricula while supporting faculty development. Her research spans physiological monitoring (e.g., EEG, HRV), AI-driven discourse analysis, and AI ethics in education. Teaching: She instructs graduate courses such as Software Architecture , Requirements Engineering , and Computational Thinking , with a focus on active learning and practical software engineering.

• Service Operations
• Operational Excellence
• Lean Six Sigma
• +۱۲ مورد دیگر

Professor Maneesh Kumar is a Professor of Service Operations and Pro Dean for Technology, Systems and Data at Cardiff Business School, Cardiff University . He also holds an honorary Visiting Professor position at Cardiff & Vale University Health Board , reflecting his interdisciplinary work in healthcare operations and digital innovation. His research focuses on Operational Excellence , particularly in the integration of Lean Six Sigma, Industry 4.0, Big Data Analytics, and Circular Economy models . His work spans sectors including healthcare, automotive, SMEs, and the drinks industry. He has led and collaborated on multiple externally funded research projects with partners such as ESRC, Welsh Government, Innovate UK, and industry leaders like Siemens and Centrica. His recent publications explore themes such as AI in manufacturing, collaborative supply chains, human factors in smart systems, and sustainability. These works reflect a strong trend toward digital transformation, interdisciplinary collaboration, and real-world application of operational excellence frameworks in both public and private sectors. He has received several honors, including: ‘Business Innovation’ Award, Cardiff University (2020) Distinguished Professor Award, IEOM Society (2020) Excellence in Teaching Award, Cardiff University (2018) Multiple best paper awards internationally Professor Kumar has successfully supervised 12 doctoral students and currently supervises 4 PhD candidates. He has served as an external examiner for 23 PhDs globally. He is actively involved in postgraduate teaching, particularly in Operations Management and Lean Thinking on Executive MBA and MSc programs, with innovative experiential teaching methods recognized by the university. He has led or co-hosted several high-impact forums, including the Lean Enterprise Research Centre (LERC) conference and the iLEGO (Lean, Green, and Innovation) workshops, fostering collaboration between academia and industry.

• Artificial Intelligence
• Explainable AI
• Medical Imaging Analysis
• +۷ مورد دیگر

Daniel Sonntag is a Research Professor at the German Research Center for Artificial Intelligence (DFKI) in Kaiserslautern, Germany, with extensive contributions to artificial intelligence, particularly in medical applications and human-AI interaction. His work spans multiple domains including explainable AI, medical imaging analysis, eye tracking technology, and clinical decision support systems. His research interests focus on developing interpretable AI systems for healthcare applications, with particular emphasis on ophthalmology and radiology. He investigates how to make AI systems more transparent and trustworthy through concept bottleneck models, multi-agent systems, and interactive learning approaches. His work bridges the gap between theoretical AI advancements and practical clinical applications, ensuring that AI tools are both effective and understandable for medical professionals. Sonntag's publication record demonstrates a consistent trend toward more interactive and interpretable AI systems, with recent work focusing on multi-agent RAG systems for radiology report generation, clinical decision support tools for ophthalmology, and biodiversity monitoring through soundscape analysis. His research shows a strong interdisciplinary approach, combining computer vision, natural language processing, and human-computer interaction to solve complex problems in healthcare and environmental monitoring. His work has appeared in leading venues including IUI, ICMI, KI, and various IEEE and ACM conferences, reflecting his contributions to both the theoretical foundations and practical applications of artificial intelligence.

• Natural Language Processing
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Chien-Sheng Wu is a prominent researcher in natural language processing and artificial intelligence, actively contributing to advancements in large language models (LLMs), dialogue systems, and information retrieval. His work focuses on enhancing factual consistency, developing frameworks for service AI agents, and exploring vision-language model limitations in arithmetic tasks. Key research areas: LLMs, knowledge grounding, dialogue summarization, and human-AI collaboration Recent publications analyze the capacity of LLM agents in CRM tasks, multihop reasoning frameworks, and content moderation tools. His collaborations span institutions like ACL, EMNLP, and NAACL. 2025 papers address workflow extraction, visual arithmetic understanding, and RAG system evaluation 2024 work includes Haystack summarization challenges and executable text-editing interfaces Scientific awards and student mentorship details are not explicitly mentioned in available data. Affiliations remain unspecified, but his contributions to NLP benchmarks and evaluation frameworks are significant.