جست‌وجوی استاد و پژوهشگر

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

Dr. Jonathan Fürst is a Researcher at the ZHAW School of Engineering, part of the Intelligent Information Systems research focus at ZHAW Zurich University of Applied Sciences. His work spans AI-driven data systems, ontology matching, IoT applications, and cross-domain data integration. Key research interests include: AI applications in healthcare and education Machine learning for data integration and systems Multi-modal data exploration and natural language interfaces IoT networks and smart building technologies His projects include: AI-based Quality Management of BIM Models (project leader) DataGEMS : A data discovery platform for generalized exploratory search Multi-lingual data exploration systems Digital Health Zurich: Patient-centered clinical innovation Recent research trends emphasize explainable AI, weakly supervised learning, and real-world system robustness. His work bridges theoretical advancements with practical implementations in domains like healthcare, education, and smart infrastructure. No scientific awards are explicitly listed, though his active role in Horizon Europe projects reflects collaborative excellence. He contributes to interdisciplinary teams across data science, engineering, and healthcare.

• Artificial Intelligence
• Bioinformatics
• Natural Language Processing
• +۳ مورد دیگر

Dr. Ahmad Aghaebrahimian is a researcher at the ZHAW School of Life Sciences and Facility Management, affiliated with the Institute of Computational Life Sciences. He specializes in computational methods applied to healthcare, natural language processing (NLP), and bioinformatics. His work integrates deep learning, ontology-based systems, and signal processing to address challenges in healthcare informatics, biomedical research, and security systems. Research Projects: Project Leader: Advancing Information Accessibility in Hospitals (LLMs) Project Leader: Multi-document Patient Records Summarization Project Leader: Plant Cell Cultures with Deep Learning Deputy Leader: Automatic Supply Chain Monitoring Research Interests: His research focuses on AI-driven solutions for healthcare, including ontology-aware relation extraction, medical text mining, and robust signal processing systems. He also explores NLP applications in question answering, entity disambiguation, and parallel corpus creation. Recent work includes drone detection using CNNs in low SNR environments and computational methods for natural products discovery. Publications Trends: Over the past decade, his publications emphasize interdisciplinary approaches combining machine learning with bioinformatics and medical informatics. Key themes include deep learning model optimization, biomedical knowledge graph construction, and practical applications of NLP in healthcare systems. Grants & Collaboration: Leads research initiatives on AI in colorectal cancer classification and supply chain monitoring, demonstrating expertise in securing project leadership roles within academic-industry collaborations.

• Machine Learning
• Natural Language Processing
• Healthcare Applications
• +۷ مورد دیگر

Frank Rudzicz is a Professor at the University of Toronto with appointments in the Faculty of Medicine and Department of Computer Science. His research bridges machine learning, natural language processing, and healthcare applications, with a particular focus on developing AI solutions for medical diagnostics, surgical assistance, and patient care. He leads several research initiatives in medical AI and collaborates extensively with clinicians and healthcare institutions. Rudzicz's research interests span multiple critical areas in healthcare AI. He has pioneered work in speech-based detection of neurological conditions including Alzheimer's and Parkinson's diseases, developing algorithms that analyze linguistic patterns to identify early signs of cognitive decline. His work in surgical AI has led to systems that provide real-time decision support during operations and analyze surgical performance. He also investigates ethical AI implementation in healthcare, addressing privacy concerns and developing frameworks for trustworthy clinical AI systems. More recently, his research has expanded into large language models for medical applications, focusing on privacy preservation, fairness, and explainability in clinical contexts. His publication record shows a clear evolution from foundational work in speech processing and dysarthria recognition toward increasingly sophisticated medical AI applications. Early work focused on improving speech recognition for impaired speakers, which naturally led to applications in neurological disorder detection. Over the past decade, his research has expanded to encompass broader healthcare applications, with recent work heavily featuring transformer models, multimodal data integration, and ethical considerations in medical AI deployment. His current research demonstrates strong emphasis on practical implementation challenges including model security, privacy preservation, and clinical integration of AI systems. Rudzicz has received recognition for his work, including best paper awards at major conferences like ICLR 2025. His research has been supported by numerous grants focusing on AI in healthcare, though specific grant details aren't provided in the available text. He has mentored numerous graduate students who have gone on to contribute significantly to medical AI research. As a leader in medical AI, Rudzicz contributes to major collaborative efforts including the Delphi consensus statement for digital surgery. His work frequently appears in top-tier AI and medical journals, demonstrating his ability to bridge the technical and clinical domains. Through his research group at the University of Toronto, he continues to advance the frontier of AI applications in healthcare while addressing the practical and ethical challenges of implementing these technologies in real clinical settings.

Sina Zarrießمشاهده پروفایل

استاد

• Computational Linguistics
• Natural Language Processing
• Dialogue Systems
• +۵ مورد دیگر

Sina Zarrieß is a Professor of Computational Linguistics at the University of Bielefeld, affiliated with the Faculty for Linguistics and Literature Studies. His research focuses on computational models of language use, with applications in natural language generation, dialogue systems, and language-vision integration. He leads the Computational Linguistics Bielefeld group and collaborates on interdisciplinary projects in ecology, hate speech analysis, and multimodal interaction. Research interests include language model architecture optimization, pragmatic reasoning in generation, and ethical NLP. He actively contributes to workshops such as ACL, EMNLP, and NLP4Ecology, publishing on topics like character-based LMs, gaze-driven hate speech detection, and visualization-oriented dialog systems. His work bridges theoretical linguistics with applied machine learning, emphasizing interpretability and real-world impact. Notable projects include developing BabyLM character-based models, studying gender-inclusive language benchmarks (SlayQA), and creating tools like VIST5 for adaptive visualization dialog. He explores how LMs encode linguistic principles like Maximize Presupposition! and investigates zero-shot learning for novel object categories. Current research trends show a focus on model efficiency (e.g., sentence selection for classification), multimodal grounding (e.g., scene context in visual tasks), and human-AI interaction dynamics (e.g., explanation effects on user perception). Despite prolific publishing, no specific scientific awards are mentioned in the provided materials. Advising and grants are not explicitly detailed, though his extensive co-authorship network indicates active mentoring. The Computational Linguistics Bielefeld group maintains open research directions in visual question answering, poetry generation diversity, and historical hate speech evolution studies.

• Machine Learning
• Data Mining
• Graph Neural Networks
• +۵ مورد دیگر

Hanghang Tong is a Professor at the Siebel School of Computing and Data Science, University of Illinois at Urbana-Champaign. He also holds affiliations with the National Center for Supercomputing Applications (NCSA) and the Center for Digital Agriculture. His research interests span machine learning, data mining, graph theory, and ethical AI, with a focus on network analysis, fairness, and large-scale data systems. Tong has been recognized with prestigious awards including the IEEE Fellow (2022), ACM Distinguished Member (2020), and NSF CAREER Award (2019). His recent work explores topics like LLM safety, time series analysis, and network alignment through tools like PLANETALIGN. Tong actively contributes to open-source libraries and benchmarks, advancing reproducibility and ethical standards in AI. Education : Not explicitly listed in provided texts. His research emphasizes practical applications of AI in climate science, healthcare, and social networks. Tong's work often addresses challenges in model fairness, scalability, and cross-domain adaptation. He collaborates widely, contributing to both foundational theory and real-world systems. Key projects include CLIMB (class-imbalanced learning benchmark) and Saffron-1 (LLM safety assurance). Tong’s lab at NCSA and Siebel School drives innovation in AI-driven data science and computational methods.

Heng Zhengمشاهده پروفایل

پژوهشگر

• Computational Argumentation
• Explainable AI
• Computational Social Science
• +۳ مورد دیگر

Heng Zheng is a Postdoctoral Research Associate at the University of Illinois Urbana-Champaign (UIUC) School of Information Sciences, mentored by Jodi Schneider, Bertram Ludäscher, and Karen Wickett. Starting August 2025, he will join the University of Kentucky as a tenure-track Assistant Professor of Information Science. His research focuses on computational argumentation, explainable AI, legal informatics, and digital libraries, with a particular emphasis on addressing information disorder and improving scientific communication. Educational Background: PhD in Artificial Intelligence (University of Groningen, 2024; advisors: Bart Verheij and Davide Grossi), MSc in Logic (Sun Yat-sen University, 2018), and BSc in Information Management (Dalian Maritime University, 2016). Research Interests: Develops formal theories for case-based reasoning in legal and scientific contexts, explores computational methods to audit scientific publications, and investigates epistemic uncertainty in information dissemination. His work bridges AI, law, and information science to enhance transparency and reliability in knowledge systems. Grants: Principal investigator on the $40k XAI-CA project (UIUC-Cardiff Collaboration) and co-recipient of IMLS and Sloan Foundation grants. Teaches Information Modeling and delivers guest lectures on argumentation in information systems. Actively reviews for journals like Studies in Logic and conferences such as ICAIL and iConference. Key Achievements: Published 15+ peer-reviewed papers (including JCDL 2024 and iConference 2024), developed datasets on scientific retractions and legal reasoning, and contributes to open science initiatives. His work on computational argumentation frameworks (e.g., AF-Xray) and entity alignment using LLMs showcases innovative applications of AI in complex domains.

• Artificial Intelligence
• Programming Languages
• Formal Methods
• +۳ مورد دیگر

Gagandeep Singh is an Assistant Professor in the Department of Computer Science at the University of Illinois. His research focuses on trustworthy AI, programming languages, and formal methods, with a particular emphasis on ensuring safety, robustness, and ethical alignment in machine learning systems. He holds a Ph.D. from ETH Zurich and has received multiple awards, including the ACM SIGPLAN Doctoral Dissertation Award. His work spans neural network verification, adversarial robustness, and federated learning, with applications in security, reliability, and ethical AI. He has contributed to journal editorships, conferences, and has taught courses on formal methods and trustworthy AI systems. Education: Ph.D. in Computer Science, ETH Zurich (2014–2020) Masters in Computer Science, ETH Zurich (2012–2014) Bachelor's in Computer Science and Engineering, IIT Patna (2008–2012) Research Interests: Artificial Intelligence Safety & Ethics Formal Verification of Neural Networks Adversarial Machine Learning Federated Learning Programming Languages & Static Analysis Recent Articles: His most recent work addresses challenges in AI alignment, robustness certification, and federated learning optimization, reflecting a focus on both theoretical foundations and practical applications. Awards & Honors: ACM SIGPLAN John C. Reynolds Doctoral Dissertation Award (2021) ETH Medal for Best Master's Thesis (2014) Teaching & Advising: He has taught courses such as 'Formal Software Development Methods' and 'Trustworthy AI Systems', and his research group explores topics like LLM robustness and certified neural networks.

Ayoub Bagheriمشاهده پروفایل

دانشیار

• Natural Language Processing
• Explainable AI
• Bias Detection
• +۲ مورد دیگر

Dr. Ayoub Bagheri is an Associate Professor of Natural Language Processing in the Department of Methodology and Statistics at Utrecht University. He coordinates the Special Interest Group in Text Mining and co-leads the Utrecht NLP Hub, a collaborative initiative supported by the Focus Areas Applied Data Science and Human-centered AI. His research focuses on explainable and trustworthy AI, personalized NLP systems, language model evaluation, multimodal language understanding, and ethical NLP applications. He develops methods to make AI transparent and aligned with human values. Bagheri teaches courses in Data Science, Text Mining, and NLP using Python and R, and coordinates the Applied Data Science master profile. His recent work examines explainability techniques for large language models and active learning approaches for systematic reviews.

Mohammad Moshirpourمشاهده پروفایل

استاد

• Applied Software Engineering
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Professor Mohammad Moshirpour is a faculty member at the University of California, Irvine (UCI), affiliated with the School of Information and Computer Sciences and the Department of Computer Science. As Director of the Software Engineering Practice and Education (SEPE) Lab, his work spans applied software engineering, artificial intelligence, machine learning, and educational methodologies. He emphasizes practical applications, collaborating with healthcare institutions (e.g., University of Calgary nursing faculty), industry partners, and academic institutions to address real-world challenges such as pipeline monitoring, video restoration, and dementia therapy through virtual reality. His research interests include software requirements engineering, AI-driven software development, and innovative education models like project-based learning (PBL) and hackathon-based assessment. He advocates for hands-on learning, comparing software engineering education to medical training. His SEPE Lab focuses on creating scalable, maintainable software solutions with a strong emphasis on AI integration. Key collaborations include work on parent-child interaction analysis via machine learning and video super-resolution techniques. Professor Moshirpour’s recent projects highlight cross-disciplinary innovation, such as leveraging federated learning for video enhancement and applying Monte Carlo methods to pipeline monitoring. His educational contributions include frameworks for agile teaching, automated feedback systems, and curriculum modernization. While no specific awards are listed, his work reflects a commitment to bridging theoretical research with practical implementation.

ALBERTO TESTOLINمشاهده پروفایل

استادیار

• Artificial Intelligence
• Neuroscience
• Machine Learning
• +۳ مورد دیگر

Alberto Testolin is an Assistant Professor at the University of Padua, Italy. His research focuses on artificial intelligence, computational neuroscience, and machine learning applications in healthcare and marine biology. He explores topics such as EEG seizure prediction, dolphin whistle detection, numerical cognition, and the capabilities of large language models. His work bridges theoretical models with practical challenges, including the design of robust classifiers and the analysis of neural network dynamics. Key research interests include deep generative models, computational models of cognitive disorders like dyscalculia, underwater acoustic signal processing, and the development of AI systems that emulate human judgment. His interdisciplinary approach integrates techniques from computer vision, neuroimaging, and reinforcement learning. Recent publications emphasize the challenges and advancements in EEG forecasting, the emergence of symbolic reasoning in LLMs, and the behavioral analysis of marine animals. He advocates for rigorous evaluation methodologies to ensure machine learning models generalize across diverse datasets and patient populations.

• Quantum Computing
• Software Engineering
• Artificial Intelligence
• +۴ مورد دیگر

Dr. Aakash Ahmad is a Lecturer in Computing and Communications at Lancaster University, UK. His research focuses on quantum software systems, AI-driven software engineering practices, and microservices architecture. He leads empirical studies on developer experiences with tools like GitHub Copilot and has contributed to foundational work in quantum architecture description languages (QADL). His work bridges practical software development challenges with cutting-edge AI and quantum computing innovations. Affiliations: Department of Computing at Lancaster University, UK. Research Interests: Quantum software architecture, AI in software engineering, microservices challenges, empirical studies on developer tools, and blockchain system development. His datasets include empirical analyses of Stack Exchange and GitHub data, exploring architecture decisions in quantum systems and challenges in Copilot usage. He has received awards for impactful studies on GitHub Copilot practices and quantum software development processes. Ahmad is actively involved in organizing workshops such as the Engineering Processes and Practices for Quantum Software (PPQS’24) and presents at international conferences. Key Projects: Quantum architecture frameworks, AI ethics in development, and blockchain system design.

Brigitte de Graaffمشاهده پروفایل

استادیار

• Sustainable Business Management
• Integrated Reporting
• Integrated Thinking
• +۵ مورد دیگر

Brigitte de Graaff is an Assistant Professor in Accounting at the School of Business and Economics (SBE) at Vrije Universiteit Amsterdam. She serves as the program director for the CMA-program at SBE Executive Education. Her research focuses on sustainable business management, integrated thinking/reporting, and ESG governance. She holds ancillary roles as a board member of the Institute of Management Accountants (since 2012) and director/owner of Expedition Anywhere (2022–2032). Education: LLM (Master of Laws) MSc (Master of Science) Research Interests: Her work examines organizational impacts of sustainability transitions, including ESG regulatory compliance, integrated management frameworks, and legal/governance implications of sustainability efforts. Key areas include CSRD/CSDDD reporting standards, ESG boardroom accountability, and management control systems for sustainability integration. Media Contributions: She has provided expert commentary on topics like CSRD implications for food businesses, CFO roles in sustainability, and integrated reporting practices, featured in media outlets such as Accountant and Strategic Finance . Grants & Advising: While specific grants are not listed, her research aligns with regulatory and organizational transformation themes. She actively advises on ESG reporting standards through her academic and ancillary roles. Labs/Teams: Engaged with SBE Executive Education through the CMA-program, focusing on professional accounting education for sustainability challenges.

Steven Verstocktمشاهده پروفایل

استاد

• Multimodal Data Processing
• Video Analysis
• Computer Vision
• +۲ مورد دیگر

Steven Verstockt is a Professor at Ghent University’s Department of Electronics and Information Systems, affiliated with the Internet Technology and Data Science Lab (IDLab) and the Multimedia Lab. His research spans multimodal data processing computer vision video analytics geospatial information technology sports data science domains. He leads interdisciplinary projects in sports, media, and cultural heritage, focusing on spatio-temporal data collection, classification, and visualization. Recent research trends highlight his work in CO2-based occupancy detection BIM semantic enrichment multi-camera sports analytics geosocial media mining historical archive digitization hyperspectral image classification published across 2025–2024. His team combines computer vision, machine learning, and geospatial tools for real-world applications.

• Machine Learning
• Natural Language Processing
• Topic Modeling
• +۴ مورد دیگر

Wray L. Buntine is a faculty member at Monash University in Melbourne, Australia, specializing in machine learning, natural language processing, and Bayesian methods. His research bridges theoretical foundations and practical applications, with a strong focus on NLP innovations, topic modeling, and efficient learning techniques. His research interests span: Machine Learning : Bayesian neural networks, active learning, and low-resource model optimization. Natural Language Processing : Topic modeling, machine translation, LLM evaluation, and dialogue systems. AI Applications : Healthcare (e.g., medication recommendation), education (e.g., dialogue classification), and graph-based anomaly detection. Recent publications (2023–2025) demonstrate a shift toward LLM-centric research, including automatic evaluation of topic models, uncertainty-aware language agents, and logical verification frameworks. His work frequently integrates Bayesian approaches with deep learning, emphasizing model interpretability and data efficiency. While no awards or grants are detailed in the source text, Buntine leads collaborative projects across NLP and ML, often co-authoring with researchers at Monash University and international institutions. Labs or teams are not explicitly referenced.

Manmohan Chandrakerمشاهده پروفایل

استاد

• Computer Vision
• 3D Reconstruction
• Scene Understanding
• +۵ مورد دیگر

Manmohan Chandraker is a Professor in the Department of Computer Science & Engineering at the University of California, San Diego (UCSD). His research focuses on computer vision, 3D reconstruction, and autonomous driving, with interdisciplinary links to robotics, machine learning, and computer graphics. He leads projects in 3D scene understanding, real-time visual odometry, and sensor fusion for autonomous systems. His work has been recognized with top academic awards and industry accolades. Education: B.Tech. in Electrical Engineering from IIT Bombay Ph.D. in Computer Science from UCSD Postdoctoral scholarship at UC Berkeley Research Interests: Chandraker’s work addresses challenges in 3D reconstruction under complex lighting and materials, autonomous driving systems, and physically-based inverse rendering. Key contributions include frameworks for non-convex optimization in geometric reconstruction and semantic-driven dense reconstruction techniques. His research bridges theory and practice, with applications in augmented reality, robotics navigation, and automotive safety systems. Key Awards: Best Paper Award at CVPR 2014 UCSD CSE Dissertation Award (2009) NEC Business Contribution Award (2015) Advising & Industry Collaboration: He has led teams at NEC Labs America, collaborating with automobile manufacturers to develop single-camera systems for real-time autonomous driving. His work on closed-loop traffic simulations and adversarial scenario generation has driven industry partnerships and patents. Current research explores multimodal LLM integration, data-centric AI tools (e.g., Ymir platform), and photorealistic scene synthesis. Labs & Projects: Chandraker’s group develops open frameworks like OpenRooms and TRoVE for 3D scene datasets, and contributes to benchmarks for domain adaptation in vision tasks. His team also works on safety-critical systems for autonomous vehicles through projects like SAFE-SIM and Drive-1-to-3.