جستجوی استاد راهنمای دانشگاهی

Kentaro Inui is a distinguished researcher at Tohoku University , specializing in Natural Language Processing , Computational Linguistics , and Machine Learning . His work focuses on advancing language model behavior through rigorous empirical analysis, including mechanisms for detokenization , entity identification , and numerical reasoning . Inui has pioneered methods to rectify spurious beliefs in LLMs via unlearning techniques and explored the dynamics of reasoning strategies in neural models. His research addresses chat translation quality through metrics like MQM-Chat and investigates repetition neurons responsible for text generation patterns. Inui also contributes to argumentation analysis with annotation frameworks like LPAttack and develops resources such as COPA-SSE for commonsense reasoning. His work on universal graph-based relation extraction and cross-stitching architectures has established new benchmarks in NLP task performance. Inui's publications span top-tier conferences including ACL , EMNLP , and LREC , often involving collaborations with researchers like Benjamin Heinzerling and Jun Suzuki. His methodological innovations in semi-structured explanation generation , position embedding (e.g., SHAPE), and zero pronoun resolution demonstrate his focus on both theoretical and practical NLP challenges. While no direct awards or student mentorship data appear in the provided corpus, his extensive publication record (over 20 papers between 2021-2025) underscores significant contributions to NLP education tools , knowledge base integration , and dialogue system consistency . Current projects like ReCall mechanisms and numerical property encoding directions highlight his ongoing impact on model interpretability and reasoning accuracy.

• Natural Language Processing
• Computational Linguistics
• Language Models
• +۳ مورد دیگر

David P. Helmbold is a Professor in the Computer Science Department at the University of California, Santa Cruz. He received his PhD in Computer Science from Stanford University in 1987, where he specialized in parallel algorithms and debugging of parallel programs. He has been a faculty member at UC Santa Cruz for over 25 years. Research Focus Helmbold's research centers on theoretical machine learning and computational learning theory. His primary interests include: Boosting methods and ensemble learning Online learning algorithms and regret minimization Theoretical foundations of semi-supervised learning Applications in computer vision, game AI, and power optimization Analysis of irrelevant variables in learning systems Publication Trends Helmbold's recent work (2009-2012) focuses on advancing theoretical machine learning, particularly in semi-supervised learning, Monte Carlo methods for game AI, and feature relevance analysis. His publications demonstrate a consistent bridge between theoretical frameworks and practical applications, spanning computer vision, geospatial analysis, and algorithmic game theory. Professional Recognition Helmbold is a long-standing member of the computational learning theory community, having hosted the COLT conference and served on its steering committee. No specific awards are mentioned in the source material.

• Machine Learning
• Computational Learning Theory
• Boosting
• +۴ مورد دیگر

Soukaina Filali Boubrahimi serves as an Assistant Professor in the Computer Science Department within the College of Engineering at Utah State University. Her academic appointment is based in the SER 332 building located at 4205 Old Main Hill, Logan, UT 84322-0001. She maintains a research-active position with a focus on computational methods for complex temporal data analysis. Dr. Filali Boubrahimi's research program centers on time series analysis , machine learning , and space weather prediction , with particular emphasis on solar flare forecasting and counterfactual explanation systems. Her work bridges theoretical machine learning advancements with practical applications in heliophysics, hydrology, and social media analysis. The research portfolio demonstrates significant expertise in handling imbalanced temporal datasets, developing novel data augmentation techniques, and creating interpretable AI systems for critical prediction tasks. Analysis of her recent publication trajectory reveals consistent contributions to counterfactual explanation frameworks for time series data (Info-CELS, M-cels, ACTS), space weather prediction systems (solar flare and energetic particle event forecasting), and generative modeling approaches (AVATAR, ChronoGAN). Her work frequently addresses the challenges of severely imbalanced datasets through contrastive learning and sophisticated preprocessing techniques, demonstrating methodological innovation in handling rare but critical space weather events. While no specific awards are documented in the available information, her research program appears substantial based on the volume and quality of recent publications spanning multiple high-impact domains. The research demonstrates strong interdisciplinary connections between computer science, space physics, and environmental science. Her laboratory activities focus on developing machine learning frameworks for temporal data analysis, with particular attention to space weather prediction systems. The research group appears to specialize in creating robust models for rare event prediction, explainable AI systems for time series classification, and novel data augmentation techniques for imbalanced temporal datasets. Current projects likely include the development of multimodal fusion approaches for solar energetic particle prediction and spatio-temporal modeling for hydrological applications.

• Machine Learning
• Time Series Analysis
• Space Weather Prediction
• +۴ مورد دیگر

Dr. Julie Kang, MD, PhD is an Assistant Professor of Therapeutic Radiology at Yale School of Medicine and a board-certified radiation oncologist specializing in head and neck, prostate, bladder, and skin cancers. She practices at Smilow Cancer Hospital Care Center at Trumbull and is affiliated with Yale Cancer Center and Yale Medicine. Her educational background includes a BS from Yale University (2002), PhD from University of Southern California (2008), MD from University of Southern California (2010), and residency at UCLA (2015). Dr. Kang's research focuses on optimizing cancer treatment outcomes while minimizing toxicity through advanced radiation techniques including intensity-modulated radiation therapy, image-guided radiation therapy, stereotactic body radiation therapy, and adaptive radiation therapy. She has extensive experience conducting clinical trials utilizing immunotherapy and personalized medicine approaches for head and neck and prostate cancers. Her work emphasizes analyzing factors that influence cancer outcomes to enhance healthcare quality, with particular interest in addressing cancer disparities and reducing barriers to care. Analysis of Dr. Kang's recent publications (2022-2025) reveals a strong focus on treatment de-escalation strategies for HPV-associated oropharyngeal cancer, radiation dosing optimization for head and neck malignancies, and innovative applications of artificial intelligence in cancer care. Her research spans clinical trials, retrospective analyses, and systematic reviews addressing critical questions in radiation oncology with emphasis on improving patient outcomes while reducing treatment-related toxicity. Kaiser Permanente Everyday Hero Award Recipient (July 7, 2017) Kaiser Permanente Physician Merit Award (July 11, 2016) Dr. Kang is actively involved in clinical trials and cooperative groups, with a focus on mentoring the next generation of medical professionals in research related to head and neck and prostate cancers. Her clinical approach is deeply informed by personal experience as "the daughter of a cancer patient," emphasizing compassion, patient education, and personalized treatment planning. She is dedicated to not only delivering the highest standards of care but also raising them through continuous research and innovation in radiation oncology. As part of Yale's Therapeutic Radiology department, Dr. Kang contributes to a comprehensive cancer care program that includes state-of-the-art facilities such as the Yale Gamma Knife Center and Smilow Cancer Hospital, working within multidisciplinary teams to provide integrated cancer care across multiple locations including Hamden, Greenwich, and Lawrence and Memorial Cancer Center.

• Head and Neck Cancer
• Prostate Cancer
• Radiation Oncology
• +۸ مورد دیگر

Michael Gadermayr serves as a Senior Lecturer and Head of the Research Group within the Department of Information Technologies and Digitalisation at Salzburg University of Applied Sciences. Based at Campus Urstein (Room 423), he can be contacted via michael.gadermayr@fh-salzburg.ac.at or +43-50-2211-1341. His research focuses on advancing medical imaging through artificial intelligence, with core expertise in deep learning for image segmentation, digital pathology, and cancer diagnosis. Key contributions include multimodal fusion techniques for CT/CBCT integration, synthetic data generation for surgical guidance, and objective wound healing quantification using vision models. His work bridges computer vision and clinical applications to solve real-world healthcare challenges. Analysis of his 15 most recent publications reveals a dominant trend toward leveraging synthetic data and multimodal fusion to enhance segmentation accuracy in oncology and surgical contexts. Over 70% of his work targets CT/CBCT integration for intraoperative navigation, while digital pathology applications (particularly thyroid and breast cancer) constitute 25% of his output. Emerging themes include wound healing quantification using SAM and parameter optimization for MIL-based pathology diagnostics. As Head of the Research Group in Information Technologies and Digitalisation, he leads initiatives focused on translating AI innovations into clinical practice, with emphasis on robustness in medical image analysis and practical deployment of segmentation tools for radiology and pathology workflows.

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

Dokyun Lee is a Kelli Questrom Chair Associate Professor of Information Systems and Computing & Data Science at Boston University , where he founded the Business Insights through Text (BIT) Lab and co-leads the BU Digital Business Institute Generative AI Lab . His research focuses on the responsible application, development, and impact of AI in business, particularly analyzing unstructured data (e.g., text, images) across digital consumer management, platform design, and human-AI collaboration. Education : PhD in Operations, Information & Decisions (Wharton School), MS in Statistics (Yale University), BA in Computer Science (Columbia University) His work addresses Generative AI (unintended consequences, human-integration frictions), Economics of Unstructured Data (content extraction, marketing), and AI Regulation in contexts like advertising, market competition, and creativity. Recent articles explore LLMs in corporate risk nowcasting, generative AI's effects on online communities, and interpretable deep learning for churn management. His research trends span NLP, computer vision, and causal inference, with applications in digital marketing, e-commerce, and innovation analytics. Scientific Awards : INFORMS ISS Gordon B David Young Scholar, INFORMS ISS Sandy Slaughter Early Career, CDO Magazine Leading Academic Data Leader, AMA Don Lehmann Award 2024, MSBA Teaching Award He has secured grants from Adobe, Google, Microsoft, MassMutual, and Prudential Foundation. His labs (BIT Lab, Generative AI Lab) mentor students and collaborate on AI-driven solutions for business challenges.

• Generative AI
• Economics of Unstructured Data
• Human-AI Collaboration
• +۵ مورد دیگر

Lena Schmidt is a Research Fellow at the University College London (UCL) Social Research Institute , specializing in methods development for systematic reviews , evidence maps , and living evidence syntheses . She leverages machine learning and neural networks to optimize data mining and classification during the screening process. Her work emphasizes automation tools for large-scale literature analysis and addressing reproducibility in evaluations. MSc Applied Health Sciences , Furtwangen University (2018) NIHR Systematic Reviews Fellowship (2018–2020) Predoctoral Fellowship in Living Reviews and Automation Her research focuses on enhancing systematic review efficiency through NLP , AI , and data engineering , particularly for grey literature in health research. Recent publications address pandemic impacts on mental health , automation in evidence synthesis , and knowledge graph development for toxicology. Scientific awards include the NIHR Fellowship and Intelligent and Adaptive Systems Certification from the University of Sussex. She actively collaborates with organizations like the Campbell Collaboration and ICASR , co-organizing automation workshops and contributing to methodological frameworks.

• Systematic Review Automation
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

David Lowe is a Professor of Physics at Brown University, where he has been a faculty member since 1997. His academic journey includes a B.A. and M.A. from Cambridge University and a Ph.D. in theoretical physics from Princeton University (1993), followed by postdoctoral research at UC Santa Barbara and Caltech. His research focuses on string theory applications to gravitational physics , particularly black hole thermodynamics, quantum gravity, and cosmological implications of holographic principles. Key contributions include foundational work on AdS/CFT correspondence, black hole information paradox resolution, and de Sitter space holography. His publications span high-impact journals including Journal of High Energy Physics and Physical Review D , with recent emphasis on quantum information aspects of black holes. Richard B. Salomon Faculty Research Award BSF Research Grant NSF Travel Award Lowe has maintained extensive international collaborations, holding visiting positions at U. Tokyo, Max Planck Institute, CINVESTAV, KITP, and Aspen Center for Physics. His teaching portfolio includes graduate courses in general relativity, quantum field theory, and advanced electrodynamics, reflecting his expertise in theoretical physics.

• String Theory
• Quantum Gravity
• Black Hole Physics
• +۳ مورد دیگر

Beyza Eken serves as Assistant Professor in the Department of Software Engineering at Sakarya University's Faculty of Computer and Information Sciences, teaching core courses including Software Project Management, Natural Language Processing, and Graduation Projects while maintaining active research in software engineering and AI applications. Her academic credentials include: Doctorate in Computer Engineering from Istanbul Technical University (2015), thesis: "Software Defect Prediction" Master's in Computer Engineering from Istanbul Technical University (2011-2015), thesis: "Entity Name Recognition in Short Texts" Bachelor's in Computer Engineering from Sakarya University (2007-2011) Dr. Eken's research integrates machine learning with software engineering, specializing in defect prediction models that incorporate personalized developer factors and industrial deployment challenges. Her work bridges natural language processing for Turkish social media analysis with software quality assurance, demonstrating expertise in both theoretical modeling and practical implementation in industrial settings. Recent expansions include neuro-symbolic AI for test oracle generation and MLOps frameworks. Publication trends reveal consistent focus on empirical software engineering from 2018-2021 (defect prediction, community smells, industrial deployment), evolving into cutting-edge domains by 2023-2025 (neuro-symbolic testing, employee feedback analysis, MLOps). Her work shows strong industry-academia collaboration patterns with increasing methodological sophistication. Dr. Eken actively contributes to academic service as reviewer for ACM Transactions on Software Engineering and Methodology (2024) and IEEE Transactions on Software Engineering (2023). She leads research projects including "Developer-specific error prediction modeling" (2020) and the Mevlana exchange project with Ryerson University on data mining for defect prediction (2018), while supervising graduation projects and research area courses that develop student expertise in software engineering practices. Her international research engagement includes participation in the ASTERIx project at Università della Svizzera Italiana's Software Testing and Analysis Research Group (2023), demonstrating ongoing commitment to global collaboration in software engineering advancements.

• Software Defect Prediction
• Natural Language Processing
• Machine Learning in Software Engineering
• +۲ مورد دیگر

Claudia Lenk serves as Full Professor at Ulm University since 2024, leading the Biomedical Sensor Systems and Microsystems research group. Her work focuses on developing bio-inspired acoustic sensors to enhance human and machine hearing capabilities, particularly for speech processing in noisy environments through MEMS-based adaptive technologies. Education: Technical Physics, TU Ilmenau PhD in Biophysics (specializing in numerical/chemical modeling of atrial fibrillation mechanisms) Postdoctoral research developing MEMS-based artificial hair cells for hearing enhancement Her research integrates bio-inspired engineering with acoustic sensor design to create noise-robust systems that mimic biological hearing mechanisms. Key innovations include tunable MEMS resonators, neuromorphic auditory processing, and integrated signal pre-processing for hearing aids and robotics. This interdisciplinary approach bridges microsystems engineering, neuroscience, and auditory perception to address limitations in current speech processing technologies. Analysis of her 15 most recent publications (2020-2025) reveals a consistent trajectory toward adaptive neuromorphic acoustic systems. Dominant themes include resonance frequency control, dynamic range expansion through nonlinear dynamics, and bio-inspired feature extraction for low signal-to-noise ratio environments. These advancements target practical implementations in energy-constrained devices like hearing aids and autonomous systems. Scientific Awards: No awards documented in source materials Advising and Grants: Source materials contain no information regarding student supervision, research grants, or collaborative funding initiatives. Labs and Teams: She directs Ulm University's Biomedical Sensor Systems and Microsystems group, which develops cutting-edge sensor technologies for hearing applications, robotics, and speech processing systems through MEMS fabrication and neuromorphic computing approaches.

• Bio-inspired Sensors
• MEMS
• Neuromorphic Engineering
• +۳ مورد دیگر

Professor Zoe Marriage is a leading scholar in the Department of Development Studies at SOAS University of London, specializing in the intersections of conflict, security, and development with particular expertise on African contexts including the Democratic Republic of Congo, Sierra Leone, Rwanda, and South Sudan. As a Principal Fellow of the Higher Education Academy, she maintains an active teaching and supervision profile alongside her research. Department of Development Studies, SOAS University of London Principal Fellow of the Higher Education Academy Active PhD supervisor with multiple recent completions Marriage's research trajectory began with humanitarian assistance studies in conflict zones, evolving into deeper investigations of security conceptualization in contexts of structural violence, and most recently exploring cultural resistance through unconventional frameworks like capoeira. Her work consistently centers on marginalized voices excluded from formal security debates, examining alternative pathways to security constructed from below. Her three major monographs trace this evolution: Not Breaking the Rules, Not Playing the Game (2006) on humanitarian assistance, Formal Peace and Informal War (2013) on Congolese security conceptualizations, and Cultural Resistance and Security from Below (2019) on capoeira as resistance. Analysis of Marriage's recent publications reveals a sustained focus on African political economies of conflict, with increasing attention to cultural and embodied forms of resistance. Her 2024 publications continue examining South Sudanese state formation while maintaining her Congo expertise, demonstrating remarkable longevity in regional specialization combined with methodological innovation through cultural analysis. As an active PhD supervisor, Marriage mentors students working on diverse security governance topics across Africa including South Sudanese displacement, Ghanaian land conflicts, Congolese judicial reform, and Sierra Leonean decentralization. Her supervision portfolio reflects her interdisciplinary approach bridging political science, anthropology, and development studies. Marriage leads the reDe research group (visible through her website URL), which appears to focus on reimagining development and security paradigms through critical and cultural lenses, though specific details about the group's structure require further investigation.

• Conflict Studies
• Security Studies
• Development Studies
• +۴ مورد دیگر

Markus Henningsson serves as an Adjunct University Lecturer at Linköping University's Department of Health, Medicine and Care (HMV) and Department of Diagnostics and Specialist Medicine (DISP). His academic work centers on advanced medical imaging techniques with particular emphasis on cardiovascular applications. His research interests span Medical Imaging , Cardiovascular Imaging , and Artificial Intelligence in Medicine . Dr. Henningsson specializes in developing and applying novel MRI techniques for cardiac analysis, particularly focusing on left atrial structure, epicardial adipose tissue, and fibrosis segmentation. His work bridges engineering methodologies with clinical cardiology applications. Analysis of his recent publications reveals a strong trend toward integrating machine learning with medical imaging, particularly using deep learning architectures like geometric UNet for tissue segmentation. His research consistently addresses clinically relevant questions in atrial fibrillation and cardiovascular disease assessment through advanced imaging biomarkers. While no formal scientific awards are documented in the available information, his work appears in reputable journals including Scientific Reports , Physics in Medicine and Biology , and Magnetic Resonance in Medicine . His collaborative approach is evident through numerous co-authorships with researchers across cardiovascular sciences. Dr. Henningsson's work appears connected to Linköping University's Cardiovascular Sciences Unit, which conducts research in cardiovascular physiology, cardiology, and related surgical disciplines. His contributions support the department's focus on both basic and clinical cardiovascular research with direct patient care applications.

• Medical Imaging
• Cardiovascular Imaging
• Cardiology
• +۳ مورد دیگر

Ioannis Tzimas is a Professor at the Department of Electrical and Computer Engineering of the University of Peloponnese. He is a highly active researcher with numerous publications in areas of Service-Oriented Architectures, Web Engineering, Big Data, and Artificial Intelligence applications. University: University of Peloponnese Department: Department of Electrical and Computer Engineering Academic Rank: Professor Email: tzimas@uop.gr Ioannis Tzimas received his education from the Department of Computer Engineering and Informatics of the University of Patras, where he also completed his PhD in Web Engineering. His research spans multiple interdisciplinary domains at the intersection of computer science and practical applications. Service-Oriented Architectures and Information Systems Web Data Engineering and Web Modeling Big Data Management and Data Science Machine Learning and Artificial Intelligence Applications Digital Ecosystems and Digital Transformation for the Public Sector Bioinformatics Professor Tzimas' recent research output demonstrates a strategic focus on applying advanced computational techniques to address contemporary challenges. His work shows particular expertise in labor market analysis using large language models, electricity demand forecasting in Greece, and social protection systems. His publications reveal a pattern of bridging theoretical computer science with practical, real-world applications across multiple sectors. Since 2018, he has served as an international consultant to the World Bank in the field of information systems and digital transformation, working on projects across Europe, Africa, the Caribbean, the Pacific Islands, and China. His earlier career included significant technical leadership roles at the University of Patras and other Greek institutions. Technical Manager of the Graphics, Multimedia and Geographic Systems Laboratory (1996-2018) Technical Coordinator of the Internet and Multimedia Technologies Research Unit (1997-2011) Scientific Manager of the Network Management Center of the TEI of Messolonghi (2009-mid 2013)

• Service-Oriented Architectures and Information Systems
• Web Data Engineering
• Web Engineering and Web Modeling
• +۶ مورد دیگر

Haoyu Wang is a Researcher in the Computer and Information Science department at the University of Pennsylvania . He previously held research positions at Shanghai Jiao Tong University and interned at Google DeepMind , Amazon AWS , ByteDance , AI2 , Tencent AI Lab , and Goldman Sachs . Education : PhD in Computer and Information Science (2021–Present), MS in Computer and Information Science (2019–2021), BS in Computer Science (2015–2019). His research focuses on Event-Centric NLP/NLU , LLM Reasoning and Planning , Knowledge Graph , and Pose Estimation in Computer Vision . His work includes event causality identification, semantic classification in context, and synthetic control for temporal reasoning. He has contributed to multimodal hallucination analysis and safety in reasoning models through projects like RESIN-11 and Devil's Advocate . His publications span venues like EMNLP , EACL , and ACL . His recent articles analyze LLM limitations in NP-hard problems , clinical trial prediction , event causality , and hallucination in vision-language models . He has served as PC Member for conferences including ACL , NAACL , NeurIPS , and EMNLP since 2019.

• Event-Centric NLP/NLU
• LLM Reasoning and Planning
• Knowledge Graph
• +۵ مورد دیگر

Andrés Bruhn is a Professor for Intelligent Systems and Dean of Computer Science Studies at the University of Stuttgart, where he leads research in the Institute for Visualization and Interactive Systems (VIS). His academic career spans over a decade with significant contributions to computer vision, particularly in optical flow, scene flow, and motion estimation. As Dean of Studies, he oversees academic programs while maintaining an active research agenda focused on cutting-edge computer vision problems. Bruhn's research interests center around computer vision with emphasis on optical flow estimation, scene flow, motion analysis, and adversarial machine learning. His work bridges theoretical foundations with practical applications, developing algorithms that address real-world challenges in motion estimation, image processing, and visual understanding. His research group has pioneered approaches that combine variational methods with deep learning, creating robust systems for motion analysis that can withstand adversarial attacks and challenging environmental conditions. The publication record demonstrates a strong focus on advancing the state-of-the-art in motion estimation, with recent work exploring adversarial attacks on optical flow systems, high-resolution datasets for benchmarking, and multi-frame fusion techniques. His research shows consistent innovation, moving from traditional variational methods to modern deep learning approaches while maintaining mathematical rigor. The work spans both theoretical contributions and practical implementations with real-world applicability. Bruhn has mentored numerous researchers who appear as first authors on publications, including Jenny Schmalfuss, Lukas Mehl, and Azin Jahedi, indicating his commitment to developing the next generation of computer vision researchers. His leadership role as Dean of Studies demonstrates institutional recognition of his expertise and administrative capabilities.

• Computer Vision
• Optical Flow
• Scene Flow
• +۵ مورد دیگر