جستجوی استاد راهنمای دانشگاهی

Erik Dernerمشاهده پروفایل

پژوهشگر

• Human-Centric Artificial Intelligence
• Large Language Models
• Robotics
• +۶ مورد دیگر

Erik Derner is an ELLIS Postdoctoral Researcher at the ELLIS Unit Alicante, focusing on human-centric AI and ethical LLMs. He collaborates with Prof. Robert Babuška's Machine Learning team at the Czech Institute of Informatics, Robotics, and Cybernetics (CIIRC) and contributes to the VIVES project under the PERTE of New Language Economy. His work addresses biases in LLMs, AI safety, and security, while also exploring symbolic regression for robotics and reinforcement learning. Education: Ph.D. in Robotics and Machine Learning (2022) from Czech Technical University in Prague, with B.Sc. and M.Sc. in Open Informatics. Erik's research spans human-centric AI , large language models , robotics , computer vision , and reinforcement learning . He emphasizes ethical and secure AI development, particularly for underrepresented languages. His work integrates symbolic regression with neural networks to create interpretable and efficient models for robotics. Recent publications highlight his focus on LLM security , gender bias analysis , and symbolic regression for dynamic systems. Notable trends include ethical AI, multimodal red-teaming, and physics-informed modeling. Scientific Awards: Werner von Siemens Award (2023) for his Ph.D. thesis. CTU FEE Dean's Award (2023) for a prestigious dissertation. Erik supervises student projects in robotics, AI ethics, and LLM security, including internships and theses on topics like mental health risks, toxicity evaluation, and real-time assistance for the visually impaired.

• Natural Language Processing
• Computational Linguistics
• Dependency Parsing
• +۷ مورد دیگر

Carlos Gómez Rodríguez is a Full Professor (Catedrático de Universidad) in the Department of Computer Science at the University of A Coruña, Spain, where he leads the FASTPARSE Lab within the LyS Research Group at the CITIC Research Center. His work bridges theoretical and practical aspects of computational linguistics with significant applications in natural language processing. His primary research interests focus on natural language parsing algorithms, with particular emphasis on improving parsing speed for web-scale applications, handling non-projective dependency structures, analyzing morphologically-rich and low-resource languages, and exploring the cognitive aspects of syntax. He maintains a critical perspective on the field's current LLM-driven revolution, advocating for continued research into alternative approaches that prioritize efficiency, explainability, and scientific insight beyond user-facing applications. His recent publications reveal a strong trend toward examining the capabilities and limitations of large language models, particularly in comparison to human language processing, while continuing to advance traditional parsing techniques. His work spans theoretical foundations, practical implementations, and diverse applications including sentiment analysis, opinion mining, and creative writing evaluation. National Young Researcher Price "María Andresa Casamayor" in Mathematics and Information Technologies by Spain's Ministry of Science Honorary Member of the Royal Spanish Mathematical Society CAEPIA 2024 Best Paper Award for research on evaluating creative writing in LLMs Professor Gómez Rodríguez has successfully led significant research projects including the ERC Starting Grant project FASTPARSE, which focused on techniques to improve the speed of parsing algorithms. His work demonstrates consistent funding support for innovative research at the intersection of computational linguistics, cognitive science, and practical NLP applications. He actively collaborates with researchers across Spain and internationally, as evidenced by his extensive publication record with diverse co-authors. He leads the FASTPARSE Lab and is a core member of the LyS Research Group, focusing on advancing both theoretical understanding and practical implementations of language processing technologies. His team explores diverse aspects of natural language processing, from fundamental syntactic analysis to applied sentiment analysis and innovative applications of language technology in domains like healthcare.

Rico Sennrichمشاهده پروفایل

استادیار

• Natural Language Processing
• Machine Translation
• Multilinguality
• +۷ مورد دیگر

Rico Sennrich is an Assistant Professor at the University of Zurich's Department of Computational Linguistics, specializing in natural language processing with a focus on machine translation and multilinguality. He is also an Honorary Fellow at the University of Edinburgh and an ELLIS Fellow. His research spans low-resource and efficient methods, interpretability and model analysis, and multimodal language processing, particularly known for work on tokenization, neural architectures, and data augmentation. His research interests include developing practical solutions for multilingual machine translation, addressing challenges in low-resource scenarios, improving model efficiency, and analyzing how neural networks process language. He has made significant contributions to subword tokenization techniques and neural architecture design that have been widely adopted in both research and industry applications. His work often bridges theoretical insights with practical implementations to advance the state of the art in NLP. His recent publications reflect trends toward addressing bias in machine translation, improving document-level translation, enhancing multilingual capabilities, and developing more efficient inference methods for large language models. His research increasingly focuses on the intersection of machine translation with multimodal processing and low-resource language scenarios. ELLIS Fellow Honorary Fellow at the University of Edinburgh Action editor for Computational Linguistics and ACL Rolling Review Standing reviewer for TACL Senior area chair for ACL 2025 Professor Sennrich has advised numerous PhD students whose work has received recognition, including ACL outstanding paper awards and best thesis awards. He leads research projects such as Evolving Language (2024-), InvestigaDiff (2024-2027), and MT for Romansh idioms (2025-2026), having previously led the MUTAMUR project (2019-2025). His laboratory focuses on practical NLP solutions with real-world impact, particularly in multilingual and low-resource scenarios. The team actively collaborates with industry partners and participates in major research initiatives like ELITR.

Emmanuel Cledatمشاهده پروفایل

دانشیار

• Photogrammetry
• Sensor Calibration
• Risk Measurement for Cyclists
• +۴ مورد دیگر

Emmanuel Cledat is an Associate Professor of photogrammetry at the National Institute of Geographic and Forest Information (IGN) and lecturer at the ENSG (National School of Geographic Sciences), where he teaches courses in sensor technology, mathematics for photogrammetry, and applied photogrammetry. As a member of the UMR Lastig research unit and ACTE research team, he conducts interdisciplinary work spanning photogrammetry, geomatics, and transportation safety. His research interests focus on photogrammetry , sensor calibration , and measurement of risks faced by cyclists . Dr. Cledat specializes in macro-photogrammetry of small objects, drone-based mapping systems, and GNSS-denied environment navigation. His methodological expertise includes camera calibration models, 3D reconstruction, and fusion of photogrammetric and LiDAR data. Dr. Cledat's publication record demonstrates consistent contributions to photogrammetry and geospatial sciences since 2016, with recent work exploring AI applications in geomatics and historical bridge modeling. His research shows a clear trajectory from foundational work on drone photogrammetry calibration to more applied projects addressing transportation safety and cultural heritage preservation. ISPRS Best Young Author Award 2020 As principal investigator, Dr. Cledat leads the CycloSafe project which quantifies cycling risks using LIDAR-equipped bicycles, and the EntrePonts project focused on 3D modeling of historical bridge models from the 17th-19th centuries. His teaching portfolio spans undergraduate and graduate courses in photogrammetry fundamentals, underwater photogrammetry, and climate change workshops. His laboratory work centers around the UMR Lastig research unit, with projects involving drone mapping systems, 3D TOF camera calibration, and photogrammetric fieldwork methodologies for both small objects and large-scale environmental mapping.

Jorge Roserمشاهده پروفایل

پژوهشگر

• Medical Physics
• Image Reconstruction
• Particle Therapy
• +۳ مورد دیگر

Jorge Roser is a Research Scientist at the Institute of Medical Engineering, University of Lübeck, where he has been working since October 2023. His research focuses on medical imaging technologies, particularly Compton cameras and their applications in particle therapy verification. Dr. Roser received his Bachelor Degree in Physics from the University of Valencia in 2016 and his M.Sc in Theoretical Physics from the same institution in 2017. He completed his PhD in Physics from the Universidad de Valencia in 2023 with summa cum laude honors and international mentions. His research interests include: Emission tomography Image reconstruction Gamma-ray detection Particle therapy range verification Compton camera technology Dr. Roser's publication record demonstrates expertise in developing novel image reconstruction algorithms and techniques for medical imaging applications. His work often involves Monte Carlo simulations, analytical modeling, and innovative approaches to improve signal-to-background ratios in Compton camera imaging for proton therapy verification. Recent publications show significant advancements in range monitoring capabilities and background reduction techniques. He has been actively collaborating with researchers from various institutions, as evidenced by his co-authorship on multiple publications with international teams working on the SiFi-CC detector and related medical imaging technologies.

Maik Stilleمشاهده پروفایل

پژوهشگر

• Medical Imaging
• Computed Tomography
• Image Reconstruction
• +۳ مورد دیگر

Dr. Maik Stille is a Research Associate at the Fraunhofer Research Institution for Individualized and Cell-Based Medical Technology (IMTE) in Lübeck, Germany, with strong affiliations to the University of Lübeck's Institute of Medical Engineering. His work focuses on advanced medical imaging techniques, particularly in computed tomography and artifact reduction methodologies. His research interests center on Computed Tomography , Metal Artifact Reduction , Image Registration , and Neuroimaging . Dr. Stille has developed innovative approaches for metal artifact correction using deep learning techniques and non-local prior image integration, significantly advancing the field of medical image reconstruction. His work bridges clinical applications with computational methodologies, particularly in addressing challenges posed by metal implants in CT imaging. Analysis of his recent publications reveals a strong trend toward deep learning applications in medical imaging, particularly using generative adversarial networks for metal artifact reduction. His research spans both clinical CT applications and industrial computed tomography, demonstrating versatility across medical and engineering domains. Key focus areas include dual-energy CT calibration, virtual monoenergetic imaging enhancement, and magnetic particle imaging techniques. Dr. Stille collaborates extensively with researchers at the University of Lübeck and participates in the Graduate School for Computing in Medicine and Life Sciences. His work has significant implications for radiation therapy planning, diagnostic imaging quality improvement, and industrial non-destructive testing applications.

Manling Liمشاهده پروفایل

استادیار

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۷ مورد دیگر

Manling Li is an Assistant Professor at the Department of Computer Science, Northwestern University. She previously served as a postdoc at Stanford University's Vision and Learning Lab under Prof. Jiajun Wu and received her Ph.D. from the University of Illinois at Urbana-Champaign (advisor: Prof. Heng Ji). Her research spans Language + Vision + Robotics with applications in Embodied AI and AI for Science . Key Research Areas : Knowledgeable Foundation Models, Reasoning & Planning, Compositionality, Multimodal Knowledge Extraction, Factuality & Trustworthiness in AI Leadership Roles : Organizing Committee for ACL 2025, NAACL 2025, EMNLP 2024 Research Trends in her 15 most recent publications show: Advancing Embodied AI through structured reasoning and planning frameworks Developing Vision-Language Models for 3D layout optimization and video understanding Addressing LLM Hallucinations via knowledge shadowing and mechanistic interpretability Creating collaborative agent systems with out-of-sync recovery mechanisms Scientific Recognition : ACL 2024 Outstanding Paper SoCal NLP 2024 Best Paper Microsoft Research Fellowships (PhD & Postdoc) DARPA Riser & EE CS Rising Star Advising Impact : Mentored 19 students in developing the UIUC information extraction system. Currently advising 12 students across PhD, Master's, and undergraduate levels, with particular emphasis on supporting underrepresented groups. Led teams to rank 1st in DARPA AIDA evaluations.

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

Prof. Dr. Mario Fritz is a Professor at Saarland University and faculty member at CISPA Helmholtz Center for Information Security. He serves as a Fellow at the European Laboratory for Learning and Intelligent Systems (ELLIS). His research focuses on Trustworthy Information Processing at the intersection of AI & Machine Learning with Security & Privacy. Mario Fritz leads numerous significant research initiatives including the European Large Open Multi-Modal Foundation Models for Robust Generalization (ELLIOT), European Lighthouse on Secure and Safe AI (ELSA), and multiple projects on privacy-preserving AI applications in healthcare. His work spans security and privacy aspects of large language models, foundation models, and medical AI applications. He has established himself as a leading researcher in trustworthy AI through his extensive publication record and leadership in major collaborative projects. His recent research (2025 publications) demonstrates a strong focus on the security and safety challenges of large language models, including model stealing attacks, causal reasoning capabilities, sampling methods, and privacy risks. His work bridges theoretical foundations with practical applications across multiple domains, particularly in healthcare and cybersecurity. Mario Fritz actively mentors PhD students and Post-Docs, seeking new researchers to join his group. He has been involved in numerous grants and collaborative projects, including those funded by BMBF and the Helmholtz Association, demonstrating his ability to secure substantial research funding and lead large interdisciplinary teams.

Neil Gongمشاهده پروفایل

دانشیار

• Cybersecurity
• Trustworthy AI
• Adversarial Machine Learning
• +۳ مورد دیگر

Neil Gong is an Associate Professor at Duke University's Pratt School of Engineering , holding primary and secondary appointments in Electrical and Computer Engineering and Computer Science respectively. His work bridges cybersecurity and trustworthy AI , with a focus on adversarial machine learning and secure systems. Education: Ph.D. in Computer Science (UC Berkeley, 2015), B.E. (USTC, 2010) Research emphasizes AI security , including prompt injection attacks , federated learning safety , and watermark robustness in generative models. Recent publications analyze vulnerabilities in LLM agents, multi-modal systems, and graph-based security mechanisms. His 2023-2025 publications reveal trends in LLM attack/defense (jailbreaking, watermarking), federated learning security (poisoning, Byzantine robustness), and privacy analytics (membership inference, data auditing). Notable Awards: NSF CAREER, Army Research Office YIP, IBM Faculty Award, Facebook Research Award Teaching includes courses on machine learning in adversarial settings and advanced topics in ECE . Actively recruiting postdocs and graduate students for cybersecurity research.

Lei Liمشاهده پروفایل

دانشیار

• Natural Language Processing
• Machine Learning
• Machine Translation
• +۱۲ مورد دیگر

Lei Li is an Associate Professor in the Department of Computer Science at the University of California, Santa Barbara (UCSB), where they serve as Co-Director of the UCSB NLP Group. Their research focuses on developing algorithms and systems for machine learning, natural language processing, machine translation, reasoning, and AI-powered drug discovery. Dr. Li received their PhD from Carnegie Mellon University and completed their undergraduate studies at Shanghai Jiao Tong University. Dr. Li's research spans multiple critical areas in artificial intelligence and machine learning. Their work in natural language processing encompasses machine translation, speech translation, multilingual NLP, large language models, text generation, program synthesis, reasoning, privacy, and watermarking. Additionally, they have made significant contributions to AI applications in drug design and efficient machine learning techniques. Their research bridges theoretical foundations with practical applications, particularly in the emerging field of AI for biological discovery. Analysis of Dr. Li's recent publications reveals a strong focus on advancing natural language processing capabilities while addressing critical challenges in model efficiency, security, and evaluation. Their work spans machine translation systems that handle hundreds of languages, techniques for improving large language model capabilities in zero-shot settings, methods for evaluating text generation quality, and novel approaches for protecting intellectual property in language models. Notably, they've also made significant contributions to applying AI to biological problems, particularly in antimicrobial peptide discovery and protein sequence design. Dr. Li has received notable recognition for their research, including: Best Paper Award at ACL 2021 for "Vocabulary Learning via Optimal Transport for Neural Machine Translation" Dr. Li actively advises PhD and Master's students in computer science at UCSB, with recent advisees working on topics including antimicrobial peptide discovery, protein sequence design, diffusion models, speech translation, and language model watermarking. Their research group, the UCSB NLP Group, appears to be well-funded and productive, with consistent publications in top-tier conferences including ACL, EMNLP, ICML, KDD, and NeurIPS. The group has developed several influential frameworks and benchmarks, including MTG (Multilingual Text Generation benchmark) and SEScore2 (text generation evaluation metric). The UCSB NLP Group, co-directed by Dr. Li, maintains an active research agenda with multiple ongoing projects spanning natural language processing, machine learning, and their applications to scientific discovery. The group collaborates with researchers across disciplines, particularly in the biological sciences for drug discovery applications.

Tianlong Chenمشاهده پروفایل

استادیار

• Artificial Intelligence
• Machine Learning
• Large Language Models
• +۵ مورد دیگر

Tianlong Chen is an Assistant Professor in the Department of Computer Science at The University of North Carolina at Chapel Hill , starting in Fall 2024. His research focuses on AI trustworthiness, efficiency, and scientific applications , particularly through sparsity, multimodal learning, and large language model (LLM) innovations. Research Interests include: Sparsity techniques for LLM optimization Multimodal learning and graph neural networks AI safety and privacy preservation Quantum computing applications Biological-informed AI systems Recent Trends in his publications emphasize Mixture-of-Experts (MoE) , LLM safety mechanisms , and lifelong learning architectures . Awards highlight recognition from Amazon, UNC Provost, NAIRR, and AAAI. Scientific Awards : Amazon Research Award (2025) UNC Accelerating AI Awards (2025) NAIRR Pilot Award (2025) CPAL/KAUST Rising Star Awards (2025) AAAI New Faculty Highlights (2025) Advising : Mentors 19 Ph.D. students across UNC Chapel Hill and remote collaborations, focusing on AI4Science, LLMs, and quantum computing. Grants include Cisco Research funding and UNC Provost AI support.

• Computer Vision
• Machine Learning
• Image Processing
• +۳ مورد دیگر

Hui Guo is an Adjunct Assistant Professor in the Department of Civil Engineering. Their research intersects computer vision, machine learning, and image processing, with a focus on advanced restoration and super-resolution techniques. Recent publications highlight contributions to image super-resolution challenges (NTIRE 2024/2025), vision transformers (QID), diffusion models for video restoration, and continual learning frameworks (PCL). Work spans OCR-free visual document analysis, adversarial degradation modeling (AND), and parametric image restoration. Contact: guoh15

• Parallel Programming
• GPU Programming
• Image Processing
• +۵ مورد دیگر

Sándor Szénási is a Professor at the Department of Informatics within the Faculty of Economics and Informatics at J. Selye University, where he serves as the person responsible for the Applied Informatics study program. With over two decades of academic experience, he has established himself as a leading researcher in parallel programming, GPU programming, and image processing, with recent expansion into machine learning applications. Eötvös Loránd University, Faculty of Science and Informatics (2001-2004): Information technology teacher Budapest Polytechnic, John von Neumann Faculty of Information Technology (1997-2001): B. Engineer in Information Technology Óbuda University (2010-2013): PhD in Applied Informatics Habilitation at Óbuda University (2019): Information Science and Technology Professor inauguration at Óbuda University (2022) Szénási's research spans computational methods with practical applications across multiple domains. His early work focused on parallel and GPU programming for image segmentation and heat transfer simulation. More recently, he has integrated machine learning techniques with traditional computational approaches, particularly in metaheuristic optimization, speech processing, and inverse problem solving. His interdisciplinary research bridges computer science with transportation safety, manufacturing, and medical applications. His recent publications reveal a clear evolution toward hybrid computational approaches that combine machine learning with traditional algorithms. There is a strong emphasis on optimization techniques, particularly metaheuristics enhanced with machine learning components. His work spans diverse application areas including speech emotion recognition, autonomous vehicle control, additive manufacturing, and heat transfer simulation, while maintaining a core focus on computational efficiency and parallel processing. Szénási has been actively involved in multiple EFOP-funded research projects including 'Improvement of higher education institutes for better teaching quality and accessibility,' 'Dynamics and control of autonomous vehicles,' and 'Solving the Inverse Heat Conduction Problem with Machine Learning.' His collaborative work with researchers like Gábor Kertész, Zoltán Vámossy, and Imre Felde demonstrates his commitment to interdisciplinary research.

Peter Procházkaمشاهده پروفایل

استادیار

• Internet of Things (IoT)
• UX Design
• Multimedia Applications
• +۳ مورد دیگر

Peter Procházka is an Assistant Professor at the University of Economics in Bratislava, affiliated with the Faculty of Economic Informatics and the Department of Applied Informatics . His teaching and research focus on IoT , UX Design , Multimedia Applications , and technical education. His research spans several subfields, including: IoT : Development of educational tools using Arduino, smart device integration, and open-source platforms. Generative AI : Prompt engineering for neural networks and machine learning applications. Web Analysis : SEO tools, linguistic summaries for data visualization, and compliance with public administration standards. His publications highlight a trend toward data-driven decision-making in technology, including big data, machine learning, and IoT frameworks. He has also contributed pedagogical works on computer hardware, multimedia, and IoT, alongside collaborative studies on digital citizenship and pandemic response in educational institutions.

Pierre Maierمشاهده پروفایل

پژوهشگر

• Multi-Level Software Languages
• Software Language Engineering
• Conceptual Modeling
• +۴ مورد دیگر

Pierre Maier is a Researcher at the University of Duisburg-Essen , affiliated with the Faculty of Computer Science and the Information Systems and Integrated Information Systems department. His research explores the intersection of information systems and software languages, with a focus on multi-level modeling (MLM), natural language generation (NLG), and the application of large language models (LLMs) in organizational problem-solving. Maier's work investigates automation techniques to enhance the usability of multi-level software languages and addresses challenges in transitioning from traditional two-level languages. He supervises theses on topics like Machine Learning-Assisted Domain Modeling , LLM-Driven Semantic Matching , and Flexible Modeling , reflecting his interest in bridging conceptual modeling with AI advancements. His recent publications analyze the integration of generative language models with UML, the evolution of low-code platforms, and the role of multi-level modeling in improving software artifacts. Maier teaches courses in Object-Oriented Modeling , Robotic Process Automation , and Data Integration , combining technical rigor with practical applications for enterprise environments.