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Yanqi SuView profile

Research Fellow

• Software Engineering
• Artificial Intelligence
• Automated Testing
• +3 more

Yanqi Su is a postdoctoral researcher at the TUM School of Computation, Information and Technology (CIT) at the Technical University of Munich, affiliated with the Chair of Software Engineering & AI . She will soon complete her Doctorate at The Australian National University under Prof. Zhenchang Xing, following academic training at Nanjing University of Aeronautics and Astronautics. Education : PhD (in progress, ANU), Master’s/Bachelor’s in Computer Science and Technology (Nanjing University of Aeronautics and Astronautics) Research Focus : Her work centers on enhancing exploratory testing through Knowledge Graphs and Large Language Models (LLMs) , with notable contributions to test scenario generation , automated exploratory testing , and bug-component triaging . She has discovered ~300 bugs in applications like Firefox (desktop/Android), many resolved in collaboration with developers. Publication Trends : Her research spans LLM applications for GUI/code analysis , automated security testing , and AI ethics . Key subfields include model context protocols , vulnerability detection , code optimization , and human-AI collaboration in software development. Scientific Recognition : Recipient of the Distinguished Paper Award at ASE 2021 , her work appears in top-tier venues (ICSE, ASE, TSE, TOSEM).

• Applied Electromagnetics
• Antennas and Propagation
• Microwave Imaging
• +3 more

David Orlando Rodriguez Duarte is a Fixed-term Assistant Professor at the Department of Electronics and Telecommunications (DET) at Polytechnic University of Turin (Politecnico di Torino), Italy. He is a member of the Interdepartmental Center PolitoBIOMed Lab - Biomedical Engineering Lab and actively contributes to the applied electromagnetics research group. His academic work bridges electromagnetic theory with practical medical applications, particularly in developing innovative imaging technologies for healthcare. Dr. Rodriguez Duarte is completing his Ph.D. degree with the Department of Electronics and Telecommunications at Politecnico di Torino while holding a prestigious Marie Sklodowska-Curie Fellowship. He is deeply involved in the EMERALD project, which focuses on modeling and designing microwave imaging systems for cerebrovascular diseases. The department's research group has over 10 years of experience advancing both theoretical and applied electromagnetism, with significant contributions in electromagnetic theory, wave propagation, and applications in communication systems and radar technology. His research spans multiple interconnected domains in applied electromagnetics. He specializes in antenna design specifically tailored for medical applications, with particular expertise in microwave imaging systems for neurological conditions. His work in electromagnetic field theory focuses on biomedical contexts, especially stroke monitoring and detection. He develops practical implementations that bridge theoretical concepts with clinical applications, addressing critical healthcare challenges. His research directly supports SDG Goal 3: Good health and well-being through the development of accessible diagnostic technologies. Dr. Rodriguez Duarte's scholarly output demonstrates a clear progression toward developing practical, portable microwave imaging solutions using off-the-shelf components. His recent work increasingly integrates machine learning techniques with traditional microwave sensing approaches, enhancing diagnostic accuracy for neurological conditions. There's a strong emphasis on creating real-time imaging solutions that can be deployed at the bedside for immediate patient care, particularly for time-sensitive conditions like stroke. Marie Sklodowska-Curie Fellowship for research on microwave imaging systems for cerebrovascular diseases Dr. Rodriguez Duarte serves as primary supervisor for two doctoral candidates: Alex Ramiro Masaquiza Caiza (40th cycle, 2024-present) and Martina Gugliermino (39th cycle, 2023-ongoing). Their research focuses on 'EM field study in medicine: characterization of materials for phantoms and stroke monitoring algorithms.' He has secured research funding through the EMERALD European project (2018-2021) and continues to develop grant-supported work in biomedical microwave imaging. His ERC sector alignments include PE7_6 (Communication technology), PE7_11 (Medical applications), and PE7_3 (Simulation engineering). He is an integral member of the PolitoBIOMed Lab - Biomedical Engineering Lab, where his team develops microwave imaging technologies for brain stroke monitoring. The lab focuses on creating practical implementations of electromagnetic theory for medical diagnostics, with emphasis on point-of-care applications. Their work addresses a critical healthcare need as stroke affects approximately one in six people worldwide and requires prompt diagnosis for effective treatment, making their research vital for improving patient outcomes through early detection technologies.

Davor RunjeView profile

Lecturer

• Artificial Intelligence
• Theoretical Computer Science
• Software Engineering
• +4 more

Davor Runje is a lecturer at Algebra University with extensive industry experience as a software engineer, computer scientist, and serial entrepreneur. He has co-founded multiple technology ventures including AIRT (an AI startup), DRAP (a digital agency acquired by Imago Ogilvy in 2018), and PlayMedia Systems (1997), which became a global leader in AMP MP3 playback technology. His research interests span artificial intelligence, theoretical computer science, parallel and distributed computing, and cryptography. Runje has made significant contributions to programming for multiprocessor/multicore systems, with work that later became the Task Parallel Library in the .NET framework during his time at Microsoft Research. His publication portfolio shows a clear evolution from theoretical computer science and concurrency research (2003-2009) toward machine learning and AI (2019-2025), with particular focus on neural networks, model interpretability, and security aspects of large language models. His recent work demonstrates expertise in constrained monotonic neural networks, attention mechanisms for tabular time-series, and vulnerabilities in large language models. His notable achievements include: Microsoft Research SSCLI and Phoenix 2005 awards Two US patents 20 publications in theoretical computer science and artificial intelligence Chairing the Croatian Independent Software Exporters board (2020-2024) As an active industry leader, Runje has participated in numerous legal and tax reform initiatives aimed at increasing the competitiveness of the Croatian IT sector in global markets, representing approximately one-third of the Croatian IT industry through his leadership role.

Klaus MuellerView profile

Professor

• Visual Analytics
• Explainable Machine Learning and AI
• Algorithmic Fairness and Transparency
• +7 more

Klaus Mueller is a Professor in the Computer Science Department at Stony Brook University , with additional appointments in Biomedical Engineering and Radiology. He serves as Director of the Visual Analytics and Imaging (VAI) Lab, Liaison for the SUNY Korea CS Program, and Interim Chair of the Department of Technology and Society . His career spans roles at Brookhaven National Lab and leadership positions at SUNY Korea. Dr. Mueller earned his PhD in Computer and Information Science (1998), MS in Computer and Information Science (1996), and MS in Biomedical Engineering (1990) from The Ohio State University , alongside a BS in Electrical Engineering (1987) from the Polytechnic University of Ulm, Germany. His research focuses on visual analytics , explainable AI , algorithmic fairness , computational imaging , and medical imaging . He has pioneered GPU-accelerated CT reconstruction techniques, bias mitigation frameworks (e.g., D-BIAS), and tools like DOMINO for causal reasoning. His work bridges data science , human-computer interaction , and medical applications , often integrating large language models for visualization tasks. Recent publications highlight advances in multivariate volume rendering , LLM-driven bias detection , and mDDPM-based medical image synthesis . His articles span IEEE Transactions , Nature Machine Intelligence , and conferences like IEEE VIS and ACM CHI . Award highlights include NSF CAREER (2000), SUNY Chancellor Award (2011), IEEE Golden Core Award (2016, 2022), induction into the National Academy of Inventors (2018), and elevation to IEEE Fellow (2024). He has chaired major conferences and served as Editor-in-Chief of IEEE Transactions on Visualization and Computer Graphics (2019-2022). He teaches graduate and undergraduate courses in visualization , medical imaging , and GPGPU programming , and leads the Visual Analytics Seminar (CSE 648). His lab ( VAI Lab ) fosters interdisciplinary research in GPU-accelerated analytics and ethical AI.

Melike Erol KantarciView profile

Associate Professor

• 5G and 6G Networks
• Artificial Intelligence (AI)
• Tactile Internet
• +9 more

Dr. Melike Erol-Kantarci is an Associate Professor at the School of Electrical Engineering and Computer Science, University of Ottawa, and a Canada Research Chair in AI-enabled Next-Generation Wireless Networks. She founded the Networked Systems and Communications Research (NETCORE) Laboratory and holds a courtesy appointment as Assistant Professor at Clarkson University's Electrical and Computer Engineering Department. With over 6,000 citations and an h-index of 40, she contributes to IEEE as a Senior Member, chairs the Green Smart Grid Communications special interest group, and serves as Editor for IEEE Communications Letters and IEEE Access . Research Focus: 5G/6G wireless networks AI/ML-driven network optimization Smart grid communications Cyber-physical system security Edge computing and IoT Reconfigurable Intelligent Surfaces (RIS) Scientific Contributions: Her recent publications highlight generative AI integration in 6G, intent-driven network management using hierarchical reinforcement learning, physical layer security for connected vehicles, and explainable AI frameworks in communications. The 15 most recent works (2024-2025) span topics like beamforming optimization, federated learning security, UAV-assisted networks, and semantic communication systems. Leadership: As director of the NETCORE Laboratory, she leads cutting-edge research in networked systems, bridging wireless communication paradigms with energy sustainability and security requirements for next-generation networks.

• Requirements Engineering
• Software Engineering
• Explainable AI Systems
• +4 more

Andreas Vogelsang is a Full Professor in Software Systems Engineering at the University of Duisburg-Essen, Germany. He maintains strong connections in the international software engineering community through his roles as a member of the Steering Committee of the REFSQ Conference, the Editorial Board of the Journal of Software Engineering for Autonomous Systems, and an associated member of the International Requirements Engineering Board (IREB). Since 2018, he has also been a Junior Fellow of the German Informatics Society (GI). Professor Vogelsang's research focuses on the intersection of Requirements Engineering, Explainable AI Systems, and Automotive User Interfaces. His work particularly emphasizes the application of Natural Language Processing and Generative Large Language Models in Requirements Engineering. Recent publications show a strong trend toward explainable systems, requirements quality, and the integration of AI technologies into software engineering processes. His research group has produced numerous publications addressing challenges in requirements traceability, explainability in autonomous systems, and the impact of requirements smells. Vogelsang has received several prestigious awards including being named 'Young Researcher of the Year' by academics/DHV in 2019, along with multiple Best Paper and Reviewer Awards. His scientific contributions have established him as a prominent figure in the requirements engineering community, with particular expertise in the application of AI technologies to traditional software engineering challenges. His current research direction increasingly focuses on the integration of generative AI into requirements engineering processes, exploring how large language models can transform traditional specification approaches. This represents a significant shift in the field, connecting established software engineering practices with cutting-edge AI developments.

Erik DernerView profile

Researcher

• Human-Centric Artificial Intelligence
• Large Language Models
• Robotics
• +6 more

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
• +7 more

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 SennrichView profile

Assistant Professor

• Natural Language Processing
• Machine Translation
• Multilinguality
• +7 more

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 CledatView profile

Associate Professor

• Photogrammetry
• Sensor Calibration
• Risk Measurement for Cyclists
• +4 more

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 RoserView profile

Researcher

• Medical Physics
• Image Reconstruction
• Particle Therapy
• +3 more

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 StilleView profile

Researcher

• Medical Imaging
• Computed Tomography
• Image Reconstruction
• +3 more

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 LiView profile

Assistant Professor

• Computer Science
• Artificial Intelligence
• Machine Learning
• +7 more

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
• +7 more

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 GongView profile

Associate Professor

• Cybersecurity
• Trustworthy AI
• Adversarial Machine Learning
• +3 more

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.