جست‌وجوی استاد و پژوهشگر

Iftekhar Ahmedمشاهده پروفایل

دانشیار

• Software Engineering
• Software Testing
• Machine Learning for Software Engineering
• +۳ مورد دیگر

Iftekhar Ahmed is an Associate Professor in Informatics at the Donald Bren School of Information and Computer Science, University of California, Irvine. His research focuses on software engineering, particularly combining software testing, static analysis, and machine learning to develop better tools and techniques for software quality assurance. His educational background includes: PhD in Computer Science (2018) from Oregon State University, advised by Carlos Jensen BSc in Computer Science & Engineering (2007) from Shahjalal University of Science and Technology Dr. Ahmed's research interests center on software testing, static analysis, and the application of machine learning to software engineering problems. He has made significant contributions to mutation analysis, particularly in scaling this technique for real-world software systems. His work often bridges theoretical advances with practical applications, focusing on how to make software testing more effective and efficient for developers. He leads the STAIRS (Software Engineering & Testing Using Artificial Intelligence for Reliable Software) research group at UCI, where his team explores innovative approaches to software reliability through AI and machine learning. His recent publications demonstrate a strong trend toward integrating artificial intelligence with traditional software engineering practices. There's a clear focus on applying machine learning to code analysis, commit message generation, and bug detection, while maintaining rigorous empirical validation through studies of real-world software projects and developer practices. His work spans multiple domains including web accessibility, quantum computing, and Jupyter notebooks, showing both depth in core software engineering topics and breadth across application areas. Dr. Ahmed has received several prestigious awards: IBM Ph.D. Fellowship for academic year 2016-2017 Graduate School tuition relief Scholarship for academic year 2016-2017 IBM Ph.D. Fellowship for academic year 2017-2018 Actively involved in the academic community, Dr. Ahmed serves on program committees for major software engineering conferences including ASE, ICSE, and ESEC/FSE. He is currently accepting PhD students into his research group and emphasizes mentorship and professional development. His research has been supported by various grants that enable his team to explore innovative approaches to software testing and analysis. Dr. Ahmed leads the STAIRS research group at UCI, which focuses on developing AI-powered techniques for software testing and reliability. The group collaborates with industry partners and academic institutions to ensure their research addresses real-world challenges in software development. Current projects include improving mutation testing scalability, analyzing code smells in emerging domains like quantum computing, and developing tools for accessibility testing.

Juliana Alves Pereiraمشاهده پروفایل

استاد

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +۸ مورد دیگر

Juliana Alves Pereira is a Professor at the Computer Science Department of the Pontifical Catholic University of Rio de Janeiro (PUC-Rio), Brazil, where she leads the Artificial Intelligence for Software Engineering lab (AISE) as part of the Software Engineering lab (LES). Her academic journey includes a Ph.D. in Software Engineering with distinction (summa cum laude) from Otto-von-Guericke-Universität Magdeburg (OvGU) in Germany (2018), a Master's degree in Computer Science from the Federal University of Minas Gerais (Brazil), and postdoctoral research at the University of Rennes, Inria/IRISA (France). Her research focuses at the intersection of software engineering and artificial intelligence, particularly in automating software engineering through software analysis, machine learning, recommender systems, and meta-heuristic optimization. Dr. Pereira's work explores applications of Machine Learning, Explainability, Transfer Learning, Generative AI, Deep Learning, Natural Language Processing, and Recommender Systems in software quality and human aspects contexts. Her publications demonstrate a consistent focus on empirical software engineering, with significant contributions in code quality analysis, pull request characteristics, exception handling, and performance prediction in configurable systems. Her research shows increasing integration of advanced AI techniques with traditional software engineering practices. Best Dissertation Prize from the School of Computer Science in 2018 (Germany) Dissertationspreis award from OvGU (2018) ACM Best Paper Award from SPLC 2021 ACM Best Paper Award from ICPE 2020 CBSoft Most Influential Paper Award from Brazilian Computer Society (SBC) Dr. Pereira actively supervises master's and doctoral research in the Software Engineering concentration area at PUC-Rio. She has served on Program and Organizing Committees for major international conferences including ICSE, ASE, SANER, MSR, and ICSME. Her laboratory collaborations span national and international research groups, reflecting her significant impact in the software engineering community.

René Peinlمشاهده پروفایل

استاد

• Multimodal Artificial Intelligence
• Resource-efficient AI
• Speech Recognition
• +۷ مورد دیگر

René Peinl is a Professor of Business Informatics at the Institute for Information Systems (iisys) within the Faculty of Computer Science at Hof University of Applied Sciences. He serves as the Institute Director and leads the research group on Multimodal Artificial Intelligence. His academic career spans over 15 years of teaching in master's programs including 'Master Informatik' and 'Master Applied Research in Computer Science', with a focus on resource-efficient AI for text understanding since 2021. Dr. Peinl's research spans multiple cutting-edge domains in AI and information systems. His primary focus is on multimodal artificial intelligence systems that integrate various data modalities. He specializes in resource-efficient AI models that can operate without cloud resources, with particular emphasis on German language processing for speech recognition, text understanding, and speech synthesis. His work extends to practical applications in Industry 4.0 through customizable manufacturing execution systems (HiCuMES) and immersive learning applications using virtual reality technology for fields like electrical engineering and healthcare training. Analysis of his recent publications reveals a strong trend toward practical, efficient AI implementations with a focus on German language processing and multimodal systems. His work addresses critical challenges in model compression, bias mitigation in generative AI, and multimodal integration of text and image understanding. A significant portion of his research focuses on making AI more accessible and practical for small and medium enterprises through resource-efficient implementations that don't require cloud infrastructure. Professor Peinl actively participates in academic service and industry collaboration. He serves as a member of Hof University's senate and supervisory board. His research is supported through EFRE projects including 'Wirtschaft im Mittelstand 4.0' and 'Digitale Transformation des Mittelstands mit KI'. He frequently engages with industry through speaking engagements, consulting, and collaborative projects with companies across various sectors. At Hof University, Peinl leads the Institute for Information Systems and the Multimodal Artificial Intelligence research group. His team works on developing privacy-friendly speech assistance systems that function without cloud resources, highly customizable manufacturing execution systems for Industry 4.0, and immersive VR learning applications for domains that are expensive, dangerous, or difficult to replicate in physical reality.

Angela Clemensمشاهده پروفایل

استاد

• Human-Computer Interaction
• Intermedial Design
• Brand Design
• +۵ مورد دیگر

Angela Clemens serves as Professor at Flensburg University of Applied Sciences within the School of Information and Communication. Her academic responsibilities span teaching in Media Informatics, Design, Film & Marketing, and System Technology programs. She leads significant institutional initiatives including the CIVU – Center for Interaction, Visualization and Usability and TechStartUp@HS-Flensburg (TeStUp), while serving as contact person for the DesignLab. Human-Computer Interaction Intermedial Design Brand Design Light Art Product Design Maritime Cultural Installations Professor Clemens' creative output manifests primarily through art installations and photographic exhibitions that explore the boundaries between interior and exterior spaces, often incorporating maritime cultural contexts. Her recent work demonstrates a consistent focus on transforming ordinary objects and spaces through contextual repositioning, creating surreal experiences that challenge conventional spatial perceptions. The research trajectory reveals progressive integration of technological elements with traditional design disciplines, particularly evident in her light-based installations and interactive spatial designs. Her leadership extends to institutional innovation through the TechStartUp initiative and DesignLab collaborations, where academic research directly informs product development for startups. The CIVU center under her guidance represents a multidisciplinary approach to solving complex interaction and visualization challenges.

Christian Beckerمشاهده پروفایل

استاد

• Film Editing
• Experimental Film
• Found Footage Techniques
• +۲ مورد دیگر

Christian Becker (born 1971) serves as Technology/Postproduction Editor at the Academy of Media Arts Cologne (KHM), where he manages edit suite bookings and technical post-production facilities. He previously held the position of acting professor for film editing at the Dortmund University of Applied Sciences during the 2022/23 academic year. Becker's research centers on the auditory and visual design possibilities of film editing, with particular focus on the surprising/unexpected and process-based nature of montage. His work spans feature films, documentaries, experimental formats, industrial films, music videos, and installations. He explores found footage techniques and experimental narrative structures, emphasizing editorial innovation as a creative force. His filmography demonstrates consistent engagement with montage as an artistic medium, evolving from early experimental shorts to contemporary feature productions. Key thematic threads include biographical documentation, musical integration, and non-linear storytelling approaches across diverse cinematic genres. NRW Prize at the International Short Film Festival Oberhausen City of Duisburg Prize at the Duisburg Film Week Becker has contributed to academia through guest lectures at multiple institutions and his temporary professorship at FH Dortmund. His technical role at KHM involves maintaining post-production infrastructure including editing stations, recording studios, and film laboratories across multiple floors of the Filzengraben facility.

• Natural Language Processing
• Dialectology
• Speech Processing
• +۷ مورد دیگر

Lea Fischbach is a Research Fellow and PhD candidate at the Research Center Deutscher Sprachatlas (DSA) within the Faculty of German Studies and Arts at Philipps University of Marburg. She is employed in the long-term Regionalsprache.de project funded by the Academy of Sciences and Literature in Mainz and also contributes to the BMBF project AnDy . Since January 2021, she has been working at the DSA, initially as a student assistant before advancing to her current PhD candidate position under the supervision of Lucie Flek and Alfred Lameli. Her educational background includes both Bachelor's and Master's degrees in Informatics from Philipps University of Marburg. Her Bachelor's thesis focused on Efficient Serialization of Objects while her Master's thesis addressed Applied Learning Analytics Process Based on a Language Learning App , building upon an Android app she helped develop as part of a student team project. Lea's research centers on the classification of German dialects using audio recordings and deep learning techniques. Her work explores how deep learning can be optimized for dialect classification, which phonetic features are crucial for distinguishing between dialects, and how insights from both deep learning and phonetic analysis can be combined for enhanced classification. This research sits at the intersection of computational linguistics, speech processing, and dialectology, with particular focus on German dialects and low-resource audio data scenarios. Her technical approach integrates programming expertise with linguistic knowledge to develop practical solutions for dialect recognition challenges. Her publications demonstrate a strong focus on practical applications of deep learning to dialect classification challenges. She has explored voice conversion techniques for data augmentation, analyzed the importance of acoustic features in deep learning models, compared speaker diarization approaches for German dialectal speech, and contributed to digital language geography projects. Her work consistently addresses the challenges of working with limited dialectal data and seeks to improve classification accuracy through innovative technical approaches that minimize speaker-related variability while highlighting dialect-specific features. At Philipps University of Marburg, Lea has served as a tutor teaching courses in programming, technical informatics, and database systems. During her time at the DSA, she has contributed to various projects including the creation of the Hessenplattform and developed the second version of the Welcome to Bavaria app, a language guide for Bavarian dialects. Her technical work bridges the gap between computational methods and traditional linguistic research, creating practical tools for dialect documentation and analysis. Her research is conducted within the Research Center Deutscher Sprachatlas, a leading institution for German dialect research that provides both the theoretical framework and computational resources for her technical investigations into dialect classification. She actively participates in international conferences including Interspeech, ACL, and NoDaLiDa, presenting her work on deep learning pipelines for dialect recognition and the challenges in developing technical solutions for dialect classification tasks.

• Artificial Intelligence
• Machine Learning
• Argument Mining
• +۷ مورد دیگر

Marco Lippi is a Professor affiliated with the University of Modena and Reggio Emilia (Italy) and the Einaudi Institute for Economics and Finance. His research spans artificial intelligence, machine learning, and their applications in domains like legal technology, transportation, and healthcare. He collaborates extensively with institutions globally, focusing on interdisciplinary projects that bridge technical innovation and societal challenges. Research Interests: Lippi's work emphasizes neuro-symbolic AI, argument mining, and ethical AI applications. He explores how AI can enhance consumer protection, improve urban transport systems, and contribute to medical diagnostics. His recent projects include autonomous systems development, causality learning in smart factories, and analyzing social media for disaster response. Publications Trends: His articles reflect a strong focus on AI ethics, legal informatics, and machine learning applications. Notable contributions include frameworks for detecting unfair contract clauses, causal modeling in industrial IoT, and human-robot interaction pipelines. His work often combines theoretical advancements with practical implementations in real-world settings. Affiliations & Collaborations: Collaborations span academia and industry, including projects with Università di Bologna and the Einaudi Institute. His team frequently addresses cross-disciplinary challenges, such as integrating legal reasoning with machine learning and optimizing smart city infrastructure through data-driven approaches.

Wolfram Burgardمشاهده پروفایل

استاد

• Artificial Intelligence
• Robotics
• Mobile Robotics
• +۳ مورد دیگر

Prof. Wolfram Burgard is a Professor for Robotics and Artificial Intelligence and Founding Chair of the Engineering Department at the University of Technology Nuremberg. Previously, he was a professor of computer science at the University of Freiburg (1999–2021), where he established the renowned Autonomous Intelligent Systems research lab. His research focuses on probabilistic techniques for robot navigation, state estimation, and control, with notable contributions to SLAM, path-planning, and mobile robot applications such as museum tour-guides (Rhino/Minerva) and autonomous vehicles. His research interests span artificial intelligence, mobile robotics, and autonomous systems. Key projects include deploying interactive museum robots, developing autonomous parking systems (2008), and the pedestrian-like Obelix robot (2012). He has authored over 350 papers, co-authored seminal books like Probabilistic Robotics , and holds the prestigious Gottfried Wilhelm Leibniz Prize (2009) and ERC Advanced Grant (2010). He is an ECCAI and AAAI Fellow, and member of the German Academy of Sciences Leopoldina and Heidelberg Academy of Sciences. Prof. Burgard coordinated the BrainLinks-BrainTools Cluster of Excellence (2012–2019), funded by the German Research Foundation. His work emphasizes real-world applications, including industrial plant supervision and autonomous driving systems. His Erdős number is ≤4, and his publications are indexed on Google Scholar.

• Information Retrieval
• Machine Learning
• Recommendation Systems
• +۴ مورد دیگر

Chenliang Li is a Professor at the School of Computer Science and Engineering, Nanyang Technological University, Singapore. He holds a PhD in Computer Science from the same institution (2013). His research focuses on information retrieval, machine learning, recommendation systems, natural language processing, and social media analysis. He has published extensively in top-tier venues such as SIGIR, CIKM, ACL, and IEEE TKDE. Key contributions include advancements in sequential recommendation systems using diffusion models and transformers, knowledge graph reasoning with GNNs, and applications of pretrained language models in NLP tasks. His work bridges theory and practice, addressing challenges in data-driven decision making and large-scale systems. Recent publications (2023-2025) explore topics like cross-city POI recommendation, bias-agnostic recommender systems, and multimodal vision-language models. He collaborates widely with industry and academia, contributing to open-source projects like ModelScope-Agent.

Daksitha Withanage Donمشاهده پروفایل

پژوهشگر

• Affective Computing
• Artificial Emotional Intelligence
• Socially Interactive Agents
• +۳ مورد دیگر

Daksitha Withanage Don is a Researcher at the Chair for Human-Centered Artificial Intelligence, affiliated with the University of Augsburg's Faculty of Applied Computer Science and Institute of Computer Science. His work focuses on developing socially interactive agents, affective computing, and generative AI applications. He holds an M.Sc. and actively contributes to projects like DEEP (Emotion Processing for Social Agents) and MITHOS (mixed reality teacher training). His research interests include self-supervised learning, multimodal analysis, and human behavior modeling. He advises students on topics such as GUI design for social agent frameworks, behavioral synchrony analysis using foundation models, and real-time listener behavior generation in Unreal Engine. Key Projects: MITHOS, FORSocialRobots, TherapAI, ReNeLiB Labs/Teams: Human-Centered Artificial Intelligence Team Supervised Theses (2024): Automated ICEP-R Annotation, Long-Term Memory Integration in LLMs, Mediapipe 3D Blendshapes for Behavior Modeling Publications include work on generative AI for HCI, automated behavioral annotation, and real-time interactive systems. Contact via email or visit his GitHub for open-source contributions.

Ioana Hulpusمشاهده پروفایل

پژوهشگر

• Knowledge Graph Mining
• Entity Linking
• Word-Sense Disambiguation
• +۷ مورد دیگر

Ioana Hulpus is a post-doctoral researcher in the Data and Web Science Group at the University of Mannheim, collaborating with Prof. Heiner Stuckenschmidt and Prof. Simone Paolo Ponzetto. Her work bridges text mining and knowledge representation, with prior research at Insight Centre (NUI-Galway) involving projects with Elsevier, RTE, and Irish Times. She holds a Ph.D. from the National Insight Centre (2014), focusing on unsupervised word-sense disambiguation and knowledge graphs under Dr. Conor Hayes. Research Interests: Knowledge Graph Mining Entity Linking & Word-Sense Disambiguation Knowledge Representation & Linked Data Financial Network Analysis Research Trends in Articles: Her recent work emphasizes predictive analytics in education and knowledge-driven argument analysis, leveraging machine learning and semantic technologies. Earlier contributions explored argumentation frameworks integrated with knowledge graphs. Advising & Grants: No specific grants or advising roles explicitly listed. Collaborations include industry-academic partnerships with Elsevier and media organizations. Labs/Teams: Core member of the Data and Web Science Group, focusing on interdisciplinary applications of knowledge representation techniques.

Angela P. Schoelligمشاهده پروفایل

استاد

• Robotics
• Artificial Intelligence
• Control Systems
• +۳ مورد دیگر

Professor Angela P. Schoellig holds the Alexander von Humboldt Professorship for Robotics and Artificial Intelligence at Technical University of Munich (TUM) and is an Associate Professor at the University of Toronto Institute for Aerospace Studies (UTIAS), affiliated with the Vector Institute. Her research focuses on integrating robotics, control systems, and machine learning to enhance autonomous systems' performance, safety, and adaptability. She leads projects in autonomous drones, self-driving vehicles, and safe learning algorithms. Schoellig is a recipient of prestigious awards including the Robotics: Science and Systems Early Career Spotlight Award (2019), Sloan Fellowship (2017), and recognition as an MIT Innovator Under 35 (2017). Educational Background: PhD in Robotics from ETH Zurich (2013), awarded ETH Medal and Chorafas Foundation Prize M.Sc. in Engineering Cybernetics from University of Stuttgart (2008) M.Sc. in Engineering Science and Mechanics from Georgia Institute of Technology (2007) Key Roles & Activities: Principal Investigator, NSERC Canadian Robotics Network Four-time winner (2018–21) of the SAE AutoDrive Challenge with the U of T team Associate Director, Centre for Aerial Robotics Research and Education (CARRE) Editor, International Journal of Robotics Research Research Themes: Her work emphasizes safe learning in dynamic environments, including delivery drones for remote regions, pollution monitoring, and medical AED delivery. She develops algorithms for robust control under uncertainty, with applications in mining, healthcare, and autonomous vehicles. Notable projects include winter-optimized autonomous driving (WinTOR initiative) and UWB-based localization systems. Awards & Honors: Mitigates risks in learning systems through safety filters and constraint-aware algorithms Pioneer in swarm robotics and language-driven choreography for drone coordination Advances reproducibility in robotics with benchmarks like safe-Control-Gym Grants & Collaborations: Infineon Stiftungslehrstuhl for "Safety, Performance and Reliability of Learning Systems" Industry partnerships with aerospace, transport, and energy sectors Labs & Teams: Leads the Dynamic Systems Lab at TUM and advises U of T’s SAE/GM AutoDrive Challenge team and UTAT Aerospace Team, fostering student innovation in autonomous systems.

• Machine Translation
• Natural Language Processing
• Language Resources
• +۵ مورد دیگر

Stelios Piperidis is a prominent researcher at the Athena Research and Innovation Center, specializing in language technologies and digital language equality initiatives across European frameworks. His work spans machine translation, language resource development, and multilingual infrastructure projects including the European Language Grid (ELG), European Language Equality (ELE) program, and META-SHARE infrastructure. His research interests focus on overcoming language barriers through advanced computational methods, with particular emphasis on scientific domain machine translation , parliamentary corpus analysis , and digital language equality metrics . He has pioneered methodologies for constructing specialized parallel corpora from scientific abstracts, news metadata, and parliamentary debates, addressing challenges in domain adaptation and multilingual resource scarcity. His publication trends reveal consistent contributions to language resource standardization, metadata schemas, and European language technology policy frameworks. Recent work centers on the European Language Data Space initiative and sustainability models for digital language equality. Piperidis has played key roles in numerous European Commission-funded projects, contributing to strategic agendas for language resource coordination and technological infrastructure development. His collaborative work spans over 50 institutions across Europe, focusing on practical implementations of language technology solutions for public sector applications and academic research. He actively participates in major language resource conferences including LREC and EAMT, contributing to community-building efforts through workshop organization and editorial work. His leadership in European language technology initiatives has helped shape strategic roadmaps for achieving full digital language equality by 2030.

Maximilian Röglingerمشاهده پروفایل

استاد

• Business Process Management
• Process Mining
• Digital Transformation
• +۱۵ مورد دیگر

Prof. Dr. Maximilian Röglinger is Chair of Business Informatics and Value-Oriented Process Management at the University of Bayreuth and Managing Director of the FIM Research Institute for Information Management. He is also Deputy Director of the Business Informatics branch at Fraunhofer FIT and previously served as an Adjunct Professor at Queensland University of Technology. He co-directs the Finance & Information Management program with TUM and co-moderates the Master's program in Digitalization & Entrepreneurship. His research spans digital transformation , process mining , AI in business processes , strategic IT management , and digital innovation . He investigates how data-driven methods can enhance process decisions, how digital technologies reshape customer interactions, and how organizations can structure digital transformation initiatives. His work combines theoretical rigor with practical application across industries. His recent publications (2023–2025) reveal a strong focus on generative AI integration in process mining and chatbots, human-AI collaboration in healthcare and logistics, process improvement systems , and digital ecosystems . He explores emerging topics like triadic delegation, explainable AI, and object-centric process mining from text. Scientific recognitions include: Paper of the Year 2019, Electronic Markets Best Associate Editor Award, ECIS 2021 Top 1.5% in BWL-Forschungsranking 2024 Certificate of Outstanding Contribution in Reviewing, European Journal of Information Systems 2023 Röglinger leads numerous publicly funded and industry-applied research projects with partners such as BMW, Siemens, Deutsche Bahn, Allianz, and Celonis. His work is supported by BMBF, ERDF, ERASMUS+, and Bavarian Research Foundation. He has co-founded the Digital Innovation Workshop, Digital Leadership Academy, and Fraunhofer Center for Process Intelligence. He is active in the international research community as Associate Editor of Business & Information Systems Engineering and editorial board member of Electronic Markets and Journal of Strategic Information Systems , and regularly organizes tracks at ECIS and BPM conferences.

Fabian Isenseeمشاهده پروفایل

پژوهشگر

• Medical Image Segmentation
• Deep Learning
• Computer Vision
• +۲ مورد دیگر

Dr. Fabian Isensee serves as Head of the Applied Computer Vision Lab within the Helmholtz Imaging Support Unit at the German Cancer Research Center (DKFZ), where he leads efforts to translate cutting-edge AI methodologies into practical solutions for Helmholtz Association researchers. His work focuses on developing domain-agnostic software for AI-based image analysis and algorithm evaluation across diverse biomedical applications. He earned his PhD from DKFZ's Division of Medical Image Computing, where he pioneered nnU-Net—the now de facto standard for medical image segmentation that earned publication in Nature Methods and multiple international competition victories. His research centers on deep learning for 3D semantic segmentation in biological and medical contexts, emphasizing automated pipeline design and open-science dissemination of tools. Analysis of his 2023–2025 publications reveals dominant trends in medical image segmentation with 85% focused on 3D architectures, particularly lesion detection (PET/CT), anatomical structure segmentation (vascular/circle of Willis), and trauma assessment (pelvic fractures/TBI). Key innovations include promptable segmentation frameworks, active learning evaluation, and federated benchmarking for healthcare AI. While specific award names aren't documented, his methods have secured victories in numerous international segmentation competitions including FeTS, AutoPET, and TopCoW challenges. His leadership extends to developing evaluation frameworks like Metrics Reloaded for rigorous validation of medical AI systems. As head of the Applied Computer Vision Lab, he directs collaborative projects across Helmholtz centers, providing consulting services and developing tools like nnU-Net variants that address segmentation challenges in oncology, neurology, and cardiology. His team's work supports over 15 major challenges including KiTS21 and PENGWIN, with active grants focused on decentralized AI evaluation and longitudinal lesion analysis.