جست‌وجوی استاد و پژوهشگر

Birte Glimmمشاهده پروفایل

استاد

• Knowledge Representation
• Automatic Reasoning
• Semantic Web Technologies
• +۳ مورد دیگر

Prof. Birte Glimm is a full Professor and Chair of the Institute of Artificial Intelligence at the University of Ulm. She holds leadership roles in academic governance, including as Dean of Studies for Cognitive Systems and Computer Science programs. Her research focuses on knowledge representation, automated reasoning, and semantic web technologies, with applications in autonomous systems and intelligent assistants. She has led projects like the 'Companion-Technology for Home Improvement' and contributed to standards such as SPARQL 1.1 Entailment Regimes. Education: PhD (2008) from the University of Manchester under Prof. Ian Horrocks, BSc from Hamburg University of Applied Sciences (2004), and prior industry experience in communication design. Research Interests: Development of efficient reasoning algorithms for Description Logics, ontology-based data management, and explainable AI. Her work emphasizes scalability, dynamic knowledge processing (e.g., for autonomous vehicles), and natural language explanations of automated reasoning. She co-leads the DFG-funded KEMAI research training group and previously led the BMBF project 2LIKE. Awards: Google Faculty Award (2017), Mileva Einstein-Marić-Preis (2016), and multiple best paper awards. She has served on numerous conference committees and editorial boards, including the OWL 2 Conformance Specification. Teaching: Courses on Knowledge-Based AI, Semantic Web Foundations, and programming for cognitive systems. She coordinates curricula for Computer Science and AI programs at Ulm. Labs/Teams: Institute of Artificial Intelligence; contributions to Collaborative Research Centers SFB/TRR 62 and EU projects in AI for industry.

• Natural Language Processing
• Vision-Language Models
• Medical AI
• +۳ مورد دیگر

Parminder Bhatia is a prominent research scientist at Amazon with over 49 publications and 1,400+ citations spanning natural language processing, vision-language models, and medical AI. As a key contributor to Amazon's AI research initiatives, Bhatia has developed influential frameworks including A³Tune for medical vision-language alignment, SIMA for visual-language modality improvement, and ReCode for evaluating code generation robustness. Their work bridges theoretical advances with practical applications across healthcare, software engineering, and multimodal systems. Bhatia's research primarily focuses on enhancing large language models through innovative alignment techniques, efficient fine-tuning strategies, and robustness evaluation frameworks. Key contributions include solving attention distribution challenges in medical VLMs, improving cross-file context understanding for code completion, and developing self-improvement mechanisms for visual-language alignment without external dependencies. Their work demonstrates consistent innovation in addressing fundamental limitations of current AI systems while maintaining practical applicability across diverse domains. Analysis of Bhatia's 15 most recent publications reveals a strong emphasis on medical AI applications (40%), code generation/analysis (30%), and foundational LLM improvements (30%). The research shows an evolving trajectory from basic NLP tasks toward complex multimodal integration, with increasing focus on practical constraints like computational efficiency, robustness to perturbations, and adaptation to specialized domains. Notably, over 60% of recent work involves medical applications, establishing Bhatia as a leader in healthcare AI.

• Control Systems Engineering
• Networked Control Systems
• Diffusion Processes
• +۴ مورد دیگر

Fangzhou Liu is currently a Professor at the Research Institute of Intelligent Control and Systems, School of Astronautics, Harbin Institute of Technology, China (2022–Present). He previously held a postdoctoral position (2019–2022) and completed his PhD (2014–2019) at the Chair of Automatic Control Engineering, Technische Universität München, Germany. His academic journey includes a visiting PhD stint at the University of Groningen (2018) and degrees from Harbin Institute of Technology: M.Sc. (2012–2014) and B.Sc. (2008–2012) in Control Science and Engineering. Research Interests: Focus on control of complex systems, including diffusion processes over complex networks, opinion dynamics, networked control systems, model predictive control, and sliding mode control. Key themes: large-scale systems, dynamic optimization, and robust control. Publication Trends: Recent works emphasize robotic control (reinforcement learning, barrier functions), switched systems (parameter estimation, identification), and epidemic modeling (deep transfer learning for forecasting). Collaborations with Martin Buss and others span robotics, control algorithms, and optimization. Awards: PhD Award 2019 of the Department of Electrical and Computer Engineering Labs & Teams: Affiliated with the Chair of Automatic Control Engineering (Technische Universität München) and Harbin Institute of Technology’s Research Institute of Intelligent Control and Systems. Collaborates on projects involving intelligent control, robotics, and networked systems.

• Software Security
• Vulnerability Analysis
• Static Analysis
• +۸ مورد دیگر

Yeting Li is a researcher at the Institute of Information Engineering, Chinese Academy of Sciences, with academic affiliation at the University of Chinese Academy of Sciences. Their work bridges software security and artificial intelligence, focusing on practical vulnerabilities in modern systems. Research spans vulnerability analysis in Kubernetes ecosystems, AI-driven binary similarity detection , and semantic-enhanced static analysis for baseband firmware. Recent work explores large language models for security applications including fuzz driver generation and data contamination mitigation in benchmarking, alongside accessibility-focused testing for speech recognition systems. Publications reveal a clear trajectory toward integrating AI with traditional security analysis, particularly in containerized environments and binary code analysis. Emerging themes include LLM-based tooling for vulnerability identification and specialized testing methodologies for emerging technologies like automatic speech recognition and deep learning operators. Key contributions include Kubernetes resource injection vulnerability studies, Aster for stutterer accessibility testing, and ACETest for deep learning operator validation. Research demonstrates consistent focus on empirical evaluation of security tools across ASE, ICSE, and ISSTA venues from 2023-2025.

Owolabi Legunsenمشاهده پروفایل

استادیار

• Software Engineering
• Software Testing
• Runtime Verification
• +۳ مورد دیگر

Owolabi Legunsen is an Assistant Professor in the Department of Computer Science at Cornell University's College of Engineering. His research focuses on software engineering with particular emphasis on software testing and runtime verification. He maintains an active teaching schedule with courses including Runtime Verification (taught Fall 2020, Spring 2022-2025) and Software Testing (taught Spring 2021, Fall 2021-2023, Fall 2025). Dr. Legunsen's research interests center around improving software reliability through advanced testing methodologies. His work spans runtime verification, where he develops techniques to monitor software behavior during execution, and regression test selection, where he creates methods to optimize the testing process as software evolves. His research also extends to cloud systems configuration testing and inline testing approaches. He is part of Cornell's growing Software Engineering Group and maintains an active research program with numerous publications in top-tier software engineering conferences. His recent publications show a clear trend toward making runtime verification more efficient and practical for real-world software testing scenarios. The research spans foundational verification techniques, tool development for practical application, and empirical studies examining overheads and effectiveness in different contexts. His work increasingly integrates evolution-aware approaches that account for how software changes over time. ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) ACM SIGSOFT Distinguished Paper Award (ISSTA 2023) ACM SIGSOFT Distinguished Paper Award (ASE 2016) Dr. Legunsen actively mentors PhD students and postdocs, with several co-authored publications indicating strong student involvement in his research program. His work has been supported by research grants that enable his investigations into software testing and verification techniques. He regularly serves on program committees for major software engineering conferences including ICSE, FSE, ASE, and ISSTA, demonstrating his standing in the research community. Dr. Legunsen maintains close collaborations with researchers at multiple institutions, particularly in the areas of cloud systems and configuration testing. His research group develops practical tools like TraceMOP for explicit-trace runtime verification, ExLi for inline-test generation in Java, and pytest-inline for Python testing. These tools bridge theoretical research with practical application, allowing his techniques to be evaluated in real-world settings and adopted by practitioners.

Mark Harmanمشاهده پروفایل

استاد

• Search Based Software Engineering
• Software Testing
• Program Analysis
• +۳ مورد دیگر

Mark Harman is a part-time Professor of Software Engineering at University College London's Department of Computer Science within the Faculty of Engineering Sciences, while working full-time as a Research Scientist at Meta Platforms in the Instagram Product Performance team. He previously served as head of Software Engineering at UCL and director of its CREST centre from 2006 to 2017 before joining Meta when his startup Majicke was acquired in 2017. Harman's research spans multiple domains of software engineering, with particular emphasis on Search Based Software Engineering (SBSE), which he co-founded in 2001. His work has evolved to include LLM-based software engineering, software testing, program analysis, and bias mitigation in machine learning systems. He has made significant contributions to automated testing through systems like Sapienz and WW that have been deployed at scale at Meta. His publication record shows a clear evolution from traditional software testing and analysis toward increasingly sophisticated integration of machine learning techniques. Recent work demonstrates strong focus on addressing fairness challenges in ML systems, improving test reliability in continuous integration environments, and exploring the applications of large language models in software engineering tasks. This reflects both his ability to identify emerging challenges and his commitment to practical, industry-relevant research. IEEE Harlan Mills Award (2019) ACM Outstanding Research Award (2019) Fellowship of the Royal Academy of Engineering (2020) Harman maintains a unique bridge between academia and industry, having co-founded the Simulation-Based Testing team at Meta and previously directing UCL's CREST research centre. His work on Sapienz grew from his startup Majicke and has had significant industrial impact while maintaining strong academic foundations. He frequently participates in academic conferences as both contributor and committee member, demonstrating ongoing commitment to the research community despite his industry position. At Meta, Harman works within the Instagram Product Performance team, building on his earlier work with the Simulation-Based Testing team where he co-developed platforms for client- and server-side testing. His research on cyber-cyber digital twins represents an innovative application of simulation techniques to virtual software systems rather than physical ones.

Torsten Brindaمشاهده پروفایل

استاد

• Computer Science Education
• Competency Modeling
• Digital Education

Prof. Dr. Torsten Brinda holds the Chair for Didactics of Informatics at the University of Duisburg-Essen's Faculty of Computer Science. His research centers on competency modeling in programming, digital education frameworks, and computer science pedagogy. He serves as chair of the GI department for computer science education and received the IFIP Service Award in 2022. Key research areas include: Modeling of digital competencies for teachers/students Automatic assessment in programming education International curriculum standards for CS education His publications (2015-2022) demonstrate consistent focus on educational informatics, featuring competency modeling studies, global CS education analyses, and contributions to the Dagstuhl Declaration on digital education. Recent work explores integrated digital competency frameworks for teacher training.

Bo Bernhardssonمشاهده پروفایل

استاد

• Automatic Control
• Control Theory
• Modeling and Control of Uncertain Systems
• +۱۷ مورد دیگر

Bo Bernhardsson is a Professor in Automatic Control at the Department of Automatic Control, Faculty of Engineering (LTH), Lund University. He has been a full-time professor at Lund since 2010, following a decade (2001–2010) as an Expert in Mobile System Design and Optimization at Ericsson. He is affiliated with major research initiatives including ELLIIT (Excellence Center in Information Technology), LCCC (Lund Center for Control of Complex Engineering Systems), and WASP-AS (Wallenberg AI, Autonomous Systems and Software research school), where he has played a leadership role since 2016. His research focuses on modeling and control of uncertain and large-scale systems, with applications spanning industrial automation, mobile communications, particle accelerators, biomedical systems, and navigation technologies. He integrates theoretical control methods with practical implementations, particularly under constraints such as communication limitations, noise, and delays. His recent publications reveal a strong trend in networked control, communication-constrained estimation, and optimization-based control design. The works span theoretical advances in signal estimation under SNR constraints, event-based and stochastic control, and practical applications like IMU-radio fusion for navigation and RF field control in particle accelerators. Keywords include Control Theory, Communication Systems, Optimization, Signal Processing, and Networked Control , with subfields such as encoder-decoder co-design, virtual antenna arrays, and dynamic programming for time-delay systems. PhD in Control, Lund University, 1992 Professor in Automatic Control, Lund University, since 1999 Expert, Mobile Systems, Ericsson, 2001–2010 Bo Bernhardsson has supervised over 20 PhD and licentiate students, including Jacob Bergstedt (immune system modeling), Anders Mannesson (navigation and radio), and Erik Johannesson (control under communication constraints). His research has been funded by major entities such as the European Spallation Source and the Wallenberg Foundation. He teaches advanced courses in Linear Systems, Convex Optimization, and Robust Control, and has contributed significantly to both academic and industrial advancements in control engineering. He leads and collaborates on interdisciplinary projects involving real-time control, autonomous systems, and machine learning, often in partnership with industry and international research centers. His work in the RobotLab at LTH and on cloud-based control systems highlights his engagement with emerging technologies.

David Ohlinمشاهده پروفایل

مدرس پاره‌وقت

• Optimal Control
• Control of Large-Scale Networks
• Social Networks
• +۴ مورد دیگر

David Ohlin is a PhD student and part-time lecturer in Automatic Control at Lund University's Faculty of Engineering (LTH), actively contributing to the Wallenberg AI, Autonomous Systems and Software Program (WASP) since 2021. His position bridges advanced doctoral research with instructional responsibilities across multiple control theory courses. He earned his MSc in Engineering Physics in 2021 with a thesis focused on machine learning applications for EEG signal interpretation, establishing his interdisciplinary approach at the intersection of control systems and biomedical engineering. His educational trajectory demonstrates a clear progression from foundational physics to specialized control theory expertise. Ohlin's research centers on optimal control methodologies for large-scale networked systems, with innovative applications to social network dynamics and opinion modeling. His work on graph search methods for controlling positive systems and nonlinear synchronization models represents significant contributions to both theoretical control frameworks and their real-world implementations in social dynamics. This dual focus on mathematical rigor and practical application characterizes his scholarly approach. His publication pattern reveals a growing expertise in network control systems, with recent work examining consensus formation in polarized networks, linear cost optimization, and heuristic search algorithms. These publications collectively demonstrate his ability to translate complex control theory concepts into solutions for networked social systems. Teaching since Fall 2021: Non-linear control, Learning-based control, and Network dynamics courses Research collaborations with prominent scholars including Anders Rantzer and Emma Tegling Active participant in WASP, Sweden's premier AI and autonomous systems initiative

Volker Heunمشاهده پروفایل

استاد

• Algorithmic Bioinformatics
• Protein Folding
• Data Structures
• +۴ مورد دیگر

Volker Heun is a Professor of Bioinformatics at Ludwig-Maximilians-Universität München (LMU), holding positions within the Faculty of Mathematics, Computer Science, and Statistics since 2003. He currently serves as Vice Dean of the faculty and holds various academic coordination roles, including Bioinformatics program coordination and ERASMUS coordination. His academic journey includes a Ph.D. from TU München (1996) and research fellowships at the International Computer Science Institute (Berkeley, 1997–1998). Education: Studied Computer Science and Mathematics at Johann Wolfgang Goethe-Universität Frankfurt (1984–1991), followed by research roles at TU München and other institutions. Professional roles include Research Assistant (1991–1996) and Research Associate (1996–2003). Research focuses on algorithmic bioinformatics, protein folding, data structures, and computational biology. Key contributions include work on RNA sequence-structure analysis, chemical shift correction tools (e.g., CheckShift), and efficient algorithms for range queries and suffix arrays. He has authored multiple influential books on algorithms and bioinformatics. Publications span over two decades, emphasizing algorithm design, bioinformatics applications, and parallel computing. Supervised PhD students include Benjamin Albrecht (2016), Luca Chiarabini (2009), and Simon W. Ginzinger (2008). Teaching includes courses on Algorithmic Bioinformatics, Algorithms on Sequences, and bioinformatics-related seminars. Administrative roles include serving as Vice Dean (2004–2007, 2013–2017, 2019–present) and Dean (2011–2013, 2017–2019). His work bridges theoretical computer science and applied bioinformatics, with a focus on interdisciplinary research.

Regina Stoddenمشاهده پروفایل

پژوهشگر ارشد

• Text Simplification
• Online Participation
• Web Accessibility
• +۶ مورد دیگر

Regina Stodden is a Research Fellow at the Department of Computational Linguistics, Heinrich Heine University Düsseldorf, since January 2019. She is affiliated with the NRW Research College for Online Participation (second funding phase) and works under the supervision of Prof. Dr. Marc Ziegele. Education: B.A. in Educational Science, Text Technology, and Computational Linguistics from Bielefeld University M.A. in Information Science and Language Technology from HHU Düsseldorf Her research focuses on automatic text processing , particularly text simplification for online discussions. This includes: Enabling participation for people with limited German proficiency Reducing manual workload in text analysis Exploring accessibility in Open Data portals She has contributed to tools like TS-ANNO for corpus annotation and EASSE-DE for simplification evaluation, with recent work extending to CEFR-based language proficiency assessment . Her research intersects Natural Language Processing , Machine Learning , and Usability Studies , often addressing accessibility challenges in digital participation. Scientific awards: No explicit awards mentioned. Advising and grants: Participates in the NRW Research College for Online Participation funding program and collaborates under Prof. Dr. Laura Kallmeyer's supervision. Her work involves grants related to text simplification for online participation processes.

• Optimization
• Control Theory
• Nonlinear Systems
• +۴ مورد دیگر

Pontus Giselsson is an Associate Professor at the Department of Automatic Control within the Faculty of Engineering, LTH at Lund University . His research focuses on optimization theory and its applications to control systems, machine learning, and robotics. He teaches advanced courses including: Optimization for Learning (Master’s level, held annually) Large-Scale Convex Optimization (PhD level, last held 2015) His research projects span: Scalable control of interconnected systems Nonlinear control theory Robot learning and control Cloud-native control systems Autonomous real-time systems Optimal transport and numerical optimization His scientific work emphasizes: Operator splitting algorithms Convergence analysis Low-rank optimization Applications to machine learning and control systems Pontus leads research initiatives in the RobotLab LTH and contributes to national projects such as WASP (Wallenberg AI, Autonomous Systems and Software Program) and ELLIIT (Excellence Center at Linköping–Lund in Information Technology).

• Implicit Psychological Assessment
• Social Categorization and Prejudice
• Expectation Violations
• +۳ مورد دیگر

Prof. Dr. Sarah Teige-Mocigemba serves as University Professor and Head at the Department of Psychological Diagnostics (Teige-Mocigemba Group) within the College of Psychology at Philipps-University of Marburg. She holds a W3 professorship in psychological diagnostics since 2023, having declined a similar position at Albert-Ludwigs-University Freiburg in the same year. Current affiliation: Philipps-University of Marburg Academic rank: Full Professor Research focus: Implicit psychological processes in attitudes, stereotypes, self-concept, and systemic approaches to psychological diagnostics Research Interests: Her work centers on developing and analyzing implicit psychological assessment methods, including: Theoretical analyses of implicit procedures Construct-independent factors affecting implicit measures Social categorization and prejudice Prejudice reduction interventions Expectation violations and stereotype maintenance Context effects in psychological assessment Systemic approaches to psychological diagnostics (in collaboration with Beatriz Arias Martin) Publication Trends: Her 15 most recent articles (2004-2025) demonstrate consistent focus on: Implicit bias measurement techniques Validation of psychological assessment tools Expectation violation models Prejudice and stereotype research Cognitive processes in aggression Online self-assessment methodologies Scientific Awards: Notable recognitions include: Spokesperson for DFG Graduate College 2271 (2017-2026) Brigitte-Schlieben-Lange Program Stipend (2017) Baden-Württemberg Foundation Postdoctoral Grant (2011-2014) Eugen-Fink Early Career Award (2009) Academic Leadership: She supervises doctoral students Paul Bacher, Violetta Biermann, and Kiara Roth. Her research team includes Dr. Manuel Becker and Dr. Massimo Köster. She has served in various academic governance roles including: Mentor in ProProfessur Mentoring Program (since 2022) Chair of the Equal Opportunities Commission (since 2020) Member of the Steering Committee for Graduate College 2271 (since 2019) Member of Marburg's Faculty Council (2018-2022)

• Software Security
• Static Analysis
• Program Analysis
• +۳ مورد دیگر

Dr. Dominik Helm is an interim professor for Software Engineering at the University of Duisburg-Essen . His research focuses on modularization and automatic parallelization of collaborative static analyses, particularly through the OPAL framework for Java VM bytecode. He is the lead maintainer of OPAL, which aims to enhance precision, soundness, and scalability in static analysis for bug and security vulnerability detection. Helm has also received the Ernst Denert Software-Engineering-Preis (2024) for his contributions. Education : Helm earned his PhD in Computer Science from Technische Universität Darmstadt in 2023, with his thesis titled Modular Collaborative Program Analysis . His academic background includes extensive work on purity analysis, call graphs, and modular static analysis techniques. Research Interests : Helm's work bridges theoretical advancements in program analysis with practical tools. His key areas include cross-language analysis (e.g., AXA framework), modular call-graph algorithms (Unimocg), and soundness theory for analysis architectures. He emphasizes improving static analysis tools to handle complex language features and ensure robust software quality. Publications & Awards : Helm has authored/co-authored over 15 papers in top-tier conferences like ASE, ISSTA, and ESOP. His 2024 work on AXA and Unimocg showcases cross-language interoperability and modular consistency. The Ernst Denert Preis recognizes his impactful contributions to software engineering research. Technical Leadership : As OPAL's lead maintainer, Helm drives collaborative research in static analysis frameworks. His projects often involve interdisciplinary teams, reflecting his commitment to practical software engineering solutions.

• Numerical Analysis
• Differential-Algebraic Equations (DAEs)
• Linear Algebra
• +۳ مورد دیگر

Prof. Diana Estévez Black is a Professor at Berlin University of Technology, affiliated with Department II (Mathematics, Physics, Chemistry). She specializes in Numerical Analysis and Differential-Algebraic Equations (DAEs), focusing on numerical methods, matrix factorization, and computational techniques for solving complex mathematical models. Teaching: She teaches courses in Applied Mathematics, Biotechnology Mathematics, Computational Engineering, and Mechanical Engineering programs, including Numerical Mathematics, Linear Algebra, and Numerics for graduate-level engineering disciplines. Research: Her work emphasizes DAE analysis, singular value decomposition, and eigenvalue problems. She has developed software like InitDAE for consistent initialization of DAEs and published extensively on topics such as robotic arm dynamics, multirate PDAEs, and tax policy modeling. Her research bridges theoretical mathematics with practical applications in engineering and economics. Labs/Tools: She contributes to software development (e.g., Python-based InitDAE ) and maintains visualization resources for numerics education, including Hinton diagrams for matrix factorization and animations for discrete mathematics concepts.