جستجوی استاد راهنمای دانشگاهی

Dongheui Leeمشاهده پروفایل

استادیار

• Human motion understanding
• Physical human-robot interaction
• Machine learning in robotics
• +۵ مورد دیگر

Dongheui Lee is an Assistant Professor at the Institute of Automatic Control Engineering (LSR) within the Faculty of Electrical Engineering and Information Technology at Technische Universität München (TUM). She leads the Dynamic Human Robot Interaction for Automation System Lab. Her research focuses on human motion understanding, physical human-robot interaction, and machine learning in robotics. Education: B.S. and M.S. in Mechanical Engineering from Kyunghee University (2001-2003), PhD in Mechano-Informatics from the University of Tokyo (2007). Prior roles include research scientist at KIST Korea (2001-2004) and project assistant professor at the University of Tokyo (2007-2009). Research Interests: Human-robot collaboration, probabilistic robotics, motion recognition, and incremental lifelong learning mechanisms. She has contributed to advancements in motion primitives, compliant physical interaction, and real-time object tracking. Selected Awards: Finalist for KUKA Service Robotics Best Paper Award (2009), Hirose Scholarship (2006-2007), and multiple grants from KRF, KOSEF, and international robotics competitions. Key Publications: Focus on prioritized inverse kinematics, motion imitation, and adaptive control systems. Her work bridges robotics theory and practical applications in humanoid robots and human-robot interaction.

• Artificial Intelligence
• Automated Planning
• Temporal Reasoning
• +۴ مورد دیگر

Alfonso Emilio Gerevini is a prominent researcher in artificial intelligence with over 30 years of continuous academic contributions. His work spans theoretical foundations of automated planning to practical healthcare applications, with recent publications demonstrating significant impact in both traditional AI domains and emerging interdisciplinary areas. His research interests focus on automated planning systems , temporal reasoning , and multi-agent coordination , with recent expansion into healthcare applications using machine learning techniques. Gerevini has made fundamental contributions to planning algorithms, particularly in width-based search, case-based planning, and privacy-preserving multi-agent planning. His work on PDDL (Planning Domain Definition Language) has been influential in standardizing planning representations. Analysis of his 15 most recent publications reveals a strategic evolution from core planning research toward impactful healthcare applications, particularly during the COVID-19 pandemic. While maintaining his expertise in planning algorithms, he has successfully integrated machine learning techniques to address real-world medical challenges including radiology report analysis, prognosis prediction, and lab test interpretation. His work demonstrates exceptional versatility across both theoretical and applied domains of artificial intelligence. Gerevini maintains a robust collaborative network, primarily with Italian researchers including Ivan Serina, Alessandro Saetti, and Luca Putelli. His publications appear consistently in top-tier AI venues including Artificial Intelligence journal, Journal of Artificial Intelligence Research, and AAAI/ICAPS conferences. The collaborative patterns suggest he leads a significant research group focused on advancing planning systems while applying them to critical real-world problems.

Jingling Xueمشاهده پروفایل

استاد

• Programming Languages
• Compilers
• Program Analysis
• +۵ مورد دیگر

Jingling Xue is a Scientia Professor at the School of Computer Science and Engineering at the University of New South Wales (UNSW) in Sydney, Australia. As an IEEE Fellow of the Computer Society, he leads the Programming Languages and Compilers research group, focusing on practical applications of compiler optimization and program analysis techniques. His work bridges theoretical foundations with real-world software systems, particularly in developing open-source tools for large-scale program analysis. Professor Xue received his B.Eng and M.Eng degrees from Tsinghua University in 1984 and 1987, respectively, followed by a PhD from the University of Edinburgh in 1992. His academic journey has established him as a leading figure in programming languages and compiler technology. Xue's research spans programming languages, compiler technology, and program analysis with emphasis on practical relevance. His current projects include compiler techniques for improving parallelism and locality, pointer/alias analysis for million-line-scale programs, and static/dynamic analysis for detecting bugs and security vulnerabilities in real-world applications like web browsers and Android apps. His group actively develops open-source tools to support scientific replicability and reproducibility in these areas. His recent publications demonstrate a strong focus on applying program analysis techniques to modern challenges including AI compilers, homomorphic encryption, security vulnerability detection, and graph processing systems. The work shows evolution from traditional compiler optimization to addressing emerging domains like privacy-preserving computation and deep learning systems while maintaining rigorous theoretical foundations. Scientific Awards: Best Paper Award at CGO'13 Best Paper Award at CGO'16 Distinguished Paper Award at ECOOP'16 Distinguished Paper Award at ICSE'18 Distinguished Paper Award at ISSTA'19 Distinguished Paper Award at ASE'19 Distinguished Artifact Award at ISSTA'23 Best Artifact Award at FSE'23 Distinguished Paper Award at ASE'23 Test-of-Time Award at CGO'21 Professor Xue has successfully supervised 30 PhD students to completion, many of whom now work as professors or researchers in academia and industry. He has served as Program Chair for major conferences including LCTES'13, CC'18, CGO'20, and General Chair for LCTES'20. His group currently focuses on memory safety in Rust, smart contract analysis, AI compilers, compilation for privacy-preserving computation, and adversarial attacks in deep learning. The Programming Languages and Compilers group maintains strong connections with industry partners, translating theoretical advances into practical tools for real-world software development challenges. Their work on pointer analysis, memory safety, and compiler optimizations continues to influence both academic research and industrial practice.

Andrea Waltherمشاهده پروفایل

استاد

• Nonlinear Optimization
• Algorithmic Differentiation
• Mathematical Optimization
• +۳ مورد دیگر

Andrea Walther is a Professor of Mathematical Optimization at the Humboldt University of Berlin, holding a position within the Faculty of Mathematics and Natural Sciences. She leads the Mathematical Optimization research group at the Institute of Mathematics, focusing on algorithmic differentiation, nonlinear optimization, and applied mathematics. Her academic journey includes a Diploma in Business Mathematics (1996, University of Bayreuth), a PhD (1999, TU Dresden), and habilitation (2008, TU Dresden). She has held roles such as Junior Professor at TU Dresden (2007–2008) and Professor at the University of Paderborn (2009–2019) before joining Humboldt in 2019 as a MATH+ Professor. Education : 1991–1996: Studies in Business Mathematics, University of Bayreuth 1996: Diploma in Business Mathematics, University of Bayreuth 1999: PhD in Mathematics, TU Dresden 2008: Habilitation, TU Dresden Her research interests center on optimization methods, particularly algorithmic differentiation (e.g., ADOL-C software), nonsmooth optimization, and applications in engineering and machine learning. She leads initiatives like the Cluster of Excellence MATH+ and contributes to projects such as the Transregio 154. Key Projects: Co-PI of DFG Project 'Mixed-integer non-smooth optimization for gas market problems' (2020–2022) Principal Investigator in MATH+ Projects (EF3-7, AA2-7) Co-developer of ADOL-C, a widely used tool for algorithmic differentiation Notable awards include being a SIAM Fellow. Her work bridges theoretical advancements and practical applications, with contributions to energy sector optimization, inverse problems, and computational frameworks for solving complex systems.

Paul Kotyczkaمشاهده پروفایل

استاد

• Control Engineering
• Nonlinear Control
• Port-Hamiltonian Systems
• +۸ مورد دیگر

Paul Kotyczka is a Professor at the Technical University of Munich (TUM) in the School of Engineering and Design , Department of Automatic Control Engineering. He leads the Energy-based Modeling and Control Working Group and focuses on modeling, geometric discretization, and control of multi-physical systems, with expertise in nonlinear and passivity-based control. His work spans applications in robotics, mechatronics, and process engineering. Education : Dipl.-Ing. in Electrical Engineering (TUM, 2005), Dr.-Ing. (TUM, 2010), Habilitation (Dr.-Ing. habil., TUM, 2019). Research : Core areas include port-Hamiltonian systems, predictive control of active chassis, structural mechanics modeling, and numerical methods for control. His projects address autonomous driving, distributed parametric systems, and passivity-based control of switching nonlinear systems. Awards : Held a Marie Sklodowska-Curie Fellowship (2015–2017). Grants : Leads DFG projects (e.g., HermInE, INFIDHEM), MSCA-IF, and Franco-German Doctoral Program on port-Hamiltonian systems. Labs : Head of the Energy-based Modeling and Control group at TUM, collaborating internationally (e.g., IIT Bombay, Grenoble INP, LCIS France).

Nicolas Mansardمشاهده پروفایل

پژوهشگر

• Robotics
• Humanoid Robotics
• Sensor-Based Control
• +۶ مورد دیگر

Nicolas Mansard is a permanent researcher at LAAS-CNRS in Toulouse, France, where he has been working since October 2008. He is a member of the Gepetto research group alongside Philippe Souères, Florent Lamiraux, Olivier Stasse, and Jean-Paul Laumond. He defended his Habilitation à Diriger des Recherches (HDR) in July 2013 on the topic of motion semiotics. In 2013, he was an invited researcher at Emo Todorov's lab at the University of Washington, Seattle. His research focuses on sensor-based control, particularly the integration of sensor-based schemes into humanoid robot applications. His work spans the intersection of robotics, automatic control, signal processing, and numerical mathematics, with humanoid robotics as his primary application field. Mansard has made significant contributions to hierarchical quadratic programming for fast online humanoid-robot motion generation, inverse dynamics control, and sensor-based control systems. Mansard has received prestigious awards including the CNRS Bronze Medal in 2015 and the Grand Prix de l'ANR in 2016 for his project ANR Entracte. His research has resulted in numerous publications in top robotics journals and conferences, with a focus on motion generation, control theory, and humanoid robotics applications. His work has been particularly influential in developing efficient algorithms for hierarchical task control and inverse dynamics. 2015 CNRS Bronze Medal for research in robotics Grand Prix de l'ANR in 2016 for project ANR Entracte Associate Editor for IEEE TRO since July 2013 Mansard has advised numerous PhD students including Justin Carpentier, Mathieu Geisert, Oscar Ramos, and Sovannara Hak. He has secured significant research funding including leading the ANR project ENTRACTE (starting November 2013) and serving as CNRS coordinator and work package leader for the FP7 EuRoc project. He has also taught courses in advanced robotics at Supaero, mathematics for motion generation at École Normale Supérieure, and experimental humanoid robotics at INSA, all in Toulouse. His research group has been involved in developing open-source software for motion generation, notably the Stack of Tasks framework, which has been widely adopted in the robotics community. His work on humanoid robot dance with HRP-2 demonstrated the practical applications of his theoretical contributions to motion generation and control.

Julia Lawallمشاهده پروفایل

پژوهشگر

• Operating Systems
• Domain-Specific Languages
• Program Transformation
• +۴ مورد دیگر

Julia Lawall is a Senior Research Scientist (Directrice de Recherche) at Inria-Paris, where she leads research in the Whisper group. She has made significant contributions to the fields of programming languages, operating systems, and software engineering, with a particular focus on program transformation and Linux kernel development. Her work bridges theoretical computer science with practical software engineering challenges. Dr. Lawall's research primarily centers on the design and implementation of domain-specific languages for operating system problems, program transformation techniques, and automated software evolution. Her most notable contribution is the Coccinelle framework, which has been instrumental in automating the evolution of Linux device drivers for over a decade. Her work spans from theoretical foundations in optimal reduction of the lambda calculus to practical tools that address real-world software maintenance challenges in large-scale systems like the Linux kernel. Her publication record demonstrates consistent contributions across multiple domains, with recent work focusing on Android API evolution, Linux kernel bug detection, and program transformation techniques. The trajectory of her research shows a progression from theoretical programming language concepts to increasingly practical applications in system software maintenance and evolution. EuroSys Test of time award for 'Documenting and Automating Collateral Evolutions in Linux Device Drivers' at EuroSys 2008 Best paper award for 'Diagnosys: Automatic Generation of a Debugging Interface to the Linux kernel' at ASE 2012 Most Influential ICFP Paper Award for foundational work on lambda calculus Best Reviewer at GPCE 2020 and Distinguished Reviewer at ASE 2020 Dr. Lawall has been actively involved in the academic community, serving as program co-chair for numerous prestigious conferences including ASE 2019, FSE 2026, and EuroSys 2025. She has also contributed to community initiatives as the Linux kernel coordinator for Outreachy (2015-2018) and as a member of the advisory board for Software Heritage. Her leadership extends to editorial roles, including associate editor for Higher-Order and Symbolic Computation and membership on the editorial board of Science of Computer Programming. She leads the Whisper research group at Inria-Paris, which focuses on program transformation techniques and their applications to system software. The group has developed several influential tools including Coccinelle, Coccinelle4J, LiLiput, Prequel, and JMake, which have had substantial impact on both academic research and industrial practice in software maintenance and evolution.

• Clinical Linguistics
• Psycholinguistics
• Aphasia
• +۵ مورد دیگر

Dr. Anna Rosenkranz is a researcher at the Institute for German Linguistics, specializing in Clinical Linguistics at Philipps-Universität Marburg. She holds a visiting professorship at the Department of Educational and Rehabilitation Sciences, University of Cologne (2024-2025), focusing on language disorders in neurologically impaired populations. Her career spans institutions including Ruhr-Universität Bochum and University of Reading, with a PhD in Linguistics (2015). Current affiliations: Philipps-Universität Marburg (since 2019), University of Cologne (2024-2025) Education: PhD Linguistics (Bochum), MA Speech Science (Marburg), BA Linguistics (Bochum) Rosenkranz's research bridges clinical linguistics and neuropsychology, focusing on: Executive function contributions to word retrieval in aphasia Complex semantic processing in cognitive communication disorders Diagnostic tools for subtle post-brain-injury communication deficits Psycholinguistic models of verbal fluency in psychiatric and neurological conditions Digital participation barriers for speech-language impaired individuals Her work emphasizes practical applications in speech therapy, including the development of the KomplexSem test and FAVRES-D assessment. Recent publications analyze digital accessibility challenges, semantic processing, and cognitive-linguistic interactions across conditions like schizophrenia and dementia. Professional activities include: Co-developer of cognitive-communication screening tools (CCCABI-DE, KomplexSem) Coordinator of continuing education programs (2022-2024) Keynote speaker at international workshops on language production and neurorehabilitation Contributor to BMBF-funded projects on 'small disciplines with high potential'

Isabel Valera Martinezمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Data Science
• +۴ مورد دیگر

Isabel Valera Martínez is a full Professor of Machine Learning at the Department of Computer Science, Saarland University, and an Adjunct Faculty at the Max Planck Institute for Software Systems (MPI-SWS). She holds a PhD in Machine Learning from the University Carlos III of Madrid and has held prestigious fellowships including the Humboldt Postdoctoral Fellowship and the Minerva Fast Track Fellowship from the Max Planck Society. Her research focuses on developing interpretable, robust, and fair machine learning methods, with applications in healthcare, social systems, and consequential decision-making like hiring and loan approvals. Education: PhD in Machine Learning (2014) and MSc in Multimedia and Communications (2012) from University Carlos III of Madrid; Master’s work at Leibniz University Hannover (2009); Telecommunications Engineering from the Technical University of Cartagena (2009). Research Interests: Fairness in AI, Bayesian nonparametric models, robust machine learning, and applications in healthcare and social systems. She co-leads the ELLIS Robust Machine Learning program and the Saarbrücken AI & Machine Learning (SAM) Unit. Key Publications: Contributions to fair classification frameworks, automatic discovery of data types, and modeling psychiatric comorbidity. Notable venues include AAAI, NIPS/NeurIPS, JMLR, and ICML. Awards: Humboldt Fellowship, Minerva Fellowship, Best Paper Honorable Mention at WWW 2017. Labs & Collaborations: Active in the ELLIS Society and MPI-SWS, focusing on interdisciplinary AI applications.

• Medical Image Analysis
• Computer Vision
• Deep Learning
• +۳ مورد دیگر

Pan Pan is a Professor in the Department of Biomedical Engineering at Huazhong University of Science and Technology, with extensive research contributions spanning medical image analysis, computer vision, and underwater wireless communications. Their work demonstrates strong interdisciplinary collaboration between biomedical engineering and computer science, with significant industry partnerships including Alibaba. Research interests focus on medical image analysis (particularly automatic breast ultrasound systems), deep learning applications in healthcare diagnostics, and secure underwater communications . Their work bridges theoretical advances with practical clinical applications, developing innovative segmentation algorithms, tumor detection systems, and secure communication protocols for specialized environments. Analysis of recent publications reveals a strong trend toward integrating multi-modal data fusion techniques with uncertainty-aware deep learning models for medical diagnostics. The research spans both fundamental algorithm development (novel segmentation networks, feature matching optimization) and domain-specific applications (ABUS tumor detection, ICU mortality prediction, underwater sensor networks). Pan Pan maintains active collaborations with major Chinese technology companies and academic institutions, evidenced by the consistent publication record in top-tier conferences including CVPR, ICCV, and NeurIPS. While specific awards aren't documented in the provided materials, the research impact is demonstrated through numerous high-impact publications across computer vision and biomedical engineering venues. The research program shows particular strength in translating computer vision techniques to medical applications, with significant contributions to semi-supervised learning approaches for medical image segmentation where labeled data is scarce. Recent work also demonstrates growing interest in secure communications for specialized environments like underwater sensor networks.

Tegawendé F. Bissyandéمشاهده پروفایل

استاد

• Program repair
• Software Security
• Code Search
• +۶ مورد دیگر

Prof. Tegawendé F. Bissyandé is a Chief Scientist in the Professor category at the Interdisciplinary Centre for Security, Reliability and Trust (SnT) at the University of Luxembourg. He holds the prestigious position of ERC Fellow and serves as Principal Investigator of the NATURAL project focused on Artificial Intelligence for Program Repair. His research spans software engineering, cybersecurity, and artificial intelligence, with particular emphasis on applying machine learning techniques to software development and security challenges. Dr. Bissyandé's research interests include: Software Debugging (especially bug localization and program repair) Software Security (especially malware detection and analysis) Code Search (both free-form and semantic code-to-code) Machine Learning and Natural Language Processing for software engineering Cyber-security applications in mobile and cloud environments His recent work demonstrates a strong focus on leveraging Large Language Models (LLMs) for various software engineering tasks. Analysis of his 15 most recent publications reveals several key trends: extensive application of LLMs to program repair and code generation; innovative approaches to Android security and malware detection; development of novel techniques for code search and understanding; and exploration of the intersection between natural language processing and software engineering. His research increasingly bridges theoretical software engineering with practical applications in mobile security and developer productivity tools, with a significant portion of his work focusing on Android ecosystem security and program repair technologies. Dr. Bissyandé has received numerous prestigious awards throughout his career: APSEC Best ERA Paper Award (2018) for 'LSRepair: Live Search of Fix Ingredients for Automated Program Repair' IPSJ SIG SE Excellent Research Award (2018) for 'FaCOY: a Code-to-Code Search Engine' FOSS Impact Paper Award (2018) for 'Characterizing Deprecated Android APIs' SANER Best ERA Paper Award (2016) for 'Parameter Values of Android APIs: A Preliminary Study on 100,000 Apps' ASE Best Paper Award (2012) for 'Diagnosys: automatic generation of a debugging interface to the Linux kernel' As an active member of the software engineering research community, Dr. Bissyandé serves on program committees for major conferences including ICSE, ASE, and ISSTA, and has been an Area Chair for ICSE 2024. His industry partnerships include significant collaborations with BGL BNP Paribas (since January 2019), Luxembourg Stock Exchange (since January 2018), and Paul Wurth (January 2015 to 2018), demonstrating the practical impact of his research. He leads the SerVAL lab at SnT, which focuses on software validation and analysis, with particular expertise in mobile security and program repair technologies, and actively mentors PhD candidates through FNR research grants.

Frank Puppeمشاهده پروفایل

استاد

• Artificial Intelligence
• Medical Image Analysis
• Document Analysis
• +۵ مورد دیگر

Frank Puppe is a Full Professor of Computer Science at the University of Würzburg, Germany, where he holds the Chair of Computer Science VI (Artificial Intelligence and Applied Computer Science) within the Faculty of Mathematics and Computer Science. He is also affiliated with the Center for Artificial Intelligence and Data Science (CAIDAS) and leads research in artificial intelligence, knowledge systems, and applied computer science. His educational background includes a Diploma in Computer Science from Bonn University (1983), a dissertation on Diagnostic Problem Solving from Kaiserslautern University (1986), and a habilitation on Problem Solving with Expert Systems from Karlsruhe University (1991). Professor Puppe's research spans multiple domains of artificial intelligence and its applications. His primary focus areas include Medical Image Analysis , where he develops AI systems for endoscopic disease detection and medical information extraction; Document Analysis and OCR , with significant contributions to processing historical documents and musical manuscripts; and Information Extraction from diverse domains including medical, legal, and literary texts. His work in E-Learning and E-Assessment has led to innovative systems for automatically evaluating programming assignments and argumentation structures. His recent publications demonstrate a strong interdisciplinary approach, bridging computer science with medicine, digital humanities, and law. A notable trend is the application of deep learning techniques to historical document analysis and medical imaging, while maintaining a strong foundation in knowledge-based systems. His research consistently focuses on practical applications of AI that solve real-world problems across multiple domains. 2015-2017: Senator at University of Würzburg 2011-2013: Dean at University of Würzburg 2008-2011: Dean of Students at University of Würzburg Professor Puppe leads multiple significant research projects including KINERGY (optimization of heating systems), DZ-PTM (order entry optimization in radiology), Corpus Monodicum (edition of medieval Latin music), and projects related to adenoma detection in colonoscopy. His laboratory develops tools such as OCR4all for historical document processing and it4all for programming assessment.

Marcel Campenمشاهده پروفایل

استاد

• Computer Graphics
• Geometry Processing
• Surface Parametrization
• +۴ مورد دیگر

Marcel Campen is a Professor at Osnabrück University specializing in Computer Graphics and Geometry Processing. His research focuses on surface parametrization, quad mesh generation, and computational geometry. He has made significant contributions to the field of geometry processing, particularly in developing algorithms for quad layout generation, surface mapping, and mesh repair. His research interests span Computer Graphics, Geometry Processing, Surface Parametrization, Quad Mesh Generation, 3D Modeling, and Mesh Repair. Campen's work addresses fundamental challenges in representing and processing complex geometric shapes, with applications ranging from animation and simulation to reverse engineering and meshing. His research often combines theoretical insights with practical implementations, resulting in algorithms that are both mathematically sound and computationally efficient. Campen's publications demonstrate a strong focus on developing robust and efficient methods for geometry processing. His work on quad layout generation, parametrization techniques, and surface mapping has resulted in several award-winning papers, including Best Paper Awards at SGP 2021 and 2022. His research often bridges theoretical concepts with practical implementations, making his contributions highly influential in both academic and industrial settings. Best Paper Award (1st place) at SGP 2022 Best Paper Award at SGP 2021 Campen has made significant contributions to the field through his doctoral thesis on quad layout generation and numerous publications in top-tier conferences including SIGGRAPH, Eurographics, and SGP. His work on directional field synthesis, similarity maps, and bijective mappings has advanced the state of the art in geometry processing. He has also contributed to practical tools like libQEx for robust quad mesh extraction, demonstrating his commitment to making theoretical advances accessible to practitioners.

Johanna Devaneyمشاهده پروفایل

دانشیار

• Music Information Retrieval
• Music Technology
• Sonic Arts
• +۳ مورد دیگر

Johanna Devaney serves as Associate Professor of Music at Brooklyn College, City University of New York (CUNY), teaching within the Music Technology and Sonic Arts programs at the Conservatory of Music under the School of Visual, Media and Performing Arts. She holds dual appointments at the CUNY Graduate Center in both the Data Analysis and Visualization program and the PhD program in Music, reflecting her interdisciplinary expertise bridging computational methods and musical scholarship. Her academic foundation includes a PhD in music technology from McGill University (2011), an MPhil in music theory from Columbia University, and an MA in composition from York University in Toronto. Previous positions include Assistant Professor of Music Theory and Cognition at Ohio State University and postdoctoral research at UC Berkeley's Center for New Music and Audio Technologies (CNMAT). Devaney's research centers on Music Information Retrieval (MIR), with emphases on computational performance analysis, audio signal processing for music, and the cognitive/social dimensions of musical expression. She investigates how technology reveals patterns in musical performance, gender identity construction through vocal curation, and interdisciplinary applications of music encoding standards. Her work consistently integrates technical innovation with humanistic inquiry into musical meaning. Analysis of her publication record reveals a trajectory toward developing open computational frameworks like AMPACT for performance comparison, advancing methodological rigor in music corpus studies, and addressing representation gaps in audio analysis—particularly regarding gender. Her scholarship demonstrates increasing integration of machine learning with musicological frameworks while maintaining critical perspectives on technology's role in music. Her research impact is evidenced by competitive external funding: National Science Foundation EAGER grant for \"Learning with less data: Capitalizing on formal pedagogies and human performance to incorporate domain knowledge into deep learning models\" National Endowment for the Humanities Digital Humanities Level II Grant for \"Automatic Music Performance Analysis and Comparison Toolkit (AMPACT)\" Devaney actively shapes her field through editorial leadership as Co-Editor in Chief of the Journal of New Music Research, service on RILM's Commission Mixte, and program committee roles for the Society for Music Theory. She co-founded the Cognitively-Informed Music Information Retrieval (CogMIR) Workshop and North Eastern Music Information Special Interest Group (NEMISIG), significantly advancing methodological standards in MIR research. As a leader in the international MIR community, she organizes initiatives like Women in Music Information Retrieval (WiMIR) and delivers keynotes at major conferences including ISMIR. Her collaborative networks span computer science, musicology, and cognitive science departments globally, fostering interdisciplinary approaches to music technology that prioritize both technical innovation and cultural context.

• Database Systems
• Information Systems
• Conceptual Modeling
• +۵ مورد دیگر

Ivan Lukovic is a Professor at the Faculty of Technical Sciences, University of Novi Sad, specializing in database systems, information systems, and conceptual modeling. His research spans over two decades with consistent publication output, demonstrating expertise in domain-specific languages, business process modeling, and data science education. His educational background isn't explicitly detailed in the provided sources, but his extensive publication record and editorial roles suggest advanced academic training in computer science. His research primarily focuses on database systems, information modeling, and applications of these technologies in various domains including education and industry. Lukovic's research interests center around database systems, conceptual modeling, and domain-specific languages. His work demonstrates particular expertise in form-based modeling approaches, integrity constraints, and model-driven development methodologies. He has made significant contributions to the understanding of business application modeling, production process modeling in Industry 4.0 contexts, and educational approaches in computer science. His recent publications show a strong focus on practical applications of database and information systems research, particularly in the areas of production process modeling, educational technologies, and data integration. The articles demonstrate increasing sophistication in methodology and application scope, with recent work addressing contemporary challenges in AI-powered literature review assistance and explainable AI methods. Lukovic has served as guest editor for multiple journal issues, including special editions on computer science applications in management and sustainability, and explainable AI methods. His editorial work reflects recognition of his expertise by the academic community. His collaborative work spans numerous projects with colleagues at the University of Novi Sad and international partners. He has contributed to educational initiatives in data science, particularly through the development of academic study programs at his institution. His work on students' preferences in computer science education indicates active engagement with educational research and curriculum development. Lukovic's research has practical applications in industry settings, particularly in manufacturing and business process domains. His recent work on production process modeling for Industry 4.0 demonstrates how his theoretical research translates into real-world applications that address contemporary industrial challenges.