جستجوی استاد راهنمای دانشگاهی

Muskaan Singhمشاهده پروفایل

مدرس

• Natural Language Processing (NLP)
• Computational Linguistics
• Artificial Intelligence (AI)
• +۹ مورد دیگر

Muskaan Singh is a Lecturer in Data Analytics at the Intelligent Systems Research Centre (ISRC) within the School of Computing, Engineering and Intelligent Systems at Ulster University . A member of the Cognitive Analytics Research Lab (CARL) , her work bridges Natural Language Processing (NLP) , Artificial Intelligence , and Practical Applications in domains ranging from machine translation to biomedical diagnostics. Education: PhD in Machine Translation (Thapar Institute of Engineering and Technology, 2016-2020) Master’s in Machine Translation (IIIT Hyderabad, India) Her research spans NLP and AI with applications in code-switched language modeling , depression detection , social media analytics , and medical diagnostics . She has developed multilingual tools for automatic minuting, including DeepCon and ALIGNMEET , and contributed to EU-funded projects like ROXANNE (criminal network analysis) and ELITR (European Live Translator). Key scientific awards include first prizes in international NLP competitions (EVAL4NLP, LT-EDI, SMM4H) and recognition at EMNLP , ACL , and COLING . She received the Inclusion and Diversity Grant (EMNLP 2021) and GHC Scholarship (2019). Current projects include AI-EPOCMON (AI-Enabled Point-of-Care Monitoring) and T3-NCP (crime prevention for safer communities). Dr. Singh has supervised grants from UKRI and Alzheimer’s Research UK , focusing on AI for health and IT operations . Her team at ISRC collaborates globally with institutions in Switzerland , Czech Republic , and India . She also leads research for the Center for Data Science and Artificial Intelligence at IIIT Lucknow, India.

• Machine Learning
• Artificial Intelligence
• Federated learning
• +۹ مورد دیگر

Rajesh M. Hegde is a Professor in the Department of Electrical Engineering at the Indian Institute of Technology Kanpur. His research spans multiple domains within artificial intelligence, signal processing, and communication systems. He has established himself as a leading researcher in multimodal information processing and has contributed significantly to speech and audio processing techniques. Education: PhD in Computer Science and Engineering, IIT Madras (2005) ME in Electronics Engineering, Bangalore University (1988) BE in IT Engineering, Mysore University Professor Hegde's research focuses on the intersection of machine learning, artificial intelligence, and signal processing, with particular emphasis on multimodal information fusion. His work bridges theoretical foundations with practical applications in speech recognition, wireless communication, and pervasive computing. He has made significant contributions to group delay analysis in speech processing and developed innovative approaches for speaker segregation and multimodal system design. His research has practical implications for improving human-computer interaction through more natural and context-aware interfaces. Analysis of his recent publications reveals a consistent focus on multimodal systems and speech processing, with increasing attention to federated learning approaches and wireless network applications. His work demonstrates a strong theoretical foundation combined with practical implementation expertise, particularly evident in his prototyping of multimedia acquisition systems. Scientific Awards: P.K Kelkar Research Fellowship (2009-2013) Mentor for best undergraduate engineering design project award at UC San Diego (2005-06) ISCA Grant for INTERSPEECH 2004 IIT Madras PhD thesis recommended for IBM best thesis award (2005) Programme Committee member for ISCA Winter schools Commendation by IIT Kanpur for teaching excellence Professor Hegde has supervised multiple research projects and mentored students in developing innovative multimedia systems. His work on multimodal situation awareness systems has received recognition for its interdisciplinary approach. He has secured research funding through fellowships and institutional support, enabling his laboratory to maintain cutting-edge research facilities. His laboratory is located in ACES 203 and 204 at IIT Kanpur, where his research team works on multimodal information processing, wireless communication systems, and speech/audio processing applications. The lab maintains strong connections with international research institutions, particularly through his involvement with ISCA and previous collaborations with UC San Diego.

Sasan Matinfarمشاهده پروفایل

پژوهشگر

• Medical Sonification
• Sonic Interaction Design
• Extended Reality (XR)
• +۴ مورد دیگر

Sasan Matinfar is a research scientist at the Technical University of Munich (TUM), affiliated with the Chair of Computer Aided Medical Procedures (Prof. Navab) and the Munich Center for Machine Learning (MCML). He serves as scientific staff at Rechts der Isar Hospital, developing XR and sonification systems for surgical environments since 2020. His educational background includes: Master’s and Bachelor’s in Computer Science, Ludwig Maximilian University of Munich (LMU) Musicology, Franz Liszt University of Music, Weimar Piano Interpretation, Art University of Tehran Matinfar pioneers medical sonification and multisensory XR, creating auditory interfaces that convert tissue properties into sound for surgical guidance. His work in user-centered design produces clinically viable tools like the Ocular Stethoscope for retinal procedures and physics-based BioSonix frameworks, merging computer vision with perceptual audio engineering to enhance intraoperative precision without visual overload. Analysis of his 12 recent publications (2017-2025) reveals an evolving research arc from foundational surgical soundtracks to sophisticated context-aware sonification. Current work integrates generative AI with real-time tissue deformation modeling, focusing on multimodal frameworks where auditory feedback complements visual navigation in complex surgeries like cardiac interventions and retinal peeling. Key recognitions include: The Data Sonification Award (2025) MICCAI 2023 Best Paper Nominee (top 3% of submissions) MICCAI Young Scientist Award (2017, top 2% of papers) As an educator, Matinfar mentors students through TUM courses including Medical Augmented Reality (WS 2025/26) and Surgical Robotics, while co-organizing the Medical Augmented Reality Summer School and IEEE ISMAR 2025’s MIX Workshop. His patented technologies emerge from collaborations with Politecnico di Milano, TU Dresden’s CeTI, and Balgrist Hospital Zurich, securing interdisciplinary grants in surgical data science. Matinfar operates within TUM’s NARVIS Lab for medical image analysis and RobUSt for robotics-ultrasound integration, leveraging the German Heart Center Munich (DHM) infrastructure to validate XR systems in live surgical workflows and advance vision-language models for intraoperative decision support.

Bart Moensمشاهده پروفایل

پژوهشگر ارشد

• Human-Computer Interaction
• Music Technology
• Spatial Audio
• +۴ مورد دیگر

Bart Moens is a postdoctoral researcher at the Institute for Psychoacoustics and Electronic Music (IPEM) within Ghent University's Department of Art, Music and Theatre Sciences. His work bridges music cognition, technology development, and rehabilitation science through embodied interaction research. He holds a PhD in Art Science and Computer Science Engineering. His doctoral research on the D-Jogger gait synchronization system generated a patent application, over 20 publications, and formed the foundation for the EU/FP7 BeatHealth personalized health project (2013-2016). Moens' research centers on human-computer interaction with focus on synchronization phenomena and artistic technology integration. He investigates how spatial audio, sonification, and adaptive music systems influence motor control in rehabilitation contexts including Parkinson's disease, stroke recovery, dementia care, and developmental disorders. His methodology combines quantitative analysis with artistic performance frameworks. Publication trends from 2014-2025 reveal consistent advancement in auditory-motor synchronization research across neurological populations. His work progresses from foundational D-Jogger studies to current neural entrainment investigations, maintaining focus on real-world rehabilitation applications through spatial audio and XR technologies. Moens has secured major research funding: Methusalem project (2009-2024): Served as postdoctoral researcher and technical staff EU/FP7 BeatHealth project (2013-2016): PhD researcher role where his D-Jogger work provided the conceptual basis He founded the artist collective Hangaar (2012-2017), producing 14 international theatre/music productions featuring interactive video projections. Currently, he leads technological deployment of IPEM's ArtScienceInteractionLab (ASIL), a 10x10x7m facility for XR research in music interaction, spatialized sound, and augmented reality.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +۳ مورد دیگر

Dr Zheyuan Liu is a Research Fellow at the Australian Institute for Machine Learning within the Faculty of Sciences, Engineering and Technology at The University of Adelaide. His research focuses on machine learning, computer vision, and multimodal learning, particularly in areas like federated learning, semantic segmentation, and vision-language models. His research interests include: Federated fine-tuning of large language models Text-to-video generation with diffusion models Weakly supervised semantic segmentation techniques Multimodal image retrieval systems Audio-visual source localization Recent publications demonstrate expertise in cross-domain adaptation, contrastive learning, and re-ranking methods. He is eligible to supervise Masters and PhD students.

• Signal Processing
• Machine Learning
• Optimization
• +۷ مورد دیگر

Hiroyuki Kasai is a Full Professor at the School of Fundamental Science and Engineering, Waseda University, where he leads research in signal processing, machine learning, and optimization. He holds a B.Eng. (1996), M.Eng. (1998), and Dr.Eng. (2000) in Electronics, Information, and Communication Engineering from Waseda University. His career includes positions as Associate Professor and Professor at the University of Electro-Communications (2007-2019), Senior Policy Researcher at Japan's Cabinet Office (2011-2013), and visiting roles at Technical University of Munich and British Telecom. His research spans: Fundamental methodologies : Riemannian optimization, stochastic gradient algorithms, tensor decomposition Applied domains : Network analysis, multimedia systems, environmental sound processing, video coding Emerging areas : Low-rank modeling, manifold learning, and large-scale anomaly detection His publications focus on efficient algorithms for high-dimensional data, with recent work emphasizing Riemannian manifold optimization and real-time tensor analysis. This includes development of open-source tools like SGDLibrary (MATLAB) and McTorch (PyTorch) for optimization tasks. Awards include: IEEE ICCE Best Paper Award (2011) Yamashita Memorial Award (2003) Ericsson Young Scientist Award (2001) 電気通信普及財団賞 (2015) IEICE Service Recognition Award (2010) He maintains memberships in IEEE, IEICE, IPSJ, and JSIAM, and has contributed to over 100 peer-reviewed publications with significant citation impact (h-index 27 via Google Scholar).

• Geophysics
• Electromagnetic Methods
• Numerical Modeling
• +۷ مورد دیگر

Thomas Kalscheuer is a Senior Lecturer and Associate Professor in Applied Geophysics specializing in electromagnetic methods at Uppsala University's Department of Earth Sciences, Geophysics section. He has been with Uppsala University since 2013 and was awarded Docent (Associate Professor) status in 2017. His work focuses on developing advanced geophysical methods for subsurface imaging and characterization. His educational background includes: PhD in Geophysics from Uppsala University (2008) Diplom-Geophysiker from University of Cologne, Germany (2004) Postdoctoral Fellowship at Swiss Federal Institute of Technology, Zurich (2009-2013) Dr. Kalscheuer's research centers on the development of multidimensional numerical forward modeling and inversion schemes for geophysical electromagnetic and potential field methods. He specializes in joint interpretation of EM, seismic, potential field and borehole data, with applications spanning geotechnical engineering, hardrock geology, tectonics, hydrology, geothermal systems, and volcanology. His current work focuses on 3D inversion algorithms for natural and controlled-source EM methods, building on his earlier contributions to 1D and 2D inversion schemes, model error analysis, and resolution studies. Analysis of his recent publications (2018-2025) reveals a strong focus on advancing 3D electromagnetic inversion techniques, with particular emphasis on transfer function data processing, mesh refinement strategies, and integration of multiple geophysical datasets. His work increasingly addresses practical applications in groundwater surveying, fracture zone detection in hard rock environments, and geothermal system characterization, demonstrating the applied value of his methodological innovations. His notable recognition includes: Distinguished University Teacher award from Uppsala University (2024) Dr. Kalscheuer has supervised numerous research projects, particularly those involving collaboration between Uppsala University and international partners. His work often involves field studies in Sweden (including the Äspö Hard Rock Laboratory site), the Okavango Delta in Botswana, and various locations in Europe. He has secured research funding for projects related to electromagnetic method development and their application to geological and hydrological problems, though specific grant details are not provided in the available information. His research is conducted within the Geophysics section of Uppsala University's Department of Earth Sciences, where he collaborates closely with colleagues specializing in seismic methods, potential field methods, and geological interpretation. His work at the Äspö Hard Rock Laboratory demonstrates involvement with a major international research facility for studying deep geological processes and repository technology.

• Systematic Musicology
• Psychoacoustics
• Crossmodal Perception
• +۴ مورد دیگر

Christoph Reuter is a Professor in the Department of Musicology at the University of Vienna. His work spans systematic musicology, psychoacoustics, and crossmodal perception, with a focus on auditory cognition, timbre analysis, and sound-environment interactions. As Director of Studies for Musicology and Linguistics, he oversees academic programs and teaches courses ranging from systematic musicology to auditory perception. His research explores the intersection of music, technology, and human perception. Key themes include crossmodal associations (e.g., sound-color mappings), acoustical analysis of natural and synthetic sounds, and auditory environments in healthcare and education. He has developed VR tools like the Incubator Experience and SOFA Native Spatializer Plugin to advance immersive audio research. The 15 most recent articles highlight his expertise in timbre studies, crossmodal perception, and medical/environmental acoustics. Topics range from the neuroscience of chord progressions to the psychoacoustic impact of neonatal ventilation sounds and metal music lyrics. His work bridges empirical experimentation with interdisciplinary applications in technology and health. Reuter contributes to musicological education through initiatives like the MediaLab of the Faculty of Philological and Cultural Studies, integrating digital methods into teaching. His collaborations span neurology, engineering, and cultural studies, reflecting a holistic approach to sound research.

Hannes Ritschelمشاهده پروفایل

پژوهشگر

• Artificial Intelligence
• Reinforcement Learning
• Human-Robot Interaction
• +۳ مورد دیگر

Dr. Hannes Ritschel is a Researcher at the Chair of Human-Centered Artificial Intelligence , Department of Computer Science, University of Augsburg , and a Professor at the University of Music Würzburg . His work focuses on adaptive AI systems for human-robot interaction, leveraging reinforcement learning to personalize robot behaviors based on social cues. His research interests span Artificial Intelligence , Reinforcement Learning , Natural Language Generation , and Multimodal Interaction . He has pioneered techniques for socially-aware robots, including humor and irony generation, adaptive linguistic styles, and real-time behavior synchronization. Recent publications highlight his work on generative AI for virtual agents , multimodal joke presentation , and privacy-by-design speech assistants . Key themes include emotion recognition, human-agent trust, and AI applications in health and music education. Scientific Awards: Best Technical Paper Award, PETRA 2019 Best Poster Award, PETRA 2019 He mentors students in projects related to reinforcement learning , social robotics , and creative AI , with advisees including Kathrin Janowski, Andreas Seiderer, and Thomas Kiderle. His teaching includes lectures on Content Creation for Virtual Environments and Reinforcement Learning , bridging art and computer science.

Kenneth Womackمشاهده پروفایل

استاد

• Beatles studies
• 20th-century British literature
• Popular music
• +۳ مورد دیگر

Kenneth Womack serves as Professor of English and Popular Music in the Department of English, Music and Theatre Arts at Monmouth University. His office is located in the Lauren K. Woods Theatre Complex 209, with regular office hours maintained throughout the academic year. Womack is recognized as one of the world's foremost authorities on the Beatles and their enduring cultural influence. Womack's research interests center on Beatles studies, 20th-century British literature, popular music, and creative writing. His scholarly work spans both academic and popular publications, demonstrating his ability to bridge academic rigor with accessible cultural commentary. He serves as the Music Culture critic for Salon and contributes regularly to major publications including Slate , Billboard , Time , Variety , The Guardian , and NBC News . His recent publications reveal a strong focus on Beatles history and biography, with particular emphasis on producer George Martin and John Lennon's final days. Womack's articles demonstrate expertise across multiple dimensions of Beatles scholarship, from recording technology analysis to cultural impact assessment, showing his comprehensive understanding of the band's artistic trajectory from their early Liverpool days through their final masterpiece, Abbey Road. Womack maintains an active presence beyond the university, frequently presenting at major institutions including Princeton University, Harvard University, the Smithsonian Institution, and the Grammy Museum Experience. He has served as an expert commentator for major media outlets including ABC's 20/20 and NBC's Access Hollywood , demonstrating his status as a trusted authority on Beatles history. As an educator, Womack teaches courses including 'Introducing the Beatles!', 'Life and Work of Bruce Springsteen', and various creative writing workshops. His teaching schedule extends through the 2025 academic year, demonstrating his ongoing active role in the university community. He also serves as Founding Editor of Interdisciplinary Literary Studies: A Journal of Criticism and Theory and Co-Editor of the English Association's Year's Work in English Studies .

Lingling Fanمشاهده پروفایل

دانشیار

• Software Security Analysis
• Software Testing and Analysis
• Big Data-driven Analysis
• +۳ مورد دیگر

Lingling Fan is an Associate Professor (100 Young Academic Leaders of Nankai University) at Nankai University, China. Her research focuses on software security analysis, software testing and analysis, and big data-driven analysis, with significant contributions to mobile application security, particularly in Android security and accessibility. Her research interests include: Software Security Analysis, with emphasis on mobile application security and vulnerability detection Software Testing and Analysis, particularly for Android applications and accessibility issues Big Data-driven Analysis for security and quality assessment of software systems Dr. Fan's publication record shows strong trends in automated security testing, vulnerability detection in open-source ecosystems, and accessibility analysis for mobile applications. Her work spans multiple disciplines including software engineering, security, and human-computer interaction, with a particular focus on practical applications for Android ecosystem and measurable real-world impact. Her notable scientific awards include: ACM SIGSOFT Distinguished Paper Award (ASE 2022) ACM SIGSOFT Distinguished Paper Award (ICSE 2021) ACM SIGSOFT Distinguished Paper Award (ICSE 2018) Research Tool Award at NASAC 2018 National Scholarship from The Ministry of Education, China (2018) ACM SIGSOFT CAPS Award (ASE 2018) Dr. Fan has served in various academic service roles including as a program committee member for major conferences such as ASE, ICSE, FSE, and ISSRE. She has also been a reviewer for prestigious journals including IEEE Transactions on Information Forensics and Security (TIFS), IEEE Transactions on Dependable and Secure Computing (TDSC), IEEE Transactions on Software Engineering (TSE), and ACM Transactions on Software Engineering and Methodology (TOSEM).

Jan Cychnerskiمشاهده پروفایل

استادیار

• Medical Imaging
• Machine Learning
• Computed Tomography
• +۵ مورد دیگر

Dr. Jan Cychnerski is an Assistant Professor at the Department of Computer Architecture within the Faculty of Electronics, Telecommunications and Informatics at Gdańsk University of Technology. His research bridges medical imaging and distributed systems, with significant contributions to AI-driven diagnostic tools and real-time communication platforms. Research Focus: His work spans: Medical imaging algorithms (CT liver segmentation, Hounsfield scale optimization) Machine learning frameworks for healthcare R&D Real-time networked systems for specialized applications like music performance Dataset construction methodologies for medical AI Recent publications demonstrate strong emphasis on adaptive AI solutions for radiology and oncology, with neural network innovations improving diagnostic accuracy. His interdisciplinary approach connects computer architecture with clinical needs. Research Team: Member of Architektura Systemów Komputerowych (Computer Systems Architecture), focusing on distributed/parallel applications and systems engineering.

Olivier Cappéمشاهده پروفایل

استاد مدعو

• Machine Learning
• Statistical Signal Processing
• Bayesian Methods
• +۳ مورد دیگر

Olivier Cappé is a CNRS Research Director at the Department of Computer Science of École normale supérieure (DI ENS - CNRS/ENS/Inria) and an Adjunct Professor at Université PSL. He is affiliated with the Centre Sciences des Données (CSD) at ENS and serves as a Chair holder and member of the Executive committee of the Pr[AI]rie-PSAI (Paris School of AI) project. Previously, he served as deputy scientific director at INS2I (2017-2023) and headed the Information Processing and Communication Laboratory (LTCI) from 2013 to 2016. Dr. Cappé's research focuses on statistical signal processing and machine learning. His work spans several areas including Bayesian methods, Markov Chain Monte Carlo, online learning, multi-armed bandit models, and differential privacy for machine learning. Starting in speech and audio processing in the 1990s, he contributed to natural language processing applications in the 2000s, and has focused extensively on online learning and bandit algorithms since 2010. His recent work also addresses privacy issues in machine learning systems. Cappé teaches Reinforcement Learning and Differential Privacy for Machine Learning courses in the IASD master program at Université PSL. His publication record shows consistent output across multiple research areas, with recent work focusing on bandit algorithms, online learning, and privacy-preserving machine learning. His research bridges theoretical foundations with practical applications in digital advertising, recommendation systems, and pandemic response analysis. Grand Prize of the EADS Corporate Foundation (Information Sciences) from the French Academy of Sciences (2013) Co-author of 'Tout comprendre (ou presque) sur l'intelligence artificielle' with Claire Marc Dr. Cappé holds a Supélec engineering degree (1990) and a doctorate from ENST (currently Télécom ParisTech, 1993). He joined CNRS as a researcher in 1996 and has maintained a productive research career spanning over 30 years, with significant contributions to both theoretical and applied aspects of statistical signal processing and machine learning.

Chris Munsonمشاهده پروفایل

استاد

• Music Media
• Recording Arts
• Audio Engineering
• +۳ مورد دیگر

Chris Munson serves as Professor and Program Coordinator of Music Media and Recording Arts within the School of Music and Performing Arts at the University of Louisiana at Lafayette, leveraging over two decades of professional music industry experience in his academic role. He earned a Bachelor of Music in Percussion Performance from Eastern Kentucky University (2006) and a Master of Arts in Jazz Studies from Middle Tennessee State University (2008). His research and teaching expertise centers on Music Media and Recording Arts, with developed curricula for audio production and music technology programs. Scholarly publications appear through Mel Bay and Alfred Publishing, while original compositions are distributed by Drop 6 Media, Tapspace, Engine Room Publishing, and Simmons Jazz. Two Grammy Nominations for album contributions Though specific student advising details are unavailable, Professor Munson teaches core courses including History of the Recording Industry, Introduction to the Music Industry, and Audio Recording Techniques across multiple levels. He has designed and installed music technology infrastructure including electronic music labs and commercial recording studios at Lane Community College, and developed the Recording Arts curriculum at Eastern Kentucky University.

Michael Lyuمشاهده پروفایل

استاد

• Software Engineering
• Cloud Computing
• AIOps
• +۵ مورد دیگر

Michael Lyu is a Professor at The Chinese University of Hong Kong specializing in software engineering with a focus on cloud reliability, AIOps, and log analysis. His research bridges the gap between theoretical advances and practical industrial applications in large-scale cloud systems. His research interests span Software Engineering , Cloud Computing Reliability , AIOps , and Log Analysis . Dr. Lyu's work addresses critical challenges in modern cloud operations, including failure diagnosis, anomaly detection, and reliability engineering. His recent research has pivoted toward leveraging large language models for software engineering tasks, particularly in code generation and log analysis. His publication portfolio demonstrates consistent contributions to major software engineering conferences (ASE, ICSE, ESEC/FSE) from 2018-2025, with a noticeable increase in LLM-related research since 2023. The trend shows a clear evolution from traditional software engineering topics toward AI-driven approaches for cloud operations. ICSE 2021 Keynote: "Reliability-Driven AIOps for Cloud Resilience" ASE 2023: Maat: Performance Metric Anomaly Anticipation for Cloud Services ASE 2024: LILAC: Log Parsing using LLMs with Adaptive Parsing Cache Dr. Lyu actively mentors students, with numerous co-authored publications showing his advisees as first authors. His work receives significant attention in both academic and industrial software engineering communities, addressing practical problems faced by large-scale cloud service providers. His research group appears focused on developing data-driven approaches for improving cloud system reliability through advanced analytics of logs, traces, and KPIs.