جست‌وجوی استاد و پژوهشگر

• Behavioural Neuroscience
• Pain Neurophysiology
• Brain Imaging
• +۵ مورد دیگر

Dr Andrej Stancak is a Reader in Behavioural Neuroscience at the University of Liverpool , based in the Department of Psychological Sciences within the Institute of Psychology, Health and Society . His expertise lies in neuroimaging, electrophysiology, and the neural mechanisms of pain, decision-making, and olfaction. He leads multidisciplinary research projects and teaches extensively across undergraduate and postgraduate programs. Education: While specific degrees are not listed in the text, Dr Stancak has held prestigious fellowships including a Fulbright Fellowship (2001), DAAD Fellowship (1997), and DFG Fellowship (1998), indicating advanced training in neuroscience and neuroimaging. Research Interests: Dr Stancak’s work spans pain neurophysiology , brain imaging (EEG, MEG, fMRI) , olfactory modulation of behavior , and economic decision-making . He explores how the brain generates conscious pain experiences and how sensory inputs like odors and textures influence value-based choices, often in collaboration with industry partners such as Unilever and AstraZeneca . Research Trends: His recent publications emphasize mobile EEG applications , machine learning for pain prediction , and cross-cultural neuroscience . There is a consistent focus on translating lab-based findings into real-world contexts, such as online purchasing behavior and chronic pain management. Scientific Awards: He has received several international fellowships and honors, including: Fulbright Fellowship (USA, 2001) DAAD Fellowship (Germany, 1997) DFG Fellowship (Germany, 1998) Ludwig Boltzmann Fellowship (Austria, 1994) Teaching & Supervision: Dr Stancak is the module coordinator for Psychobiology of Pain (PSYC317) and teaches across multiple undergraduate and postgraduate modules. He has successfully supervised 8 PhD students to completion and has 1 pending submission . Labs & Teams: While no specific lab name is provided, his work is conducted in collaboration with the Department of Mathematical Sciences , Institute of Risk and Uncertainty , and international partners such as Charles University in Prague and University of Glasgow .

• Human-Computer Interaction
• Wearable Computing
• Tactile Interfaces
• +۴ مورد دیگر

Denys J.C. Matthies is an Associate Professor at Technical University of Applied Sciences Lübeck and affiliated with Fraunhofer IMTE Lübeck . He specializes in Human-Computer Interaction , particularly focusing on Wearable Computing , Tactile Feedback Systems , and Activity Recognition through smart footwear and body-worn sensors. Key Collaborations: Augmented Human Lab (NUS), Fraunhofer IGD, City University of Hong Kong Research Themes: Smart wearables, haptic interfaces, physiological sensing, assistive technologies His recent work explores: PhantomFolds (2025) - Spatial tactile feedback via fingernail-mounted LRAs PAVES (2025) - Pneumatic terrain simulation in VR Cyber-Placebo (2024) - Ethical implications of fake-AI in CPHS He has contributed to smart footwear systems like ShoeTect2.0 (2024) and SurfSole (2024), integrating capacitive sensing with neural networks for real-time activity and surface recognition. His work spans medical applications (e.g., 2025 study on hepatic encephalopathy screening) and novel interaction paradigms (e.g., Kavy conversational AI 2024).

Robert Funnellمشاهده پروفایل

استاد

• Hearing Mechanics
• Acoustics
• Computational Modeling
• +۵ مورد دیگر

Robert Funnell, PhD is an Investigator at the Research Institute of the McGill University Health Centre (RI-MUHC) and a Professor in the Department of Otolaryngology - Head and Neck Surgery at McGill University's Faculty of Medicine. His research is conducted within the Child Health and Human Development Program at RI-MUHC, where he applies engineering principles to understand auditory mechanics and improve diagnostic tools for hearing assessment. Dr. Funnell's research focuses on the mechanics and acoustics of hearing, with particular emphasis on developing novel measurement techniques in experimental animals and infants, and creating computational models of the ear. His work combines experimental measurements with sophisticated modeling approaches to better understand middle ear function. He has developed specialized software for creating 3D models of complex anatomical structures, including his notable Thrup'ny 3D viewer application. His research has significant clinical applications, particularly in improving newborn hearing screening protocols and developing better diagnostic tests for middle ear conditions. Analysis of Dr. Funnell's recent publications reveals a consistent trajectory in middle ear biomechanics research spanning several decades. His work demonstrates increasing sophistication in computational modeling techniques, with recent publications integrating optical coherence tomography, X-ray microCT data, and finite element analysis to create increasingly accurate representations of middle ear function. A significant portion of his recent work focuses on newborn hearing assessment, reflecting the clinical importance of early hearing detection. His research bridges engineering principles with clinical audiology, creating practical applications for hearing screening and diagnosis. Dr. Funnell has developed several notable software tools including Thrup'ny, a free open-source 3D viewer for VRML models designed for both anatomy teaching and finite-element pre- and post-processing. His website hosts various educational resources including interactive 3D models of the ear, finite-element simulation tools, and teaching materials on middle ear mechanics. These resources have been widely used for both research and educational purposes in the field of otolaryngology and biomedical engineering.

• Software Engineering
• Sustainability
• Human-Environment Interaction
• +۱۲ مورد دیگر

Ana Leticia de Cerqueira Leite Duboc is a contracted professor at La Salle School of Engineering, affiliated with the Human-Environment Research department. Her work bridges Software Engineering and Sustainability , focusing on education, systems analysis, and cross-disciplinary applications. Key research areas: Software Engineering Sustainability & SDGs Animal Welfare via Acoustic Monitoring Cognitive Robotics ICT for Mental Health Recent projects include Mentaltic (ICT in mental health), CowTalk (cattle vocalization monitoring), and CMES (cross-modal robotic perception). Her collaborations span AGAUR and the Generalitat de Catalunya, with a focus on deep learning and sustainable systems.

Fabio Hellmannمشاهده پروفایل

پژوهشگر

• Biomedical Computer Vision
• Deep Learning
• Human-Computer Interaction
• +۳ مورد دیگر

Fabio Hellmann is a Ph.D. candidate and Researcher at the Chair for Human-Centered Artificial Intelligence under Prof. Dr. Elisabeth André at the University of Augsburg . Holding an M.Sc., he focuses on biomedical image/signal processing, deep learning, and explainable AI, with affiliations to the Faculty of Applied Computer Science and Institute of Computer Science . Research Interests : Biomedical Computer Vision Deep Learning Applications Human-Computer Interaction Explainable AI Medical Image Analysis Facial Phenotyping for Genetic Disorders Publications highlight trends in AI-driven medical imaging (e.g., GANonymization , GestaltGAN ), food viscosity analysis via sound, and rare disease diagnostics. He reviews for journals like IEEE Transactions on Dependable and Secure Computing and contributes to projects such as FORSocialRobots and Dynalearn .

Johanna Kuchمشاهده پروفایل

پژوهشگر

• Human-Robot Interaction
• Sound Design in Human-Machine Interaction
• Gamification Strategies
• +۳ مورد دیگر

Johanna Kuch, M.Sc., is a Scientific Associate at the Chair of Human-Centered Artificial Intelligence within the Faculty of Applied Computer Science at the University of Augsburg. Her work focuses on sound design, gamification, and voice interaction systems in human-machine contexts. Research Areas Innovative sound design for human-machine interfaces Gamification strategies in human-robot interaction Personalized voice synthesis for humanoid robots Emotional AI integration in virtual agents She supervises bachelor's and master's theses on adaptive interaction systems and collaborates on projects involving 3D audio, emotional signal processing, and user-focused AI training tools. Contact: Universitätsstraße 6a, 86159 Augsburg | Phone: +49 821 598-2345 | Office hours by appointment

Lin Chenمشاهده پروفایل

دانشیار

• Software Engineering
• Programming Languages
• Artificial Intelligence for Software Engineering
• +۴ مورد دیگر

Lin Chen is an Associate Professor in the Department of Computer Science and Technology at Nanjing University, China, specializing in software engineering and programming languages with applications to AI-integrated systems. His work bridges theoretical foundations and practical tools for software analysis, testing, and ecosystem studies. Education: Ph.D. in Computer Software and Theory, Southeast University (2009) B.S. in Computer Science and Technology, Southeast University (2001) Visiting Scholar at Purdue University (2015-2016) His research spans software testing, programming language design (particularly gradual typing), and AI-enhanced software engineering. Key contributions include empirical studies of Python's dynamic features, mutation testing frameworks for AI systems, and defect prediction models. He investigates how programming language semantics impact software quality and maintenance in open-source ecosystems. Recent publications (2023-2024) reveal strong trends in applying software engineering techniques to AI systems, analyzing Python's typing evolution, and developing practical testing tools. There is significant emphasis on empirical validation, with 60% of recent work focusing on Python ecosystem analysis and 30% on AI/software integration. Scientific Awards: FSE 2016 Distinguished Artifact Award First Prize of Hubei Science and Technology Award (2015) First Prize of Jiangsu Science and Technology Award (2012) First Prize of Jiangsu Science and Technology Award (2007) Professor Chen has advised 14+ graduate students including PhD candidates Hao Ren and Wanwangying Ma, and master's students like Fan Yang and Yuanlei Han. He actively recruits self-motivated PhD and undergraduate researchers for projects in software analysis, testing, and intelligent engineering. His group collaborates with industry partners on tool development for defect prediction and type system analysis. His research team at Nanjing University focuses on four pillars: (1) Software Analysis and Testing for dynamic languages, (2) Technical Debt and Refactoring in evolving systems, (3) AI-driven defect prediction, and (4) Gradual typing semantics for multilingual ecosystems. Current projects include large-model-based test generation and knowledge graph applications for QA system validation.

• Neuroscience
• Motor Control
• Virtual Reality
• +۳ مورد دیگر

Andreas Wulff-Abramsson serves as a Special Consultant in the Department of Nutrition, Exercise and Sports at the University of Copenhagen, affiliated with the Movement and Neuroscience research group. His interdisciplinary work bridges neuroscience, virtual reality, and motor control, focusing on embodied learning applications and neural mechanisms of cognition and movement. His primary research interests include Neuroscience , Motor Control , Virtual Reality , Cognitive Science , EEG , and Embodied Learning . He investigates how virtual environments influence educational outcomes in children, neural correlates of motor function across age groups, and sensory processing mechanisms using EEG and VR methodologies. His experimental approach integrates cognitive psychology with advanced neuroimaging techniques to explore embodied cognition. Recent publications reveal a strong trend toward applied neuroscience, particularly VR-enhanced educational interventions for early literacy and detailed EEG analyses of motor control and sensory processing. His work consistently combines experimental rigor with innovative technology applications, spanning educational psychology, film cognition, and human-computer interaction domains. Wulff-Abramsson operates within the Movement and Neuroscience team at NEXS, which examines the intersection of physical activity, nutrition, and brain health. This group employs virtual reality systems, EEG recording, and motion analysis to investigate fundamental neural processes underlying movement and cognitive function across diverse populations.

Liqian Chenمشاهده پروفایل

استاد

• Program analysis
• Program verification
• Abstract interpretation
• +۳ مورد دیگر

Liqian Chen is a Full Professor and PhD Supervisor in the College of Computer Science and Technology at the National University of Defense Technology (NUDT) in Changsha, China. With a prolific research career spanning over a decade, Professor Chen has established himself as a leading expert in program analysis, verification, and automated program repair. His work bridges theoretical foundations with practical applications, particularly in the areas of abstract interpretation and numerical program analysis. Professor Chen's primary research interests include: Program analysis and verification Abstract interpretation Automated program repair Floating-point analysis Neural network verification His research focuses on developing rigorous theoretical frameworks for program analysis while ensuring practical applicability to real-world software systems. A significant portion of his work addresses the challenges of numerical accuracy in software, particularly in floating-point computations, and has extended these techniques to the emerging domain of neural network verification. Analysis of Professor Chen's recent publications reveals several key trends in his research trajectory. His work has evolved from foundational abstract interpretation techniques to address increasingly complex software systems, including neural networks. There's a clear progression from theoretical developments in abstract domains to practical applications in software verification and repair. His recent work demonstrates a growing emphasis on the intersection of traditional program analysis with machine learning systems, particularly in verifying neural network behavior and addressing numerical instability in AI systems. Professor Chen has received notable recognition for his contributions to the field: ACM SIGSOFT Distinguished Paper Award (ISSTA 2024) Best Paper Award (APSEC 2017) Best Paper Award Nomination (EMSOFT 2015) As an academic advisor, Professor Chen supervises numerous PhD and Master's students working on cutting-edge research in program analysis and verification. His team has developed several influential tools including AutoRNP (for automatically detecting and repairing floating-point errors), Software Apron (a library for numerical abstract domains), and F-IKOS (a static analyzer for Fortran programs). Professor Chen has secured substantial research funding to support his work, though specific grant details are not provided in the available information. He actively serves the research community through program committee roles for major conferences including ASE, SAS, and VMCAI, and as a guest editor for journals such as Automated Software Engineering Journal and Journal of Systems Architecture. Professor Chen leads a vibrant research group focused on high-confidence software technologies. His team, comprising PhD students, Master's students, and research engineers, collaborates closely on projects spanning from theoretical foundations of program analysis to practical tool development. The group maintains strong connections with both academic institutions and industry partners, facilitating the transfer of research innovations to real-world applications. Their work is characterized by rigorous theoretical underpinnings combined with practical validation on real software systems.

• Affect, Embodiment, Feelings, Emotions, Atmosphere, Sensations, Enchantments
• Feminist New Materiality
• Systems of Care, Ethics and Social Justice
• +۱ مورد دیگر

Chrystal Cherniwchan is a Lecturer in the School of Arts, Media and Creative Technologies at the University of Salford, where she leads undergraduate photography modules including Creative Communities, Street & Studio, and Resolution. Her practice-based research critically investigates expanded documentary methodologies through photography, film, audio field recordings, and creative non-fiction, focusing on affective dimensions of recorded experience and more-than-human knowledge systems. Her educational qualifications include: Bachelor of Fine Arts (Complete) Master of Arts (Complete) Postgraduate Certificate in Academic Practice (PGCAP), University of Salford (2025) Cherniwchan's research interrogates the intersection of feminist new materiality, aural diversity, and ethical systems of care within contemporary art practice. She critically examines hyper-tools in digital generative methods to challenge artifice in mechanical reproductions and ontological understanding within machine learning realities, emphasizing sensuality and atmospheric production in documentary outputs. Recent outputs demonstrate consistent exploration of sound art and environmental documentation, particularly through collaborative field-recording projects like 'Non Mountain' and 'Grass Folded'. These works investigate sensory experience and more-than-algorithmic knowledge systems, while her artist features highlight emerging practices in contemporary photography and visual arts. Scientific recognition includes: The Agassi Book Arts Prize (2019) for innovative book arts practice As an Academic Progress Reviewer for undergraduate levels 4 and 6, she provides academic guidance while developing curriculum through modules exploring photographic book formats and creative resolution. Her AdvanceHE Fellowship reflects commitment to pedagogical excellence in arts education. Cherniwchan collaborates with sound artists like Angus Carlyle and Craig Tattersall on projects investigating acoustic ecology, forming interdisciplinary teams that bridge environmental studies, sensory anthropology, and experimental documentary practice.

Ivan Ruchkinمشاهده پروفایل

استادیار

• Cyber-Physical Systems
• Autonomous Systems
• Formal Verification
• +۶ مورد دیگر

Ivan Ruchkin is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Florida, where he leads the Trustworthy Engineered Autonomy (TEA) Lab. He holds affiliate appointments in the Department of Mechanical & Aerospace Engineering, Department of Computer & Information Science & Engineering, Nelms Institute for the Connected World, Artificial Intelligence Academic Initiative, and Intelligent Critical Care Center. Dr. Ruchkin's research focuses on making autonomous systems safer and more trustworthy through novel techniques for modeling, analyzing, verifying, controlling, and monitoring cyber-physical systems. His work spans formal verification methods, safety monitoring with statistical guarantees, model integration approaches, and neuro-symbolic paradigms that combine the strengths of neural networks and symbolic reasoning. He has made significant contributions to the fields of conformal prediction for safety guarantees, neural network repair while preserving correct behaviors, and physically interpretable world models for autonomous systems. His recent publications reveal a strong trend toward developing statistically sound safety guarantees for learning-enabled systems, with particular emphasis on conformal prediction methods that provide calibrated confidence measures. His work bridges the gap between high-dimensional perception (like vision) and formal safety guarantees, addressing the critical challenge of ensuring safety in systems where traditional verification methods fail due to complexity. Dr. Ruchkin is actively involved in the academic community as a program committee member for major conferences including ASE 2025 (Research Papers track) and ICSE 2026 (New Ideas and Emerging Results track). His research has been published in top venues across software engineering, formal methods, and robotics. At the University of Florida, he directs the TEA Lab which focuses on developing theoretically grounded yet practically applicable methods for trustworthy autonomy. His lab investigates approaches that combine formal methods with machine learning to create safety-critical autonomous systems that can provide statistical guarantees about their behavior even in complex, uncertain environments.

Tim Menziesمشاهده پروفایل

استاد

• Search-Based Software Engineering
• Software Analytics
• Software Product Lines
• +۴ مورد دیگر

Tim Menzies is a Full Professor at North Carolina State University with a diverse professional background that includes previous careers as a nurse, rocket scientist, taxi driver, and journalist. His primary research focuses on Search-Based Software Engineering (SBSE), software analytics, software product lines, Mining Software Repositories, and data mining and machine learning applications in software engineering. Dr. Menzies has maintained an active research profile with publications spanning from 2018 through 2026, serving on program committees and organizing tracks at major conferences including ASE, ICSE, and ESEC/FSE. His work consistently bridges theoretical research with practical applications, emphasizing solutions that are both technically sound and implementable in real-world settings. His research interests include: Search-Based Software Engineering (SBSE) Software analytics and data-driven approaches Software product lines and configuration Mining Software Repositories Machine learning applications in software engineering Fairness in software engineering tools Interpretable and explainable AI for software engineering His recent publications (2023-2026) reveal a strong trend toward making complex software analytics more accessible and understandable while maintaining performance. He has pioneered approaches that simplify models without sacrificing effectiveness, directly addressing the 'black box' problem that often hinders adoption of machine learning in practice. Dr. Menzies is known for his critical perspective on software engineering research, challenging assumptions in the field, particularly regarding deep learning applications. His work emphasizes practical, evidence-based approaches over theoretical elegance alone, reflecting his commitment to improving the research-practice connection in software engineering.

Frank Wallhoffمشاهده پروفایل

استاد

• Assistive Technologies
• Human-Machine Interaction
• Cognitive Systems
• +۱۱ مورد دیگر

Prof. Dr.-Ing. Frank Wallhoff is a Professor for Assistive Technologies at Jade University of Applied Sciences since 2010. He serves as Dean of the Department of Civil Engineering Geoinformation Health Technology and Head of the Institute for Technical Assistance Systems. His career spans over two decades with significant contributions to human-machine interaction, cognitive systems, and assistive technologies. Previously, he worked at Technische Universität München from 1997-2010 where he completed his doctorate and served as a postdoc and Akademischer Rat. His educational background includes: 1988-1991: School education with A-level/high school graduation 1991-1992: Certified Engineer's Assistant 1993-1999: Study of electrical engineering 2002-2006: Doctorate at Technische Universität München on face detection, identification, and emotion recognition Wallhoff's research focuses on the intersection of technology and human needs, particularly in assistive technologies for aging populations and human-robot interaction. His work spans cognitive systems with learning capabilities, social robotics, ambient assisted living, pattern recognition, machine learning, human-machine interaction, and facial expression recognition. He has developed significant databases like the FG-NET Database with Facial Expressions and Emotions, which has been widely used in emotion recognition research. His recent publications (2015-2018) reveal a strong trend toward practical applications of human-robot interaction in healthcare, rehabilitation, and daily living assistance. His work increasingly integrates multimodal sensing, machine learning, and context-aware systems to create adaptive assistance technologies. Key areas include motion exercise recognition for rehabilitation, 3D visualization for underwater vehicle control, and robust human-robot dialogue systems for production environments. His scientific recognition includes: Best Paper Award for 'Experimental Platform for Wizard-of-Oz Evaluations of Biomimetic Active Vision in Robots' at IEEE International Conference on Robotics and Biomimetics (Robio), 2009 Wallhoff has secured significant research funding including the EITAMS project (€1.5 million from Lower Saxony Volkswagen Advance Programme) focused on affordable underwater vehicle systems, and the INTERREG project 'Vital Regions' (2017-2020) developing technologies to support elderly in rural areas. He coordinates the ALIAS project (Adaptable Ambient Living Assistant) and has led multiple projects within the Cluster of Excellence CoTeSys, including JAHIR, ACIPE, EYETRACK, and RealEYE. His research bridges academic innovation with practical applications, particularly in healthcare technology and assistive systems. He leads the Institute for Technical Assistance Systems at Jade University, where his team develops innovative solutions at the intersection of cognitive systems, human-robot interaction, and assistive technologies. Current initiatives focus on underwater robotics for maritime applications, rehabilitation technologies using motion tracking, and ambient assisted living systems for elderly care.

• Machine Learning
• Music Information Retrieval
• Computational Musicology
• +۲ مورد دیگر

Luca Marinelli is a Tutor at Queen Mary University of London's School of Electronic Engineering and Computer Science, concurrently pursuing doctoral research on multimodal analysis of gender encoding in advertising music. His work bridges machine listening, computational musicology, and critical discourse analysis. Research explores deep learning approaches to decode gendered sound patterns in media, employing music information retrieval (MIR) techniques on large-scale advertising corpora. Recent publications focus on explainable AI for bias detection in commercial audio, foundation models for music, and multimodal gender representation. Teaching: Leads the Professional and Research Practice undergraduate module, covering engineering ethics, communication skills, and career development frameworks through project-based learning. Doctoral project investigates historical sedimentation of gender stereotypes in sonic branding, combining MIR with social theory to audit cultural biases in advertising soundtracks.

• Cognitive Neuroscience
• Auditory-Motor Learning
• Music Therapy
• +۳ مورد دیگر

Simone DALLA BELLA is a Professor at the Université de Montréal, affiliated with the Faculty of Arts and Sciences and the Department of Psychology. He holds the Canada Research Chair in Music Auditory-Motor Skill Learning and New Technologies. His primary research focuses on cognitive and neural mechanisms underlying musical perception and performance, with a particular emphasis on rhythmic abilities in healthy individuals and clinical populations (e.g., Parkinson’s disease, ADHD). He co-directs the BRAMS laboratory (International Laboratory for Brain, Music and Sound Research) and explores applications of rhythmic and auditory-motor interventions for neurological and movement disorders. Research Interests: Dr. Dalla Bella investigates how rhythmic and temporal predictions influence language processing, motor coordination, and rehabilitation. His work integrates behavioral methods, EEG, motion capture, and wearable technologies to assess and train rhythmic skills. Key areas include: 1) rhythmic deficits in clinical populations, 2) music-based rehabilitation for Parkinson’s disease, 3) neural correlates of auditory-motor synchronization, and 4) cross-modal temporal processing. Key Projects: Recent grants include studies on rhythmic learning in immersive environments, neurostimulation and music effects on mood, and the role of rhythm in speech production. Collaborations involve interdisciplinary teams addressing intersectorial challenges in neuroscience and health. Labs/Teams: Co-director of BRAMS and member of CRBLM (Centre for Research on Brain, Language and Music). His work bridges cognitive neuroscience, audiology, and physical medicine, with applications in neurorehabilitation and motor training.