جست‌وجوی استاد و پژوهشگر

• AI
• Internet of Things (IoT)
• Data Science
• +۶ مورد دیگر

Dr. Muhammad Diyan is a Lecturer in Computer Science at Teesside University, UK, with over 10 years of experience in academia and industry. He holds a Ph.D. from Kyungpook National University, South Korea (2017–2021), where his research focused on scheduling sensor duty cycling using Bi-Directional LSTM and Reinforcement Learning. He has held prestigious roles including Research Associate at University of Glasgow and Edinburgh Napier University, and Lecturer at University of Dundee. His research spans AI, IoT, Data Science, Cybersecurity, Healthcare, and Computer Vision. Notable contributions include deep learning models for brain tumor detection, skin cancer early diagnosis via mobile applications, and energy-efficient IoT protocols. He has authored over 25 journal papers and conference proceedings, and received the UK Royal Academy of Engineering's Global Talent Visa for early-career innovators. Diyan actively serves as a reviewer for high-impact journals, conference track chair, and guest editor for special issues. His work emphasizes real-world applications in healthcare, smart cities, and cybersecurity, blending theoretical rigor with practical implementation.

Cumhur Erkutمشاهده پروفایل

دانشیار

• Virtual Reality
• Embodied Interaction
• Augmented Reality
• +۴ مورد دیگر

Cumhur Erkut is an Associate Professor at Aalborg University's Department of Architecture, Design and Media Technology, part of The Technical Faculty of IT and Design. His work focuses on Embodied Interaction, Virtual Reality, and Sound and Music Computing. He leads the Multisensory Experience Laboratory and has contributed to projects like MAT-DYN-NET (mathematical modeling for network dynamics) and SooC (sound in urban environments). Research interests include sonic interactions in virtual environments, movement-sound relationships, and audio-based human-machine interaction. He has published over 100 articles, with recent work on AI-generated speech in VR, voice conversion algorithms, and multimodal improvisation systems. Awards include the 2018 Best Student Paper Award and a 2015 3D User Interface contest win. His editorial roles include co-editing the Journal of Somaesthetics and serving on the Audio Engineering Society board. Projects emphasize applied research in wearable tech, urban soundscapes, and interactive systems for health and creativity.

• Automatic Speech Recognition
• Machine Learning
• Acoustic Phonetics
• +۶ مورد دیگر

Mark Hasegawa-Johnson is a Professor of Electrical and Computer Engineering at the University of Illinois at Urbana-Champaign, where he has been faculty since 1999. He holds affiliations with the College of Engineering and leads the Statistical Speech Technology Group. His academic roles include serving as Editor-in-Chief of the IEEE Transactions on Audio, Speech and Language, and membership in the ISCA Diversity Committee. Education: PhD in Electrical Engineering and Computer Science from MIT (1996), postdoctoral research at UCLA (1996-1999). Research interests span automatic speech recognition, machine learning applied to phonetics and prosody, and accessibility technologies for under-resourced languages and speech disorders. Key projects include the Speech Accessibility Project, which improves speech recognition for individuals with dysarthria, and international competition successes in audio event detection and multilingual broadcast retrieval. Scientific achievements include Fellowships from the IEEE (2020), Acoustical Society of America (2011), and ISCA. Awards also include NIH’s National Research Service Award (1998-1999) and the Frederic Vinton Hunt Post-Doctoral Fellowship (1996-1997). Teaching focuses on courses like Artificial Intelligence, Multimedia Signal Processing, and Speech Processing. Research supervision emphasizes undergraduate projects in signal processing and speech recognition, with notable student contributions to prosody-dependent speech recognition and audio source separation. Labs/Teams: Leads the Speech Accessibility Project and collaborates with interdisciplinary teams on projects like Mandarin language education tools and audio-visual speech models. Current work explores unsupervised learning, cross-lingual speech recognition, and AI-driven accessibility solutions.

Dirk Slockمشاهده پروفایل

استاد

• Wireless Communications
• Signal Processing
• MIMO Systems
• +۶ مورد دیگر

Dirk Slock is a Professor at EURECOM's Communication Systems department. His research focuses on advanced signal processing for wireless communications, including transmitter/receiver design for 4G/5G systems, Massive MIMO, stochastic geometry, and audio signal processing. He has contributed to areas like interference management, compressive sensing, and Bayesian methods. Slock teaches courses on statistical signal processing and wireless communication techniques. His notable awards include IEEE Fellow (2006) and EURASIP Fellow (2015). Collaborations with students like Christo Kurisummoottil Thomas have yielded Best Student Paper Awards at SPAWC 2018. His work addresses challenges in cell-free MIMO, semi-blind channel estimation, and secure communication systems. Recent research trends explore ultra-massive MIMO signal detection, dynamic channel prediction with tensor methods, and cell-free network optimization. His publications span 639 entries, emphasizing practical implementations of theoretical signal processing advancements.

Simon Leglaiveمشاهده پروفایل

استاد

• Signal Processing
• Machine Learning
• Audio
• +۳ مورد دیگر

Simon Leglaive is a Professor at the Rennes Institute of Electronics and Telecommunications, leading research in signal processing, machine learning, and audio/speech technologies. His work focuses on advancing methods for speech enhancement, audio source separation, and generative models for audiovisual data. He has contributed extensively to techniques involving variational autoencoders (VAEs), dynamical systems, and probabilistic modeling in noisy and reverberant environments. His research interests span signal processing fundamentals, machine learning applications to audio, and interdisciplinary projects combining speech, audio, and computer vision. Notable contributions include frameworks for unsupervised domain adaptation in speech enhancement, advanced source separation under reverberation, and human motion analysis using latent space modeling. Recent work emphasizes generative models for speech emotion recognition and robust audiovisual fusion. Key technical trends in his publications include: Integration of deep learning with classical signal processing methods Probabilistic modeling of reverberant audio mixtures Unsupervised learning for speech/noise separation Applications of vector quantization and masked autoencoders He has collaborated extensively with researchers from institutions like INSA Rennes and Sorbonne University, contributing to international challenges like the CHiME-7 speech enhancement task. His laboratory develops open-source tools and benchmarks for speech processing research.

Rohan Chandraمشاهده پروفایل

استادیار

• Robotics
• Autonomous Driving
• Multi-Robot Systems
• +۴ مورد دیگر

Rohan Chandra is an Assistant Professor in the Department of Computer Science at the University of Virginia, leading the Chandra Robot Autonomy Lab (CRΔL). He previously served as a postdoctoral research fellow at the University of Texas at Austin (2022–2024), advised by Drs. Joydeep Biswas and Peter Stone. His research focuses on algorithms and systems enabling robots to navigate safely and efficiently in dynamic environments, particularly among humans. Education background includes: Ph.D. and M.S. in Computer Science, University of Maryland, College Park (2022, 2018) B.Tech. in Electronics and Communication Engineering, Delhi Technological University, India (2016) Research interests span robotics, autonomous driving, multi-robot systems, and human-robot interaction. He has developed the METEOR dataset for complex traffic analysis and pioneered methods like LIVEPOINT for decentralized multi-robot control. His work appears in top-tier conferences (CVPR, ICRA, IROS) and involves collaborations on game-theoretic planning and emotion-aware AI. Awardees include the 2023 Microsoft Future Leader in Robotics and AI, KAUST Rising Star in AI, and Drones Young Investigator Award (2024). He serves on editorial boards for journals like RA-L and actively contributes to conference committees in robotics and AI. Teaching includes CS 2120 (Discrete Mathematics and Theory) at UVA. His lab focuses on advancing safe, socially aware autonomy in dynamic environments.

Markus Schedlمشاهده پروفایل

استاد

• Recommender Systems
• Information Retrieval
• Algorithmic Fairness
• +۶ مورد دیگر

Markus Schedl is a Full Professor at Johannes Kepler University Linz , Austria, where he leads the Multimedia Mining and Search (MMS) group within the Institute of Computational Perception . He also heads the Human-centered Artificial Intelligence (HCAI) group at the Linz Institute of Technology (LIT) AI Lab . Education: Computer Science (TU Wien), PhD (JKU Linz), Master's in International Business Administration (WU Wien, University of Gothenburg) Research Interests span recommender systems , information retrieval , algorithmic fairness , user modeling , and machine learning . His recent publications focus on: Multimodal and hybrid AI for human-centric personalization Context-aware music recommendation and emotion recognition Graph neural networks for session-based recommendation Technical and regulatory compliance in fair, transparent, and privacy-preserving systems Projects are funded by the Austrian Science Fund (FWF) , Austrian Research Promotion Agency (FFG) , and European Commission (EC) . He maintains industry collaborations with Siemens, Spotify, and Deezer. Teaching includes courses like Introduction to Machine Learning and Social Media Mining and Analysis at JKU, with guest lecturing experiences at Pompeu Fabra, Queen Mary London, and KTH Stockholm. Labs & Teams: MMS group at JKU's Institute of Computational Perception and HCAI group at LIT AI Lab.

• Radar Technology
• Microwave Engineering
• Wireless Sensor Systems
• +۳ مورد دیگر

Patrick Kwiatkowski is a researcher at the Ruhr University Bochum , affiliated with the Faculty of Electrical Engineering and Information Technology and the Integrated Systems department. His work focuses on advanced radar technologies, particularly in the development of FMCW and MIMO radar systems for automotive, biomedical, and environmental applications. Role: Researcher Institution: Ruhr University Bochum Department: Integrated Systems His research spans Radar Technology , Microwave Engineering , and Wireless Sensor Systems , with a strong emphasis on 3D target localization , frequency-steered antennas , and harmonic RFID tag integration . Recent work highlights radar-based navigation aids for visually impaired individuals and high-frequency sensor systems for biomedical monitoring. The publications demonstrate expertise in Microwave Sensors , Millimeter-Wave Technology , and Signal Processing , with applications in Automotive Safety , Medical Engineering , and Environmental Imaging . Key trends include the integration of MIMO architectures and ISM band systems for enhanced spatial resolution and functional diversity. Dr. Kwiatkowski works within the Integrated Systems team, collaborating with colleagues like Nils Pohl and Andreas Orth on cutting-edge radar solutions. His research is documented in peer-reviewed journals and conferences including IEEE transactions and European Radar Conference proceedings.

• Radar Technology
• Microwave Engineering
• Embedded Systems
• +۳ مورد دیگر

Vincent Lange-Nowak is a Researcher at the Ruhr University Bochum , affiliated with the Faculty of Electrical Engineering and Information Technology and the Integrated Systems department. His work focuses on radar technology, embedded systems, and sensor applications for medical and automotive contexts. Research Interests: Radar systems, microwave sensors, assistive technology, and signal processing. Publications: Specializes in 77-81 GHz MIMO FMCW radar, harmonic RFID tags, and navigation aids for the visually impaired.

Dale Johnsonمشاهده پروفایل

پژوهشگر ارشد

• Psychoacoustics
• Software Development
• Virtual Room Acoustics
• +۴ مورد دیگر

Dale Johnson is a Research Fellow in the Department of Computer Science at the School of Computing and Engineering , University of Huddersfield. He is affiliated with the Centre for Audio and Psychoacoustic Engineering , focusing on psychoacoustics and 3D audio technology development. Research Fellow, Department of Computer Science (2025) Member, Centre for Audio and Psychoacoustic Engineering Active in open-access audio research and software tool creation His research spans Binaural Audio , Microphone Array Optimization , and Acoustic Modeling . Publications highlight expertise in Convolutional Neural Networks for spatial audio discrimination and Impulse Response Analysis for immersive environments. He contributes to datasets like 3D Microphone Array Comparison and collaborates on international acoustic projects. Recent work (2020-2022) demonstrates a focus on improving 3D Audio Perception through Virtual Room Acoustics simulations and calibration methods. Keywords include Transfer Functions , Crosstalk Reduction , and Digital Signal Processing .

Ang Liمشاهده پروفایل

استادیار

• cybersecurity
• privacy in networked systems
• AI/ML-powered wireless and mobile systems
• +۹ مورد دیگر

Ang Li is an Assistant Professor in the Computer and Information Science Department at the University of Michigan-Dearborn's College of Engineering and Computer Science. His research focuses on cybersecurity, privacy in networked systems, and AI/ML-powered wireless technologies, including RFID, IoT, and 5G/6G networks. Ph.D., Arizona State University M.S., Beihang University B.E., Guangxi University His research integrates cybersecurity with wireless/mobile systems, emphasizing secure authentication, IoT protocols, and machine learning applications. Recent work explores RFID security, drone-aided key generation, and magnetic wearable authentication. Notable scientific awards include the USENIX Security Student Travel Grant (2023), University Grad Fellowship Summer (2023), Electrical Engineering Department Scholarship (2022), and IEEE CNS Student Travel Grant (2018). Ang actively seeks motivated Ph.D. students with interests in security, AI/ML, IoT, and mobile sensing. Contact: anglial@umich.edu

• Audiology
• Auditory Electrophysiology
• Audiologic Rehabilitation
• +۵ مورد دیگر

Samuel Atcherson, Ph.D., is a Professor at the University of Arkansas for Medical Sciences (UAMS) in both the College of Health Professions Department of Audiology and Speech Pathology and the College of Medicine Department of Otolaryngology—Head and Neck Surgery. His research spans audiologic rehabilitation, auditory electrophysiology, and health literacy, with a focus on addressing challenges faced by individuals with hearing loss, particularly in healthcare communication during mask use and accessibility for people with disabilities. Research Interests: Atcherson's work integrates audiologic rehabilitation through innovations like transparent face masks and amplified stethoscopes, auditory electrophysiology using evoked potential systems, and health literacy research examining readability of medical materials, especially for those with disabilities. Publications: With over 60 peer-reviewed articles, his recent publications explore AI integration in audiology, masked speech challenges, and health disparities. A recurring theme is the intersection of technology, accessibility, and multidisciplinary collaboration. Grants & Awards: He has secured over $477,000 in grants, including NIH funding, and holds the Distinguished Scholar Fellow title from the National Academies of Practice. Leadership: Atcherson serves on the Editorial Board of the Journal of the American Academy of Audiology and has contributed to textbooks like Auditory Electrophysiology: A Clinical Guide .

JASON HANNAمشاهده پروفایل

استادیار

• Cancer Biology
• Molecular Biology
• Angiosarcoma Development
• +۴ مورد دیگر

Dr. JASON HANNA is an Assistant Professor in the Department of Biological Sciences at Purdue University, affiliated with the College of Science. His research focuses on cancer cell and molecular biology, particularly studying microRNAs in angiosarcoma development and metastasis. He leads the Hanna Lab, which investigates vascular sarcomas such as angiosarcoma and epithelioid hemangioendothelioma (EHE), aiming to uncover genetic drivers, tumor suppressors like DICER1, and develop precision therapies for these aggressive cancers. Education includes a Ph.D. from Yale University and postdoctoral research at St. Jude Children's Research Hospital. His work integrates genetic models, in vivo studies, and cell-line investigations to address tumor initiation, progression, and therapeutic design. Research interests span molecular mechanisms of sarcomagenesis, microRNA-mediated regulation, and translational approaches to combat rare vascular cancers. His lab’s goals include identifying metastasis mediators and advancing targeted treatments for these poorly understood malignancies. Publications span digital imaging forensics (e.g., JPEG compression detection, printer source identification) and biomedical engineering (e.g., force myography for locomotion analysis), reflecting interdisciplinary collaboration. Awards and grants are not explicitly listed, but his work demonstrates sustained innovation in both biological and computational domains.

Oliver Behrendمشاهده پروفایل

استاد

• Neuroscience
• Auditory System
• Comparative Neurobiology
• +۳ مورد دیگر

Prof. Oliver Behrend is a Professor and Managing Director at the Munich Center for Neurosciences (LMU Munich), affiliated with the Faculty of Biology. His research focuses on auditory and sensory neurobiology, including interaural time difference coding, bat echolocation, amphibian sensory systems, and neural processing of environmental stimuli. Education & Affiliations: Munich Center for Neurosciences (LMU) Graduate School of Systemic Neurosciences Collaborative Research Center 870 Research Interests: Behrend's work spans auditory neuroscience, comparative neurobiology, and neuroethology. Key areas include: Binaural hearing mechanisms in mammals and bats Lateral line sensory systems in amphibians Neural coding of acoustic and water wave parameters Echolocation signal processing in bats like Rhinolophus rouxi Publications Trends: Recent work emphasizes interaural time difference processing (e.g., glycinergic inhibition in MSO), comparative analyses of sensory systems (e.g., Xenopus laevis), and editorial contributions to neurobiology conferences. Over 15 peer-reviewed articles since 1997 highlight expertise in auditory physiology, neuroethology, and sensory integration. Grants & Labs: Behrend leads the Munich Center for Neurosciences, a collaborative hub for systemic neuroscience research. His involvement in CRC 870 and graduate programs underscores his role in training the next generation of neuroscientists.

Goce Trajcevskiمشاهده پروفایل

دانشیار مدعو

• Mobile Data Management
• Moving Objects Databases
• Sensor Networks
• +۵ مورد دیگر

Goce Trajcevski is an Adjunct Associate Professor at Northwestern University's McCormick School of Engineering, Department of Electrical and Computer Engineering, and holds the Kingland Associate Professor title at Iowa State University's Department of Electrical and Computer Engineering. His research focuses on mobile data management, sensor networks, and reactive behavior in distributed systems. He earned his Ph.D. in Computer Science from the University of Illinois at Chicago. Education: Ph.D. Computer Science (UIC, 2002), M.S. Computer Science (UIC, 1995), B.S. Informatics and Automatics (University of Sts. Kiril and Metodij, 1989). Research interests include software systems, spatial-temporal data analysis, and machine learning applications. His work spans trajectory analysis, human mobility modeling, and cybersecurity. Recent publications explore graph neural networks for geolocation, adversarial learning in mobility, and heterogeneous data integration for vehicular networks. Key contributions include trajectory-based social circle inference, deep learning for user identity linkage, and privacy-preserving environmental sensing. He collaborates on projects involving autonomous systems, smart transportation, and academic impact analysis.