جست‌وجوی استاد و پژوهشگر

Kristen Graumanمشاهده پروفایل

استاد

• Artificial Intelligence
• Computer Vision
• Graphics and Visualization
• +۸ مورد دیگر

Kristen Grauman is a Full Professor in the Department of Computer Science at the University of Texas at Austin, where she leads the UT Computer Vision Group. Her research focuses on computer vision and machine learning, with applications in visual recognition, video analysis, and multi-modal perception. She received her B.A. from Boston College and her Ph.D. from MIT. Her research interests span visual recognition, image and video search, video analysis, first-person vision, embodied and multi-modal perception, and interactive machine learning. She has made significant contributions to the field, particularly in developing algorithms for understanding visual content and human activities from video, including foundational work on the Pyramid Match Kernel and relative attributes. Her recent publications reveal a strong emphasis on egocentric (first-person) vision, audio-visual learning, and view-invariant representations. There is a clear trajectory toward multi-modal integration (vision, audio, language) and real-world applications in instructional videos, human activity understanding, and embodied AI systems. She has received numerous awards including: AAAI Fellow (2019) J. K. Aggarwal Prize, International Association for Pattern Recognition (2018) Helmholtz Prize (2017) UT Austin Academy of Distinguished Teachers (2017) Best Paper Award, Asian Conference on Computer Vision (2016) Presidential Early Career Award for Scientists and Engineers (2014) Computers and Thought Award, International Joint Conferences on Artificial Intelligence (2013) Pattern Analysis and Machine Intelligence Young Researcher Award (2013) Alfred P. Sloan Research Fellow (2012) Marr Prize (2011) Prof. Grauman serves as Associate Editor-in-Chief for the IEEE Transactions on Pattern Analysis and Machine Intelligence. She has secured substantial research funding including the Presidential Early Career Award, NSF grants, and industry partnerships. Her advising has produced numerous influential publications and students who are now leaders in computer vision. She leads the UT Computer Vision Group, which collaborates closely with the Electrical and Computer Engineering Department. The group is pioneering large-scale egocentric video research through projects like Ego4D and Ego-Exo4D, focusing on real-world applications in human activity understanding, audio-visual perception, and interactive systems.

Almut Sophia Koepkeمشاهده پروفایل

پژوهشگر

• Multimodal Learning
• Audio-Visual Processing
• Text-to-Audio Retrieval
• +۵ مورد دیگر

Almut Sophia Koepke is a junior research group leader at the Technical University of Munich and University of Tübingen, focusing on multimodal learning problems integrating sound, vision, and text. Her work bridges foundational research in audio-visual understanding with practical applications in few-shot learning, zero-shot translation, and cross-modal attention mechanisms.

• Data Science
• Machine Learning
• Computational Harmonic Analysis
• +۶ مورد دیگر

Pierre Vandergheynst is a Full Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) in the Department of Electrical Engineering, with a courtesy appointment in Computer and Communication Sciences. He serves as EPFL’s Vice-Provost for Education since 2015 and leads the Signal Processing Laboratory 2 (LTS2). His research spans harmonic analysis, sparse approximations, mathematical data processing, and applications in signal/image processing, computer vision, machine learning, and graph-based data analysis. PhD in Mathematical Physics (1998), Université catholique de Louvain Postdoctoral Researcher at EPFL (1998-2001) Assistant Professor at EPFL (2002-2007) His research explores geometry/symmetry in high-dimensional data, redundant dictionaries for dimensionality reduction, and computational harmonic analysis on manifolds. Recent work focuses on protein structure modeling, geometric deep learning, and graph-based signal processing. Key article trends include graph neural networks for protein analysis, geometric deep learning in neuroscience, and structured knowledge priors in neural models. His 2023-2025 publications emphasize interpretable AI, long-range dependencies in graphs, and molecular representation learning. Scientific Awards: IEEE Signal Processing Magazine Best Paper Award (2023) Signal Processing Society Best Paper Award (2022) Apple ARTS Award (2007) De Boelpaepe Prize, Royal Academy of Sciences of Belgium (2009-2010) He has supervised over 30 PhD theses and contributed to foundational work in graph signal processing, compressive sensing, and geometric deep learning. His lab develops tools for data science on non-Euclidean structures, with applications in medicine, astronomy, and wireless systems.

Jeremy Dahlمشاهده پروفایل

استاد

• Medical Ultrasound Imaging
• Ultrasonic Beamforming
• Image Reconstruction
• +۷ مورد دیگر

Jeremy Dahl is a Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine. He directs the Ultrasound Imaging & Instrumentation Lab and serves as Director of Research Academic Affairs in the Department of Radiology since 2020. He holds multiple affiliations across Stanford including Bio-X, the Cardiovascular Institute, Wu Tsai Human Performance Alliance, Maternal & Child Health Research Institute, Stanford Cancer Institute, and Wu Tsai Neurosciences Institute. Dr. Dahl received his B.S. in Electrical Engineering from the University of Cincinnati (1999) and Ph.D. in Biomedical Engineering from Duke University (2004). His research focuses on developing ultrasonic beamforming and image reconstruction methods for diagnostic imaging applications, particularly techniques that generate high-quality images in difficult-to-image patients. His laboratory specializes in B-mode and Doppler imaging techniques that utilize additional information from ultrasonic wavefields to improve image quality and develop real-time imaging systems for clinical applications including cardiac, liver, and fetal imaging. Dr. Dahl's research has led to significant advancements in ultrasound molecular imaging platforms, sound speed estimation, aberration correction, and reverberation noise suppression. His work often bridges engineering innovation with clinical applications for cancer detection and other diseases. His recent publications demonstrate strong focus on machine learning applications in ultrasound, distributed aberration correction, and molecular imaging techniques. Fellow, American Institute of Ultrasound in Medicine (2021) Senior Member, Institute of Electrical and Electronics Engineers (2020) Distinguished Investigator Award, The Academy for Radiology & Biomedical Imaging Research (2018) Outstanding Paper Award, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (2011) Dr. Dahl serves in editorial roles for major journals including IEEE Transactions on Medical Imaging (2017-2024) and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2013-Present). His laboratory has successfully translated numerous innovations into clinical applications, with multiple patents including recent developments in pulsed focused ultrasound therapy and speed of sound quantification.

Sara B. Pritchardمشاهده پروفایل

استاد

• Environmental History
• Science and Technology Studies (STS)
• History of Technology
• +۴ مورد دیگر

Professor Sara B. Pritchard is a leading scholar in environmental science and technology studies (STS) at Cornell University's College of Arts and Sciences. She holds a Ph.D., M.A., and B.A. in History from Stanford University and University of Puget Sound. Her research critically examines light pollution, environmental ethics, and the intersection of technology and nature. Pritchard has received grants from the National Science Foundation and Cornell's Atkinson Center for a Sustainable Future, among others. Her major works include Night as Environment: Light Pollution and the Anthropocene (2024) and Confluence: The Nature of Technology and the Remaking of the Rhône (2011). She co-edited influential volumes such as New Natures: Joining Environmental History with Science and Technology Studies (2013) and pioneered the concept of 'envirotechnical analysis.' Pritchard advises students across disciplines including Anthropology, Asian Studies, and Natural Resources. Her research explores dark histories, nuclear disasters, and sustainable energy transitions. She collaborates with global scholars through special journal issues on topics like climate justice and polar night studies. Pritchard’s work bridges environmental humanities with technological critique, emphasizing interdisciplinary approaches to urgent ecological challenges.

Maarten Hornikxمشاهده پروفایل

استاد

• Building Acoustics
• Computational Sound Propagation Modeling
• Mixed Reality Sound Reproduction
• +۴ مورد دیگر

Maarten Hornikx is a Full Professor in Building Acoustics at the Department of the Built Environment , Eindhoven University of Technology. He serves as Vice-Dean of the department, leads the Building Acoustics Chair of Unit Building Physics and Services (BPS), and coordinates the Science of Sound and Music course series since 2013. His research focuses on computational modeling of sound propagation in built environments, with applications in mixed reality platforms and numerical analysis of outdoor/indoor propagation effects like vegetation and meteorological influences. Education : PhD in Applied Acoustics (Chalmers University of Technology, 2009); MSc in Architecture, Building and Planning (2004) Hornikx promotes open research software in acoustics and has received multiple grants, including Marie Curie Individual and Career Integration Grants. His recent publications explore AI-driven diffusion equation modeling, acoustic absorber optimization, and advanced numerical methods like the discontinuous Galerkin technique. He has held international leadership roles, including chairing the Computational Acoustics Technical Committee of the European Acoustics Association and serving as Associate Editor for Acta Acustica . Scientific Awards : Marie Curie Fellowship (2009-2011); Marie Curie Career Integration Grant (2012); 4TU.Built Environment Center Scientific Director (2020-2021); eScience Center Fellow (2022) As a research leader, Hornikx guided the H2020-ITN Acoutect project and conducted sabbaticals at Aalto University, Stockholm University (2018), and Politecnico Torino (2022). His group emphasizes computational acoustics and open-source tools to enhance reproducibility and collaboration.

Alec Wrightمشاهده پروفایل

پژوهشگر ارشد

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

Alec Wright is a Chancellor’s Fellow in Audio Machine Learning at the University of Edinburgh, affiliated with the Edinburgh College of Art and the Music department. He focuses on applying machine learning to musical audio signal processing and synthesis, particularly through neural network-based audio effects modeling. Education: Doctor of Science (DSc) in Neural Modelling of Audio Effects, Aalto University (2023) Master of Science (MSc) in Acoustics and Music Technology, University of Edinburgh (2018) Master of Engineering (MEng) in Mechanical Engineering, University of Manchester (2014) His research explores neural network architectures for real-time audio processing, guitar amplifier emulation, and diffusion-based distortion restoration. Key methodologies include Recurrent Neural Networks (RNNs), neural ordinary differential equations, and synthetic data generation for foundation models. Recent publications highlight sample rate conversion techniques, interpolation filters, and nonlinear distortion modeling. These works span domains like signal processing, computational audio, and physical modeling synthesis. Scientific Awards: Chancellor’s Fellow, University of Edinburgh As an active researcher, he collaborates internationally and contributes to frameworks like Open-Amp for audio effect modeling. No formal student advising details are currently available.

Dorte Hammershøiمشاهده پروفایل

استاد

• Acoustics
• Hearing Science
• Audio Engineering
• +۵ مورد دیگر

Dorte Hammershøi is a Professor in the Department of Electronic Systems at The Technical Faculty of IT and Design, Aalborg University, Denmark. Her research focuses on acoustics, sound engineering, and hearing science with significant contributions to human hearing, ear canal acoustics, and audio technology applications. Her research interests include: Temporary Threshold Shift and frequency resolution in human hearing Ear canal acoustics and sound pressure level measurement Distortion Product Otoacoustic Emission (DPOAE) analysis Impulse response and acoustic impedance studies Hearing aid technology and rehabilitation methodologies Virtual reality audio interfaces and accessibility applications Professor Hammershøi's recent publications demonstrate a strong clinical-engineering interdisciplinary approach, bridging theoretical acoustics with practical hearing rehabilitation applications. Her work on hearing aid fitting methodologies, occupational noise exposure effects, and virtual reality audio interfaces shows consistent innovation in translating engineering principles to clinical practice. The research shows particular attention to individualized hearing solutions and accessibility technologies. Her scientific contributions have been recognized with: Dansk Lydpris 2020 (awarded November 17, 2021) Ambassadør for Aalborg (awarded September 15, 2004) Professor Hammershøi has supervised 5 PhD students and led numerous research projects including the ongoing "Audio Only VR for Blind Gamers" project (2024-2028) funded by the Independent Research Foundation of Denmark, and the completed "BEAR: Better Hearing Rehabilitation" project (2016-2022). Her research has attracted significant media attention with 110 press/media appearances discussing hearing damage prevention, tinnitus, and public health implications of noise exposure. She maintains active professional engagement through committee memberships (46 documented activities), international collaborations, and contributions to clinical practice guidelines. Her work continues to influence both academic research and practical applications in hearing science and audio engineering.

Cecilia Mascoloمشاهده پروفایل

استاد

• Mobile Systems
• Wearable Computing
• Machine Learning
• +۵ مورد دیگر

Cecilia Mascolo is a Professor of Mobile Systems at the University of Cambridge , specifically in the Department of Computer Science and Technology . She co-directs the Centre for Mobile, Wearable System and Augmented Intelligence and is a Fellow of Jesus College, Cambridge . Her research focuses on mobile systems , machine learning for mobile health , and earable technology . She has been awarded prestigious grants such as the ERC Advanced Research Grant (2019-2025) and the EPSRC Open Research Fellowship (2025-2030). Currently on sabbatical at Harvard University , her work bridges systems and machine learning for health applications. Education: PhD in Computer Science from the University of Bologna, Italy. Previous Affiliation: Faculty at University College London before 2008. Her research spans mobile and wearable systems for health and behavior monitoring, focusing on on-device machine learning , uncertainty-aware models , and audio-based diagnostics . Key areas include federated learning , edge computing , and respiratory disease progression analysis via wearables. She explores earable technology for physiological monitoring, gait analysis, and even toothbrushing tracking using in-ear sensors. Her recent publications highlight advancements in earable-based health monitoring , including heart rate estimation , respiratory rate detection , and ECG analysis using machine learning. She emphasizes longitudinal health data from consumer devices, advocating for scalable diagnostics beyond traditional clinical standards. Scientific Awards: ERC Advanced Research Grant EPSRC Open Research Fellowship Best Paper Award - IEEE Percom 10-Year Impact Award - ACM Ubicomp Computer Laboratory Ring Hall of Fame Best Paper Award Student: Andrea Ferlini - ACM SIGMOBILE Doctoral Dissertation Runner-up She leads the Mobile Systems Research Laboratory , mentoring a team of 15 researchers (postdocs and PhD students), and has graduated over 25 PhD students. Her teaching includes Mobile Health courses at the University of Cambridge, and she serves as Director of Studies for Computer Science at Jesus College.

• Additive Manufacturing
• Micro Metal Forming
• Tribology
• +۴ مورد دیگر

Dr Dongbin Wei is an Associate Professor at the School of Mechanical and Mechatronic Engineering , University of Technology Sydney (UTS), with a career spanning academia and industry. He holds a PhD in Materials Processing Engineering from the University of Science and Technology Beijing (2001) and academic appointments from 2005–2012 at the University of Wollongong (Research Fellow to Lecturer) and 2013–2017 at UTS (Senior Lecturer) before his promotion to Associate Professor in 2018. His research lies at the intersection of Mechanical Engineering , Manufacturing Engineering , and Materials Processing , focusing on: Ultrasonic Additive Manufacturing (UAM) Micro Metal Forming and Size Effects Tribology and Lubrication Numerical Simulations of Material Processing Composite Material Fabrication Key contributions include: Development of the Springback Path–Displacement Adjustment (SP-DA) method for stamping accuracy Advancements in femtosecond laser texturing for silicon wettability control Studies on nanolubrication in hot rolling Optimization of micro-deep drawing parameters He has secured competitive grants from the Australian Research Council (ARC) and industry partners like Weir Minerals Australia Ltd , including projects on: Revolutionizing mineral separation via additive manufacturing Super high-speed grinding technologies Mechanics of micro composite drill fabrication As a lead supervisor, he guided the 2022 thesis 'Creation and Validation of 3D Printable Mineral Separation Spiral' . His work bridges theoretical analysis, computational modeling (FEM/FEA), and practical validation in advanced manufacturing systems.

Bradley J. Siwickمشاهده پروفایل

دانشیار

• Chemical Physics
• Materials Science
• Condensed Matter Physics
• +۳ مورد دیگر

Bradley J. Siwick is an Associate Professor in the Department of Chemistry at McGill University, holding the Canada Research Chair in Ultrafast Science (Tier II). He specializes in developing ultrafast electron-based techniques to study atomic and molecular dynamics in materials and chemical systems. His work bridges chemical physics, materials science, and condensed matter physics, focusing on structural dynamics, phase transitions, and nonequilibrium states. Education: B.A.Sc. (Engineering Physics, University of Toronto, 1997), M.Sc. (Physics, 1998), Ph.D. (Physics, 2004). Postdoctoral training at FOM-AMOLF Amsterdam (2004–2006). Awards: NSERC Doctoral Prize (2005). Research interests include ultrafast electron diffraction/scattering, electron-phonon coupling, and imaging transient structural changes. Techniques developed in his lab combine electron microscopy with ultrafast laser spectroscopy to observe atomic motions on femtosecond timescales. Key areas of study are phase transitions in materials (e.g., VO₂, cuprates), nanocomposites, and extreme states of matter using facilities like the Advanced Laser Light Source (ALLS). Recent articles highlight advances in momentum-resolved phonon dynamics, polaron formation, and ultrafast imaging of 2D materials. His lab, based in Otto Maass and Rutherford buildings, collaborates on frontier projects in nonequilibrium materials science. Advising: Leads the Siwick Research Group, focusing on graduate students in chemical physics and materials science. Grants: Supported by NSERC and Canada Research Chairs funding. Labs and facilities: Otto Maass 25 laboratory and ALLS (Varennes, Quebec) for high-power laser experiments.

• Digital Music
• Human-Machine Interaction
• Electroacoustic Composition
• +۳ مورد دیگر

Dominic Thibault is an Assistant Professor at the Faculty of Music, Université de Montréal . His research-creation explores human-machine interaction in musical contexts, focusing on embodied cognition through electroacoustic compositions, audiovisual performances, and musical software development. Co-director, Laboratoire Formes·Ondes Active member, CIRMMT (Centre for Interdisciplinary Research in Music Media and Technology) Research axis leader, Expanded Musical Practice (CIRMMT) Member, Québecor Millénium entrepreneurship committee Scientific committee member, ACFAS

• Linguistics
• Phonetics
• Sociophonetics
• +۵ مورد دیگر

Nicholas Henriksen is an Arthur F. Thurnau Professor and Professor of Spanish & Linguistics at the University of Michigan, where he has held faculty positions since 2012. His research focuses on laboratory phonology, experimental phonetics, sociophonetics, and intonational grammar in Spanish and other Romance languages. Notable projects include studies on sound change in Andalusian and Patagonian Spanish, bilingualism (Afrikaans-Spanish), and second language speech learning. He directs the Michigan Speech Production Lab and co-leads initiatives integrating gender-diverse language into Romance curricula. Henriksen earned a PhD in Linguistics from Indiana University (2010), MA in Hispanic Linguistics (2006), and BA in Spanish/Mathematics from Rutgers (2003). His teaching spans undergraduate and graduate courses in Spanish linguistics, sociolinguistics, and phonetics, emphasizing applied research and pedagogical innovation. He has received prestigious awards, including the LSA Early Career Award (2021) and the Class of 1923 Teaching Award (2018). His research has been funded by grants from the University of Michigan ADVANCE Program and the Humanities Collaboratory, supporting projects like From Africa to Patagonia: Voices of Displacement . Collaborations include work with scholars in Argentina, South Africa, and Spain, examining language contact, heritage language maintenance, and sociolinguistic identity. Publications span over 30 peer-reviewed articles and book chapters, with a focus on phonetic variation, intonation, and bilingualism. He mentors graduate students in experimental phonetics and sociolinguistics, with advisees contributing to studies on Andalusian Spanish, heritage language acquisition, and voice quality.

Omer T Inanمشاهده پروفایل

استادیار

• Biomedical Engineering
• Medical Devices
• Physiological Monitoring
• +۷ مورد دیگر

Omer T Inan is the Regents Entrepreneur Endowed Chair and Assistant Professor at the School of Electrical and Computer Engineering (ECE) at Georgia Institute of Technology. His work bridges biomedical engineering and wearable technology, focusing on non-invasive physiological monitoring for chronic disease management. He holds a Ph.D. in Electrical Engineering from Stanford University (2009) and previously worked at Countryman Associates (2007-2013) as Chief Engineer, developing professional audio systems. Education: B.S., M.S., Ph.D. in Electrical Engineering, Stanford University (2004-2009) His research interests include medical devices for home-based cardiovascular monitoring, musculoskeletal sound analysis, and neuromodulation of stress responses. He has pioneered technologies for heart failure patients, PTSD treatment, and osteoarthritis diagnostics. Recent publications highlight innovations in AI-driven cardiac parameter estimation, motion artifact removal in seismocardiograms, and multimodal stress tracking via wearables. His work spans biomedical signal processing, clinical translation, and portable diagnostic systems. Scientific Awards 2024 IEEE Fellow 2023 IEEE Distinguished Lecturer 2023 American College of Cardiology Fellow 2022 American Institute for Medical and Biological Engineering Fellow 2021 Academy Award for Technical Achievement (The Oscars) 2018 ONR Young Investigator Award 2018 NSF CAREER Award At Georgia Tech, Inan leads the Inan Research Lab, which develops technologies for physiological monitoring and modulation. Projects include musculoskeletal sound analysis for joint health, non-invasive cardiovascular sensing, and neuromodulation to treat PTSD via vagal nerve stimulation.

Milad Abediمشاهده پروفایل

مدرس

• Historical Linguistics
• Language Contact
• Ancient Iranian Languages
• +۴ مورد دیگر

Milad Abedi is a PhD candidate in Linguistics at the University of Zurich and Institut national des langues et civilisations orientales (INALCO), Paris, focusing on the cultural history of Sino-Iranian lexical contacts. He has taught Zoroastrian Middle Persian and Middle Iranian languages at the University of Zurich while conducting research on topics including language contact mechanisms and historical Persian grammar. PhD in Linguistics (2020–Present), University of Zurich & INALCO, Paris MA in Ancient Languages and Cultures of Iran (2015–2019), Allameh Tabataba'i University (Highest Distinction) BSc in Cellular-Molecular Biology (2010–2015), Islamic Azad University His research spans ancient Iranian language contact (Elamite-Iranian, Sino-Iranian, Arabic-Iranian), historical grammar of Persian, and Classical Persian literature. He has published on topics such as kinship term evolution, cuneiform epigraphy, and metonymic phrases in Achaemenid Elamite. Recent publications highlight his focus on lexical diffusion across Eurasia, with key works on donkey terminology in cross-cultural contexts and the origins of felt words in Iranian languages. His collaborative research with Wolfgang Behr and Samira Müller demonstrates interdisciplinary approaches combining linguistics and archaeology. 2021 Travel grant, University of Zurich 2016 Travel grant, Allameh Tabataba'i University 2016 Top student award, Allameh Tabataba'i University As a Lecturer at the University of Zurich, he has taught courses on Zoroastrian Middle Persian and historical linguistics. His digital humanities expertise includes ELAN software, GIS, and SQL for language documentation.