Dr. Andrew Hines is a Researcher at the School of Computer Science, University College Dublin, specializing in machine learning applications for signal processing in speech, audio, and video domains. His work focuses on Quality of Experience (QoE) modeling, speech quality assessment, and immersive media analysis. He has held leadership roles in European COST Actions like Qualinet and CryptoAction, and previously worked in industry as a Director of Engineering. University: University College Dublin Role: Director of Research, Innovation and Impact Key Collaborations: IEEE (Senior Member), Audio Engineering Society (Ireland) Research interests center on machine learning for QoE optimization, audio-visual integration, and healthcare applications like heart sound classification and stroke rehabilitation. His recent publications explore self-supervised learning, neural speech codecs, and contextual factors in speech/audio quality assessment. Scientific contributions include awards like IEEE Senior Membership, and his work spans both academic research and industrial engineering in finance and aviation sectors. He leads the QxLab research team at UCD and develops open-source platforms such as WARP-Q and AQP for quality metrics.
Mark Bocko is a Distinguished Professor of Electrical and Computer Engineering at the University of Rochester, affiliated with the Hajim School of Engineering & Applied Sciences. He holds roles as Director of the Center for Emerging and Innovative Sciences (CEIS) and Director of Audio & Music Engineering. He earned his PhD in Physics from the University of Rochester in 1984, focusing on gravitational wave detectors. His research spans audio signal processing, sensors, superconductivity, and quantum computing. Notable contributions include flat-panel loudspeaker development, non-contact ECG sensors, and quantum coherence studies in Josephson junctions. Research interests include audio and acoustic signal processing, computer audition, and sensor technologies. His work integrates interdisciplinary approaches, combining electrical engineering, physics, and computer science. Awards include the 2012 Goergen Award for Teaching and Mercer Brugler Distinguished Teaching Professor (2008–2011). Recent publications address modal crossover networks for loudspeakers, vibrational touch sensing, and room impulse response modeling. He has advised PhD students on topics like spatial audio rendering and musical vibrato analysis. His labs focus on advancing audio engineering and smart sensor systems through collaborative industry partnerships.
Frederick A. A. Kingdom is a Professor in the Department of Ophthalmology at McGill University's Faculty of Medicine, focusing on Perception, Cognition and Cognitive Neuroscience . His research explores the interplay between early visual feature detection (edges, bars) and intermediate stages forming contours, textures, and surfaces through spatial vision, color vision, stereopsis, texture perception, brightness/lightness perception, and transparency studies . Email: fred.kingdom@mcgill.ca Key research domains include: Perceptual Mechanisms : Lateral inhibition, contrast normalization, spatial bandpass filters, and their role in brightness/lightness perception and illusions like simultaneous brightness contrast. Color Vision : Red-green vs blue-yellow system distribution, chromatic contrast requirements for stereopsis, color-based depth processing limitations, and color-shading effects that parse surfaces vs illumination. Texture Analysis : Detection thresholds for orientation/frequency/contrast modulated textures, co-circularity in texture perception, and texture statistical sensitivity (e.g., kurtosis importance). Shape Processing : Shape-frequency/shape-amplitude aftereffects, global vs local shape coding, and contour inflection adaptation. His work combines psychophysics , fMRI , image processing , and computational modeling to dissect visual system architecture, particularly how color and luminance signals are integrated/separated in early cortical processing.
Richard M. Stern is a Professor of Electrical and Computer Engineering at Carnegie Mellon University (CMU), holding courtesy appointments in the Language Technologies Institute and Department of Computer Science, and serving as an Artist Lecturer in the School of Music since 2007. His interdisciplinary work bridges engineering and music technology through the School of Music's programs. Education: Ph.D. in Electrical Engineering from Massachusetts Institute of Technology (MIT), 1976 Professor Stern's research spans sound, speech, hearing, and music, with core emphases on robust speech processing in variable acoustic environments, music information retrieval, automated accompaniment, and foundational contributions to binaural perception theory. His work integrates psychoacoustic principles with machine learning to address challenges in speech recognition and human-robot interaction. Recent publications (2022-2025) reveal intensified focus on deep learning for speech enhancement in reverberant/noisy conditions, human-robot interaction scenarios, and music tagging—highlighting innovations in beamforming, source separation, and temporal modulation modeling. Awards and Honors: Fellow of the IEEE Fellow of the Acoustical Society of America Fellow of the International Speech Communication Association (ISCA) ISCA Distinguished Lecturer Allen Newell Award for Research Excellence (1992) Lutron Award for Teaching Excellence (2018) Professor Stern has advised numerous graduate students in speech and audio research, though specific names are unlisted in source materials. His grant portfolio includes significant National Science Foundation and industry-funded projects in speech technology, with leadership roles in initiatives like Interspeech 2006. He actively collaborates with CMU's Language Technologies Institute and Music and Technology program. He maintains strong ties to CMU's interdisciplinary ecosystem through the Language Technologies Institute and School of Music's Music and Technology program, contributing to research that merges acoustic engineering with musical applications.
Govind P. Agrawal is a Professor of Optics and Physics at the University of Rochester. He holds concurrent positions as a Fellow of the IEEE and the Optical Society of America (OSA). His research focuses on theoretical optics, nonlinear optics, laser physics, and semiconductor lasers, with notable contributions to fiber optics and optical communications. Education: Agrawal earned his MS (1971) and PhD (1974) from the Indian Institute of Technology, New Delhi. He has held prior positions at École Polytechnique (Paris), City University of New York, and AT&T Bell Laboratories before joining the University of Rochester in 1989. Research Interests: Agrawal's work explores quantum electronics, soliton dynamics, Raman scattering, and temporal reflection phenomena. He has authored seminal books such as Nonlinear Fiber Optics and Semiconductor Lasers , and published over 300 journal articles. His recent studies emphasize spatiotemporal dispersion, multimode fiber optics, and integrated photonics systems. Editorial Roles: He served as Topical Editor for Journal of the Optical Society of America B (1993–1998) and currently contributes to editorial boards for optics journals and book series. Awards: Honored as a Fellow of the IEEE and OSA for his transformative contributions to optics and photonics. Key Contributions: Pioneered theories on graded-index fiber amplifiers, temporal reflection in nonlinear media, and soliton-based technologies. His research bridges fundamental optics with practical applications in telecommunications and photonics systems.
Scott McDougall is an Associate Professor in the Department of Earth, Ocean & Atmospheric Sciences at the University of British Columbia (UBC). He specializes in geohazards, particularly landslides and related risks, with a focus on improving risk assessment and mitigation strategies. His work integrates field data, statistical analysis, numerical modeling, and laboratory experiments to understand landslide dynamics. Education: PhD in Geological Engineering, UBC (2006) BASc in Civil Engineering, University of Toronto (1998) Research Interests: Landslide mobility and runout modeling Tailings dam breaches and their impacts Risk evaluation frameworks for geohazards Shoreline erosion and landslide-generated waves Applications of machine learning in hazard prediction Key Contributions: Development of the UBC Geohazards Research Team to advance landslide risk reduction Leadership in the CanBreach project to improve tailings dam breach analysis Pioneering probabilistic runout prediction models for rock avalanches and debris flows Awards: Engineering Geology Best Paper Award 2024 CDA Published Paper Award Advising & Grants: Supervised over 15 graduate students and postdoctoral fellows Active industry collaborations with mining and engineering firms Funded by NSERC, Mitacs, and industry partnerships Labs & Teams: UBC Geohazards Research Team CanBreach Collaborative Research and Development Project
Matej Varga is a Scientific Assistant and Postdoctoral Researcher at ETH Zurich's Department of Civil, Environmental and Geomatic Engineering, working in the Geosensors and Engineering Geodesy group under Prof. Andreas Wieser since 2021. His research spans geometrical geodesy, physical geodesy, and satellite geodesy, with applications in both theoretical and practical domains. Dr. Varga's research interests focus on spatial, temporal and spectral analysis of geodetic data, with particular expertise in geodetic reference systems and frames, gravity and geomagnetic field modeling at all temporal and spatial scales, and multi-GNSS multi-frequency positioning and monitoring. His work integrates geometrical and physical aspects of geodesy to address complex Earth observation challenges, particularly in infrastructure monitoring and geophysical applications. His recent publications demonstrate a strong trend toward high-precision geodetic applications for major scientific infrastructure, most notably the Future Circular Collider project, alongside important contributions to earthquake impact analysis, geomagnetic network development, and gravity field modeling. His research bridges traditional geodetic methods with modern computational approaches, including machine learning applications for point cloud registration. Dr. Varga is actively involved in the GSEG research group at ETH Zurich, contributing to the development of geodetic infrastructure and reference systems. His work has practical applications in infrastructure monitoring, earthquake analysis, and scientific projects requiring extreme geodetic precision.
John Serences is a Professor in the Department of Psychology at the University of California, San Diego (UCSD). He leads the Perception and Cognition Lab, which participates in the Neuroscience Graduate Program. His research focuses on how behavioral goals and attention influence perception, memory, and decision-making, employing techniques like psychophysics, computational modeling, EEG, and fMRI. Key projects explore serial dependence, neural adaptation in visual cortex, and the interplay between sensory processing and mnemonic storage. Recent work highlights mechanisms reconciling repulsive neuronal adaptation with attractive behavioral biases. Affiliations: Department of Psychology, UCSD; Neuroscience Graduate Program. Research Themes: Visual perception, working memory, decision-making, neuroimaging. His lab investigates neural dynamics underlying cognitive processes, with particular emphasis on how attentional modulations and stimulus history shape neural representations. Notable contributions include studies on adaptive sensory coding and the role of top-down signals in perceptual stability.
Ben Seiyon Lee is an Assistant Professor in the Department of Statistics at George Mason University's College of Science. His work bridges computational statistics, climate modeling, and environmental risk assessment. Education: PhD in Statistics, Pennsylvania State University (2020) Lee specializes in computational methods for high-dimensional spatiotemporal data and uncertainty quantification in climate models. His research explores climate change impacts on extreme hydrological events, wildfire emissions, and medical decision-making. Recent publications focus on Bayesian spatiotemporal frameworks for extreme precipitation analysis, zero-inflated spatial models, and multisector uncertainty quantification. His work addresses challenges in flood risk assessment, agricultural yield projections, and healthcare compliance metrics.
Ebrahim Bedeer Mohamed is an Assistant Professor in the Department of Electrical and Computer Engineering at the University of Saskatchewan. He joined in July 2019, following roles as an Assistant Professor (Lecturer) at Ulster University, UK, and postdoctoral fellowships at Carleton University and the University of British Columbia. He holds a Ph.D. (Distinction) from Memorial University of Newfoundland (2014), with expertise in signal processing and wireless communications. His research focuses on optimizing communication systems through advanced signal processing techniques, including faster-than-Nyquist signaling, IoT network design, AI integration, and energy-efficient protocols. Key areas include next-generation communication networks, non-orthogonal modulation, and MIMO systems. Notable contributions include work on channel estimation for FTN signaling, RIS-aided wireless systems, and LR-FHSS protocols in IoT. His publications span spectral efficiency, interference minimization, and energy management in 5G/6G contexts. He actively seeks Ph.D. students with strong backgrounds in signal processing fundamentals. Awards and grants are not explicitly listed in the provided texts. His work emphasizes practical applications, such as UAV trajectory optimization for IoT data collection and energy-efficient caching strategies in dynamic networks.
Jesper Rindom Jensen is an Associate Professor in the Department of Electronic Systems at Aalborg University, Denmark, under the Technical Faculty of IT and Design. He is the Head of the Audio Analysis Lab, a leading research group in audio signal processing, since 2023. His work bridges theoretical signal processing and practical applications in artificial intelligence and audio systems. Full Name: Jesper Rindom Jensen Institution: Aalborg University School: The Technical Faculty of IT and Design Department: Department of Electronic Systems Research Lab: Audio Analysis Lab Email: jrj@es.aau.dk Office: Fredrik Bajers Vej 7B, B5-206, 9220 Aalborg Øst, Denmark Education: M.Sc. in Electronic Systems, Aalborg University (cum laude, 2009) Ph.D. in Signal Processing, Aalborg University (2012) Research Interests: Jesper Rindom Jensen's research centers on audio signal processing, with a strong emphasis on artificial intelligence, speech enhancement, noise reduction, beamforming, and multichannel systems. His work applies to diverse domains including robot and drone audition, spatial audio, and active noise control. He develops novel filtering techniques, including variable span linear filters and harmonic beamformers, to improve speech quality and intelligibility in noisy and reverberant environments. Publication Trends: His recent publications (2023–2025) show a strong trend toward integrating deep learning with classical signal processing, particularly in direction-of-arrival estimation, underwater acoustics, and robust multichannel systems. There is a clear focus on real-world applications, including sound zone control, active noise control, and limited-data scenarios using knowledge distillation. His work consistently emphasizes robustness, efficiency, and practical deployment. Scientific Awards and Recognition: AAU Talent for emerging research leaders Recipient of a competitive postdoc grant from the Danish Independent Research Council Advising and Grants: Jesper has supervised multiple PhD and master’s students, including Nørholm, Karimian-Azari, Zhang, and Wang. He has led significant research projects such as 'Sound Processing for Robots and Drones' (2018–2020) and participated in others related to joint audio-visual tracking and speech enhancement. His research has been supported by national funding bodies, reflecting its innovation and impact. Labs and Teams: He is a founding and core member of the Audio Analysis Lab at Aalborg University, which focuses on cutting-edge audio signal processing and AI-driven solutions. The lab fosters interdisciplinary collaboration and has produced numerous publications, datasets, and real-world applications. Jensen’s leadership since 2023 underscores his pivotal role in shaping the lab’s research direction.
Maiken H. Mikkelsen is the James N. and Elizabeth H. Barton Associate Professor in the Department of Electrical and Computer Engineering at Duke University, with a joint appointment in the Department of Physics . Her research focuses on quantum nanophotonics , plasmonics , and light-matter interactions in nanoscale materials, aiming to advance optoelectronics, quantum science, and biomedical diagnostics. Education B.S. in Physics, University of Copenhagen (2004) Ph.D. in Physics, University of California, Santa Barbara (2009) Postdoctoral Fellowship, University of California, Berkeley Her work explores nanophotonic engineering for quantum optics , spintronics , and ultrafast optoelectronics , with recent studies on nonlinear metasurfaces and plasmonic enhancement of immunoassays for point-of-care diagnostics. Publications highlight 2D semiconductor emission control , ultrafast single-photon sources , and metasurface-based photodetectors . Scientific Awards Maria Goeppert Mayer Award (2017) NSF CAREER Award (2015) Moore Inventor Fellow (2021) ONR/Air Force/Army Young Investigator Awards (2015-2017) Cottrell Scholar (2016) Stansell Family Distinguished Research Award (2021) She advises graduate students in Duke’s Electrical & Computer Engineering and Physics programs and leads the Mikkelsen Lab , which emphasizes ultrafast spectroscopy and quantum material development . The lab has graduated PhD students like Eunso Shin and Hengming Li (2025).
Esa Ollila serves as Associate Professor in the Department of Signal Processing and Acoustics at Aalto University, Finland, and holds an adjunct professorship in Statistics at the University of Oulu. His academic appointments include Academy of Finland Research Fellow (2010-2015) and prior senior research/lecturing roles at both institutions. His educational background features: M.Sc. in Mathematics, University of Oulu (1998) Ph.D. in Statistics (with honors), University of Jyväskylä (2002) D.Sc.(Tech) in Signal Processing (with honors), Aalto University (2010) Professor Ollila's research centers on statistical signal processing and robust statistical methodologies , with significant contributions to array processing, high-dimensional data analysis, and covariance matrix estimation. His work bridges theoretical statistics with practical applications in radar systems, wireless communications, and big data analytics, emphasizing robustness against outliers and computational efficiency in modern data-intensive environments. Current focus areas include compressed sensing, sparse approximation, and blind source separation techniques. Analysis of his 15 most recent publications (2024-2025) reveals three dominant trends: (1) robust covariance learning for massive random access systems, (2) integrated sensing and communications (ISAC) for 6G networks using advanced beamforming, and (3) geometric approaches to elliptical distributions in statistical inference. His work increasingly incorporates deep learning (GANs, graph neural networks) while maintaining strong foundations in classical signal processing theory. Key recognitions include: Academy of Finland Postdoctoral Fellowship (2004-2007) Academy of Finland Research Fellowship (2010-2015) His research has been supported through prestigious Academy of Finland grants totaling over a decade of continuous funding. Professor Ollila currently leads an active research group at Aalto University, supervising doctoral candidates and collaborating internationally with institutions including Princeton University (where he served as Visiting Post-doctoral Research Associate during 2010-2011). He maintains strong ties with the University of Oulu through his adjunct professorship and has contributed to EURASIP's Special Area Team on Theoretical and Methodological Trends in Signal Processing. The Esa Ollila Research Group focuses on cutting-edge challenges in statistical signal processing, with current projects spanning robust DOA estimation under non-Gaussian noise, covariance matrix learning for massive MIMO systems, and machine learning-enhanced radar-communication integration. The group actively develops open-source tools like the fitHeavyTail R package for heavy-tailed distribution modeling and maintains collaborations with industry partners in wireless communications.
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.
Prof. Bernhard U. Seeber is an Extraordinary Professor at the Technical University of Munich (TUM), leading the Chair of Audio Signal Processing within the TUM School of Computation, Information and Technology. His work bridges auditory neuroscience and engineering, focusing on improving hearing aids, cochlear implants, and virtual acoustic systems. He holds affiliations with the Bernstein Center for Computational Neuroscience, Munich Institute of Biomedical Engineering, and others. Education: Studied and earned his PhD (2003) in Electrical Engineering and Information Technology at TUM. Postdoctoral research included time at UC Berkeley and the MRC Institute of Hearing Research (UK), where he pioneered studies on binaural hearing and cochlear implant optimization. Research Interests: Combines experimental and theoretical approaches to explore auditory scene analysis, binaural unmasking, and spatial hearing. Key areas include signal coding for cochlear implants, virtual acoustics, and non-destructive acoustic monitoring. His work emphasizes interdisciplinary collaboration with industry and academia. Awards: Lothar Cremer Award (2010), Emmy Noether Fellowship (2007), and recognition from the German Acoustical Society. Teaching: Offers courses on audio communication, computational neuroscience, and technical acoustics. Projects: Leads initiatives like HAPPAA and Auralization, advancing sound field synthesis and hearing aid algorithms. Current Roles: Head of Chair of Audio Signal Processing, Board Member of DEGA, and spokesperson for the ITG Technical Committee on Hearing Acoustics.
