Janne Lindqvist is an Associate Professor at the Department of Computer Science, Aalto University. His research bridges security engineering, human-computer interaction (HCI), and privacy, with a focus on making security systems usable and user-centric. University: Aalto University Department: Department of Computer Science Rank: Associate Professor Email: janne.lindqvist@aalto.fi Lindqvist’s work spans security engineering , privacy systems , and user research , emphasizing practical authentication methods, password management, and human behavior in security contexts. He explores how users interact with systems like TPM APIs, gesture passwords, and mobile authentication mechanisms. Recent publications highlight trends in authentication systems , ubiquitous computing security , and mobile user behavior . Key themes include biometric authentication, gesture-based security, and balancing usability with cryptographic robustness. CHI'25 Honorable Mention Award CHI'24 Best Paper Award His research integrates empirical studies with technical implementations, such as analyzing password forgetting patterns and developing acoustic sensing for vehicle detection (e.g., Auto++, BO-Ear). Collaborative efforts span machine learning, psychology, and embedded systems.
Prof. Dr. Steffen Marburg is a Full Professor at the Chair of Acoustics of Mobile Systems within the TUM School of Engineering and Design at the Technical University of Munich. His research focuses on numerical methods in vibroacoustics, structural optimization, and acoustic modeling for applications in automotive, maritime, and musical instrument domains. Education: PhD from Technical University of Dresden (1998). Academic Career: Junior Professor at TU Dresden (2004), Chair of Technical Dynamics at University of the Federal Armed Forces Munich (2010), Full Professor at TUM (2015–present). Editorial Roles: Co-Editor-in-Chief of Journal of Theoretical and Computational Acoustics, Associate Editor of Journal of the Acoustical Society of America, Editor of Acoustics Australia and Mechanical Systems and Signal Processing. His research integrates computational acoustics, boundary element methods, and machine learning to address noise control and structural optimization challenges. Recent work explores acoustic metamaterials, viscothermal losses, and data-driven modeling. He has co-authored over 150 publications and led advancements in multifrequency solution methods and noise-insulating structures. Scientific awards include the Innovation Award of the Industrieclub Sachsen e.V. (1999). His editorial contributions and leadership in journals highlight his influence in computational acoustics and structural dynamics.
Ivan Dokmanic is an Assistant Professor at the Coordinated Science Laboratory (CSL) within the University of Illinois . His research bridges signal processing , machine learning , and applied inverse problems , with a focus on acoustics, biomedical imaging, and distance geometry. Current Role : Assistant Professor, CSL Email : dokmanic@illinois.edu Research Interests : Dokmanic explores machine learning applications in inverse problems , particularly distance geometry for molecular imaging and acoustics . His work includes unlabeled sensing , where distances between points are known but their arrangement is not. This has implications for powder diffraction , indoor localization , and echo modeling . Article Trends : His recent publications emphasize distance geometry in machine learning , acoustic signal processing , and inverse problem theory . Key areas include molecular imaging , audio encryption , and sensor positioning . Collaborative work spans medical imaging , cyberphysical systems , and geometric invariants . 2016 Google Faculty Award NSF Grant (1 year, $157,079) Students and Grants : Dokmanic mentors PhD students like Puoya, Shuai, and Anadi. His research is funded by the National Science Foundation , Google , VISA , and nVidia .
Craig Jin is an Associate Professor at the University of Sydney, leading the CARlab (Computing and Audio Research Laboratory) and Spatial Audio Research initiatives within the School of Electrical and Computer Engineering. He holds a BS from Stanford University, an MS from Caltech, and a PhD from the University of Sydney. His work focuses on immersive audio technologies, biomedical signal processing, and assistive technologies for sensory augmentation. Research interests include spatial audio reproduction, binaural processing, acoustic sensing for accessibility, and machine learning applications in signal processing. Key contributions span HRTF interpolation, noise reduction algorithms, and acoustic touch systems for the visually impaired. Recent projects include real-time MRI analysis of vocal tract dynamics and sparse recovery techniques for sound field reconstruction. His publications span over 150 peer-reviewed articles in journals like IEEE Transactions on Audio, Speech, and Language Processing, and conferences such as ICASSP. He advises four current PhD/Master’s students on projects like predictive gesture tracking, voice disorder classification, and magnetic resonance imaging techniques.
Dr. Kim Yong-Joe is an Associate Professor in the J. Mike Walker ’66 Department of Mechanical Engineering at Texas A&M University. He serves as the Director of the Acoustics and Signal Processing Laboratory (ASPL), founded in 2009. His research focuses on acoustics, applied signal processing, nonlinear acoustics, biomedical acoustics, noise and vibration control, and structural dynamics. He has received notable awards including the 2014 Department of Mechanical Engineering Graduate Teaching Award and the 2014 Pioneer Natural Resources Faculty Fellow II. His lab specializes in wave propagation analysis, ultrasonic structural health monitoring, and acoustophoresis in microfluidic systems. He has collaborated with sponsors like the National Science Foundation, Qatar National Research Fund, and Samsung Techwin. His research has led to advancements in noise reduction technologies, biomedical diagnostics, and nondestructive evaluation methods.
F. Levent Degertekin is a Regents' Entrepreneur and the George W. Woodruff Chair in Mechanical Systems and Professor at the George W. Woodruff School of Mechanical Engineering at Georgia Institute of Technology. His office is located in Love Building, room 311B, and his contact email is levent.degertekin@me.gatech.edu. Dr. Degertekin's academic journey includes a Ph.D. in Electrical Engineering from Stanford University (1997), an M.S. in Electrical Engineering from Bilkent University, Turkey (1991), and a B.S. in Electrical Engineering from Middle East Technical University, Turkey (1989). Dr. Degertekin's research focuses on micromachined ultrasonic devices and systems for medical applications, particularly in intravascular ultrasound imaging, therapeutic ultrasound, and acousto-optical sensors for MRI. His work spans from fundamental research on novel transduction methods to complete catheter-based imaging systems close to commercialization. He has made significant contributions to capacitive micromachined ultrasonic transducers (CMUTs), developing diffraction grating based optomechanical sensing methods now commercialized by Silicon Audio, novel atomic force microscopy imaging probes, and micromachined ultrasonic ejector structures for cell transfection commercialized by OpenCell Technologies. His research integrates acoustics, optics, and their combinations for various medical applications, utilizing conventional microfabrication (MEMS) and integrated circuit technologies. The Degertekin lab exposes students to applied physics, electrical, mechanical and biomedical engineering, biology, and biomimetic systems, providing them with thorough theoretical and experimental education in acoustics and optics while learning interdisciplinary research. Dr. Degertekin's work has received significant media attention, including coverage in IEEE Spectrum, Wired Magazine, The New York Times, and Fox Business News, highlighting innovations such as handheld ultrasound probes, MRI safety sensors, and minimally invasive cardiac imaging technologies. IEEE Fellow for 'Contributions to micromachined ultrasonic and optomechanical transducers and systems,' 2022 IEEE UFFC Society Inaugural Carl Hellmuth Hertz Ultrasonic Achievement Award, 2014 George W. Woodruff School Outstanding Achievement in Commercialization and Entrepreneurship Award, 2024 National Science Foundation CAREER Award, 2004-2009 Whitaker Foundation Biomedical Engineering Research Grant Award, 2001 66 US and 6 International Patents Dr. Degertekin has mentored numerous students who have gone on to make significant contributions in the field. Several of his students have received IEEE Ultrasonics Symposium Best Student Paper Awards, including Jeff McLean (2003), Sheng-Yu Peng (2006), Rasim O. Guldiken (2005 and 2007), and Toby Xu (2014). His research has been supported by various grants including the NSF CAREER Award and Whitaker Foundation grant. His work has led to multiple commercial ventures including Silicon Audio and OpenCell Technologies. The Degertekin Group at Georgia Tech focuses on transducers and systems for medical imaging and sensing, with current projects including capacitive parametric transducers, acousto-optic sensors for MRI, novel transducer methods for focused ultrasound in the brain, microsystems for intravascular and intracardiac ultrasound imaging, and CMUT-on-CMOS systems for IVUS imaging.
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.
Riccardo Raheli is a Full Professor at the University of Parma , Department of Engineering and Architecture, with a career spanning over three decades in Information and Communication Technologies (ICT). He has served as Chair of the Councils for Telecommunications and Communication Engineering programs, and as representative of the University of Parma in CNIT and its Members' Assembly. Education: Laurea in Electronic Engineering (University of Pisa, 1983), M.Sc. in Electrical and Computer Engineering (University of Massachusetts, 1986), Postgraduate Diploma (Scuola Superiore Sant'Anna, 1987) Key Roles: President of Degree Councils (2002-2018), CNIT Committee Member (2000-2005), Editorial Board member for IEEE Transactions, Springer and MDPI journals His research bridges telecommunications , digital signal processing , and healthcare applications , producing extensive international publications and industrial patents. He has co-authored monographs including Detection Algorithms for Wireless Communications (Wiley, 2004) and LDPC Coded Modulations (Springer, 2009). Recent article trends show interdisciplinary work in automotive stress monitoring (IoT/Matlab-based systems), video processing for healthcare (neonatal seizures, respiratory monitoring), and acoustic field control (microphone virtualization, personal sound zones). His work spans machine learning applications in automotive systems, stochastic acoustic modeling , and power-line communications . Scientific Leadership : Co-Chair for IEEE conferences (ICC 2010, GLOBECOM 2011, ISPLC 2020) Editorial roles in 7+ international journals Grants & Collaborations : Led industrial patents in communications systems Coordinated CNIT Technical Reports series (2025) He teaches Wireless Communications and Digital Signals Laboratory , emphasizing Matlab/Simulink proficiency. His laboratory sessions focus on practical implementation of signal processing algorithms, requiring full software installation on personal devices.
Prof. Gabriele Schrag holds the Professorship of Microsensors and Actuators at the Technical University of Munich (TUM), within the TUM School of Computation, Information and Technology. Her research focuses on MEMS (Micro-Electro-Mechanical Systems), including microsensors, actuators, and their applications in acoustics, microfluidics, and bioengineering. She has pioneered work in virtual prototyping for system-level modeling to enhance device robustness and performance. Education: PhD (summa cum laude) from TUM on 'Modeling coupled effects in microsystems' Habilitation in sensor systems technology (2018) Acting head of the Chair of Technical Electrophysics (2018-2023) Research emphasizes acoustic MEMS transducers , electrohydrodynamic printing , and physics-based modeling . Notable projects include developing piezoelectric MEMS microphones with corrugated membranes and integrated micropump systems. Awards include the Bavarian Prize for Good Teaching (2021) and Eurosensors Fellow Award (2019). Her work bridges virtual prototyping with real-world applications , addressing challenges in miniaturization, energy efficiency, and sensor integration for medical and industrial systems.
Professor Jordan Cheer holds a Royal Academy of Engineering Research Chair in Smart Acoustic Control Technologies at the University of Southampton's Institute of Sound and Vibration Research (ISVR). His research focuses on active control of noise and vibration, including smart structures and metamaterials. He is an Associate Editor for Journal of the Acoustical Society of America and Subject Editor for Journal of Sound and Vibration . Education: BMus (Tonmeister) in Music and Sound Recording, University of Surrey (2008) MSc in Sound and Vibration, ISVR, University of Southampton (2009) PhD in Active Control of Automobile Cabin Acoustics, ISVR, University of Southampton (2012) Research Interests: Active Noise/Vibration Control, Smart Structures, Acoustic Black Holes, and Metamaterials. Current projects include the BAE Systems-funded Smart Acoustic Control Technologies Chair and the IN-NOVA MSCA Doctoral Network. Recent Work Trends: Publications emphasize neural network controllers, head-tracking systems, and metamaterial applications. Patents focus on vibration damping and acoustic control devices. Awards: Royal Academy of Engineering Research Chair (2024) Grants & Labs: Leads projects funded by EPSRC, MOD, and industry partners. Collaborates with teams like the Southampton Marine and Maritime Institute and the Centre for Defence and Security Research.
Lin Cai is a Professor in the Department of Electrical & Computer Engineering at the University of Victoria, Canada. She holds prestigious fellowships including NSERC Steacie, IEEE, CAE, and Royal Society of Canada. Her research focuses on wireless communications, networking, and mobile computing, with emphasis on protocols for multimedia and IoT systems. She has led projects in vehicular networks, UAV-assisted systems, and federated learning for edge intelligence. Dr. Cai has advised over 20 students, many of whom have received awards and prominent roles in academia and industry. She has authored numerous high-impact papers, secured grants from NSERC, CFI, and industry partners, and serves in leadership roles at IEEE and educational institutions. Notable contributions include work on congestion control, network security, and autonomous systems. Education: BEng (Nanjing U. of Sci. & Tech.), MASc/PhD (University of Waterloo) Affiliations: IEEE Vehicular Technology Society Board of Governors, IEEE ComSoc Distinguished Lecturer Awards: 2020 IEEE N2Women 'Star in Networking', RSC Fellow 2024, Best Paper Awards (ICC 2008, WCNC 2011) Research Labs: Connected Autonomous Vehicles (CAV) Lab, AI-driven Networking Group Her work integrates networking, AI, and control theory to address challenges in 6G, IoT, and smart transportation. She actively promotes diversity through initiatives like the 'Riko-chan' STEM manga series.
Fernando Lopez-Lezcano is a Lecturer at Stanford University's Center for Computer Research in Music and Acoustics (CCRMA) where he has been working since 1993. His work combines music composition, electronic engineering, and programming with a focus on spatial audio and sound diffusion technologies. He was the Edgar Varese Guest Professor at TU Berlin during the Summer of 2008 and is the 2014 winner of Stanford's Marsh O'Neill Award for Exceptional and Enduring Support of Stanford University's Research Enterprise. Lopez-Lezcano's research interests center on computer music, spatial audio technologies, and sound diffusion systems. He has pioneered work in High Order Ambisonics (HOA), developing novel decoders and reverberation architectures for immersive sound environments. His work on the SpHEAR Project has created innovative 3D-printed soundfield microphone arrays, while his development of the GRAIL (Giant Radial Array for Immersive Listening) has revolutionized concert speaker arrays for HDLAs (High Density Loudspeaker Arrays). He has also created numerous open-source tools and software environments for spatial sound composition and diffusion. His recent creative output demonstrates a consistent exploration of 3D sound spatialization, modular synthesis, and interdisciplinary collaborations. Across his compositions, he frequently integrates custom-built hardware like his modular synthesizers (including the famous 'El Dinosaurio' built in 1980-81) with sophisticated software environments written in SuperCollider. His work often bridges acoustic and electronic sound sources, creating rich spatial experiences that explore the relationship between technology and artistic expression. Among his notable achievements is the 2014 Marsh O'Neill Award, recognizing his exceptional support of Stanford's research enterprise. This prestigious award was inspired by Marsh O'Neill, Associate Director of the W.W. Hansen Laboratories, and honors outstanding staff members who support faculty research activities. Lopez-Lezcano has mentored numerous students in the development of musical instruments and performance systems, most notably the 'Ensemble AnaLocos' (Analógicos Locos or 'Crazy Analogs') which created the 'Noise Toaster' synthesizers. He has also developed important infrastructure for CCRMA including 'Planet CCRMA,' a collection of open-source audio software for Linux. His teaching includes the 'Sound in Space' course (Music 222), which covers historical background, techniques, and theory on the use of space in music composition and diffusion. He directs the CCRMA Stage concerts and has been instrumental in developing CCRMA's spatial audio infrastructure, including the GRAIL system used in Bing Concert Hall. His work with the SpHEAR Project has advanced 3D sound recording techniques, while his collaborations with performers like Michiko Theurer (violin) and Chris Chafe (celletto) have produced innovative interdisciplinary performances.
Dr Sean Anderson is a Senior Lecturer at the Department of Automatic Control and Systems Engineering , University of Sheffield , with over 15 years of experience in interdisciplinary research spanning robotics, control systems, and computational biology. He earned his MEng and PhD from the University of Sheffield, focusing on control systems and chemical engineering. Education: MEng in Control Systems Engineering, University of Sheffield (2001) PhD in Chemical and Process Engineering, University of Sheffield (2005) Research Interests include: Bioinspired robotics Adaptive and optimal control in biological systems Nonlinear system identification Computational neuroscience Acoustic and visual sensor fusion for localization His recent publications highlight innovations in robotic localization in hazardous environments, interpretable deep learning for control systems, acoustic sensing technologies, and data-driven modeling of complex systems. Key projects involve autonomous navigation in pipe networks, turbulence modeling, and biomedical signal processing. Grants and Funding: He has secured major grants from EU H2020 (£4M), EU FP7 (£2.9M), and EPSRC (£5.7M), focusing on bioinspired control algorithms, robotic safety, and infrastructure assessment. Teaching: He leads the ACS61011 Deep Learning module, emphasizing practical applications in robotics and signal processing.
Archontis Politis is an Assistant Professor in the Department of Computing Sciences at Tampere University's Faculty of Information Technology and Communication Sciences. His research focuses on signal processing, machine learning, and their applications in audio engineering, particularly in spatial audio, sound source separation, and parametric audio coding. He explores topics such as Ambisonics, reverberation control, and neural network-based approaches for audio processing. His work emphasizes spatial audio reproduction, including six degrees of freedom (6DOF) rendering, microphone array processing, and efficient compression techniques for higher-order Ambisonics. He also investigates sound event localization and detection, leveraging machine learning for real-world acoustic scenarios. His contributions span theoretical advancements in spherical harmonics and practical implementations of spatial audio systems. Recent research highlights include developing datasets for music source separation, improving synthetic-to-real generalization in classical music, and creating neural encoding models for irregular microphone arrays. His methodologies often integrate deep learning with traditional signal processing to address challenges in multi-speaker environments and dynamic acoustic scenes.
Professor Jon Barker is a faculty member at the University of Sheffield , where he holds a Personal Chair in the School of Computer Science . He leads the Speech and Hearing (SpandH) research group and co-founded the CHiME international workshop series on robust speech recognition. Education : PhD in Computer Science (University of Sheffield, 1999); BA in Electrical and Information Sciences (Cambridge University). Research Focus : His work bridges machine listening and human auditory perception , with key contributions to noise-robust speech recognition , speech intelligibility prediction , and hearing aid signal processing for speech and music. Recent projects include the Clarity Challenges and Cadenza Challenges , large-scale machine learning initiatives to improve accessibility for hearing-impaired users. Publication Trends : Recent articles emphasize machine learning for hearing aid optimization , dysarthric speech recognition , audio-visual integration , and music demixing algorithms . Collaborations span speech processing, psychoacoustics, and biomedical engineering. Scientific Awards : EURASIP Best Paper Award (2009) ISCA Best Paper Award (2008) Grants and Leadership : He has secured major EPSRC grants including EnhanceMusic (2022-2026) and Challenges to Revolutionise Hearing Device Processing (2019-2025). He co-led the TAPAS Marie Curie Training Network (2017-2022) and led projects like AV-COGHEAR (2015-2018) and CHiME (2009-2012). Labs and Teams : Barker collaborates closely with the Speech and Hearing Research Group and contributes to international initiatives like the CHiME Workshop . His lab develops open datasets such as the Clarity Speech Corpus and Audio-Visual Lombard Corpus .
