Dr. Thomas Sullivan is a Teaching Professor in the Department of Electrical and Computer Engineering (ECE) at Carnegie Mellon University, with a courtesy Lecturer appointment in the School of Music. He holds a PhD (ECE '96) and BS (EE '85) from Carnegie Mellon and an MS in Computer Music from MIT's Media Lab (MAS '88). His research interests focus on signal processing for music and audio applications, including audio recording advancements, music synthesis, and controller design for synthesizers. He teaches core ECE courses such as Introduction to ECE, Senior Capstone Design, and Electro-acoustics, while also emphasizing STEM outreach for under-represented K-12 students. Education: PhD in Electrical and Computer Engineering, Carnegie Mellon University (1996) MS in Computer Music, MIT Media Lab (1988) BS in Electrical Engineering, Carnegie Mellon University (1985) His work bridges engineering and music technology, contributing to both academic and industry applications. Sullivan actively promotes STEM engagement through outreach programs and is involved in interdisciplinary initiatives like the Music Technology BS/MS programs. Outside academia, he enjoys music performance as an amateur guitarist/bassist, ice hockey, and distance running.
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.
David Lindlbauer is an Assistant Professor at the Human-Computer Interaction Institute (HCII) within Carnegie Mellon University's School of Computer Science. He leads the Augmented Perception Lab and co-directs the CMU Extended Reality Technology Center (XRTC) . His research bridges Human-Computer Interaction, Computer Graphics, and Computer Vision to create adaptive interfaces that enhance human-digital interaction. Education : PhD (summa cum laude) from Technische Universität Berlin , MSc and BSc from University of Applied Sciences Upper Austria Previous Affiliation : Postdoctoral Researcher at ETH Zurich (2018-2020) David's research focuses on understanding human perception of digital information and developing computational approaches to optimize AR/VR interface usability. Key areas include: Context-aware adaptive interfaces Visual saliency and attention modeling Spatial audio-haptic systems Optimal placement algorithms Object manipulation in Remixed Reality Diminished/ambient MR interfaces His 15 most recent publications (2024-2025) span topics in adaptive XR interfaces, multimodal notifications, haptic systems, and spatial cognition. These works appear at venues like ACM CHI, ACM UIST, IEEE VR, and Frontiers in VR. Common themes include: Machine learning for interface adaptation Human factors in XR design Real-time environment analysis Privacy-aware display systems Collaborative MR interfaces Accessibility enhancements Scientific Recognition : Best Paper Honorable Mention Award (ACM CHI 2024) Best Paper Award (ACM ISS 2023) ETH Zurich Postdoctoral Fellowship Multiple best paper recognitions at CHI, UIST, and IEEE VR Teaching & Leadership : Course developer for CMU's "Interactive Extended Reality" Mentor for NASA SUITS Challenge team Co-chair roles at CHI and UIST Overseeing PhD students and research interns
Ruohan Gao is an Assistant Professor in the Department of Computer Science at the University of Maryland, College Park , with affiliate appointments at the University of Maryland Institute for Advanced Computer Studies (UMIACS) , Maryland Robotics Center (MRC) , and Artificial Intelligence Interdisciplinary Institute at Maryland (AIM) . His research focuses on Computer Vision and Machine Learning , emphasizing Multisensory Machine Intelligence that integrates sight, sound, and touch . He aims to enable machines to perceive, understand, and interact with the world as humans do, with applications in robotic manipulation , audio-visual localization , and differentiable rendering . Article Trends : Span 2018–2025 , centering on audio-visual perception , multisensory datasets , and robotics . Recurring themes include object-centric learning , sound synthesis , and cross-modal consistency . Scientific Awards : Michael H. Granof Award (UT Austin’s Top 1 Doctoral Dissertation, 2021) Best Paper Award Runner-Up (BMVC 2021) Best Paper Award Finalist (CVPR 2019) Highlight Paper (CVPR 2023) He leads the UMD Multisensory Machine Intelligence Lab and collaborates with institutions like Stanford and The University of Texas at Austin . Contact: rhgao@umd.edu .
Carey E. Priebe is a Professor in the Department of Applied Mathematics and Statistics at the Whiting School of Engineering, Johns Hopkins University. He maintains strong affiliations with multiple research centers including the Johns Hopkins University Center for Imaging Science, the Mathematical Institute for Data Science, and the Human Language Technology Center of Excellence. His academic career spans several decades with significant contributions to statistical methodology and theory. Dr. Priebe's research focuses on computational statistics, statistical pattern recognition, and statistical inference for high-dimensional and graph data. His work bridges theoretical statistics with practical applications in areas such as brain connectome mapping, network analysis, and image processing. He has made significant contributions to spectral graph theory, graph matching, and vertex nomination, with applications ranging from neuroscience to national security. His publication record demonstrates consistent contributions to statistical methodology, with a notable emphasis on graph-based statistical methods. His research trajectory shows increasing focus on network data analysis, particularly in the last decade, with applications to brain mapping and connectome analysis as evidenced by his NSF BRAIN Initiative grant and Nature publication. 2013 Erskine Fellow (University of Canterbury) 2011 McDonald Award for Excellence in Mentoring and Advising 2010 ASA SDNS Distinguished Achievement Award 2009 Erskine Fellow (University of Canterbury) 2008 National Security Science and Engineering Faculty Fellow 2008 Pond Award for Excellence in Teaching NSF BRAIN EAGER grant recipient (2014) Professor Priebe has supervised an extensive number of doctoral students whose work spans statistical methodology, network analysis, and machine learning. His students have secured positions at prestigious institutions including academia (University of Wisconsin, Boston University), government research labs, and major technology companies (Microsoft, Facebook, Amazon). His research has been supported by significant grants from NSF, DARPA, and other agencies focused on national security applications and fundamental statistical methodology development. He maintains active collaborations across multiple disciplines and institutions, as evidenced by his numerous conference presentations and visiting appointments including at The Alan Turing Institute and The Isaac Newton Institute. His work bridges theoretical statistics with practical applications in neuroscience, security, and data science.
Florence d'Alché-Buc is a Professor at Télécom Paris (Institut Polytechnique de Paris), holding an Isaac Newton Institute Simons Chair (2025) and leading the Data Science and Artificial Intelligence for Digitalized Industry & Services (DSAI) Chair. She heads the Image, Data, and Signal Department and is part of the Signal, Statistics, and Learning (S2A) team at the LTCI laboratory. Her research focuses on machine learning, bioinformatics, and industrial applications, emphasizing kernel methods, structured prediction, and reliable AI. Education: Previously a professor at Université d’Evry and deputy director of the IBISC lab. Co-director of the Paris-Saclay Data Science Master and creator of specialized AI programs (e.g., Certificate of Specialized Studies in AI). Research highlights include contributions to operator-valued kernel methods, graph prediction, and frugal AI. She actively collaborates with institutions like Inria, École Polytechnique, and industry partners (Airbus, Engie, etc.). Notable roles: Scientific director of Digicosme Labex, Ellis Fellow, and board member of IVADO (Montreal). Her recent work addresses AI explainability, robustness, and sustainability, including projects on interpretable networks and energy-efficient models.
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
Dr. Aniket Bera is an Associate Professor in Computer Science at Purdue University and holds an Adjunct Associate Professor role at the University of Maryland at College Park (UMIACS). He directs the IDEAS Lab at Purdue and previously served as a Research Assistant Professor at UNC Chapel Hill. His research focuses on Affective Computing, Computer Graphics (AR/VR), AI & Robotics, Social Robotics, and medical AI applications for mental health diagnostics. Affiliations: Purdue University (Primary), University of Maryland (Adjunct), UMIACS Career: Joined Purdue in 2017, extensive industry collaborations with Disney Research, Intel, and C-DAC Research Interests: Affective Computing: Emotion perception via gait analysis, speech, and facial/body expressions AR/VR: Redirected walking, virtual environments, and human motion modeling Medical AI: AI-driven mental health detection systems (e.g., VidSole dataset) in collaboration with medical schools Key Contributions: Developed Project Dost (mental health initiative) Received 2020 Brain & Behavior Seed Grant ($X) for emotion-gait research Authored 65+ papers (1,800+ citations) with awards at IEEE VR 2021 Funding & Leadership: Serves as Senior Editor for IEEE RA-L (Planning/Simulation) Conference Chair for ACM SIGGRAPH MIG 2022 Labs/Teams: IDEAS Lab (Purdue), UMD GAMMA Group
Prof. Raimon Jané Campos is a leading figure in biomedical signal processing at the Universitat Politècnica de Catalunya (UPC) and Universitat de Barcelona (UB). As co-director of UPC's Biomedical Signal and System Group (CREB) and coordinator of the Biomedical Engineering PhD Programme, he bridges engineering and clinical applications. His work focuses on respiratory and sleep disorder diagnostics, with significant contributions to COPD and sleep apnea monitoring through wearable devices and machine learning. PhD in Biomedical Engineering (UPC, 1989) Visiting researcher at Université de Nice-Sophia Antipolis Vice-president of Spanish Society of Biomedical Engineering Research spans respiratory mechanics , sleep-disordered breathing , acoustic biomarkers , bioimpedance , and machine learning in biomedical contexts . His 2025 work on microcalorimetric pathogen classification and 2024 spiking neural networks for apnea detection demonstrate cutting-edge integration of computational methods with physiological monitoring. Articles from 2017-2024 reveal consistent focus on non-invasive diagnostics , cardiorespiratory synchronization , and smartphone-based health solutions . Awarded the Barcelona City Technology Research Award (2005) and serving on the International Advisory Board for Physiological Measurement since 2010, his career combines academic leadership with real-world clinical translation through IBEC's technology transfer initiatives.
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.
Donguk Lee is an Assistant Professor in the Department of Audiology and Speech Language Pathology at the University of North Texas. He holds a Ph.D. from the University of Tennessee Health Science Center (2023) and previously worked as an audiologist in clinical and hearing aid settings. He also served as a medic in the South Korean Navy, providing medical support in naval and land-based environments. His research focuses on preventing noise-induced hearing loss through studies of auditory efferent systems, leveraging techniques like otoacoustic emissions and auditory evoked potentials. Dr. Lee’s work addresses both clinical and environmental aspects of hearing conservation. His recent studies explore variability in medial olivocochlear reflex responses to noise exposure and the interplay between efferent unmasking and cortical processing. Earlier work assessed noise levels in stadiums, subways, and other public spaces, linking environmental noise to hearing health risks and public attitudes. He was awarded the 2022 Student Research Grant in Audiology from ASHA for his innovative contributions to hearing loss prevention. His articles span from 2014 to 2023, reflecting a trajectory from hearing aid technology development to advanced auditory physiology and environmental noise policy implications.
Professor Moncef Gabbouj is a distinguished academic and researcher currently serving as Professor of Signal Processing at the Department of Computing Sciences, Faculty of Information Technology and Communication Sciences, Tampere University, Finland. Previously, he held the same position at Tampere University of Technology before the merger in 2019. He has also held visiting professorships at prestigious institutions including Hong Kong University of Technology and Science, University of Southern California, and Purdue University. Ph.D. and MSc. in Electrical Engineering from Purdue University, USA (1989 and 1986) B.Sc. in Electrical Engineering from Oklahoma State University, USA (1985) Prof. Gabbouj's research spans multiple domains within signal and image processing, with a strong focus on machine learning applications. His primary research interests include artificial intelligence, machine learning, Big Data analytics, multimedia content-based analysis, indexing and retrieval, nonlinear signal and image processing, voice conversion, and video processing and coding. His work bridges theoretical advancements with practical applications across various industries, particularly in multimedia communications and biomedical applications. His extensive publication record demonstrates a clear evolution from traditional signal processing techniques toward more sophisticated machine learning and deep learning approaches. Recent work shows increasing focus on convolutional neural networks for various applications including ECG classification, video processing, financial time-series analysis, and image recognition tasks, reflecting the broader trend in the field toward deep learning methodologies while maintaining strong foundations in signal processing theory. IEEE Fellow (2011) Member, Finnish Academy of Science and Letters (2014) Knight, First Class, of the Order of the White Rose of Finland (2006) Nokia Foundation Recognition Award (2005) Nokia Foundation Visiting Professor Award (2012) Finnish Cultural Foundation for Art and Science Award (2017) TUT Foundation Grand Award (2015) Prof. Gabbouj has supervised 64 doctoral and 72 Master's theses, demonstrating his significant contribution to academic mentoring. His research has been supported by substantial funding, including research grants totaling 8.5 million Euro (2001-2015). He has served as Academy of Finland Professor during 2011-2015 and has been involved in numerous EU research projects including Horizon, ESPRIT, HCM, IST, COST, Tempus and Erasmus programs. As Editor, Guest Editor or member of the Editorial Board of 6 international scientific journals, he has significantly influenced the academic discourse in his field. He leads the Signal Analysis and Machine Intelligence (SAMI) research group at Tampere University and serves as the Finland Site Director of the NSF IUCRC funded Center for Visual and Decision Informatics. His research unit focuses on applying advanced machine learning techniques to solve complex problems in signal processing, computer vision, and multimedia analytics, with applications ranging from healthcare to multimedia communications and financial analysis.
Chen Sun is an Assistant Professor of Computer Science at Brown University and a part-time Staff Research Scientist at Google DeepMind . His research bridges computer vision, machine learning, and artificial intelligence , focusing on multimodal representation learning, visual commonsense, and controllable video generation . He directs the PALM🌴 research lab , which explores scalable models for robotic planning, video understanding, and human activity recognition . Chen Sun earned a Ph.D. in Computer Science from the University of Southern California (2016) , advised by Professor Ram Nevatia , and a Bachelor of Science in Computer Science from Tsinghua University (2011) . His lab's work has been supported by Adobe, Honda, Meta, NASA, and Samsung , and he is affiliated with the NSF AI Research Institute on Interaction for AI Assistants . His research spans multimodal transformers, embodied agents, and physics-informed video generation . Key trends include Learning from unlabeled videos for human activity recognition Developing controllable generation techniques using motion trajectories and physics-based signals Advancing scalable frameworks for video-language tasks Scientific awards include the Brown University Richard B. Salomon Faculty Research Award and Samsung Global Research Outreach Award . He has served as Workshop Chair (CVPR 2025) , Action Editor (TMLR) , and Area Chair for top conferences like ICLR, CVPR, and NeurIPS . Chen Sun mentors a dynamic team including Ph.D. students Apoorv Khandelwal (Presidential Fellow) Calvin Luo (Research Mobility Fellow) Nate Gillman (Math Department) Shijie Wang Tian Yun (co-advised with Ellie Pavlick) Yuan Zang Zilai Zeng Zitian Tang and alumni now pursuing Ph.D. programs at Princeton, Cornell, UBC, and UNC . His teaching portfolio includes graduate-level courses on Deep Learning (CSCI 2470) , Advanced Topics in Deep Learning (CSCI 2952N) , and a short course on Multimodal Transformers at ICASSP 2022 and AAAI 2023 .
Paul Boersma is a Professor of Phonetic Sciences at the University of Amsterdam within the Faculty of Humanities. His research explores how phonetic, phonological, and morphological phenomena emerge through computational modeling using artificial neural networks and Optimality Theory, with a focus on multi-level constraint interactions and distributional learning. University of Amsterdam Faculty of Humanities Phonetic Sciences Key research areas include: Computational Modeling : Simulations of phonological category emergence from phonetic data Optimality Theory : Gradual Learning Algorithm applications BiPhon Framework : Parallel bidirectional phonology/phonetics models Statistical Learning : Cross-situational and distributional learning mechanisms Recent publications emphasize: 2025: Inclusive speech recognition systems using Whisper model 2025: F0 ratio analysis for creaky voice diagnostics 2024: Prosodic clitics in child speech and checked tones in Shanghai Chinese 2023: Distributional learning in developmental language disorder contexts 2022: Substance-free phonological features and ghost segment phenomena He has also contributed extensively to the Praat software for phonetic analysis, with continuous updates since 1993.
Dr. Siwei Lyu is a SUNY Distinguished Professor and SUNY Empire Innovation Professor in the Department of Computer Science and Engineering at the University at Buffalo. He serves as Co-Director of the Center for Information Integrity (CII) and Director of the UB Media Forensic Lab (UB MDFL). His research focuses on digital media forensics, computer vision, and machine learning, with significant contributions to counter-deepfake technologies. Education includes a PhD in Computer Science from Dartmouth College (2005), MS from Peking University (2000), and BS in Information Science from Peking University (1997). He has held academic positions at the University at Albany and New York University. His work spans media forensics, adversarial machine learning, and AI security. Notable achievements include developing the Celeb-DF dataset, leading NSF-funded projects, and testifying before U.S. and NYS legislative bodies on disinformation threats. Over $11.3M in grants have supported his research on AI-generated media detection, including a $5M NSF Convergence Accelerator grant. Key awards include IEEE and IAPR Fellowships, Google Faculty Award, and SUNY Chancellor's Research Award. He has authored 230+ papers, 4 patents, and serves on editorial boards of top journals and conferences (e.g., CVPR, ICCV).
