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Changxi ZhengView profile

Associate Professor

• Computer Graphics
• Scientific Computing
• Physics-Based Sound Synthesis
• +7 more

Changxi Zheng is an Associate Professor in the Department of Computer Science at Columbia University's School of Engineering and Applied Science (SEAS). He directs Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC). After receiving his PhD from Cornell University, he joined the faculty of Computer Science Department at Columbia, where he has established himself as a leading researcher in computer graphics and scientific computing. Dr. Zheng's research spans multiple areas of applied computer science with a particular focus on computer graphics and scientific computing. His work centers around developing numerical models for simulating physical phenomena involving complex motions such as fluids, bubbles, and thin rods, along with their resulting acoustic waves. Leveraging computational insights from these models, he devises methods for improving tangible object creation, enabling novel human-computer interactions, and developing software tools for acoustic and photonic devices. His research has attracted significant public interest and media coverage, including projects like FontCode, AirCode, and Computational Metallophone Design. His recent publications reveal a strong interdisciplinary approach, bridging computer graphics, physics simulation, machine learning, and hardware design. His work demonstrates consistent innovation in computational methods for simulating physical phenomena and applying these techniques to practical problems in 3D printing, acoustic modeling, and interactive systems. The breadth of his research spans from fundamental physics-based simulations to practical applications in industry. Columbia SEAS Dean's Fellow (for advised students) NSF Graduate Research Fellow (for Ruilin Xu) Snap Research Fellow (for Rundi Wu) CKGSB Fellow (for Yun Fei) Adobe Research Fellow (for Gabriel Cirio) Marie Sklodowska-Curie Individual Fellow (for Rundi Wu) Best Paper Award at ACM International Conference on Multimedia (ACMMM), 2019 Dr. Zheng actively mentors a diverse group of students, including current PhD candidates and postdoctoral researchers. His research group has received support from various sources that enable their innovative work in computational graphics and physics-based simulation. He has supervised numerous successful students who have gone on to positions at leading technology companies including Adobe, Tencent, Facebook, and academic institutions. As director of Columbia's Computer Graphics Group (C2G2) within the Columbia Vision and Graphics Center (CVGC), Dr. Zheng leads a vibrant research team focused on advancing the state of the art in computer graphics, physics-based simulation, and their applications. The group maintains strong collaborations with industry partners and academic institutions worldwide, fostering an environment of innovation and practical application of theoretical concepts.

Kyle Bellucci JohansonView profile

Visiting Assistant Professor

• Collaborative practice
• Digital media
• Drawing
• +14 more

Kyle Bellucci Johanson serves as a Visiting Critic in Cornell University's College of Architecture, Art, and Planning, Department of Art. Holding an MFA from California Institute of the Arts and a BA in Art and Reconciliation Studies from Bethel University, he merges artistic practice with critical theory through performance architecture and collaborative media. Whitney Independent Study Program (2022–23) Founding fellow at land's edge free school (Los Angeles) Teaching experience at SAIC, UIC, CUNY, and Cooper Union His research investigates: Power structures through spatial interventions Critical theory applications in art practice Post-capitalist imaginaries Hauntology and historical materialism Transdisciplinary approaches to social critique Project trends reveal: Reinterpretation of Soviet design principles Investigation of worker identity in post-industrial contexts Temporal and spatial dislocation in artistic practice Monument critique through performative methods Quantum theory-inspired architectural concepts Reimagining of common spaces as political sites Scientific awards include: Creative Research Grant (2025) Whitney ISP participation (2022–23) Automata Studio Residency (2022) Artists Run Chicago Grant (2021) BOLT Residency (2020–21) Through projects like KLUB WRKR —a nomadic workers' club reimagined for the gig economy—and table (2018–2022), a Chicago-based experimental space for artistic practice, he interrogates institutional frameworks while fostering community through discursive meals and collaborative exhibitions.

Anand V NatarajanView profile

Assistant Professor

• Quantum Information Theory
• Quantum Complexity Theory
• Nonlocality and Bell Inequalities
• +3 more

Anand V Natarajan is the ITT Career Development Professor in Computer Technology and Assistant Professor in the Department of Electrical Engineering and Computer Science (EECS) at MIT, with affiliation to the Computer Science and Artificial Intelligence Laboratory (CSAIL). He holds a PhD in Physics from MIT (2018), advised by Aram Harrow, and previously served as a postdoc at Caltech's Institute for Quantum Information and Matter. His research focuses on theoretical quantum information, emphasizing quantum complexity theory (e.g., MIP* and QMA(2) classes), nonlocality (Bell inequalities, nonlocal games), and semidefinite programming hierarchies. He teaches courses such as Quantum Cryptography, Quantum Systems Engineering, and Introduction to Algorithms at MIT. Notable contributions include resolving the MIP* = RE problem and the FOCS 2019 Best Paper Award for NEEXP ⊆ MIP*. He advises PhD students in quantum computing topics and collaborates extensively on quantum verification protocols and entanglement testing.

Jiajun WuView profile

Assistant Professor

• Computer Vision
• Machine Learning
• Robotics
• +5 more

Jiajun Wu is an Assistant Professor of Computer Science and, by courtesy, of Psychology at Stanford University. He holds multiple affiliations including membership in Bio-X, Faculty Affiliate status at the Institute for Human-Centered Artificial Intelligence (HAI), and membership in both the Wu Tsai Human Performance Alliance and Wu Tsai Neurosciences Institute. Dr. Wu earned his Ph.D. and S.M. in Electrical Engineering and Computer Science from the Massachusetts Institute of Technology before joining Stanford. Dr. Wu's research program focuses on creating AI systems that understand and interact with the physical world through the integration of computer vision, machine learning, robotics, and cognitive science. His work emphasizes physics-based modeling combined with deep learning to develop systems capable of perceiving, reasoning about, and predicting physical interactions. Key research areas include 3D scene understanding, neurosymbolic AI approaches, multimodal perception (combining vision, sound, and language), and embodied intelligence for robotics applications. His lab develops novel frameworks that bridge the gap between neural networks and symbolic reasoning to create more interpretable and robust AI systems. Analysis of Dr. Wu's recent publications reveals a strong trajectory toward integrated multimodal understanding for embodied AI. His work increasingly combines vision, sound, and language processing with physical reasoning to create systems that can interact meaningfully with the physical world. There's a clear progression from foundational computer vision research toward practical robotics applications, with significant emphasis on foundation models for robotics, sim2real transfer techniques, and creating comprehensive datasets for embodied AI research. Dr. Wu's exceptional contributions have been recognized with numerous prestigious awards including the NSF CAREER award (2024), Young Investigator Programs from ONR (2024) and AFOSR (2023), the Okawa research grant (2024), and being named to IEEE Intelligent Systems' 'AI's 10 to Watch' (2024). He has received multiple best paper awards at leading conferences including ICRA (2024), SIGGRAPH Asia (2023), and CoRL (2023). Dr. Wu actively mentors a large cohort of students across multiple levels, serving as primary advisor for doctoral candidates, master's students, and numerous independent researchers. His research is supported by substantial funding from major technology companies including Google, Meta, Amazon, Samsung, and J.P. Morgan, as well as government agencies like NSF, ONR, and AFOSR, reflecting the significance and impact of his work in physical AI and multimodal perception systems. Dr. Wu leads a dynamic research group at Stanford that collaborates extensively with the Wu Tsai Neurosciences Institute and Institute for Human-Centered AI. Current projects include developing neurosymbolic models for computer graphics, creating multisensory datasets like OBJECTFOLDER 2.0 for sim2real transfer in robotics, and building foundation models for embodied intelligence that can understand and manipulate objects with human-like physical intuition.

Adam FinkelsteinView profile

Professor

• Computer Science
• Audio Processing
• Image Processing
• +3 more

Adam Finkelstein is a Professor in the Department of Computer Science at Princeton University, where he has been a faculty member since 1997. He holds a PhD and Master's in Computer Science from the University of Washington and a dual degree in Physics and Computer Science from Swarthmore College. Finkelstein is renowned for his interdisciplinary work at the intersection of computer graphics, audio processing, and machine learning, and he co-organized the Art of Science exhibition at Princeton. Education: PhD, Computer Science, University of Washington MS, Computer Science, University of Washington BA, Physics and Computer Science, Swarthmore College His research spans audio processing (e.g., speech enhancement, voice conversion, audio metrics), computer graphics (e.g., line drawing algorithms, stylized rendering, image manipulation), and machine learning (e.g., self-supervised learning, differentiable programming). His work often bridges technical and creative domains, exemplified by collaborations at Pixar and Adobe Creative Technologies Lab. Recent publications highlight advancements in audio super-resolution and voice conversion using deep learning frameworks, as well as stylized line rendering for animated 3D models. His contributions to perceptual audio metrics and shader optimization further underscore his impact on human-centric computational systems. Scientific Awards: NSF CAREER Award Alfred P. Sloan Fellowship Fellow of the Association for Computing Machinery (ACM) Finkelstein has secured foundational grants for his research and actively mentors students, though no specific advisees are listed. He also explores collaborative tools for internet music performance, reflecting his broader interest in distributed systems and user interfaces.

Golan LevinView profile

Professor

• Interactive Art
• Digital Fabrication
• Information Visualization
• +4 more

Golan Levin is Professor of Electronic Art at Carnegie Mellon University's School of Art, with courtesy appointments in the School of Design, School of Architecture, School of Computer Science, and Entertainment Technology Center. From 2009 to 2022, he served as Director and Co-Director of CMU's Frank-Ratchye STUDIO for Creative Inquiry, a laboratory dedicated to supporting atypical, anti-disciplinary research at the intersection of arts, science, technology, and culture. Levin's research spans interactive art, digital fabrication, information visualization, and generative design. He explores the critical potential of visualization, generative computation for expressive form, and how abstraction connects to realities beyond language. His practice engages interactive gestural robotics, nonverbal interaction aesthetics, and tactical applications of personal digital fabrication through responsive artifacts and media provocations that highlight human-machine relationships. His publications reveal consistent innovation in real-time audiovisual systems, interactive fabrication interfaces, and pedagogic tools for artists. Key trends include slit-scan video techniques, tangible augmented-reality performance, and noise-based visualizations, demonstrating his commitment to expanding human action vocabulary through digital media while bridging artistic expression with computational systems. Levin's work has received significant recognition: Permanent collection inclusion at Museum of Modern Art (MoMA) Whitney Biennial exhibition "50 Designers Shaping the Future" by Fast Company (2012) Competitive grants from National Endowment for the Arts, National Endowment for the Humanities, Creative Capital, and Rockefeller MAP Fund As an educator, Levin teaches "studio courses in computer science" on interactive art, experimental capture, and generative design, emphasizing computation as a medium for critical inquiry. His research funding from major cultural institutions supports interdisciplinary exploration at the arts-technology nexus. The Frank-Ratchye STUDIO for Creative Inquiry, which he directed for 13 years, remains a vital hub for anti-disciplinary collaboration, continuing his legacy of fostering creative agency through technology-infused artistic practice.

Gary ScavoneView profile

Professor

• Music Technology
• Musical Acoustics
• Acoustical Signal Processing
• +3 more

Dr. Gary Scavone is a Professor and Department Chair in the Music department at McGill University's Schulich School of Music. He holds a PhD in Computer-Based Music Theory & Acoustics and MS in Electrical Engineering from Stanford University, alongside degrees from Syracuse University in Music and Electrical Engineering. His research focuses on music technology, including acoustic modeling, sound synthesis, and instrument design. He directs the Computational Acoustic Modeling Laboratory (CAML), which explores advanced techniques for simulating musical instruments and developing software tools. As a saxophonist, he specializes in contemporary concert music performance. Research interests include physically-based sound synthesis, wind instrument acoustics, and digital waveguide modeling. He has contributed to studies on brass and woodwind impedance measurements, violin soundpost dynamics, and free-reed instrument modeling. His work bridges engineering and artistry, with applications in music pedagogy, instrument design optimization, and virtual acoustic replication. Key contributions include open-source projects for wind instrument modeling and the development of tools for automated timbre assessment. His research often combines experimental methods with computational simulations, addressing challenges in both theoretical and applied music acoustics. Current projects focus on deep learning for friction modeling, impedance measurement systems, and cross-cultural instrument analysis.

Alec WrightView profile

Research Fellow

• Audio Machine Learning
• Neural Networks
• Audio Signal Processing
• +3 more

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.

• Artificial Intelligence
• Deep Learning
• Recurrent Neural Networks
• +12 more

Juergen Schmidhuber is Associate Professor at the Faculty of Informatics of Università della Svizzera italiana and a leading researcher at the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI). He is also Chief Scientist at NNAISENSE, a company dedicated to building practical general-purpose AI. His work has profoundly influenced modern artificial intelligence, particularly through the development of Long Short-Term Memory (LSTM) networks in 1991, now deployed across billions of devices for speech recognition, machine translation, and virtual assistants. His research interests span Artificial Intelligence, Deep Learning, Recurrent Neural Networks, Universal AI, Meta-Learning, Algorithmic Information Theory, Artificial Curiosity, Robotics , and Low-Complexity Art . He has pioneered mathematically rigorous frameworks for self-improving AI systems and formal theories of creativity and beauty. His work bridges theoretical foundations with real-world applications in computer vision, natural language processing, and autonomous robotics. The recent articles reflect a consistent trajectory of innovation, combining deep theoretical insights with scalable machine learning architectures. His publications emphasize sequence modeling, universal learning, intrinsic motivation, and computational creativity , demonstrating both foundational contributions and industrial impact. From LSTM to Goedel machines, his work consistently targets the long-term goal of self-improving general AI. Scientific Awards: Numerous awards in AI and machine learning (specific names not listed) Schmidhuber leads a research group at IDSIA, where he mentors students and researchers in advancing the frontiers of AI. His lab has secured significant recognition and industrial collaboration, though specific grants are not detailed. He promotes the 'New AI'—general, sound, and relevant to physics—and continues to explore the convergence of intelligence, computation, and the universe. Labs and Teams: Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI) NNAISENSE (as Chief Scientist)

• Mobile operating systems
• Mobile computer architecture
• Low-power mobile systems
• +5 more

Robert Likamwa is an Associate Professor at Arizona State University, affiliated with the School of Arts, Media and Engineering and the School of Electrical, Computer and Energy Engineering. His research focuses on the intersection of mobile computing, augmented reality, and sensor design through Meteor Studio. Ph.D., Rice University M.S., Rice University B.S., Rice University His work spans three core research arcs: (i) advanced visual capture systems, (ii) hybrid virtual-physical immersion through sensory augmentation, and (iii) data-driven frameworks for AR/VR storytelling. He explores mobile operating systems, low-power architectures, computational imaging, and holographic computing. Recent publications analyze multi-resolution visual sensing, geospatial cross-virtuality collaboration, planetary data visualization, spatial audio optimization, and multi-sensory virtual environments. His 2013 paper on energy-proportional image sensors received the Best Paper Award at ACM MobiSys. Best Paper Award, ACM MobiSys 2013 LiKamWa advises graduate students through research and thesis courses and leads grants related to mobile vision systems, haptic interfaces, and energy-efficient sensor design. He directs Meteor Studio, a research group focused on immersive technology innovation.

Thomas HermannView profile

Researcher

• Auditory Data Science
• Interactive Sonification
• Smart Environments
• +5 more

Dr. rer. nat. Thomas Hermann is a faculty member at Bielefeld University's Faculty of Engineering, leading the Ambient Intelligence Group and coordinating the Computer Science program. He specializes in sonification, auditory data science, and smart environments. Head of Ambient Intelligence Working Group Computer Science Program Coordinator Member of multiple academic advisory boards His research focuses on interactive sonification for biomedical applications, quantum systems, and smart environments. Key projects include ECG sonification for cardiac diagnosis, real-time auditory feedback in swimming, and sonic interfaces for AR cooperation. Recent publications span 2025 with Python-based sonification tools ( pya AGen ), quantum system sonification, and ST-elevation myocardial infarction monitoring. He contributes to open-access supplementary materials and interdisciplinary workshops. As a researcher , Hermann develops practical sonification frameworks like Panson for facial behavior analysis, CardioScope for portable ECG monitoring, and Base Cube One for smart environments. His work bridges academic research with industry applications.

• Sound and Music Computing
• Music technology
• Artificial intelligence
• +8 more

Koray Tahiroglu is a University Lecturer at Aalto University's School of Arts, Design and Architecture, specializing in Sound and Music Computing. His work bridges artificial intelligence, digital musical instruments, and embodied interaction, with a focus on deep learning applications in audio synthesis and human-AI creative collaboration. His research explores New Interfaces for Musical Expression (NIME), sonic interaction, and physical computing. He collaborates with SOPI Research Group and Google Brain Team (Magenta) on AI-driven artistic innovation. Recent Publications : 2024 studies on dance-sound cross-correlation and intra-action frameworks; 2023 work on AI-terity and deep learning syllabi; 2022 explorations of GAN synthesis, musical expectations, and lifeworld sonification. Scientific Awards : Co-Creative Artificial Intelligence of Music (2022) 2010 grant for scientific publications and artistic activities 2017 Honorable Mention for mobile cultural heritage research Tahiroglu contributes to digital art education and leads projects at Media Lab Helsinki, advancing sonic interaction and generative audio systems.

Doug L. JamesView profile

Professor

• Computer Graphics
• Computer Sound Synthesis
• Physically Based Modeling and Animation
• +3 more

Doug L. James is a Full Professor of Computer Science at Stanford University since 2015, following roles as Associate Professor at Cornell University (2006-2015) and Assistant Professor at Carnegie Mellon University (2002-2006). He holds a PhD in Applied Mathematics from the University of British Columbia (2001), alongside earlier degrees from the same institution and the University of Western Ontario. His research focuses on computer graphics, sound synthesis, and physically-based modeling, with notable contributions to fluid simulation, cloth animation, and medical modeling. Key achievements include the 2012 Technical Achievement Award from the Academy of Motion Picture Arts and Sciences for 'Wavelet Turbulence,' and the 2013 Katayanagi Prize. He serves as a consulting Senior Research Scientist at Pixar Animation Studios and has led roles like Technical Papers Chair at SIGGRAPH 2015. His work integrates physics-based principles with interactive systems, emphasizing real-time applications and data-driven methods. Research interests span sound synthesis for animations (e.g., cloth, water, impact sounds), deformable models for medical simulation, and tools like 'svMorph' for virtual surgery planning. His publications reflect a blend of algorithmic innovation and practical applications in film, gaming, and healthcare.

• Speech Communication Technology
• Speech Signal Processing
• Machine Learning Applications in Speech Analysis
• +3 more

Paavo Alku is a Professor of Speech Communication Technology at Aalto University's Department of Information and Communications Engineering. With academic credentials from Helsinki University of Technology (M.Sc. 1986, Lic.Tech. 1988, Dr.Sc.Tech. 1992), he has held academic positions at Asian Institute of Technology (1993) and University of Turku (1994-1999). Current research focuses on speech production analysis, parametric speech synthesis, and speech-based biomarkers for health monitoring Actively develops machine learning models for voice disorder detection and Parkinson's disease classification Principal investigator for projects including SymptoSonic (2024-2025) and HEART (2020-2024) His recent publications emphasize: Wavelet scattering for neurological speech analysis Fisher vector representations in voice disorder classification Machine learning approaches to vocal intensity categorization Respiratory aerosol emission during speech production Formant tracking through hybrid neural network/LP methods Awarded: IEEE Fellow (2020) Academy Professor (2015-2019) Multiple best student paper awards at ICASSP and Interspeech

Stefan BleeckView profile

Professor

• Hearing Science
• Audiology
• Signal Processing
• +5 more

Professor Stefan Bleeck is a Professor of Hearing Science and Technology at the University of Southampton, leading the Hearing and Balance Centre and directing the Institute of Sound and Vibration Research (ISVR). His research focuses on the intersection of hearing science, audiology, and signal processing, with specialties in bio-inspired auditory modeling, speech intelligibility in noise, cochlear implants, and auditory evoked potentials. He holds a PhD in computational neuroscience and has held roles including Head of the Hearing and Balance Centre. Awards include Vice-Chancellor's Teaching Awards (2009) and Google Research Awards (2012). Education: Diploma in Physics (University of Darmstadt, 1995), PhD in Computational Neuroscience (University of Darmstadt, 2000). Research spans experimental, computational, and clinical approaches to improve hearing aids and cochlear implants. Active projects include developing speech enhancement algorithms, antiphasic speech tests for hidden hearing loss, and neural-space speech processing. Supervises multiple PhD students in engineering and computer science. Publications highlight advancements in speech enhancement, bio-inspired models, and cross-linguistic hearing tests. Collaborates with institutions like Google and the European Union on projects funded by EPSRC, Cancer Research UK, and others. His work aims to enhance speech understanding for hearing-impaired individuals through innovative signal processing and auditory modeling.