Professor Annie Mahtani is a Professor of Electroacoustic Composition and Practice at the University of Birmingham's Department of Music. She holds a BMus (Hons), MA, MPhil, and PhD from the University of Birmingham and Birmingham City University. Her work focuses on electroacoustic music, acousmatic composition, multichannel audio, spatialisation, and field recordings, with significant contributions to site-specific installations and cross-disciplinary collaborations. She co-directs the SOUNDkitchen collective and serves on key boards such as the British ElectroAcoustic Network (BEAN) and the British Section of ISCM. Her research explores the sonic identity of environmental sounds, large-scale multichannel composition, and ambisonic audio techniques. As a performer with BEAST (Birmingham Electroacoustic Sound Theatre), she develops live acousmatic performances and soundwalks. Recent projects include Becoming Tree (an immersive audio retelling of a literary work) and Minimum Monument , performed at Birmingham Hippodrome. She actively collaborates with dance and theatre companies like Rosie Kay Dance Company. Professor Mahtani teaches undergraduate and postgraduate modules in electroacoustic composition and is involved in admissions and curriculum leadership. Her works have been performed globally in festivals like Klang! Électroacoustique and Sound + Environment, showcasing her innovative contributions to contemporary music and sound art.
Abhinav Shrivastava is an Associate Professor in the Department of Computer Science at University of Maryland, College Park, with a joint appointment in the Institute of Advanced Computer Studies (UMIACS). Previously, he served as an Assistant Professor at the same institution from August 2018 to June 2024, and spent one year as a Visiting Research Scientist at Google Research from September 2017 to August 2018. His educational background includes: PhD in Robotics and Artificial Intelligence from Carnegie Mellon University (2017), advised by Abhinav Gupta, with thesis titled 'Discovering and Leveraging Visual Structure for Large-scale Recognition' MS in Artificial Intelligence from Carnegie Mellon University (2011), supervised by Alyosha Efros and Martial Hebert BTech in Computer Science and Engineering from Jaypee Institute of Information Technology (2010) Professor Shrivastava's research focuses on computer vision and machine learning, with particular expertise in object detection, image recognition, and neural representations. His work bridges theoretical advances with practical applications, exploring how visual systems can discover and leverage structure in large-scale recognition problems. He has made significant contributions to understanding the role of supervision in vision transformers, developing novel approaches for object-state composition recognition, and creating efficient neural representations for videos and 3D scenes. His research often addresses fundamental challenges in visual recognition, including handling novelty in open-world environments and improving the efficiency of visual systems. An analysis of his recent publications reveals a strong emphasis on neural representations, particularly for dynamic content like videos and 3D scenes. His work demonstrates increasing sophistication in handling open-world vision problems, with research spanning object discovery, localization, and representation learning. The publications show a clear progression toward more efficient and scalable models, with recent work focusing on model compression, sparse representations, and addressing the challenges of working with limited annotations. His scientific contributions have been recognized with several prestigious awards: Best Paper Award (Applications) at IEEE Winter Conference on Applications of Computer Vision (2020) Microsoft Research PhD Fellowship (2014-2016) Best Student Paper Award at IEEE Winter Conference on Applications of Computer Vision (2014) Outstanding Reviewer Award at IEEE CVPR (2015) Professor Shrivastava has successfully mentored numerous graduate students, many of whom have become prominent researchers in computer vision. His Amazon Research Awards (2020 and 2023) have supported innovative projects including 'The pursuit of knowledge: discovering and localizing new concepts using dual memory' and 'Audio-conditioned Diffusion Models for Generating Lip-synchronized Videos.' He has served as Area Chair for major conferences including ICCV, CVPR, and AAAI, demonstrating his leadership in the computer vision community. His research has attracted significant funding from both academic and industry sources, supporting his exploration of fundamental questions in visual recognition and representation learning.
Junier Oliva is an Assistant Professor in the Department of Computer Science at the University of North Carolina at Chapel Hill and Lead Faculty of the Master of Applied Data Science program. His research focuses on machine learning, artificial intelligence, and nonparametric statistics, particularly in high-dimensional density estimation, sequential modeling, and learning from complex/structured data. He holds a B.S., M.S., and Ph.D. in Computer Science from Carnegie Mellon University, with prior industry experience at Yahoo! and Uber ATG. Research Interests: Machine learning, artificial intelligence, nonparametric statistics, deep learning, statistical data mining, signal processing, kernel methods, and scalability. His work bridges machine and human learning via collective approaches, emphasizing simple yet flexible models for massive datasets. Awards/Grants: $592K AIM-AHEAD/NIH Grant for Human+AI Collaboration $594K NSF Grant for Scientific Discovery $500K NSF Grant for 'Machine Detectives' Project ACM BCB Best Paper Award (2022) for transparent single-cell classification work Labs/Teams: Director of the LUPA Lab, which develops machine learning techniques for holistic data understanding across domains like healthcare, earth science, and computer vision.
Dina El-Zanfaly serves as an Assistant Professor in the School of Design at Carnegie Mellon University (CMU), where she directs the hyperSENSE: Embodied Computations Lab. Her work bridges computational design and human-centered interaction, focusing on how physicality shapes sensory experiences and cognitive processes through intelligent systems. Education: PhD in Design and Computation, Massachusetts Institute of Technology (MIT) Master of Science in Design and Computation, MIT (Fulbright scholar) Her research critically examines computational methods for augmenting sensory perception, with emphasis on embodied sense-making in hybrid environments. She investigates co-creative interactions between humans and intelligent systems, exploring how computational tools empower designers and non-designers to shape products, social spaces, and interconnected technologies. Key questions address mutual learning between humans and machines through improvisation and creative production. Analysis of her 2022-2025 publications reveals dominant themes in mixed reality interfaces, AI-augmented skill acquisition (particularly in crafts and welding), and tangible co-creation with generative AI. Her work consistently integrates physical computing with mindfulness applications and privacy-aware smart environments, demonstrating interdisciplinary reach across education, manufacturing, and therapeutic contexts. Scientific Awards: Fulbright Scholarship As lab director, El-Zanfaly mentors students in computational making and embodied interaction projects. Her research is supported through initiatives like Fab Lab Egypt and collaborations with MIT, where she co-founded the Computational Making Group. She chairs major conferences including Fab15 in Egypt and serves on the DESFORUM program committee, indicating significant leadership in maker education and design research communities. She founded and leads the hyperSENSE Lab at CMU, which investigates computational embodiment through projects like Origami Sensei and Sand-in-the-loop. Previously, she co-established the Computational Making Group at MIT and co-founded Fab Lab Egypt (the first community maker space in North Africa/Arab world), demonstrating sustained commitment to global maker ecosystems and interdisciplinary team building.
Emma Mercier is an Associate Professor and Associate Head & Director of Graduate Programs in the Department of Curriculum & Instruction at the University of Illinois, Urbana-Champaign's College of Education. She also holds a secondary appointment in the Department of Educational Psychology, demonstrating her interdisciplinary approach to educational research. Dr. Mercier's research focuses on the relationship between social interaction and learning, with particular emphasis on collaboration and computer-supported collaborative learning (CSCL) in classroom settings. Her work examines how technology influences group interactions and learning, especially through the use of multi-touch tables in classrooms. She investigates between-group and whole-class interactions, device ecologies, teacher tools, and classroom contexts that shape learning opportunities in technology-enhanced environments. Her research spans K-12 and higher education settings, with significant contributions to engineering education and the design of collaborative learning spaces. Analysis of Dr. Mercier's recent publications reveals a strong focus on orchestration tools that support instructors in facilitating collaborative learning, the role of technology (particularly augmented and virtual reality) in collaborative problem solving, and the design of effective collaborative tasks in engineering education. Her work bridges educational theory with practical classroom applications, often employing design-based implementation research methodologies. A notable trend is her increasing focus on machine learning applications to analyze and support collaborative interactions in real-time classroom settings. Dr. Mercier has been actively involved in mentoring graduate students and teaching courses related to educational research methods, child development and technology, and advanced study of education. Her work has involved significant collaboration with researchers across institutions and disciplines, particularly in the fields of educational technology, learning sciences, and engineering education. Her research has been supported through various projects, including the CSTEPS (Collaborative Support Tools for Engineering Problem Solving) initiative, which has developed and evaluated tools to support collaborative learning in engineering classrooms. This work has involved partnerships with teaching assistants, course assistants, and faculty to implement and refine collaborative learning approaches in undergraduate engineering courses.
Berit Greinke is an Assistant Professor of Wearable Computing at the Berlin University of the Arts (UdK) and the Einstein Center Digital Future (ECDF) since 2018, previously serving as a researcher at UdK's Design Research Lab and DFKI (2016-2018). Her academic foundation includes a PhD from Queen Mary University of London (2017), an MA from Central St Martins (2009), and a Diploma from Weissensee Academy of Art Berlin (2007). Her educational trajectory: PhD in Media and Arts Technology, Queen Mary University of London (2017) MA in Design for Textile Futures, Central St Martins College of Art and Design (2009) Diploma in Textile and Surface Design, Berlin Weissensee School of Art (2007) Greinke's research pioneers the convergence of craft, textile design, and digital technology, with core expertise in electronic textiles and smart materials. She investigates metamaterial-based 'metatextiles' for electromagnetic applications and explores transdisciplinary collaboration between designers and scientists, particularly regarding 'negative data' in creative and scientific workflows. Her current UdK work focuses on four interconnected domains: performing materials for expressive textile/fashion design; multi-modal sensing converting visual processes into haptic/audible experiences; micro-to-macro material design spanning nanostructures to final products; and transdisciplinary processes for technology-art-science collaboration. Analysis of her 2020-2025 publications reveals dominant trends in sustainable textile electronics, with emphasis on knitted/folded sensor structures, origami-inspired capacitive shape estimation, and social sustainability in e-textile communities. Her work uniquely bridges fundamental material science (e.g., textile metamaterials) with artistic applications (e.g., interactive orchestra garments) and industrial production challenges. Berit Greinke supervises PhD students including Giorgia Petri. Her junior professorship is co-financed by SAP under a public-private partnership model, supporting projects like WEAR (Wearable technologists engage with artists for responsible innovation) and STELEC (Sustainable Textile Electronics), which emphasize ethical co-design and industry-academia collaboration. She leads research within UdK's Institute for Product and Process Design and collaborates with the Design Research Lab (formerly part of Connected Textiles group), focusing on sustainable industrial production of electronic clothing and transdisciplinary innovation frameworks.
Andrea Wollensak is Professor of Art at Connecticut College, where she has taught since 1993. She is an interdisciplinary artist and designer whose work explores the convergence of place, identity, and history through site-based artworks that combine new media technology with traditional design and fabrication. Her educational background includes a B.F.A. from the University of Michigan and an M.F.A. from Yale University. She has held visiting faculty positions at numerous institutions including the University of Gothenburg, Nova Scotia College of Art and Design, Concordia University, Rhode Island School of Design, and the Estonian Academy of Arts. Wollensak's research focuses on social design and place-based storytelling, employing collaboration with community partners, computer scientists, musicians, poets, and scientists. Her work addresses themes of place-based narratives, environment, and memory through forms including audio/video, interactive installations, data visualization, artist books, and digitally fabricated works. She examines how design can be used as a tool for social impact and civic participation. Her most recent projects include Mapping Your Journey to School (2023), which engages Estonian and Ukrainian refugee students in creative writing workshops; Water Stories: Visual Poetics and Collective Voices (2022), a year-long artist residency at the Anchorage Museum; and Open Waters (2019-2020), an interactive installation examining Arctic environmental change. U.S. Fulbright Scholar in Estonia (2023) Artistic Excellence Award in Digital Art from Connecticut Office of the Arts Artist residency at Hafnarborg Contemporary Art Museum in Iceland (2011) Connecticut Commission on Culture and Tourism Grants in New Media (2010, 2006) IASPIS International Artist Studio Program in Sweden (2007) Rockefeller Study and Conference Center residency (2000) Wollensak has advised numerous students in design and new media projects, often collaborating with community partners. She served as Director of the Ammerman Center for Arts and Technology at Connecticut College from 2014-2020 and currently serves on the Advisory Council of the Winterhouse Institute and the Advisory Board for Digital Media Connecticut. Her teaching emphasizes design as an interdisciplinary practice that prepares students to be creative problem solvers who can use design to create positive social impact. Her work has been exhibited internationally at venues including the Göteborg International Contemporary Art Biennial, Nova Scotia Art Gallery, Brno Design Biennial Moravian Gallery, Center for Visual Art in Denver, Brown University's Granoff Center Gallery, and the Burchfield Penney Arts Center in Buffalo.
Dimitris Samaras is a SUNY Empire Innovation Professor in the Department of Computer Science at Stony Brook University, affiliated with the College of Engineering and Applied Sciences. He leads the Computer Vision Lab and holds adjunct roles in Biomedical Informatics and Radiology. His research focuses on computer vision, machine learning, medical imaging, and computational behavioral sciences, with interdisciplinary collaborations in psychology and neuroscience. Education: Ph.D. in Computer Science (University of Pennsylvania, 2001), M.S. in Computer Science (Northeastern University, 1994), Diploma in Computer Engineering (University of Patras, Greece, 1992). Research Interests: Modeling 3D shape and illumination interactions, facial expression analysis, medical image analysis, and applying machine learning to brain imaging. Current funded projects include NIH/NIDA grants, NSF initiatives, and collaborations with institutions like Brookhaven National Lab and Adobe. Publications: Over 150 peer-reviewed papers in top venues like ICCV, CVPR, and MICCAI, with impactful work on shadow removal, face relighting, and digital pathology. Recent trends emphasize medical AI, generative models, and multimodal interactions. Awards: SUNY Chancellor’s Award (2018), Dean’s Millionaire’s Club (2016), and multiple NIH/NSF grants. Recognized for contributions to scholarship and creative activities in academia. Grants & Teams: Leads over $10M in active grants, including projects on AI for penguin population tracking, histopathology image analysis, and robotic assistance. Collaborates with interdisciplinary teams in medicine, engineering, and cognitive science. Labs & Initiatives: Directs the Computer Vision Lab, contributes to the ColdSteel/NSF CVDI-NY SPIR consortium, and co-leads the Sensor and Transportation Security Center with Farmingdale State College.
Robert S. Allison is a Professor in the Department of Electrical Engineering & Computer Science at York University's Lassonde School of Engineering. His research focuses on human perceptual responses in virtual environments, stereoscopic vision, and eye movement analysis. He is affiliated with the York Centre for Vision Research, Sensorium (Digital Arts & Technology), and the Centre for Innovation in Computing at Lassonde. His research interests include depth perception in natural and virtual environments, human-computer interface design for VR, machine vision applications, and the measurement of human motion. He has supervised multiple graduate students and contributed to over 260 publications. His work spans topics like cybersickness mitigation, display lag effects, and perceptual adaptation in VR. Key grants include NSERC-funded projects on perception in virtual environments and collaborations with institutions like the Australian Research Council. His teaching includes courses on human perception in human-computer interaction and digital logic design. Recent articles highlight advancements in understanding motion perception, VR-induced sickness, and multisensory integration. He collaborates widely, with affiliations including the VISTA program and York's Connected Minds initiative.
Prof. Vesa Välimäki is an Audio Signal Processing Professor at Aalto University's School of Electrical Engineering, leading the Audio Signal Processing Research Group within the Aalto Acoustics Lab. He also serves as Vice Dean for Research and Head of the Doctoral Programme at the university. His research focuses on digital signal processing, machine learning, and their applications in audio, acoustics, and music technology, particularly in artificial reverberation, audio filter design, and virtual analog modeling. He has pioneered techniques like velvet noise for reverberation synthesis and contributed to open-source tools like FLAMO. His academic accolades include IEEE, AES, and AAIA Fellowships, along with multiple best paper awards at venues like DAFx and ICASSP. He has advised numerous students, including recipients of prestigious awards like the Huawei Master's Thesis Award. Prof. Välimäki has held editorial roles at the Journal of the Audio Engineering Society and organized major conferences such as SMC-17. His work extends to applied projects like acoustic optimization for early childhood education facilities and immersive audio in virtual reality (e.g., the 'Space Walk' project). Key Projects: NordicSMC (Nordic University Hub for Sound and Music Computing), Aalto Acoustics Lab, FLAMO library Grants: NordForsk funding (2018–2023), Foundation for Aalto University Science and Technology His research spans both theoretical advancements (e.g., diffusion models for audio restoration) and practical implementations (e.g., real-time equalizers, headphone compensation systems). He collaborates internationally, contributing to acoustic measurement techniques and noise reduction strategies for diverse environments.
Professor David Taubman is a distinguished academic serving as Professor and Deputy Head of School (Research) at the School of Electrical Engineering and Telecommunications (EE&T) at the University of New South Wales (UNSW) in Sydney, Australia. He is also co-director of Kakadu Software Pty. Ltd. and its affiliates Kakadu R&D and Kakadu GPU. With a career spanning over three decades, Professor Taubman has made significant contributions to the field of image and video compression, most notably as the author of the EBCOT coding algorithm adopted in the JPEG2000 international standard. Professor Taubman earned his B.Sc. in Mathematics and Computer Science (1986) and B.E. (Medal) in Electrical Engineering (1988) from the University of Sydney, followed by an M.Sc. (1992) and Ph.D. (1994) in Electrical Engineering from the University of California at Berkeley. His professional journey includes engineering work at the Electricity Commission of N.S.W. (1988-1990), research positions at Hewlett-Packard Laboratories in Palo Alto (1994-1998), and an academic career at UNSW where he progressed from Senior Lecturer (1998-2003) to Associate Professor (2004-2009) and finally to Professor (2009-present). He has held various leadership roles including Head of the EE&T Telecommunications Research Group (2003-2014), Head of the EE&T Signal Processing Research Group (2014-present), Director of Research for the School of EE&T (2011-2016), and Deputy Head of School (Research) since 2017. Professor Taubman's research interests center on image and video compression, with particular expertise in JPEG2000 standards and implementations. His work spans signal processing, wavelet transforms, scalable video coding, motion modeling, and multimedia systems. He has pioneered numerous compression algorithms and frameworks, including the EBCOT coding algorithm that became central to the JPEG2000 standard. His recent research focuses on efficient motion modeling with cuboidal partitioning, learned lifting-based transform structures, and high-throughput implementations of JPEG2000 for video applications. His work bridges theoretical foundations with practical implementations, as evidenced by the commercially successful Kakadu Software tools that have garnered around 500 commercial licensees. Analysis of Professor Taubman's recent publications reveals a consistent focus on advancing compression technologies with particular emphasis on scalability, efficiency, and adaptability. His work spans traditional image compression (JPEG2000 extensions), video coding (cuboid-based partitioning for UHD/360-degree video), and emerging applications (nanopore sequencing data compression). A notable trend is the integration of machine learning techniques with traditional compression frameworks, as seen in his work on learned lifting-based transform structures. His research maintains strong connections to real-world applications across diverse domains including medical imaging, astronomical data processing, and genomic sequencing. IEEE Fellow Engineers Australia Fellow (by invitation) Professor Taubman has served as Associate Editor for the IEEE Transactions on Image Processing for two four-year appointments (2003-2005 and 2010-2013). He has been actively involved in numerous research grants focused on image and video compression technologies, particularly those related to the JPEG2000 standard and its extensions. His work has received significant industry support, reflected in his consultancy with various U.S., Japanese, and Australian corporations. He has also contributed to international standards development as a member of Standards Australia Technical Committee MS-065 (mirroring ISO TC42 on Digital Photography) and as a constitutional member of Standards Australia Technical Committee IT-029 (Coded Representation of Picture, Audio and Multimedia/Hypermedia Information). Professor Taubman co-directs Kakadu Software Pty. Ltd. and its research affiliates Kakadu R&D and Kakadu GPU, which have developed the commercially successful Kakadu Software tools for JPEG2000. His research group at UNSW focuses on advanced image and video compression techniques, with particular expertise in wavelet-based methods, scalable coding, and motion modeling. The group maintains strong industry connections and has contributed significantly to the development and standardization of image compression technologies worldwide.
Steven Livingstone is an Associate Professor in the Department of Computer Science at Ontario Tech University, Faculty of Science. His research focuses on affective data science, applying machine learning and statistical modeling to understand emotion and its disorders. He leads the Affective Data Science Lab (ADSL), specializing in emotion recognition technologies using physiological data like EEG and motion capture. Livingstone holds a PhD from The University of Queensland (2008) and has published over 70 peer-reviewed papers, with over 2,400 citations. His RAVDESS dataset is widely used in speech emotion recognition research. His research interests span data science, affective computing, and music's role in emotion. Recent work emphasizes data provisioning for deep learning applications. Livingstone teaches courses including Scientific Data Analysis and Information Visualization. He actively mentors undergraduate and graduate students in his lab, focusing on research assistantships in emotion technology development. Key contributions include studies on musical tempo’s physiological effects, Parkinson’s disease facial mimicry deficits, and ensemble performance dynamics. His work has been featured in The Atlantic, NBC Today, and on the cover of Informatik Spektrum. The ADSL lab collaborates on projects combining data analytics with human-computer interaction to advance emotion-aware systems.
Henrik von Coler is an Assistant Professor at Georgia Institute of Technology's School of Music within the College of Design. His work bridges engineering, electronic music, and empirical research, focusing on spatial audio systems, live electronics, and human-computer interaction in musical contexts. He joined Georgia Tech in 2023 after serving as director of the TU Studio for Electronic Music at Technische Universität Berlin from 2015 to 2023, where he founded the Electronic Orchestra Charlottenburg (EOC) to explore live electronic ensembles in multichannel environments. His research emphasizes the integration of sound, space, and HCI to enhance compositional and performative expressiveness. Notable projects include immersive audio installations, virtual instrument design, and AI-assisted composition. Coler has performed globally on immersive audio systems and curated international concerts. His technical contributions span spatial sound synthesis algorithms, networked music systems, and real-time signal processing tools. Key areas of exploration include volumetric music performances in metaverse environments, hybrid spatial interaction via ARCube, and statistical models for spectral synthesis. He has developed open-source systems like Orchestra (for metaverse performances) and SPRAWL (for ensemble interaction). Coler’s work often combines empirical research with artistic practice, aiming to redefine the boundaries of live electronic music through technological innovation. His academic output spans over 30 peer-reviewed articles since 2011, with recent focus on metaverse applications, AI-human collaboration, and immersive audio design. While no formal awards are listed, his leadership roles and project outcomes highlight significant contributions to music technology and spatial audio engineering.
Dr. Sam Ferguson is a Senior Lecturer at the School of Computer Science, University of Technology Sydney (UTS), with a multidisciplinary background in music performance, cognitive science, and psycho-acoustics. His research explores the intersection of sound, music, and human experience through creative coding, machine learning, and interactive systems. Key Research Areas: Sound and Music Computing, Human-Computer Interaction, Creative Coding, Cognitive Science, Installation Art, and Acoustics. Current Projects: ARC Linkage project on creative coding and multiplicitous media; industry collaborations on IoT-based audiovisual systems. Recent Publications: Focus on spatial audio complexity, gestural interaction with networked sound, music emotion recognition frameworks, and robotic performance through genre-based cultural platforms. Leadership Roles: Director of Teaching & Learning Engagement; former Deputy Head of School (Teaching and Learning); active in ACM Creativity and Cognition Steering Committee. Teaching: Courses like Digital Media Studio , Prototyping Physical Interaction , and Data Processing using R within UTS's interdisciplinary Software Development Studio.
Dr. Lucie Kruse is a researcher at the Department of Informatics, University of Hamburg, specializing in Human-Computer Interaction (HCI) and Virtual Reality (VR). Her work focuses on immersive user interfaces for cognitive and physical training, particularly for older adults and those with dementia. She has been an active member of the University of Hamburg's HCI group since 2018 and served on the Ethics Commission since 2023. Her research interests include: Virtual Reality Exergames Serious Games Assistive Technologies Accessibility in VR Mental Health Applications Her publications from 2021-2025 demonstrate expertise in designing VR systems for healthcare, analyzing age-related interaction patterns, and developing inclusive interfaces. She has received multiple awards including the 2024 Honorable Mention for Best Poster at ACM SUI and the 2023 Honorable Mention at ACM CHI. Scientific Awards: Honorable Mention for Best Poster Award at ACM SUI (2024) Runner-Up Prize at Metaverse for the Good (2024) Honorable Mention at ACM CHI'23 Interactive Demo (2023) Honorable Mention at ACM VRST (2021) She has supervised multiple theses on topics like AI agents for mental health, accessibility of chatbots for seniors, and VR exergame design. Her work spans collaborations with institutions like HITLab NZ and Western Sydney University's MARCS Institute.
