Associate Professor Ng Bing Feng leads research in Additive Manufacturing, Aerospace Engineering, and Thermofluids at Nanyang Technological University's School of Mechanical & Aerospace Engineering. As Cluster Director for Smart & Sustainable Building Technologies at NTU's Energy Research Institute, he oversees projects spanning energy-efficient cooling technologies, advanced filtration systems, and bio-inspired materials. His interdisciplinary work integrates advanced manufacturing with environmental engineering, focusing on sustainable solutions for air quality management and thermal regulation. Current research explores radiative cooling technologies, acoustic agglomeration for emissions control, and crashworthy structures via biomimetic design. Professor Ng's publications demonstrate consistent innovation in multi-scale manufacturing and fluid dynamics, with growing emphasis on sustainable urban technologies. Methodologies combine experimental fluid dynamics with computational modeling and AI-driven design. Leadership Roles: Cluster Director, Energy Research Institute @ NTU Principal Investigator for multiple industry collaborations in sustainable materials Research Output: 42+ publications in high-impact journals Multiple patents in filtration technologies and cooling systems
Dhruv Jain is an Assistant Professor in the Computer Science and Engineering Department at the University of Michigan , with affiliations in the School of Information and Michigan Medicine . He leads the Soundability Lab , focusing on transforming hearing into a programmable interface through human-centered AI systems. PhD from University of Washington MS from MIT Media Lab Formerly worked at Microsoft Research, Google, and Apple Research Interests intersect Human-Computer Interaction (HCI) , Audio AI , Accessibility , and Hearing Health . His work develops systems like SoundWatch (smartwatch-based sound awareness) and HomeSound (IoT sound visualization) to expand auditory access for DHH individuals and other domains. Recent Publications (2023–2025) span top venues like CHI , ASSETS , and ICMI , with trends including generative AI for real-time captioning , adaptive soundscapes , and clinical communication tools . SIGCHI Outstanding Dissertation Award (2023) William Chan Memorial Dissertation Award (2023) Best Poster Award at ASSETS 2024 Google Academic Research Award (2024) NIH Grant ($450k, 2023) William Demant Foundation Grant ($740k, 2025) Teaching includes Accessible Computing (undergraduate) and Advanced Accessibility (graduate), alongside global DIY workshops in five countries. The Soundability Lab collaborates with neuroscientists, clinicians, and Deaf scholars to create deployable systems like clinical communication tools now at Michigan Medicine and features integrated into Apple devices.
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.
Takako Fujioka is an Associate Professor of Music at Stanford University, affiliated with the Center for Computer Research in Music and Acoustics (CCRMA). Her research focuses on the neural mechanisms underlying auditory perception, auditory-motor coupling, and music-supported therapy for neurorehabilitation. She holds a Ph.D. in Physiology from the Graduate University for Advanced Studies, Japan, and M.Sc./B.Eng. degrees in Electrical Engineering from Waseda University. Her work combines neurophysiological techniques such as MEG and EEG to study brain plasticity in development, aging, and stroke recovery. Notable contributions include investigating how music influences motor and cognitive recovery in stroke patients, as well as exploring the neural basis of musical perception through rhythmic synchronization and pitch discrimination studies. Supported by awards from the Canadian Institutes of Health Research during her postdoctoral work at the Rotman Research Institute, her research bridges clinical neuroscience and music cognition. Dr. Fujioka’s expertise spans auditory neuroscience, neurorehabilitation, and technology-assisted music therapy. She has pioneered studies on tactile mapping for cochlear implant users and networked music performance systems, emphasizing cross-modal perception and human-technology interaction. Her findings contribute to both theoretical understanding of auditory processing and practical applications in medical and educational settings. Awards: Canadian Institutes of Health Research Awards (postdoctoral phase) Labs/Teams: CCRMA, Stanford Music Perception Laboratory, Rotman Research Institute collaborations Key Themes: Neuroplasticity, Music-Mediated Rehabilitation, Auditory-Motor Integration, Multisensory Processing Her recent work examines aging-related changes in binaural hearing and the role of beta/gamma oscillations in rhythmic processing. She advocates for translational research that connects neural mechanisms with real-world therapeutic interventions.
Dr A. I. Shihab is a Senior Lecturer at Kingston University's Faculty of Engineering, Computing and the Environment, Department of Networks and Digital Media. He teaches programming languages (C++/Java), data structures, web development, and AI/machine learning. His research focuses on affective computing and machine learning applications including: Acoustic event detection in sports environments Audio signal analysis for tennis match modeling Multi-camera visual surveillance systems Medical imaging analysis using fuzzy clustering techniques Publications demonstrate expertise in combining audio/video modalities for sports analytics (tennis rallies, court-shots) and developing Markov models for sound event sequence analysis. Contact: a.shihab@kingston.ac.uk
Rong Zheng is a Professor in the Department of Computing and Software and a member of the School of Biomedical Engineering at McMaster University, Canada. She holds a Tier-1 Canada Research Chair in Mobile Computing and serves as Acting Chair of the Computing and Software department from July to December 2025. She is also an Associate Member of the Electrical & Computer Engineering department. Education: Ph.D. in Computer Science, University of Illinois, Urbana-Champaign, USA Master of Engineering (thesis) in Electrical Engineering, Tsinghua University, Beijing, China Bachelor of Engineering in Electrical Engineering, Tsinghua University, Beijing, China Dr. Zheng's research lies at the intersection of mobile computing, wireless networking, and machine learning, with a strong focus on applications for aging populations. She directs the NSERC Smart Mobility for the Aging Population CREATE program. Her work encompasses sensor development, wireless network design, and mobile data analytics to address real-world challenges in healthcare, mobility, and data center monitoring. She has developed innovative solutions like the MacQuest campus navigation app and has captured first prize in indoor localization competitions. Her recent publications demonstrate a clear trajectory toward applying wireless sensing technologies (particularly acoustic, Wi-Fi, and mmWave) to health monitoring and mobility assessment for older adults. There's a strong emphasis on developing efficient edge computing solutions that can process data in real-time on resource-constrained devices, as exemplified by her TeamNet framework for collaborative inference on the edge. Her work bridges theoretical advances with practical applications that have social impact. Scientific Awards: Tier-1 Canada Research Chair in Mobile Computing US National Science Foundation CAREER Award (2006) Joseph Ip Distinguished Engineering Fellow (2015-2018) Dr. Zheng leads the Wireless System Research Group (WiSeR) at McMaster University, which has secured significant funding including a $1.65M NSERC CREATE grant for smart mobility research for older adults. Her research has been supported by multiple funding agencies including NSERC, NSF, UH GEAR, and DURIP. She actively mentors graduate students and has developed specialized courses including CAS 772 (Mobile Data Analytics) and CAS 781 (Mobility in the Aging Population). The WiSeR group conducts impactful research on communication, networking, and data analytics issues in Cyber Physical Systems, with applications spanning healthcare, smart infrastructure, and data center monitoring. Their work on data center infrastructure monitoring networks has been featured in EurekAlert and Data Center Dynamics, and they've made significant contributions to indoor localization technology.
Mark Lee is an Adjunct Professor in the People Analytics department at NYU’s Tandon School of Engineering, specializing in Technology Management and Innovation. He holds a Ph.D. in Engineering Psychology from Georgia Institute of Technology (1996). Currently, he serves as Head of Research, Analytics, and Business Development at UL ComplianceWire, focusing on pharmaceutical and medical device manufacturing training. His research leverages large datasets to improve healthcare safety through regulatory compliance and best practices. Courses taught include Human Factors Engineering, Workplace Design, and Predictive Analytics. Education: Ph.D. in Engineering Psychology, Georgia Tech (1996) Key Roles: Adjunct Professor, Head of Research at UL ComplianceWire Research Focus: Human Factors, Training Systems Design, Healthcare Compliance His work spans auditory display systems for aviation (e.g., 3D audio cockpit interfaces) and ergonomic design for industrial products. Recent projects emphasize data-driven solutions for regulatory challenges in life sciences. Publications highlight studies on visual search strategies, age-related cognitive performance, and application of signal detection theory in decision-making. He actively collaborates with industry and government entities, exemplified by the FDA-UL Cooperative Research Agreement.
Andrew Ollett is an Associate Professor and Director of Graduate Studies in the Department of South Asian Languages and Civilizations at the University of Chicago , where he has taught since 2019. His research focuses on the literary and intellectual traditions of South and Southeast Asia, particularly through works in Sanskrit, Prakrit, Apabhramsha, and Kannada from the first millennium CE. Co-founder and editor of NESAR (New Explorations in South Asia Research) Director of NEH-funded edition/translation of the Kavirājamārga (earliest Kannada literary manual) Expert in Prakrit and Kannada literary traditions His work explores the 'question of language'—examining how language choice shaped cultural production and change in premodern India. Current projects include a monograph on context-sensitivity in Indian language theories and a study of manuscript technology's impact on early South Asian knowledge communities. He has authored The Mirror of Ornaments (Naples: Unior Press, 2025) and Language of the Snakes (UC Press, 2017), and co-authored Lilavai (Harvard, 2021). Recent publications include interdisciplinary studies on: Sanskrit-Prakrit campū genre reevaluation Pragmatics in Mīmāṁsā interpretive concepts Manuscript literacy's influence on literary forms Historical linguistics of Middle Indic syllable reduction Philosophy of language in Śālikanātha and Kumārila Bhaṭṭa Awarded the NEH 'Scholarly Editions and Translations' grant , he teaches classical South Asian literature courses covering Sanskrit, Prakrit, and Tamil traditions. He maintains digital resources including a Prakrit Digital Texts Project and Mīmāṁsādigdarśinī digital text repository, and actively contributes to open-access scholarship.
Ian Horrocks is a Professor of Computer Science at the University of Oxford and a Fellow of Oriel College. His research focuses on knowledge representation, description logics, automated reasoning, and semantic web technologies. He has held academic positions at the University of Manchester (2003–2007) and served as Chief Scientist at Cerebra Inc. (2001–2006). Horrocks earned his BSc (1st class), MSc, and PhD in Computer Science from the University of Manchester (1981–1997). His work includes foundational contributions to ontology languages (e.g., OWL) and reasoning systems such as HermiT and ELK. He has supervised over twenty doctoral students and postdoctoral researchers. His honors include Fellowships from the Royal Society (2011), ECCAI (2009), and the British Computer Society (2005). He serves as Editor-in-Chief of the Transactions on Graph Data and Knowledge and leads initiatives in semantic web standards and knowledge graph applications. Key Roles: Editor-in-Chief (Journal of Web Semantics), Co-Chair (W3C OWL Working Group) Grants: EPSRC Senior Research Fellowship (2005), numerous international collaborations Labs: Oxford Semantic Technologies, involvement in projects like RDFox and PAGOdA
Professor Peter F. Driessen is a faculty member in the Department of Electrical and Computer Engineering at the University of Victoria, with a cross-appointment in the School of Music. He holds a BSc and PhD from the University of Victoria and is a Professional Engineer (PEng). His research focuses on communication systems, signal processing, control, and interdisciplinary projects in computer music and wireless technologies. Key areas include audio/video signal processing, radio propagation, sound recording, and multimedia systems. He leads the University of Victoria Propagation Laboratory, which explores radio wave propagation and Amateur radio integration with engineering education. His work spans theoretical research and applied projects like ECOSat satellite systems, software-defined radio (SDR), and innovative musical instruments such as the Radio Drum. He supervises undergraduate and graduate projects in these domains through ELEC 499 courses. Notable contributions include the APEGBC Editorial Board Award for Best Paper (2002) and patents in wireless networking and signal processing. His teaching includes courses in signal analysis and electromagnetics, and he collaborates on interdisciplinary programs like the Music/Computer Science degree. Education: BSc in Electrical Engineering, University of Victoria PhD in Electrical Engineering, University of Victoria Research Interests: Audio and video signal processing for music and media Software-defined radio and Amateur radio technologies Satellite communication and ground station development Gesture-based interfaces and musical instrument design Error mitigation in streaming audio/video Optical and microwave-photonic systems Labs & Collaborations: Propagation Laboratory (radio wave research) UVic Experimental Radio Group (Amateur radio club) UVic Satellite Design Team (ECOSat projects) UVic Centre for Aerospace Research Grants & Awards: APEGBC Editorial Board Award (2002) Multiple US patents in wireless systems and signal processing
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 .
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.
Habib Ullah is an Associate Professor in Data Science at the Norwegian University of Life Sciences (NMBU), Norway, where he conducts research at the intersection of computer vision and machine learning. He is affiliated with the Institute of Data Science under the Faculty of Science and Technology. He has previously held academic positions at COMSATS University Islamabad, Pakistan, and the University of Ha'il, Saudi Arabia, and served as a postdoctoral researcher at The Arctic University of Norway. Educational Background: PhD in Information and Communication Technology (Computer Vision), University of Trento, Italy (2011–2015) MSc in Electronics and Computer Engineering, Hanyang University, South Korea (2007–2009) BSc in Computer Systems Engineering, NWFP University of Engineering and Technology, Pakistan (2002–2006) Habib Ullah's research is primarily focused on computer vision and machine learning, with applications in aquaculture, agriculture, and human behavior analysis. He investigates underwater fish feeding sounds using audio classification, develops zero-shot learning models for recognizing unseen classes, and applies deep learning to detect stress in salmon via skin dot patterns. He also explores AI-driven controlled environment agriculture, leveraging sensors and automation for optimal crop growth. His work emphasizes practical AI solutions for real-world challenges in environmental and biological domains. The recent publications highlight a strong trend in leveraging deep learning for zero-shot and semi-supervised learning, particularly in computer vision tasks such as sea ice classification, crowd anomaly detection, and agricultural monitoring. His research spans remote sensing, biomedical signal processing, and human activity recognition, demonstrating interdisciplinary versatility. The keywords reflect a focus on robust feature representation, knowledge transfer, and model generalization. Scientific Awards and Funding: Industrial PhD grant 'Advancing Controlled Environment Agriculture AI' from The Research Council of Norway (Project number 354125, 2 million NOK, 2024) Team member (Coordinator-Participant) in the Battery Cell Assembly Twin (BatCAT) project funded by Horizon Europe (7 mEuro, 2023–2027) Development of an AI-Based Image Analysis System for Monitoring Plant Status (Funding: 1.8 mNOK, starting 2025) Habib Ullah actively supervises PhD projects and contributes to academic service through editorial and organizational roles. He has served as an Associate Editor for IEEE Access, Guest Editor for MDPI Remote Sensing, and Editor of the Springer book Machine Learning Techniques and Sensor Applications for Human Emotion, Activity Recognition, and Support (ML-SHEARS) . He has also been a Track Chair and Program Committee Member for several international conferences, reflecting his leadership in the academic community. His research is supported by significant grants and collaborative projects, indicating strong institutional and international engagement. He is involved in multiple research teams and projects, including the BatCAT project on battery manufacturing and AI applications in controlled environment agriculture with RIFT LABS AS. His lab work integrates deep learning, sensor fusion, and data analytics for environmental and biological monitoring systems.
Suresh Krishna is an Associate Professor in the Department of Physiology at McGill University's Faculty of Medicine. His research focuses on the neurophysiological and computational basis of sensory processing, attention, and eye movements, with applications to brain-machine interfaces and human health. He works with human subjects, non-human primates, and open datasets using in-vivo electrophysiology, eye-tracking, and computational modeling. Research interests include visual attention mechanisms, saccadic eye movement control, neural coding of motion perception, and the interplay between attention and decision-making. His work bridges basic neuroscience with translational applications such as improving neural prosthetics and understanding perceptual disorders. Recent work highlights how neural remapping processes during saccades underlie spatial perception, and how attention modulates neural activity patterns in visual cortex. The lab's publications reveal critical insights into the temporal dynamics of attentional shifts and their neural substrates, particularly in areas MT and MST. Dr. Krishna's team also investigates auditory temporal processing in the inferior colliculus, exploring correlations between neuronal responses to sound modulation. Their findings contribute to understanding how sensory systems encode temporal information across modalities. Research is conducted in the M2B3 Lab (http://m2b3.lab.mcgill.ca), which integrates experimental and computational approaches to study brain mechanisms underlying perception and action. No specific awards are listed, but ongoing work involves major contributions to primate neurophysiology and translational neuroscience.
Michael S. Horn is a Professor of Learning Sciences and Computer Science at Northwestern University, serving as Program Coordinator for Learning Sciences. He holds a PhD in Computer Science from Tufts University and a ScB in Computer Science from Brown University. His research focuses on the intersection of human-computer interaction and learning, emphasizing the use of emerging technologies in museums, classrooms, and informal settings. Notable projects include multi-touch tabletops in natural history museums, tangible programming languages for early education, and the TIDAL Lab’s innovative exhibits like Build-a-Tree and DeepTree. Education background includes: PhD in Computer Science (Tufts University, 2009) MS in Computer Science (Tufts University, 2006) ScB in Computer Science (Brown University, 1997) Research interests span tangible interaction design, computational literacy, and museum-based learning. He has led projects such as the ‘Energy Monsters’ board game and the TunePad music-coding platform. His work integrates technology with pedagogical practices to foster collaborative and exploratory learning environments. Active collaborations include the Computer History Museum and the California Academy of Sciences. Advising contributions include mentoring doctoral students like Dr. Mmachi Obiorah and Pei-Yi. His lab, TIDAL, explores interactive technologies for education, with grants from NSF and industry partnerships. Recent articles highlight innovations in computational thinking integration, music-coding hybrid practices, and AI-driven qualitative analysis.
