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John WhynotView profile

Associate Professor

• Music Production
• Audio Engineering
• Sound Mixing
• +6 more

John Whynot is an Associate Professor in the Music Production and Engineering (MP&E) department at Berklee College of Music, where he actively teaches and mentors students in audio engineering, music production, and sound mixing. He integrates decades of professional experience into his pedagogy, emphasizing hands-on learning, artistic inquiry, and technical mastery. His research and professional interests span Music Production , Audio Engineering , Sound Mixing , Film Scoring , and Recording Technology . He advocates for a holistic understanding of music creation, asking students to reflect on the purpose and audience of their recordings. His teaching blends storytelling, theory, and live demonstrations to make technical concepts engaging and practical. The 15 most recent articles reflect a consistent focus on high-profile film and album projects, particularly in sound mixing and audio engineering . These works demonstrate expertise in cinematic sound design, narrative audio, and hybrid analog-digital production. Subfields such as score-to-picture synchronization , spatial audio engineering , and acoustic clarity in vocal-centric albums recur, indicating a deep engagement with both technical precision and emotional impact in sound. Grammy-winning work on 'Timeless: A Hank Williams Tribute' by Lucinda Williams John Whynot has advised numerous artists in professional settings and contributed to major recording projects, though formal student advisees are not listed. He has been involved in grants and collaborations through film and album productions. He leads no formal lab but operates as a core faculty member in Berklee’s MP&E program, influencing curriculum and student development through direct instruction and industry integration.

Steven FentonView profile

Senior Lecturer

• Low Power Systems
• Health Innovation
• Remote Health/Wound Monitoring
• +6 more

Steven Fenton is a Senior Lecturer and Subject Area Leader in Engineering at the Department of Engineering & Technology, School of Computing and Engineering, University of Huddersfield. He holds a 1st Class Honours degree in Electronic & Information Engineering from the University of Huddersfield and has extensive industry experience in audio engineering, DSP, and embedded systems design. Current research focuses on low power systems, remote health monitoring, audio signal processing, and immersive audio technology. Active member of the Audio Engineering Society and Centre for Audio and Psychoacoustic Engineering. His work spans audio quality measurement , dynamic range optimization , and assistive technologies for the visually impaired. Recent publications highlight innovations in immersive audio mixing and ultra-low energy distributed monitoring systems. Scientific contributions include a Fellow of the Higher Education Academy and an h-index of 67. Available for PhD supervision in engineering and audio technology domains.

• Geometric Deep Learning
• Machine Learning
• Data Science
• +11 more

Michael A. Perlmutter is an Assistant Professor in the Department of Mathematics at Boise State University and an affiliate faculty member in the Computing Ph.D. program. His research bridges theoretical and applied aspects of machine learning, data science, and inverse problems, with interdisciplinary applications in biomedicine and environmental systems. Research Interests: His work primarily focuses on Geometric Deep Learning , particularly neural network models for non-Euclidean data such as graphs and manifolds. This includes theoretical development of the geometric scattering transform and designing deep learning architectures for signed and directed graphs. He also investigates Phase Retrieval in ptychographic imaging, emphasizing robust, efficient algorithms with provable guarantees. His applied interests span AI-aided drug discovery, analysis of metabolic networks, single-cell data prediction, tensor compression, audio denoising, and data science for ecological and hydrological systems. Scientific Awards: Advising and Grants: While specific students and grants are not listed, his active research program and Ph.D. affiliation suggest involvement in mentoring graduate students and pursuit of external funding, particularly in interdisciplinary data science and machine learning. His career trajectory includes multiple competitive postdoctoral appointments, indicating a strong research profile. Labs and Teams: Though not explicitly named, his work implies collaboration with interdisciplinary teams in computational mathematics, biomedical data science, and environmental modeling, possibly within or affiliated with Boise State's data science and computing initiatives.

• Multimedia Systems
• Image Processing
• Signal Processing
• +4 more

Luis Torres Urgell is a Professor at the Department of Signal Theory and Communications, Universitat Politècnica de Catalunya (UPC), affiliated with the Higher Technical School of Telecommunication Engineering of Barcelona. His research focuses on multimedia systems, image processing, and signal processing with notable contributions to video compression, audio-visual indexing, and computer vision applications. He has been actively involved in numerous competitive research projects, including initiatives on genomic data compression and multimedia security. His work spans over 410 academic activities, including over 140 conference presentations and 78 scientific documents. Notable contributions include advancements in face recognition algorithms, distributed video coding, and the development of tools for automated video summarization in sports content. Torres has also made significant strides in education through project-based learning in telecommunications and the integration of virtual ethnography in social web platforms. He has been recognized as a Senior Member of IEEE and received institutional recognition from UPC for his research contributions. His research has been supported by grants from the Spanish and Catalan governments, including projects under the RIS3CAT strategy and the National Plan for Scientific Research.

Huacheng ZengView profile

Associate Professor

• Computer Networking
• Wireless Communication Systems
• Sensing Technologies
• +3 more

Huacheng Zeng is an Associate Professor in the Department of Computer Science and Engineering (CSE) at Michigan State University (MSU), part of the College of Engineering. His research focuses on computer networking, wireless communication systems, and sensing technologies with applications in IoT security, signal processing, and machine learning. He received his Ph.D. in Computer Engineering from Virginia Tech in 2015 and was awarded the NSF CAREER Award in 2019. Dr. Zeng’s work spans innovative areas such as radar-based human motion tracking (e.g., RadEye), acoustic emotion decoding, and mmWave network optimization. His recent publications address challenges in device localization, vehicular communication, and secure RFID systems. His research often integrates machine learning techniques with traditional signal processing to enhance system performance and security. Education: Ph.D., Computer Engineering, Virginia Tech (2015) Awards: NSF CAREER Award (2019) His contributions to interference management, jamming-resilient communications, and distributed inference frameworks have advanced both theoretical and applied aspects of wireless networks. While no specific grants or advising details are listed, his extensive publication record reflects active collaboration in cutting-edge research domains.

Sri KrishnanView profile

Professor

• Biomedical Signal Analysis
• Wearables/Textile Computing
• Assistive Technology
• +5 more

Dr. Sri Krishnan is a Professor and Dean at the Department of Electrical, Computer, and Biomedical Engineering at Toronto Metropolitan University. With a focus on interdisciplinary research, he bridges electrical engineering, computer science, and biomedical applications. His work emphasizes mathematical signal analysis for healthcare solutions. PhD, University of Calgary (1999) MSc, University of Calgary (1996) BE, College of Engineering, Guindy, Anna University (1993) Research Interests: Dr. Krishnan specializes in biomedical signal analysis, affective computing, and compressive sensing. He develops wearable technologies and machine learning algorithms for applications in mental health, cardiovascular diagnostics, and neurological disorders. Scientific Contributions: His recent publications highlight advancements in fetal ECG separation, seizure detection via EMD dictionaries, acoustic scene classification using local binary patterns, compressive sensing in gait signals, and wavelet-based gait diagnostics. Fellow, Canadian Academy of Engineering Outstanding Canadian Biomedical Engineer Award Canada Research Chair in Biomedical Signal Analysis Sarwan Sahota Distinguished Scholar Award Laboratory: He leads the Signal Analysis Research Group, fostering innovation in time-frequency theory and non-stationary signal processing.

• Natural Language Processing
• Speech Recognition
• Machine Learning
• +5 more

Dietrich Klakow is a prominent researcher at Saarland University in Saarbrücken, Germany, with an extensive publication record spanning from 1997 to 2025. His work primarily focuses on natural language processing, speech recognition, and machine learning with significant contributions to multilingual models and African language processing. His research interests span a wide range of topics within computational linguistics and artificial intelligence. Klakow has made substantial contributions to Natural Language Processing , particularly in multilingual contexts and low-resource languages. His work on African language technologies has been particularly impactful, developing resources and models for languages that are often neglected in mainstream NLP research. He has also conducted significant research in speech recognition , transformer models , and computational linguistics , with a focus on practical applications and theoretical foundations. Klakow's recent publications demonstrate a strong focus on large language models, their capabilities, limitations, and applications across diverse linguistic contexts. His work spans both theoretical investigations of model architectures and practical applications addressing real-world challenges in language technology. His collaborative work spans numerous international partnerships, particularly with researchers working on African language technologies and multilingual NLP systems.

• Computational Biology
• Animal Behavior
• Bioacoustics
• +3 more

Grace Smith Vidaurre is an Assistant Professor in the Department of Computational Mathematics, Science and Engineering at Michigan State University, affiliated with the College of Natural Science. Her research integrates computational methods with ecological and behavioral studies of birds and other animals. Michigan State University, College of Natural Science, Department of Computational Mathematics, Science and Engineering Research Focus: Computational biology and animal behavior, with emphasis on: Bioacoustic analysis of avian communication Machine learning applications in sound event detection Genetic and ecological dynamics of invasive species Behavioral responses to environmental stressors Development of open-source acoustic analysis tools Interdisciplinary approaches to conservation biology Article Trends: Recent work combines deep learning with ecological datasets to analyze animal vocalizations, track behavioral patterns, and develop software for automated acoustic monitoring. Earlier studies focused on population genetics, invasive species dynamics, and multimodal communication in birds. Technological Contributions: She has developed two R packages (warbleR and ohun) to advance computational methods in bioacoustic research.

• Video Quality
• Video Streaming
• Immersive Media
• +7 more

Anthony Adeyemi-Ejeye is an Associate Professor in the Department of Music and Media at the Faculty of Arts, Business and Social Sciences, University of Surrey. His research focuses on technical and creative aspects of video quality, streaming technologies, immersive media experiences, and video compression methodologies. Key research areas include: Development of immersive media systems Video quality assessment and optimization Streaming protocol innovations Advanced video compression techniques

Aude OlivaView profile

Researcher

• Computer Vision
• Deep Learning
• Generative Models
• +5 more

Aude Oliva serves as MIT director of the MIT-IBM Watson AI Lab and director of strategic industry engagement at the MIT Schwarzman College of Computing. As a Senior Research Scientist at MIT CSAIL, she leads the Computational Perception and Cognition group, driving interdisciplinary research at the intersection of human intelligence and artificial systems. Her roles position her at the forefront of translating academic AI research into real-world applications through major industry partnerships. Dr. Oliva earned her MS and PhD in cognitive science from Institut National Polytechnique de Grenoble, France, establishing her foundation in human perception and computational modeling. Her research integrates computer vision, deep learning, and cognitive neuroscience to understand visual information processing in both biological and artificial systems. She develops computational models that mimic human visual recognition while creating AI systems capable of compositional reasoning and efficient video understanding. Current work emphasizes neuroscience-inspired architectures, resource-efficient deep learning, and multimodal representation learning, with applications spanning healthcare, robotics, and human-computer interaction. Her cross-disciplinary approach uniquely bridges theoretical neuroscience with practical AI development. Analysis of recent publications reveals a clear trajectory toward tighter integration of neuroscience and AI, particularly through brain imaging datasets like BOLD Moments. Her group consistently advances efficient deep learning techniques (Trans-LoRA, VA-RED²) while exploring fundamental questions in visual cognition through projects like the Algonauts Challenge. The work demonstrates increasing industry relevance with strong representation in NeurIPS and Nature Communications. Her major recognitions include: NSF Career Award in computational neuroscience Guggenheim fellowship in computer science Vannevar Bush Faculty Fellowship in cognitive neuroscience As director of the $240M MIT-IBM Watson AI Lab, Dr. Oliva oversees substantial research funding while advising graduate students through MIT's EECS department. Her lab benefits from unique industry-academic synergy, with students gaining access to IBM resources and real-world deployment challenges. The collaborative environment fosters innovation in efficient AI systems with tangible societal impact. The Computational Perception and Cognition group operates as a dynamic hub where computer scientists, neuroscientists, and cognitive scientists collaborate on fundamental questions of intelligence. Current projects focus on making AI systems more human-like in visual reasoning while ensuring computational efficiency for real-world deployment, leveraging the unique resources of the MIT-IBM partnership.

Chuang GanView profile

Assistant Professor

• Computer Vision
• Graph Deep Learning
• Multimodal Learning
• +5 more

Chuang Gan is a distinguished researcher holding dual positions as a Principal Research Staff Member at the MIT-IBM Watson AI Lab and an Assistant Professor at the University of Massachusetts Amherst. His work bridges academic research and industrial applications in artificial intelligence, with particular focus on advancing the frontiers of computer vision and multimodal learning systems. Dr. Gan's research interests span multiple interconnected domains within artificial intelligence. He specializes in video understanding, with deep expertise in representation learning, neural-symbolic visual reasoning, audio-visual scene analysis, and embodied intelligence. His work frequently integrates graph deep learning techniques with neuro-symbolic approaches to create more interpretable and robust AI systems. The recurring themes across his research portfolio include developing models that can understand physical dynamics from visual inputs, creating systems capable of embodied reasoning, and building bridges between symbolic and neural approaches to artificial intelligence. His publications reveal a strong trend toward increasingly sophisticated multimodal systems that integrate visual, auditory, and linguistic information. Over time, his work has evolved from basic video understanding tasks to complex embodied reasoning systems capable of physical simulation, 3D scene understanding, and multi-agent collaboration. A notable pattern is the progression from analyzing static scenes to understanding dynamic physical interactions and embodied agent behaviors in increasingly complex environments. Microsoft Fellowship Baidu Fellowship Dr. Gan's research has received significant recognition from major technology companies through prestigious fellowships and has been widely covered by leading media outlets including CNN, BBC, The New York Times, WIRED, Forbes, and MIT Tech Review. His work at the MIT-IBM Watson AI Lab provides him with access to substantial resources for cutting-edge AI research, while his academic position enables him to train the next generation of AI researchers. His collaborations with prominent researchers like Antonio Torralba demonstrate his integration within the top echelons of the computer vision and AI research community. At the MIT-IBM Watson AI Lab, Dr. Gan leads research initiatives focused on advancing video understanding and embodied intelligence. His work contributes to the lab's mission of developing AI systems that can perceive, reason about, and interact with the physical world in more human-like ways. His research group likely focuses on developing novel architectures for multimodal learning, creating benchmarks for physical reasoning, and building systems that can transfer knowledge between simulation and real-world environments.

Bo WuView profile

Researcher

• Computer Vision
• Multimodal Learning
• Natural Language Processing
• +5 more

Bo Wu is a Researcher at the MIT-IBM Watson AI Lab in Cambridge, MA, where he conducts pioneering research in deep learning, computer vision, natural language processing, and multimodal learning. Previously, he served as a postdoctoral research scientist at Columbia University after completing his Ph.D. at the Chinese Academy of Sciences (CAS) in Beijing, with additional research experience at Microsoft Research Asia (MSRA) and Academia Sinica. His academic foundation includes: Ph.D. in Computer Science, Chinese Academy of Sciences (CAS) Research internships at Microsoft Research Asia and Academia Sinica Wu's research focuses on advancing situated reasoning in real-world contexts, integrating neuro-symbolic approaches with deep learning for enhanced interpretability. His work spans video question answering, temporal forecasting, and multimodal understanding, with applications in social media prediction, enterprise AI, and personalized dialogue systems. He emphasizes bridging symbolic reasoning with neural networks to develop robust systems capable of handling open-world knowledge and dynamic environments. Analysis of his recent publications reveals three dominant trends: the creation of novel benchmarks for situated video reasoning (STAR, SOK-Bench), development of efficient multimodal architectures for enterprise applications (Granite Vision), and personalization techniques for language models. His research consistently merges computer vision with linguistic understanding while addressing practical constraints like real-time processing and model compression, demonstrating strong industry-academia translation. His scientific excellence is evidenced by prestigious recognitions including: IBM Master Inventor Award (2023) IBM Research Level-A Accomplishment Award (2021) ACL Best Demo Paper Award (2020) ICIP Prediction Challenge Champion (2020) Alibaba Global Vision AI Challenge Top 3 (2018) NIST TAC SM-KBP Top 1 (2019) Wu actively mentors emerging talent, currently recruiting students for vision-language projects. He provides significant academic service as Area Chair for ACM Multimedia, Senior Program Committee Member for AAAI and IJCAI, and organizer of the SMP Challenge at ACM Multimedia since 2017. His leadership extends to CVPR workshops on Multimodal Foundations Models (MMFM) and Multimodal Video Content Understanding (MVCS), while serving on program committees for NeurIPS, CVPR, ACL, and other top-tier conferences. As a core member of the MIT-IBM Watson AI Lab, Wu operates within a unique industry-academia ecosystem that fosters rapid translation of fundamental research into practical applications. His collaborative work with Chuang Gan and other researchers leverages IBM's computational resources and MIT's academic rigor, positioning him at the forefront of enterprise AI innovation where theoretical advances directly address real-world business challenges.

• Automating Design and Diagnosis of Information Systems
• Computer Graphics
• Web Technologies

Konstantin Nikolaevich Kasyan serves as Associate Professor at the Department of Computer Systems and Networks within the Faculty of Computer Science and Technologies at Zaporizhzhia National Technical University, where he has maintained academic activity since 1998. He graduated with honors from Zaporizhzhia Machine-Building Institute's Faculty of Electronic Engineering in 1993, specializing in Radio Engineering with qualification as Radio Engineer. His Candidate of Sciences degree (defended 1998/1999) established his expertise in diagnostic methodologies for electronic systems. His research spans three core domains: automating design and diagnosis of information systems, computer graphics methodologies, and web technologies. This interdisciplinary focus manifests in practical applications ranging from hardware diagnostics to smart home systems. His work demonstrates consistent evolution from foundational electronics reliability research in the 1990s toward contemporary IoT and computer vision applications. His publication trajectory reveals distinct chronological phases: 1990s-2000s concentrated on electro-radio diagnostics and reliability engineering; 2000s-2010s expanded into computer graphics and text recognition; while 2010s-2021 shifted toward IoT integration, smart home technologies, and machine learning applications. This progression reflects both technological advancements and his adaptability across computing subfields. Kasyan teaches modern Internet technologies, computer graphics, computer systems design, and computational methods in scientific research, directly translating his research into pedagogical practice. His academic presence is maintained through Scopus, Web of Science, Google Scholar, and ORCID profiles, indicating active scholarly engagement despite the absence of formal awards or documented grants in available records.

Oluwaseun Priscilla OLAWALEView profile

Lecturer

• Software Engineering
• Artificial Intelligence
• Machine Learning
• +5 more

Oluwaseun Priscilla Olawale is a Lecturer in Software Engineering at Near East University (Nicosia, Cyprus), where she is concurrently pursuing her master's degree in Software Engineering. She previously served as a teaching assistant at the same institution. Her academic background includes a Bachelor's degree in Computer Science from Bowen University, Nigeria, complemented by professional experience as an IT Specialist at the Standards Organisation of Nigeria and Software Developer at Royal Niger Company. Research Focus: Her work centers on developing reliable software solutions with emphasis on artificial intelligence applications. Primary domains include healthcare technology (medical imaging, remote healthcare, IoHT security), industrial systems (Industry 4.0 automation), cybersecurity (blockchain integration, anomaly detection), and educational technology (post-pandemic learning environments). She demonstrates consistent interest in interdisciplinary AI implementations bridging theoretical models and practical challenges. Publication Trends: Recent articles (2020-2025) reflect a progression from foundational surveys in data mining and blockchain verification toward applied AI solutions. Emerging themes include disaster prediction systems, audio-visual signal processing, and security-enhanced healthcare networks. Her technical approach frequently employs deep learning architectures (CNNs, VGG-16), blockchain frameworks, and signal processing techniques to address real-world problems across diverse sectors.

Wen HuView profile

Professor

• Cyber-physical systems
• Internet of Things
• Sensor network systems
• +3 more

Professor Wen Hu is a distinguished academic at the School of Computer Science and Engineering at the University of New South Wales (UNSW), where he holds a professorship focusing on cutting-edge research in cyber-physical systems and the Internet of Things (IoT). With extensive experience in both academia and industry, Professor Hu has established himself as a leading researcher in sensor network systems, low-power communications, security, and compressive sensing. His work bridges theoretical research with practical applications, as evidenced by his active commercialization efforts through roles as Chief Technology Officer at Parking Spotz and former Chief Scientist at WBS Tech. Professor Hu's research interests span multiple domains within cyber-physical systems and IoT. His work particularly focuses on low-power wireless communications , sensor network security , compressive sensing techniques , and novel applications of IoT technologies . His research group explores how these technologies can be applied to solve real-world problems in areas such as smart buildings, environmental monitoring, and human-computer interaction. The research integrates hardware design, communication protocols, security mechanisms, and data analytics to create efficient and robust IoT systems. Professor Hu's publication record demonstrates a consistent focus on advancing the state-of-the-art in wireless sensor networks and IoT. His recent work shows increasing integration of machine learning techniques with traditional sensor systems, exploring applications in areas ranging from healthcare monitoring to agricultural technology. There's a clear trend toward more sophisticated signal processing approaches, particularly using mmWave technology and visible light communications, while maintaining the core focus on energy efficiency and security that has characterized his earlier work. CSIRO Office of Chief Executive (OCE) Julius Career Award (2012-2015) Professor Hu has successfully secured multiple research grants from the Australian Research Council, CSIRO, and industry partners, enabling his team to pursue ambitious research projects with both theoretical significance and practical applications. His editorial leadership as Editor-in-Chief of ACM TOSN and organizational roles in major conferences like CPS-IoT Week 2020 and ACM/IEEE IPSN 2023 demonstrate his significant contributions to the research community. As a senior member of both ACM and IEEE, he actively mentors students and early-career researchers while fostering collaborations across academia and industry. Professor Hu leads a vibrant research group at UNSW that combines theoretical research with practical implementation. His team works closely with industry partners to ensure research relevance and facilitate technology transfer. The group maintains strong connections with international research communities and participates in collaborative projects that address global challenges in IoT and cyber-physical systems.