جست‌وجوی استاد و پژوهشگر

Michael Inouyeمشاهده پروفایل

استاد

• Systems Genomics
• Cardiovascular Disease
• Population Health
• +۳ مورد دیگر

Professor Michael Inouye is a leading systems genomics and population health researcher at the University of Cambridge (Department of Public Health and Primary Care) and Baker Heart & Diabetes Institute (Melbourne). His work bridges statistical genetics, computational biology, and clinical translation, with a focus on cardiovascular/respiratory diseases and sustainable research practices. Director of Research (Research Professor) since 2021 Honorary Professorial Fellow at University of Melbourne since 2021 Co-leader of Cambridge Baker Systems Genomics Initiative since 2017 Research Interests include: Polygenic risk score development and clinical implementation Multi-omic integration (genomics, proteomics, microbiomics) Host-microbiome interactions in disease Sustainable computational methodologies Cardiometabolic disease prevention Scientific Contributions show leadership in: Developing the Polygenic Score Catalog infrastructure Creating environmentally sustainable algorithmic frameworks Elucidating microbiome contributions to chronic diseases Advancing multi-modal risk prediction models Integrating genetic and metabolic pathway analysis Recent Publications demonstrate expertise across: Polygenic score optimization for blood traits and diabetes Microbiome-disease interaction networks Computational sustainability in biomedical research Cardiovascular risk stratification Multi-omic data harmonization Genetic determinants of molecular phenotypes

• Audio Processing
• Machine Learning
• Sound Event Detection
• +۳ مورد دیگر

Paul Primus is a researcher at the Institute of Computational Perception, Johannes Kepler University Linz, specializing in audio processing and machine learning. His work focuses on sound event detection, acoustic scene classification, and language-based audio retrieval, with significant contributions to the DCASE (Detection and Classification of Acoustic Scenes and Events) challenges. Education: Dr. (PhD) MSc BSc Research Interests: Primus's research bridges audio signal processing and deep learning, addressing real-world challenges in machine listening. His work emphasizes device invariance, data efficiency, and transformer architectures for audio analysis. Key contributions include knowledge distillation for audio retrieval, multi-stage transformer training, and novel approaches to language-audio interaction. He actively explores low-complexity solutions suitable for embedded systems and edge deployment. Publication Trends: Primus's recent work (2023-2025) shows a clear trajectory toward multimodal audio-language systems, leveraging transformers and pretraining techniques. His publications increasingly focus on data efficiency, device generalization, and practical deployment constraints, as evidenced by his DCASE challenge submissions. The integration of metadata and cross-modal alignment represents a growing research emphasis. Activities: Adversarial Robustness in Data Augmentation (2020) Exploiting Parallel Audio Recordings to Enforce Device Invariance in CNN-based Acoustic Scene Classification (2019) Labs and Teams: Primus is a core member of the Institute of Computational Perception at JKU, which leads research in computational audio analysis. The institute maintains strong participation in international challenges like DCASE and collaborates extensively on audio transformer development and language-audio interaction systems.

Ao Duمشاهده پروفایل

استادیار

• Probabilistic Seismic Risk Assessment
• Multi-hazard Resilience of Infrastructure
• Surrogate Modeling
• +۵ مورد دیگر

Ao Du, Ph.D. is an Assistant Professor in Structural Engineering at the Klesse College of Engineering and Integrated Design , The University of Texas at San Antonio . His research focuses on enhancing the resilience of infrastructure systems against natural hazards and aging through advanced modeling and data analytics. Education : Ph.D. in Civil Engineering (Rice University, 2020), M.S. in Civil Engineering (Tongji University, 2016), B.S. in Civil Engineering with minor in Mathematics (Tongji University, 2013). Research Interests include probabilistic seismic risk assessment, multi-hazard resilience analysis, surrogate modeling, uncertainty quantification, and structural reliability. His work integrates physics-based modeling , stochastic simulations , and artificial intelligence to address infrastructure risk management. Labs & Teams : Leads the Smart and Sustainable Infrastructure Resilience (S2IR) Lab , focusing on multi-modal sensing, optimization methods, and life-cycle analysis for infrastructure resilience under deep uncertainties.

F. Frank Chenمشاهده پروفایل

استاد

• Advanced Manufacturing
• Lean Systems
• Artificial Intelligence
• +۳ مورد دیگر

F. Frank Chen is a Professor in the Department of Mechanical Engineering at the University of Texas at San Antonio (UTSA) and holds the Lutcher Brown Distinguished Chair in Advanced Manufacturing. He is a Fellow of both the Society of Manufacturing Engineers (SME) and the Institute of Industrial and Systems Engineers (IISE). Ph.D. & MS, University of Missouri-Columbia BS, Tunghai University (Taiwan) Dr. Chen specializes in flexible manufacturing, lean systems, and AI integration. His research spans predictive maintenance, computer vision for defect detection, cybersecurity in industrial IoT, and sustainable production. He actively combines AI (deep learning, NLP) with lean methodologies to optimize manufacturing and healthcare workflows. Recent publications focus on AI-enabled sustainability (waste reduction, parking efficiency), advanced diagnostics (cancer detection via CNNs, transformers), and cybersecurity enhancements. His work bridges theoretical innovation with practical applications in smart manufacturing and lean healthcare. Fellow, IISE (2019) Operational Excellence Division Teaching Award, IISE (2015) SME College of Fellows (2011) Dr. Chen leads the Flexible Manufacturing and Lean Systems Lab, contributing to AI-aided lean manufacturing, intrusion detection systems, and voice-of-customer extraction. His interdisciplinary approach impacts both industrial processes and healthcare diagnostics, emphasizing efficiency, sustainability, and technological integration.

Valentin Deschaintreمشاهده پروفایل

پژوهشگر

• Computer Graphics
• Computer Vision
• Machine Learning
• +۵ مورد دیگر

Valentin Deschaintre is a Researcher at Adobe Research in London, focusing on image and 3D/appearance asset understanding, generation, and authoring with emphasis on user control. He previously held academic roles at Imperial College London and Inria Sophia-Antipolis , where he earned his PhD under Adrien Bousseau and George Drettakis, collaborating with Optis (Ansys). His work bridges computer graphics, computer vision, and machine learning. His research interests span generative models , diffusion-based image manipulation , material appearance modeling , and 3D scene reconstruction . Recent publications like IntrinsicEdit (2025) and MaterialPicker (2025) highlight his work in intrinsic space editing and multi-modal material generation, often integrating user control mechanisms. Scientific Awards : French Computer Graphics Thesis Award (2020), UCA Academic Excellence Thesis Award (2020), Eurographics Junior Fellow (2024), Eurographics Young Researcher Award (2025). Internship Mentoring : He actively mentors PhD students in Adobe's internship program, including Michael Fischer (2024), Julia Guerrero-Viu (2023-2024), and Ruben Wiersma (2023).

• Signal processing
• Audio processing
• Spatial audio
• +۷ مورد دیگر

Professor Christian Ritz serves as Director of SMART and Professor in the School of Electrical, Computer and Telecommunications Engineering within the Faculty of Engineering and Information Sciences at the University of Wollongong. He has held his professorship since 2017 and served as Associate Dean (Research) in 2022 and Associate Dean (International) from 2016-2021. Professor Ritz's research focuses on signal processing for speech, audio, acoustics and visual information. His current projects include microphone array signal processing for enhancing sound recorded in classrooms, sound scene classification for dementia care environments, spatial audio for Virtual and Augmented Reality, and undersea surveillance. His work spans multiple technical domains including signal processing, audio engineering, computer vision, and communications engineering. His recent publication trends show a strong focus on neural network applications for audio processing, particularly for classroom monitoring and spatial audio reproduction. His work integrates deep learning with traditional signal processing techniques to address challenges in spatial audio synthesis, room impulse response generation, and directional audio reproduction. He has also contributed to applications in mining automation, public safety surveillance, and positioning systems. Professor Ritz is a member of the Centre for Signal and Information Processing (CSIP), part of the Signals, Information and Communications Research Institute (SICOM) at the University of Wollongong. His research is funded by the ARC, overseas research institutes, and defense organizations. His grant portfolio is extensive, including projects such as AI/IoT-powered Airborne System for Monitoring Water Level and Tidal Floods, Multi-Modal Satellite-based Vessel Surveillance, Net Zero Industry and Innovation Program, Smart Eye: Airborne and AI-Driven Assessment Solution of Sugarcane, and numerous projects related to spatial audio and underwater surveillance. He has secured funding from diverse sources including the Australian Research Council, defense organizations, and international collaborations. Professor Ritz maintains an active research laboratory environment focused on signal and information processing. His work spans multiple application domains from classroom monitoring to underwater mine detection, with strong connections to both academic and industry partners. His research group appears to focus on practical applications of signal processing techniques to solve real-world problems across various domains.

Bing Yeمشاهده پروفایل

استاد

• Neuroscience
• Neurodevelopment
• Neurodevelopmental Disorders
• +۴ مورد دیگر

Bing Ye is the Burton L. Baker Collegiate Professor of the Life Sciences at the University of Michigan. He holds academic roles across multiple institutions: Research Associate Dean at the U-M Life Sciences Institute (LSI), Research Professor at LSI, Professor of Cell and Developmental Biology at the U-M Medical School, and Adjunct Professor in the Department of Molecular, Cellular, and Developmental Biology (MCDB) within the College of Literature, Science, and the Arts (LSA). His research focuses on neuronal development and its impact on nervous system assembly, with a particular emphasis on understanding how defects in this process lead to brain disorders like Down syndrome. He employs a multidisciplinary approach integrating genetics, cell biology, biochemistry, advanced imaging, electrophysiology, computational methods (including machine learning), and behavioral studies. Notably, his lab developed LabGym, an AI-based tool for analyzing animal behavior across species. Bing Ye has a robust educational background: B.S. in Physiology (Nanjing University), M.S. in Neurobiology (Chinese Academy of Sciences), Ph.D. in Neuroscience (Johns Hopkins), and postdoctoral training at UCSF. He is committed to mentoring, training future scientists, and promoting inclusivity in science. His awards include Pew Scholar (2010), Kavli Fellow (2012), and AAAS Fellow (2021). He serves on editorial boards for journals like PLOS Biology and is affiliated with multiple institutes, including the Michigan Neuroscience Institute and the Kresge Hearing Research Institute. His lab actively seeks postdoctoral researchers to advance AI-driven behavior analysis and mouse models of neural disorders.

Joshua I Glaserمشاهده پروفایل

استادیار

• Computational Neuroscience
• Neural Control of Movement
• Interpretable Machine Learning
• +۷ مورد دیگر

Joshua I Glaser is an Assistant Professor at Northwestern University, holding dual appointments in the Department of Neurology (Feinberg School of Medicine) and the McCormick School of Engineering. His research focuses on computational neuroscience, particularly the neural control of movement and interpretable machine learning. Glaser earned a BS in Physics and Math from the University of Illinois Urbana-Champaign (2011), a PhD in Neuroscience from Northwestern University (2018), and completed postdoctoral training at the University of Pennsylvania and Columbia University. Research Interests: Glaser’s lab develops interpretable machine learning tools to understand neural computations underlying movement and behavior. Key areas include neural population interactions, flexible behavior control, and disease-related neural dynamics. Projects include interpretable dimensionality reduction, decoding models linking neural activity to behavior, and multi-population neural interaction analysis. Lab & Collaborations: The Glaser Lab collaborates with experimental neuroscientists and statisticians. Current projects involve developing novel methods for analyzing neural data while maintaining interpretability. They host a team of PhD, master’s, and undergraduate students. Grants & Awards: While no specific awards are listed, his work reflects sustained funding in computational neuroscience and engineering. Code & Resources: Lab-developed tools include Neural_Decoding (machine learning for neural decoding), SSM (state-space models), and Sparse Component Analysis (interpretable latent factor analysis). These are publicly available on GitHub.

• Plant Biology
• Genetics
• Molecular Biology
• +۳ مورد دیگر

Dr. Jingyi Han is a Postdoctoral Research Associate in the Department of Biosciences at Durham University. Their research spans interdisciplinary fields including plant molecular biology, genetics, and computer vision. Early work focused on computer vision applications such as human pose classification in near-infrared imagery and multi-modal target detection for surveillance systems. Later research transitioned to plant biology, investigating stem cell factors in cambium development, auxin signaling pathways, and transcriptional regulation mechanisms. This shift reflects expertise in integrating computational methods with biological systems. Key contributions include studies on auxin response networks (Nature, 2021), root tip gene expression regulation (iScience, 2024), and cambium stem cell positioning (Science, 2024). Their publications demonstrate a progression from engineering-focused computer vision projects (2008-2013) to molecular genetics research in plant developmental biology. No scientific awards or grants are explicitly mentioned in the provided materials. Dr. Han has not listed formal advisees or lab affiliations in the available data.

Timo Gerkmannمشاهده پروفایل

استاد

• Statistical Signal Processing
• Machine Learning
• Speech and Audio Processing
• +۳ مورد دیگر

Prof. Timo Gerkmann is a Professor at the University of Hamburg's Department of Informatics, leading the Signal Processing Research Group. His research focuses on statistical signal processing and machine learning for speech and audio applications, including communication devices, hearing aids, audiovisual media, and human-machine interfaces. He previously held roles at Technicolor Research & Innovation, KTH Royal Institute of Technology, and Siemens Corporate Research. His work emphasizes generative models, diffusion-based approaches, and acoustic signal enhancement. He currently serves as Senior Area Editor of the IEEE/ACM Transactions on Audio, Speech, and Language Processing. Research Interests: Statistical Signal Processing for Speech and Audio Machine Learning Applications in Acoustic Environments Diffusion Models for Audio Restoration Audio-Visual Speech Enhancement Human-Machine Interaction Systems Acoustic Scene Analysis Publications Highlight Trends: Recent works focus on diffusion models for speech enhancement, generative approaches to dereverberation, and audiovisual multimodal analysis. He has pioneered frameworks like ReverbFX datasets and FlowDec codecs, emphasizing perceptual quality and unsupervised domain adaptation. Advising & Grants: While no specific students or grants are listed, his research group actively publishes in top venues, indicating sustained academic contributions. His work bridges theoretical signal processing with applied systems engineering. Labs/Teams: Leads the Signal Processing (SP) Research Group at UHH, specializing in cutting-edge audio technologies and human-centric signal processing solutions.

Aritra Duttaمشاهده پروفایل

استادیار

• Machine Learning
• Federated Learning
• Optimization
• +۷ مورد دیگر

Aritra Dutta is an Assistant Professor at the AI Initiative of the University of Central Florida (UCF), primarily affiliated with the Department of Mathematics and secondarily with the Department of Computer Science. He also holds an affiliation with the Pioneer Centre for AI (P1), Denmark. His research focuses on optimization (stochastic/nonconvex), distributed computing (including federated learning), numerical linear algebra, machine learning, low-rank approximation, and computer vision applications such as image/video analysis and object detection/tracking. Dr. Dutta’s work emphasizes interdisciplinary approaches, blending mathematical rigor with computational efficiency. He actively seeks Ph.D. students and postdocs with strong foundations in mathematics (optimization, linear algebra) or computer science (ML, distributed systems), prioritizing candidates with publication records in top-tier venues. His teaching includes courses in applied mathematics and computational methods. His research outputs span communication-efficient distributed learning frameworks, convergence analysis of optimization algorithms, and vision transformer architectures. Notable projects include GAEA (geolocation-aware conversational models) and MAVREC (multi-view aerial visual recognition). Postdoc opportunities: Open for exceptional candidates. Labs/Initiatives: UCF AI Initiative (UCF Aii), UCF Center for Research in Computer Vision (CRCV). Grants: Competitive research assistantships with tuition support.

Yan Zhaoمشاهده پروفایل

دانشیار

• Anomaly Detection
• Autoencoder
• Attention (Machine Learning)
• +۵ مورد دیگر

Yan Zhao is an Associate Professor in the Department of Computer Science at Aalborg University, Denmark, affiliated with The Technical Faculty of IT and Design. His research focuses on data engineering, science, and systems, with a particular emphasis on anomaly detection, machine learning, and spatio-temporal data analysis. He holds a Ph.D. in Computer Science (specific education details not explicitly provided). Research Interests: Dr. Zhao's work spans anomaly detection, autoencoders, attention mechanisms, preference learning, multivariate time series, and computational efficiency. His research often integrates machine learning with real-world applications in spatial crowdsourcing, trajectory analysis, and data privacy. Publications: With 72+ publications, his recent work emphasizes spatio-temporal prediction frameworks, federated learning, and efficient time series analysis. Notable contributions include frameworks for continuous learning on streaming data and privacy-preserving clustering in spatial crowdsourcing. Grants & Supervision: He has supervised one Ph.D. student and actively contributes to research grants focusing on data engineering and smart systems. His work bridges theoretical advancements with practical applications in transportation, social networks, and IoT.

Guillemette Marotمشاهده پروفایل

استاد

• Biostatistics
• Statistical Learning
• Omics Data Analysis
• +۱ مورد دیگر

Guillemette Marot is a Professor at University of Lille's Faculty of Medicine, affiliated with the METRICS research unit (ULR 2694) and Inria's MODAL project team. Serves as scientific head of Bilille bioinformatics platform and leads the Clinical Evaluation research axis. Research expertise includes statistical methods for bioinformatics: variance modeling in omics analyses, biomarker detection, multi-block data integration, and change-point detection in genomic data. Teaching focuses on statistics for non-experts and specialized omics analysis courses. Holds engineering degree from ENSAI and Ph.D. from INRA, with postdoctoral training at Inria. Office locations include Faculty of Medicine (Lille) and Inria Lab (Villeneuve d'Ascq).

Jixin Maمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Science
• Temporal Representation
• +۳ مورد دیگر

Professor Jixin Ma is Director of the PhD/MPhil Programme and Professor of Computer Science (Artificial Intelligence) at the University of Greenwich's School of Computing and Mathematical Sciences. He leads the Artificial Intelligence Research Group and has published over 200 research papers in top-tier journals and conferences. His research spans artificial intelligence, data science, temporal reasoning, information security, machine learning, and case-based reasoning. Research interests focus on: Temporal representation and reasoning Secure computation and encrypted search Machine learning applications in security Computer vision and pattern recognition He has received multiple awards including 6 Best Paper awards and has secured research grants exceeding £330,000.

Zhu Hanمشاهده پروفایل

استاد

• Game Theory
• Wireless Networking
• Security
• +۴ مورد دیگر

Dr. Zhu Han is the John and Rebecca Moores Professor at the University of Houston's Cullen College of Engineering, Department of Electrical and Computer Engineering. His research focuses on game theory, wireless networking, security, data analysis, and smart grid applications. He holds doctoral and master's degrees from the University of Maryland and a bachelor's from Tsinghua University. Research interests span: Next-generation wireless systems (6G/7G) AI/ML integration in communications Reconfigurable intelligent surfaces Quantum machine learning applications Secure and efficient network architectures His recent publications demonstrate strong focus on generative AI integration in wireless systems, quantum networking, semantic communications, and security frameworks for future networks. Awards and honors include: IEEE/ACM/AAAS Fellow status IEEE Kiyo Tomiyasu Award (2021) Highly Cited Researcher since 2017 IEEE Distinguished Lecturer (2015-2018) Dr. Han leads research in wireless communications and networking, with extensive industry collaboration. His lab focuses on developing theoretical foundations and practical implementations for next-generation communication systems.