جست‌وجوی استاد و پژوهشگر

Philip Jacksonمشاهده پروفایل

استاد

• Machine Audition
• Audio-Visual Machine Learning
• Spatial Audio
• +۹ مورد دیگر

Philip Jackson is a Professor in Machine Audition at the University of Surrey's Centre for Vision, Speech and Signal Processing (CVSSP) within the School of Computer Science and Electronic Engineering. His research focuses on audio-visual machine learning, spatial audio technologies, object-based media production, and responsible AI applications. He has over 200 publications and an h-index of 30, contributing to projects like Nephthys, BALTHASAR, and SAVEE. He served as an associate editor for Computer Speech and Language and reviewer for major journals like IEEE/ACM Transactions on Audio, Speech and Language Processing. Key research interests include sound localization, audio-visual tracking, reverberation modeling, and ethical implications of AI in media. His work spans datasets such as Tragic Talkers, SurrRoom, and ForecasterFlexOBM, advancing immersive audio-visual systems and personalized media experiences. Notable projects include spatial audio reproduction for VR, parametric room acoustics, and citizen council research on algorithmic transparency. He has advised over 20 PhD/Master’s students and collaborates with industry on applications like the Catalan Pavilion's Venice Biennale installation. His grants include UK postdoctoral fellowships and leadership in EU-funded initiatives like UDRC2 and QESTRAL.

Mohammad Naser Sabet Jahromiمشاهده پروفایل

استادیار

• Explainable Artificial Intelligence
• Biometrics
• Machine Learning
• +۴ مورد دیگر

Mohammad Naser Sabet Jahromi is an Assistant Professor at the Department of Architecture, Design and Media Technology, Aalborg University, Denmark. He is affiliated with the Visual Analysis and Perception Centre for AI Ethics, Law and Policy. His research focuses on explainable AI (XAI), biometrics, machine learning, and ethical AI applications in legal and medical domains. He actively participates in interdisciplinary projects like REPAI: Responsible AI for Value Creation (2023-2027), which explores AI ethics, computational discourse analysis, and value-driven AI systems. His educational background is not explicitly detailed in the provided text, but his research trajectory indicates strong expertise in computer science and AI systems. Key research interests include interpretable machine learning models, privacy-preserving biometric systems, and AI applications in asylum adjudication and educational assessment. Recent work emphasizes developing XAI frameworks like SIDU-TXT for NLP, verifying machine unlearning mechanisms, and automating large-classroom assessments. His projects bridge technical AI advancements with societal implications through collaborations with legal and ethical scholars. Notable contributions include datasets evaluating XAI methods in medicine and methodologies for transparent AI decision-making. He has participated in conferences such as ICPR 2024 and JURISIN 2023, showcasing interdisciplinary research impact.

Sivakumar Rathinamمشاهده پروفایل

استاد

• Motion Planning
• Autonomous Vehicles
• Collaborative Decision Making
• +۳ مورد دیگر

Dr. Sivakumar Rathinam is a Professor in the Department of Mechanical Engineering at Texas A&M University, affiliated with the College of Engineering and the Computer Science & Engineering department. He holds certifications as a Fellow of ASME (2021) and Senior Member of IEEE (2019). His research focuses on motion planning for autonomous vehicles, collaborative decision-making, combinatorial optimization, and vision-based control systems. He leads the Autonomy Lab, addressing challenges in multi-agent systems, path planning, and rural autonomous vehicle accessibility. Education: Ph.D., Civil Systems Engineering, University of California, Berkeley (2007) M.S., Electrical Engineering & Computer Science, UC Berkeley (2006) M.S., Mechanical Engineering, Texas A&M University (2001) B.Tech., Mechanical Engineering, Indian Institute of Technology Madras (1999) Research Highlights: Dr. Rathinam's work spans autonomous vehicle navigation, UAV coordination, and sensor fusion for adverse conditions. His lab develops algorithms for multi-agent pathfinding and persistent monitoring missions. Recent efforts include rural road detection datasets (R2D2) and thermal/LIDAR sensor fusion for safety in challenging environments. Awards: Outstanding Faculty Contribution Award (2021) Best Paper Runner-Up, ICAPS (2021) Teaching Excellence Award (2012) Labs & Teams: Directs the Autonomy Lab, collaborating with industry partners through the Mechanical Engineering Industry Advisory Council. Active in NSF-funded projects on equitable rural autonomy and multi-UAV recharging frameworks.

Lingzhou Xueمشاهده پروفایل

استاد

• High-Dimensional Statistics
• Nonparametric Statistics
• Statistical Learning
• +۹ مورد دیگر

Lingzhou Xue is a Professor of Statistics at The Pennsylvania State University, affiliated with the Eberly College of Science. He holds dual roles as a faculty member and the Associate Director of the National Institute of Statistical Sciences (NISS). His research focuses on high-dimensional statistics, nonparametric methods, statistical learning, and optimization, with applications in biomedical, environmental, and social sciences. He leads the SLDM (Statistical Learning and Data Mining) Lab and MDS (Microbiome Data Science) Lab. Education: B.Sc. in Statistics from Peking University (2008), Ph.D. in Statistics from the University of Minnesota (2012), postdoctoral training at Princeton University (2012–2013). Professional roles include Associate Editorships at the Journal of the American Statistical Association, Annals of Applied Statistics, and ACM Transactions on Probabilistic Machine Learning. Research Interests: Federated learning, causal inference, graphical models, reinforcement learning, optimal transport, and large-scale optimization. Recent work emphasizes theoretical guarantees for sparse PCA and federated Q-learning algorithms. Awards: IMS Fellow (2024), ASA Fellow (2023), COPSS Emerging Leader Award (2021), and Bernoulli Society New Researcher Award (2019). He has mentored 17 Ph.D. students and 3 postdocs, with four students securing tenure-track faculty positions. Service: Organized multiple NISS writing workshops, co-chaired the Ingram Olkin Statistics Serving Society Forum on Gun Violence, and contributed to the ASA whitepaper 'Discovery with Data' (2014).

Huy Truong-Baمشاهده پروفایل

مدرس

• Reliability and Degradation Modelling
• Maintenance Optimization
• Engineering Asset Management
• +۵ مورد دیگر

Dr. Huy Truong-Ba is a Lecturer at Queensland University of Technology (QUT), affiliated with the School of Mechanical, Medical and Process Engineering and the Centre for Data Science. He holds a Doctor of Philosophy from QUT. His research focuses on reliability and degradation modeling, maintenance optimization, and engineering asset management, with expertise in Markov decision processes and condition-based maintenance strategies. His academic journey includes roles as an Associate Supervisor for multiple PhD projects, such as 'Dispatch Optimisation for Concentrating Solar Tower Power Plant Under Uncertainty' and others in solar energy systems and mechanical engineering. He has collaborated with institutions like the Asset Institute and contributed to interdisciplinary projects in energy systems and transportation. Research interests span mechanical engineering, manufacturing engineering, and engineering practice, with a strong emphasis on applying data-driven methods to optimize maintenance and asset management. His work often integrates statistical modeling and stochastic optimization to address challenges in energy infrastructure, solar power, and industrial equipment degradation. Key collaborations involve Associate Professor Michael Cholette and Professor Tommy Chan, focusing on solar thermal systems, rail infrastructure maintenance, and material degradation under high-temperature conditions. Dr. Truong-Ba's contributions bridge theoretical models with practical applications, driving advancements in sustainable energy and infrastructure resilience.

Justin John Monizمشاهده پروفایل

دانشیار

• Vocal Pedagogy
• Performance Studies
• Vocal Hygiene & Wellness
• +۵ مورد دیگر

Dr. Justin John Moniz is an Associate Professor of Vocal Performance and Associate Director of Vocal Performance & Coordinator of Vocal Pedagogy at New York University's Steinhardt School. His roles include leading NYU's graduate vocal pedagogy program, teaching courses on pedagogic theory and applied voice, and directing the award-winning NYU Pop/Rock Ensemble. Additionally, he serves as a vocal faculty member at Manhattan School of Music and Hawaii Performing Arts Festival's Executive Director. He is a 2025 Ellen Faull Master Teacher and recipient of NYU's Steinhardt Teaching Excellence Award. Education: Four degrees from Florida State University, University of Miami, and SUNY Potsdam, with certifications from Harvard Business School, Duke University, and NASM. Notable awards include Yamaha's Top 40 Music Educator and recognition as one of Florida State's Notable Noles and Miami's 30 Under 30. Research focuses on vocal pedagogy, vocal hygiene, and outreach programs like NYU's Vocal Pedagogy Outreach Program (VPOP), which has engaged 1,800+ students in underserved communities. His work bridges voice science with sports medicine, emphasizing vocal load management and sustainability. Over 30 peer-reviewed articles by his students reflect his mentorship impact. Awards include The American Prize in Vocal Performance (three-time winner), BroadwayWorld accolades, and leadership roles in NATS-NYC as President. His administrative achievements include securing $2M in grants for Hawaii Performing Arts Festival, doubling its enrollment. Performance highlights include over 90 opera/musical theatre roles, a debut album *Along the Way*, and collaborations with orchestras like Chicago Symphony Center. He also directs NYU's Broadway Orchestra and leads ArtsBridge Summer Program for high school singers.

Hans Martin Kjerمشاهده پروفایل

دانشیار

• Ultrasound Imaging
• Super-Resolution Imaging
• Biomedical Image Analysis
• +۷ مورد دیگر

Hans Martin Kjer is an Associate Professor in the Department of Applied Mathematics and Computer Science at the Technical University of Denmark (DTU), where he is affiliated with the UltraSound and Biomechanics group within the Visual Computing Center and the Center for Fast Ultrasound Imaging. His research bridges engineering and medical imaging, with a strong emphasis on developing and validating advanced ultrasound techniques for biomedical applications. Research Interests: His work focuses on super-resolution ultrasound imaging, microvascular analysis, 3D reconstruction of biological structures, and image registration. He applies computational methods to improve the resolution and accuracy of ultrasound, particularly in renal and lymph node vasculature imaging. His research contributes to the UN Sustainable Development Goals in health and well-being through innovative diagnostic tools. Publication Trends: Over the past several years, Kjer has consistently published in high-impact journals and conferences in biomedical engineering and imaging. His recent work emphasizes the validation of super-resolution ultrasound against micro-CT, realistic 3D blood flow simulation, and the application of AI in enhancing imaging resolution. These studies reflect a strong trend toward quantitative, reproducible, and clinically relevant imaging solutions. Scientific Contributions: While no specific awards are listed, his leadership in major research projects and frequent collaborations with leading experts in ultrasound (e.g., Jørgen Arendt Jensen) underscore his significant role in the field. Advising and Funding: Kjer serves as a supervisor and principal investigator in several funded research initiatives, including AI for Extreme Super-Resolution CT , 3DIM: 3D Imaging Center , and QIM: Center for Quantification of Imaging Data from Max IV . He mentors PhD students and collaborates across disciplines, contributing to both biomedical and materials science imaging projects. Laboratories and Teams: He is an integral member of the Center for Fast Ultrasound Imaging and the Visual Computing Center at DTU. These teams focus on cutting-edge ultrasound technologies, image processing algorithms, and multimodal imaging integration, positioning Kjer at the forefront of computational biomedical imaging in Denmark.

• Robotic Vision
• Autonomous Vehicles
• Computer Vision
• +۷ مورد دیگر

Dr. Annette Stahl is a Professor at the Department of Engineering Cybernetics, Norwegian University of Science and Technology (NTNU). She is an Onsager Fellow and leads the Robot Vision Group, overseeing the AILARON project funded by the Research Council of Norway. Her roles include affiliation with NTNU AMOS and the SFI AutoShip Centre for Research-based Innovation. Stahl holds a PhD in Applied Mathematics (Computer Vision) from Heidelberg University. Affiliations: NTNU AMOS, SFI AutoShip, and AILARON project leadership Research Focus: Robotic vision, autonomous systems, underwater exploration, and aquaculture monitoring Education: PhD in Applied Mathematics (Computer Vision) – Heidelberg University Research Interests: Her work spans robotic and computer vision, control theory, autonomous vehicles, mathematical image analysis, and machine learning applications in aquaculture and maritime systems. Key focus areas include underwater SLAM, sensor fusion for autonomous ships, and plankton detection using AI. Articles: Recent publications emphasize underwater robotic systems, sensor fusion for autonomous navigation, and AI-driven aquaculture monitoring. Key themes include 3D reconstruction, visual SLAM, and maritime tracking algorithms. Grants & Projects: AILARON (FRINATEK/IKTPLUSS) AUTOSIGHT (IKTPLUSS) AROS (IKTPLUSS) SFI AutoShip (SFI) Advising: Main supervisor for 10+ PhD candidates (e.g., Trym Nygård, Mauhing Yip) Co-advisor for projects like INDISAL (fish identification) Labs/Teams: Robot Vision Group at NTNU Collaborations with SINTEF Ocean and Zebop AS

• Cloud Computing
• Computer Networking
• Machine Learning
• +۱ مورد دیگر

Xiaoqian (Tiffany) Zhang is an Assistant Professor in the Department of Computer Science at the University of Nebraska at Omaha (UNO) College of Information Science & Technology. Her work focuses on cloud computing, computer networking, and cybersecurity. Education: Ph.D. in Computer Science (University of Massachusetts Boston, 2023), MS in Mathematics (New York University, 2014), BA in Mathematics and Economics (Skidmore College, 2012) Her research explores cloud computing and networking with emphasis on machine learning and cybersecurity . Recent work addresses challenges in disaggregated storage systems , edge computing , and augmented reality . She received the DOE NNSA Joule Award (2022) for her scholarship. Her publications span topics including network congestion control , distributed file systems , and audio noise filtering . She teaches courses in cloud computing and computer networking .

Henrike Mollمشاهده پروفایل

استاد

• Developmental Psychology
• Cognitive Development
• Shared Intentionality
• +۵ مورد دیگر

Henrike Moll is a Professor of Psychology at the University of Southern California (USC), cross-appointed to Philosophy. She leads the Minds in Development Lab, focusing on infants' and young children's cognitive growth through social interaction, particularly shared intentionality and perspective awareness. Her work integrates developmental psychology with philosophy of mind and education. Moll holds a PhD from the University of Leipzig and has held research positions at the Max Planck Institute and the University of Washington. She has been funded by major foundations including the Spencer Foundation, Templeton Foundation, and Volkswagen Foundation. Research Interests: Ontogeny of social cognition (e.g., teaching, perspective-taking) Cultural evolution mechanisms Philosophical anthropology Human uniqueness in relational cognition Key Contributions: Pioneered studies on children’s pedagogical competence and knowledge transmission Developed the ‘Transformative Cultural Intelligence’ hypothesis Advanced interdisciplinary frameworks linking philosophy and empirical child development Funding & Recognition: Spencer Foundation Midcareer Grant ($149K, 2017) Joseph B. Gittler Award (2023) Templeton Fellowship (2015–2016) Over $1M in grants from NSF, ONR, and private foundations Labs & Teams: Director of USC’s Minds in Development Lab, collaborating with philosophers (e.g., Andrea Kern), cognitive scientists, and education researchers.

• Networking
• Operating Systems
• Distributed Systems
• +۶ مورد دیگر

Kang Shin is a Professor in the Department of Electrical Engineering and Computer Science at the University of Michigan's College of Engineering. His research spans multiple domains in computer science and engineering, focusing particularly on automotive security, wireless communications, and distributed systems.

• Web Science
• Scientific Workflow Systems
• Semantic Web
• +۳ مورد دیگر

David Newman serves as a Senior Enterprise Fellow within the Electronics and Computer Science department at the University of Southampton's Faculty of Engineering and Physical Sciences. His research focuses on the intersection of web science, scientific workflow systems, and semantic technologies, with particular emphasis on developing infrastructure for collaborative research environments. His primary research interests center around Web Science , Scientific Workflow Systems , and Research Objects , where he investigates how digital platforms can enhance scientific collaboration. Newman's work explores the social dimensions of scientific computing through projects like myExperiment, examining how researchers share and reuse computational workflows. His research bridges technical infrastructure development with social computing aspects of scholarly practice, contributing to the evolution of virtual research environments that support modern scientific collaboration across disciplines. Analysis of Newman's publication history reveals consistent contributions to the development of semantic platforms for scientific collaboration, particularly through the myExperiment project. His work spans from foundational research on scientific social objects (2011) to practical implementations like Erica the Rhino (2016) that demonstrate real-world applications of workflow systems. The publications show progression from theoretical frameworks for research objects toward applied implementations in digital art and news enrichment, reflecting both technical depth and interdisciplinary reach. While no specific scientific awards are documented in the available materials, Newman's contributions to the myExperiment platform represent significant impact in the research infrastructure community. His work has helped shape how scientists share and discover computational workflows, contributing to more efficient and collaborative research practices across multiple disciplines. As a Senior Enterprise Fellow, Newman contributes to the university's research ecosystem through development of digital research infrastructure rather than traditional student supervision. His work focuses on creating platforms that support researcher collaboration at scale, with implications for how scientific communities organize and share knowledge in the digital age. The myExperiment platform, in particular, has served as an important testbed for concepts now mainstream in research computing. Newman's research is closely associated with the Web & Internet Science group at Southampton, where he has contributed to the development of semantic technologies for research communities. His work represents an important strand of the university's leadership in web science and digital research infrastructure, connecting technical innovation with practical applications for scholarly communication and collaboration.

Shenghua Gaoمشاهده پروفایل

دانشیار

• Spatio-temporal Data Analysis
• Machine Learning
• Database Systems
• +۴ مورد دیگر

Professor Shenghua Gao is an Associate Professor at the School of Computing and Data Science of the University of Hong Kong (HKU), concurrently serving as Assistant Director for Shanghai Initiatives. He holds a PhD from Nanyang Technological University. His research focuses on integrating machine learning, spatio-temporal data analysis, and database systems to address challenges in mobility prediction, traffic management, and geospatial representation learning. He has contributed significantly to trajectory modeling, indexing frameworks for multi-dimensional data, and the application of large language models (LLMs) in spatio-temporal contexts. Key research interests include: Spatio-Temporal Data Science: Developing frameworks for efficient processing and analysis of point cloud, trajectory, and traffic data. Machine Learning for Databases: Innovating indexing algorithms (e.g., BMTree, MAST) and query optimization techniques leveraging ML. Trajectory and Mobility Prediction: Creating personalized models for next-location prediction and transfer learning across regions. Geographic AI (GeoAI): Enhancing road network representation and urban function inference using physics-guided and foundation models. Recent work highlights include the ST-LLM+ framework for traffic prediction, the MAST system for point cloud analytics, and the exploration of City Foundation Models for urban challenges. His publications span top venues in databases (SIGMOD, VLDB) and AI/data science (ICML, NeurIPS). While no awards are explicitly mentioned, his prolific output and leadership roles indicate significant academic contributions. He is actively involved in teaching and supervising research in the School’s undergraduate and postgraduate programs, including MSc(AI) and MPhil/PhD tracks.

• Target Tracking
• Navigation
• SLAM
• +۴ مورد دیگر

Edmund Førland Brekke is a Professor at the Department of Technical Cybernetics, Norwegian University of Science and Technology (NTNU). He leads the Autosit and Autosight projects, and serves as a work package leader in the SFI Autoship center. His research focuses on target tracking, navigation, and SLAM, with applications in collision avoidance for unmanned vessels. He co-founded the company Zeabuz for marine surface autonomy solutions. PhD in Technical Cybernetics (NTNU, 2010) MSc in Industrial Mathematics (NTNU, 2005) Research Interests: Edmund specializes in theoretical foundations of multitarget tracking, integrating target tracking with navigation/SLAM, and applying these to autonomous maritime collision avoidance. His work addresses challenges in heavy-tailed clutter, sensor fusion, and situational awareness for autonomous ferries and river barges. Publications Trends: His recent work (2018–2022) emphasizes collision avoidance algorithms, sensor fusion, and SLAM for autonomous vessels. Key areas include maritime radar tracking, trajectory prediction using AIS data, and integration of visual/Lidar sensors for navigation. Guidance: He has supervised numerous PhD candidates on topics like digital twins, multi-sensor tracking, and risk assessment for autonomous ships, including graduates from 2014–2024. Projects: Active in Autoferry (autonomous ferries), Autobarge (river barges), and the ORCAS initiative. Collaborates with the SFI Autoship center.

Evangelos E. Miliosمشاهده پروفایل

استاد

• Visual Text Analytics
• Modelling and Mining of Networked Information Spaces
• Text Mining
• +۸ مورد دیگر

Evangelos E. Milios is a Professor in the Faculty of Computer Science at Dalhousie University , Halifax, Nova Scotia. He has been a faculty member since 1998 and leads the MALNIS (Machine Learning and Networked Information Spaces) research group. He is affiliated with the Institute of Big Data Analytics and served as Scientific Director of DeepSense , an innovation hub for ocean data analytics. Education: PhD in Electrical Engineering and Computer Science, MIT (1986) SM & EE, MIT (1983) Dipl. Eng. in Electrical Engineering, NTUA, Greece (1980) His research focuses on visual text analytics, text mining, graph mining, social network analysis, and machine learning . He has made significant contributions to modeling and mining of networked information spaces, with applications in data science and AI. The recent publications reflect a strong trend in data mining, robotics, pattern recognition, and semantic analysis , particularly in log analysis, pose estimation, and information retrieval. His work bridges theoretical algorithms with practical applications in robotics and web technologies. Scientific Awards and Honors: Distinguished Research Professor (2017–2022) Killam Chair in Computer Science (2006–2011) Senior Member, IEEE Professional Engineer, Ontario (1998–2024) He has served in key administrative roles including Associate Dean, Research (2008–2017) and Director of the Graduate Program (1999–2002) . He has supervised numerous graduate students and taught a wide range of courses in AI, machine learning, data science, and networking. His research is supported by major grants and collaborations, including NSERC and industry partnerships. Research Labs and Teams: MALNIS – Focuses on machine learning and networked information spaces. DeepSense – Ocean data analytics and AI innovation. Institute of Big Data Analytics – Cross-disciplinary big data research.