جست‌وجوی استاد و پژوهشگر

Jianxi Gaoمشاهده پروفایل

دانشیار

• Network Science
• Artificial Intelligence
• Control Theory
• +۳ مورد دیگر

Jianxi Gao is an Associate Professor in the Department of Computer Science at Rensselaer Polytechnic Institute (RPI). His research focuses on network science, particularly network resilience, robustness, and control, integrating network theory, control theory, statistical physics, and operations research. He also explores the intersection of network science and AI, including applications of AI to network analysis and vice versa. His work aims to understand, predict, and control the resilience of complex systems against cascading failures. Key research areas include network resilience in transportation systems, quantum networks, and biological systems, with applications to pandemic response and infrastructure optimization. Gao's contributions span theoretical frameworks and computational tools, such as the NuRsE MATLAB package for network resilience analysis. His GitHub repositories (e.g., NuRsE and NON) showcase his open-source contributions to network science and computational methods. His recent publications address topics like AI-driven network analysis, quantum network percolation, and pandemic-induced healthcare system stress. He actively collaborates on interdisciplinary projects, emphasizing real-world applications of network science principles.

Alina Roitbergمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Human Activity Recognition
• +۷ مورد دیگر

Alina Roitberg is a Junior Professor (Assistant Professor) at the University of Stuttgart , affiliated with the Faculty of Computer Science, Electrical Engineering and Information Technology . Her research focuses on advancing computer vision, machine learning, and robotics applications, particularly in human activity recognition, domain adaptation, and synthetic data generation. She explores challenges in action understanding, cross-domain generalization, and real-world deployment of AI systems in fields like healthcare, autonomous vehicles, and industrial automation. Her work emphasizes robust learning under noisy conditions, multimodal data fusion, and ethical AI applications. Recent projects include foundational studies on large language models in construction (AEC), video-based muscle group estimation, and improving driver activity recognition for autonomous vehicles. She also investigates circular factory design through uncertainty-aware process optimization and human-robot interaction. Dr. Roitberg's contributions span academic publications and industrial collaborations, addressing both theoretical advancements and practical implementations. Her research bridges computer vision techniques with real-world problems, emphasizing scalability and ethical considerations in AI deployment.

Dinh Phungمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Generative AI
• +۸ مورد دیگر

Professor Dinh Phung is the Head of the Department of Data Science & AI at Monash University. His research focuses on machine learning, deep learning, generative AI, and robust AI systems. He has authored over 250 publications, with applications in NLP, computer vision, digital health, and cybersecurity. Phung holds a PhD and BSc(Hons) in Computer Science from Curtin University. He leads major projects like 'Can Machines Unlearn?' and 'Trustworthy Generative AI', funded by the Australian Research Council and the Department of Defence. Education: Doctor of Philosophy, Computer Science, Curtin University (2005) Bachelor of Science (Honours), Computer Science, Curtin University (2001) Research Interests: Machine learning, deep learning, and generative models Optimal transport and Bayesian methods Robust and trustworthy AI Applications in digital health, cybersecurity, and autism research Key Projects (2023–2029): Can Machines Unlearn? (2025–2029): Safety in AI Trustworthy Generative AI (2024–2026): Foundation models Robust Machine Learning via Optimal Transport (2023–2025) Awards and Grants: Australian Research Council grants for AI safety and robustness Department of Defence funding for robust learning systems Collaborations: Global partnerships in AI ethics, cybersecurity, and healthcare. Active advisory roles, including with the Victorian Parliamentary Library.

Paul Cohenمشاهده پروفایل

استاد

• Artificial Intelligence
• Data Analysis
• Education Informatics
• +۴ مورد دیگر

Paul Cohen is a Professor of Computer Science at the University of Pittsburgh's School of Computing and Information (SCI), where he also directs the Modeling and Managing Complicated Systems Institute (MOMACS). Previously, he served as the founding Dean of SCI from 2017 to 2020. Before joining Pitt, he was a Program Manager at DARPA (2013–2017), leading initiatives like Big Mechanism and Communicating with Computers. Earlier roles include founding director of the University of Arizona’s School of Information: Science, Technology and Arts (SISTA), and professor at the University of Southern California’s Information Sciences Institute and the University of Massachusetts. Education: PhD in Computer Science and Psychology (Stanford University), MS in Psychology (UCLA), BS in Psychology (UC San Diego). Research Interests: Focuses on artificial intelligence, machine learning, natural language processing, and modeling complex systems like cell signaling pathways and socio-environmental interactions. His work emphasizes explainable AI, human-computer communication, and interdisciplinary problem-solving. Key Contributions: Authored Empirical Methods for Artificial Intelligence and over 200 peer-reviewed articles. His research spans robotics, education technology (e.g., the AnimalWatch tutoring system), and collaborative analysis tools like COLAB. He has won a Telly Award for his video on systemic challenges and a Best Paper award for spatial language learning frameworks. Awards & Recognition: Elected Fellow of the AAAI, recipient of the Telly Award, and winner of the Best Paper Award at the IEEE Conference on Development and Learning. Leadership & Outreach: Advocates for polymathy in education to address global challenges. His work includes developing curricula for complex systems thinking and promoting diversity in STEM through initiatives like AnimalWatch.

Shamya Karumbaiahمشاهده پروفایل

استادیار

• Human-Centered AI in Education
• Equitable and Responsible AI
• Algorithmic Bias in Education
• +۳ مورد دیگر

Shamya Karumbaiah is an Assistant Professor in the Learning Sciences department at the University of Wisconsin–Madison's School of Education. She holds a PhD in Learning Sciences from the University of Pennsylvania (2021) and previously served as a postdoc fellow at Carnegie Mellon University. Her research focuses on equitable and responsible use of AI in education, addressing algorithmic bias, and methodological innovations in learning analytics. Education: PhD in Learning Sciences (University of Pennsylvania, 2021), MS in Computer Science (University of Massachusetts Amherst, 2017), BE in Computer Science (Sri Jayachamarajendra College of Engineering, 2011). Research Interests: Human-centered AI in education, algorithmic bias mitigation, affective computing, and teacher practices in human-AI collaboration. She critiques colonial continuities in algorithmic systems and advocates for upstream equity in AI development. Awards: Includes the Nellie McKay Fellowship (UW-Madison), multiple best paper nominations, and the Dean’s Fellowship (UPenn). Grants & Labs: Active in projects analyzing multimodal classroom data and AI ethics. Collaborates with institutions like Cisco and USC ICT.

Jingxian Wangمشاهده پروفایل

استادیار

• Artificial Intelligence
• Wireless Systems
• Satellite Networks
• +۹ مورد دیگر

Jingxian Wang is an NUS Presidential Young Professor and Assistant Professor in the Department of Computer Science at the National University of Singapore's Faculty of Computing. His research builds next-generation wireless systems and satellite networks, with primary focus on integrating AI with wirelessly networked devices from WiFi to satellites. He earned his PhD from Carnegie Mellon University and previously served as a research scientist at Microsoft Research in Redmond, where he led the Smart Surface for 6G and Space initiative. His educational journey includes: PhD, Carnegie Mellon University Wang's research spans Wireless Systems , Satellite Networks , Artificial Intelligence , and Internet of Things , emphasizing AI-augmented wireless systems. His interdisciplinary work bridges robotics , materials science , and AI to develop sustainable sensing methods, robust communication networks, and multimodal AI techniques. Key projects include Multimodal AI for IoT (funded by Microsoft's Accelerate Foundation Models Program) and Satellite IoT Networks. His publication trends reveal accelerating integration of AI into wireless systems, with recent focus on satellite networking, soft robotics actuation, and generative models for IoT. The research consistently targets real-world deployment challenges in battery-free systems and space networks. His scientific contributions have earned prestigious recognition: ACM SIGMOBILE Doctoral Dissertation Award 2023 Communications of the ACM Research Highlights (2021, 2022) ACM SIGMOBILE Research Highlights 2021 Best Paper Awards at IPSN 2021 and UbiComp 2020 Microsoft Research Fellowship 2020 Emerging Rockstar in IEEE Pervasive Computing 2024 Wang actively mentors doctoral students and postdoctoral researchers through his AIoT Group. His grant portfolio includes Microsoft's Accelerate Foundation Models Research Program funding for multimodal AI projects, with ongoing work targeting satellite IoT infrastructure and wireless-powered soft robotics. Future directions emphasize foundation models for space networks and battery-free IoT systems. He leads the AIoT Group, fostering cross-disciplinary collaboration between computer scientists, roboticists, and materials engineers to pioneer wireless sensing and actuation technologies.

• Machine Learning
• Signal Processing
• Biomedical Engineering
• +۷ مورد دیگر

Professor Danilo Mandic is a leading academic in Machine Intelligence and Signal Processing at Imperial College London's Department of Electrical and Electronic Engineering. He holds roles including President of the International Neural Network Society and Distinguished Lecturer for IEEE Computational Intelligence and Signal Processing Societies. His research spans Statistical Learning, Wearable Sensing (Hearables), Financial Signal Processing, and Tensor Networks for Big Data. Key contributions include pioneering in-ear physiological sensing and developing quaternion-based adaptive filters. He has authored over 600 publications, including seminal monographs on neural networks and complex-valued signal processing. Education: PhD in Nonlinear Adaptive Signal Processing from Imperial College (1999). Professional accolades include the 2019 Dennis Gabor Award and multiple IEEE Best Paper Awards. His labs include the Financial Signal Processing & Machine Learning Lab and collaborations with the Centre for Neurotechnology. He advises numerous students and leads projects on AI ethics, graph signal processing, and biomedical applications. His work emphasizes translating research into educational curricula via participatory sensor-based learning.

Taylor Johnsonمشاهده پروفایل

دانشیار

• Formal Verification
• Cyber-Physical Systems (CPS)
• Artificial Intelligence
• +۷ مورد دیگر

Taylor Johnson is an Associate Professor of Computer Science and Electrical and Computer Engineering at Vanderbilt University's School of Engineering. He directs the Verification and Validation for Intelligent and Trustworthy Autonomy Laboratory (VeriVITAL) and serves as a Senior Research Scientist in the Institute for Software Integrated Systems. Previously, he was an Assistant Professor at the University of Texas at Arlington from 2013 to 2016. His research focuses on formal verification techniques for cyber-physical systems (CPS), emphasizing safety, reliability, and security through hybrid systems, formal methods, and control theory. He has published extensively on neural network verification, earning best paper awards and recognition from IEEE, IFIP, and ACM. Education: Ph.D., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2013) M.Sc., Electrical and Computer Engineering (University of Illinois at Urbana-Champaign, 2010) B.S.E.E., Electrical and Computer Engineering (Rice University, 2008) Research Interests: Formal verification of neural networks and CPS, safety-critical systems, autonomous systems, and AI/ML security. His work bridges theoretical foundations (e.g., hybrid systems) with practical applications in aerospace, energy systems, and robotics. Key Contributions: Developed the NNV tool for neural network verification, led the Verification of Neural Networks Competition (VNN-COMP), and pioneered techniques for robust federated learning and malware detection. Awards: AFOSR YIP Award (2016), NSF CRII Award (2015), and multiple best paper honors. His research is funded by AFRL, NSF, Intel, NVIDIA, and industry partners. Labs & Collaborations: VeriVITAL Lab (Vanderbilt), collaborations with United Technologies Research Center, Boeing, and Toyota.

• Computer Vision
• 4D Vision
• Machine Learning
• +۹ مورد دیگر

Professor Adrian Hilton is a distinguished faculty member at the University of Surrey, serving as Director of the Centre for Vision, Speech and Signal Processing (CVSSP) and Director of the Surrey Institute for People-Centred AI. He is affiliated with the School of Computer Science and Electronic Engineering and leads the Visual Media Research Lab (V-Lab). His research focuses on pioneering next-generation 4D computer vision technologies that enable machines to understand and model dynamic real-world scenes. Key areas include 3D/4D shape capture, computer vision, machine learning, graphics, and animation for applications in sports analysis, film/TV production, virtual reality, and medical imaging. His work bridges the gap between real and computer-generated imagery, with notable contributions in volumetric capture, motion capture, and free-viewpoint video. Hilton's recent publications demonstrate a strong trend toward multimodal integration, particularly combining audio and visual processing for spatial audio applications, while advancing 4D reconstruction techniques for human performance capture. His work increasingly incorporates transformer architectures and neural rendering techniques for improved illumination estimation, shadow modeling, and multi-view consistency. Scientific Awards and Recognition Two EU IST Innovation Prizes Manufacturing Industry Achievement Award Royal Society Industry Fellowship (2008-2011) Royal Society Wolfson Research Merit Award in 4D Vision (2013-2018) Fellow of the Royal Academy of Engineering (FREng) Fellow of the International Association for Pattern Recognition (FIAPR) Fellow of the Institution of Engineering and Technology (FIET) Hilton actively mentors PhD and post-doctoral researchers through his leadership of CVSSP, which has a grant portfolio exceeding £31M and comprises 170 researchers. He has successfully commercialized several technologies, including systems used by the BBC for sports commentary visualization. His research collaborations span major industry partners including BBC, BT, Sony, Framestore, and The Foundry. He co-founded the G3 Games forum and the CVMP Conference on Visual Media Production, demonstrating strong engagement with the creative industries. Current research projects include the S3A Programme Grant in Future Spatial Audio and InnovateUK's ALIVE project for 360 video reconstruction.

Ole Andreas Andreassenمشاهده پروفایل

استاد

• Mental Disorders
• Neuropsychiatric Disorders
• Genetics
• +۶ مورد دیگر

Ole Andreas Andreassen is a Professor of Psychiatry and Director of the Centre for Precision Psychiatry at the University of Oslo. He holds a joint appointment as an Attending Psychiatrist (20%) at Oslo University Hospital. His academic career includes leadership roles such as Director of the Centre of Excellence NORMENT (2013–2023) and the KG Jebsen Centre for Psychosis Research (2012–2018). He completed his MD (1993) and PhD (1996) at the University of Bergen, followed by postdoctoral training at Harvard Medical School and Massachusetts General Hospital. Education: MD (1993, University of Bergen), PhD (1996, University of Bergen) Postdoc: Harvard Medical School (1998–2000) Specialist in Psychiatry: Oslo University Hospital (2006) His research focuses on mental and neuropsychiatric disorders, emphasizing genetics, brain imaging, and precision medicine. Key areas include the interplay of genetic and environmental factors in schizophrenia, bipolar disorder, and Alzheimer’s disease. He has pioneered studies on predictive tools and clinical interventions, with a strong emphasis on genomic and neuroimaging biomarkers. His publications span over 10 years, addressing topics like cortical abnormalities in bipolar disorder, genetic overlap between psychiatric conditions, and cannabis use impacts. These works highlight interdisciplinary approaches, blending genetics, neuroimaging, and clinical data. Awards: Brain Council Award (2024), Fridtjof Nansen Award (2023), University of Oslo Research Prize (2020) Leadership: Co-Director of the KG Jebsen Centre for Neurodevelopmental Disorders, ENIGMA Bipolar Working Group Chair, and member of the Psychiatric Genomics Consortium. Andreassen has supervised 42 PhD students and co-supervised numerous postdoctoral researchers. His grants and collaborations span EU-funded initiatives (e.g., CoMorMent, RealMent) and global networks like the Precision Psychiatry consortium. Labs and teams include the Centre for Precision Psychiatry and the Scandinavian Collaboration for Psychiatric Etiology (SCOPE), focusing on translational research and multimodal data integration.

Jia Wuمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Data Mining
• Deep Learning
• +۷ مورد دیگر

Dr. Jia Wu is an Associate Professor and Research Director of the Centre for Applied Artificial Intelligence at Macquarie University. He holds a PhD in Computer Science from the University of Technology Sydney (2009) and is an IEEE Senior Member. His research focuses on artificial intelligence, data mining, graph neural networks, and anomaly detection, with over 200 publications in top-tier journals/conferences like IEEE TPAMI, TKDE, and conferences like KDD, IJCAI, and NeurIPS. He has received awards including the Heidelberg Laureate Forum Fellowship (2019) and multiple best paper awards. Education: PhD in Computer Science (UTS, 2009). Current roles include Director of HDR (Higher Degree Research) and Associate Editor for IEEE TNNLS and ACM TKDD. He leads projects in AI-driven cybersecurity, personalized banking solutions, and disaster response systems. Research interests emphasize graph-based learning, fake news detection, and deep learning applications. His recent work explores hypergraph neural networks for fraud detection and brain graph analysis for neurological disorders. He has pioneered scalable semi-supervised clustering techniques and transformer-based hypergraph models for anomaly detection. Awards include CIKM'22 Best Paper Runner-Up, ICDM'21 Best Student Paper, and the 2023 Faculty of Science and Engineering Collaboration Award. His work spans 13 active research projects, including mitigating AI deepfakes in identity systems and enhancing disaster response networks through graph-based simulations. Labs/Teams: Leads teams in the Data Horizons Research Centre, Future Communications Research Centre, and Hearing Research Centre. Collaborates internationally in AI, data mining, and social network analysis.

Daniel Angusمشاهده پروفایل

استاد

• Artificial Intelligence
• Social Media Analysis
• Computational Social Science
• +۳ مورد دیگر

Professor Daniel Angus is a faculty member at Queensland University of Technology (QUT), holding the position of Professor of Digital Communication in the School of Communication and serving as Director of QUT's Digital Media Research Centre (DMRC). His research focuses on computational methods applied to communication and media studies, with a particular emphasis on AI, automation, misinformation, and digital societal impacts. He holds a PhD in computer science from Swinburne University of Technology and has extensive experience in interdisciplinary research across computer science, design, communication, linguistics, and journalism. Affiliations: ARC Centre of Excellence for Automated Decision Making & Society, ARC Centre of Excellence for the Dynamics of Language. Research Projects: Leads projects like 'Using Machine Vision to Explore Instagram’s Everyday Promotional Cultures' and 'Evaluating the Challenge of ‘Fake News’ and Other Malinformation'. Research Interests: Daniel’s work bridges technology and society, exploring AI ethics, algorithmic transparency, social media governance, and computational methodologies for analyzing communication patterns. He develops tools like Discursis and PauseCode to study discourse and conversational dynamics in healthcare, aged care, and media contexts. Grants & Awards: Principal Investigator on multiple ARC grants and collaborates with industry stakeholders to address challenges like unhealthy food advertising and platform accountability. His research has informed policy submissions to parliamentary committees on social media regulation and AI adoption. Supervision: Current PhD students focus on topics like algorithmic transparency, computational methods for meme analysis, and AI in publishing. Labs/Teams: Directs the Digital Media Research Centre, fostering interdisciplinary projects on digital culture and platform studies.

Raja Jurdakمشاهده پروفایل

استاد

• Distributed Systems
• Blockchain Technology
• Network Modeling
• +۷ مورد دیگر

Professor Raja Jurdak is a leading academic in distributed systems and applied data sciences at Queensland University of Technology (QUT), where he directs the Trusted Networks Lab. He holds dual roles as Professor of Distributed Systems and Chair in Applied Data Sciences, alongside leadership in the Centre for Data Science. His research focuses on dynamic network modeling, blockchain-based trust frameworks, and IoT applications, with particular emphasis on cybersecurity, energy efficiency, and mobility-driven diffusion processes. Jurdak formerly led CSIRO's Distributed Sensing Systems Group and maintains a visiting scientist role there. Education: PhD in Information and Computer Science, University of California, Irvine MS in Computer Networks and Distributed Computing, University of California, Irvine BE in Computer and Communications Engineering, American University of Beirut Research Interests: Network science, blockchain technology, IoT security, sustainable energy systems, and data-driven decision-making. His work bridges theoretical advancements with practical applications in smart grids, health surveillance, and urban mobility. Awards: Finalist for the 2019 Eureka Prize, multiple CSIRO accolades, and IEEE Senior Member status. His research has received industry recognition for interdisciplinary innovation, including the DiNeMo project's real-time disease surveillance system. Advisory & Grants: Leads high-impact projects funded by government and industry partnerships. Supervises PhD candidates in areas like decentralized data processing and privacy-preserving AI. Holds editorial roles at journals such as Ad Hoc Networks and PLoS ONE . Labs & Teams: Directs the Trusted Networks Lab at QUT, fostering collaborations with institutions like Oxford University and MIT. His work emphasizes cross-disciplinary teams to address global challenges in cybersecurity and sustainable systems.

Hao Chenمشاهده پروفایل

دانشیار

• Anomaly/signal detection for streaming data
• Graph-based methods
• Two-sample test for high-dimensional and non-Euclidean data
• +۷ مورد دیگر

Hao Chen, Ph.D. is an Associate Professor in the Department of Statistics at the University of California, Davis. His research focuses on statistical methodology for high-dimensional and non-Euclidean data, including anomaly detection, graph-based methods, and change-point analysis. He also explores AI security, multimodal models, and geospatial applications. His work bridges statistical theory and practical machine learning challenges. Education: Ph.D., Graduate Group in Biostatistics, Stanford University Research Interests: Dr. Chen’s expertise spans statistical methods for streaming data, categorical data analysis, and allele-specific copy number variation. He has pioneered work in detecting signals in complex datasets and developing robust AI systems. His recent focus includes mitigating modality interference in LLMs, enhancing model safety via guardrails, and advancing geospatial AI through projects like GeoLM. Publications: His recent work addresses cutting-edge topics such as multimodal model vulnerabilities, unlearning algorithms, and clinical radiology applications. Key themes include improving model robustness, ethical AI design, and interdisciplinary data integration. Labs/Teams: Engages in collaborative projects at UC Davis, though specific lab names are not listed in the provided information.

Jiaxiang Zhangمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Cognitive Neuroscience
• +۲ مورد دیگر

Jiaxiang Zhang is Professor of Artificial Intelligence in the Department of Computer Science at Swansea University's Faculty of Science and Engineering. He holds a PhD in Computational Neuroscience from the University of Bristol and previously held positions at the University of Birmingham, MRC Cognition and Brain Sciences Unit (Cambridge), and Cardiff University where he founded the Cognition and Computational Brain Lab. Zhang's research integrates computational modeling, machine learning, brain imaging (MEG/EEG/fMRI), and experimental approaches to study human cognition, aging, and neurological disorders. Key focus areas include: Neural mechanisms of decision-making and problem-solving Computational models of cognitive processes AI applications in healthcare diagnostics and neuroimaging Brain network dynamics in neurological conditions Recent publications emphasize deep learning models for neural data, multimodal brain connectivity, decision-making impairments in Parkinson's disease, and neuroinformatics tools. His work shows strong clinical translation through epilepsy biomarker development and emergency department outcome prediction. Zhang has led research grants from ERC, MRC, BBSRC, and Wellcome Trust. As primary investigator for multiple projects, he oversees significant computational neuroscience initiatives. He is available for postgraduate supervision.