جست‌وجوی استاد و پژوهشگر

Jeffrey A. Bilmesمشاهده پروفایل

استاد

• Machine Learning
• Optimization
• Graphical Models
• +۶ مورد دیگر

Jeffrey A. Bilmes is a Professor in the Department of Electrical and Computer Engineering at the University of Washington, Seattle, with adjunct roles in Computer Science & Engineering and Linguistics. He founded the MELODI Lab, focusing on machine learning, optimization, and data interpretation. Bilmes holds a Ph.D. from UC Berkeley and a Master's from MIT. His research spans graphical models, speech recognition, bioinformatics, and submodular optimization, with notable contributions like the GMTK toolkit and pioneering work in submodularity. He has received prestigious awards including the NSF Career Award (2001), NAE Gilbreth Lectureship (2008), and best paper awards at ICML/NIPS (2013). Bilmes has held leadership roles in UAI and NeurIPS conferences, and his work bridges theoretical foundations with practical applications in computational systems and human-computer interaction. Education: Ph.D., Computer Science, UC Berkeley; M.S., MIT. Research Interests: Machine learning, temporal graphical models, submodularity, speech interfaces, and algorithmic optimization. His work on submodular functions has been recognized with multiple awards, including the 25-year ICS award for his 1997 matrix optimization research. He actively contributes to academic service through conference organization and editorial roles at JMLR. The MELODI Lab develops cutting-edge tools like GMTK, PhiPAC, and Vocal Joystick for real-world applications. Recent Activities: Invited lectures at Yale (2016), Harvard (2015), and IIT Bombay. Co-organized NIPS workshops on discrete optimization (2013–2016). Authored influential papers on submodular optimization, semi-supervised learning, and parallel computing. Current research emphasizes submodular applications in large-scale data management and distributed systems.

• Artificial Intelligence
• Digital Pathology
• Self-supervised Learning
• +۵ مورد دیگر

Adalberto Claudio Quiros serves as a Lecturer in Artificial Intelligence within the School of Cancer Sciences at the University of Glasgow, where he bridges advanced machine learning techniques with oncological research to transform cancer diagnostics and treatment. His work focuses on developing AI-driven solutions for analyzing histopathological data to improve cancer outcome prediction and therapeutic decision-making across multiple malignancies. His research program centers on self-supervised learning applied to digital pathology, with core interests including: Development of deep learning frameworks for histomorphological pattern discovery Predictive modeling of treatment response and cancer progression Unsupervised representation learning from unlabeled whole-slide images Integration of AI with clinical oncology for precision medicine applications Quantitative analysis of tumor microarchitecture and spatial heterogeneity Analysis of his publication trajectory reveals a concentrated effort in leveraging self-supervised learning to decode histopathological features predictive of clinical outcomes. His recent work demonstrates consistent innovation in applying these techniques across diverse cancers—including cutaneous squamous cell carcinoma, mesothelioma, osteosarcoma, and lung adenocarcinoma—with particular emphasis on identifying morphological biomarkers for recurrence, metastasis, and treatment resistance. This research program shows increasing clinical translatability through collaborations with oncology and pathology departments. No scientific awards are documented in available materials. Regarding academic mentoring, no student names or advising activities are mentioned in the provided information. Similarly, details about research grants, laboratory infrastructure, or collaborative research teams are not specified in the source content.

Reza Hoseinnezhadمشاهده پروفایل

استاد

• Electrical and Electronic Engineering
• Artificial Intelligence and Image Processing
• Mechanical Engineering
• +۳ مورد دیگر

Professor Reza Hoseinnezhad is a faculty member in the School of Engineering at RMIT University, Australia. His research focuses on advanced engineering systems, including robotics, artificial intelligence, and autonomous systems. His work spans domains such as multi-object tracking, sensor fusion, and control systems with applications in underwater vehicles, autonomous driving, and manufacturing. He actively supervises research projects in areas like electronic seatbelt systems, anomaly detection, and swarm tracking. Research interests include Electrical and Electronic Engineering, Artificial Intelligence, Mechanical Engineering, and Manufacturing Engineering. His contributions leverage statistical methods, machine learning, and optimization to solve complex engineering challenges. Recent projects emphasize robust filtering, adversarial attack defenses, and distributed information fusion in connected systems. Professor Hoseinnezhad’s publications address cutting-edge topics like geometrically-informed particle filters, reinforcement learning for quadrupedal robots, and defect detection via point pattern analysis. His work bridges theoretical advancements with practical industrial and safety applications.

• Computer Networks
• Software-Defined Networking (SDN)
• Telecommunications
• +۵ مورد دیگر

José Manuel Arco Rodríguez is an Associate Professor in the Department of Automation at the Universidad de Alcalá (Spain). He holds a Doctorate from the same institution for his thesis Propuesta de optimización de la interconexión de redes con calidad de servicio para aplicaciones multimedia (2000), supervised by Dr. Daniel Meziat Luna. His research focuses on intelligent networks, software-defined networking (SDN), edge computing, and telecommunication systems, with particular emphasis on network optimization, protocol design, and AI integration in network architectures. He leads the NetIS research group (Networks and Intelligent Systems), exploring cutting-edge solutions for scalable, reliable data center networks and industrial IoT ecosystems. Key research areas include in-band network control, hybrid SDN architectures, multi-hop edge computing with IoT devices, and low-latency bridging protocols. His work spans theoretical frameworks to hardware implementations (e.g., NetFPGA), with a strong emphasis on practical applications in data centers, campus networks, and industrial environments. Recent contributions address AI-driven network management, plug-and-play routing protocols, and zero-configuration network architectures. Publications highlight advancements in SDN topology discovery (TEDP), scalable addressing (GA3), and path exploration protocols (ARP-Path, All-Path Bridging). His research bridges theoretical networking principles with real-world deployment challenges, often emphasizing interoperability between legacy and modern systems. Arco Rodríguez has collaborated on EU-funded projects and contributed to international standards for network protocols. His lab (NetIS) actively develops open-source tools for network analysis and simulation. Current research trends include AI-enhanced softwarized networks and cloud-edge continuum architectures for IIoT.

Stephanie C. Hicksمشاهده پروفایل

دانشیار

• statistics
• data science
• missing data
• +۹ مورد دیگر

Stephanie C. Hicks is an Associate Professor in the Department of Biostatistics at the Bloomberg School of Public Health, Johns Hopkins University. Her work bridges statistics, data science, and genomics, with a strong emphasis on developing computational tools for modern biological data. Research Interests: Statistical methods for genomics and single-cell data Missing data imputation and uncertainty quantification Spatial transcriptomics and multi-assay data integration Open-source software development for reproducible research Functional and epigenomic analysis Her recent research, as reflected in publications from 2025, centers on benchmarking cellular deconvolution methods, integrating spatial transcriptomics across individuals, and developing combinatorial approaches for marker gene selection. These works span high-impact journals in genomics, bioinformatics, and computational biology, indicating a strong and active research program in statistical genomics and computational biology. Scientific Contributions: Development of the HuBMAPR R package for accessing the HuBMAP data portal Leadership in benchmarking studies for cellular deconvolution Advancing methods for label-free marker gene identification Addressing reproducibility and integration challenges in spatial omics Dr. Hicks mentors students and contributes to graduate education in biostatistics, emphasizing statistical programming and workflows. She is actively involved in the development of open-source tools that enhance reproducibility and accessibility in genomic research.

Farshid Amirabdollahianمشاهده پروفایل

استاد

• Human-Robot Interaction
• Rehabilitation Robotics
• Assistive Technology
• +۶ مورد دیگر

Farshid Amirabdollahian is a Professor of Human-Robot Interaction at the University of Hertfordshire's School of Engineering and Computer Science. He leads the Robotics Research Group and directs the Robot House and Kaspar projects. His academic roles include Theme Champion in Information and Security. He has held prior positions at the University of Salford (THINKlab) and Newcastle University (CREST). His research focuses on adaptive human-robot interaction for rehabilitation, assistive technology, and AI applications. He actively contributes to ISO standards for robot safety and ethics. Research interests include: rehabilitation robotics, assistive technology, deep learning for HRI, human performance assessment, and robot ethics. He has led EU-funded projects like Accompany and SCRIPT, and current projects include SWAG (wearable assistive garments) and Hospital@home virtual ward initiatives. His work spans over 115 publications and 16 research projects. Teaching includes robotics and AI courses at undergraduate and postgraduate levels. Collaborations involve multidisciplinary teams in healthcare tech, AI, and robotics engineering. Current projects emphasize soft wearable robotics, ambient assisted living, and humanoid robots in special education.

• Medical Image Processing
• Visualization
• Extended Reality (XR)
• +۳ مورد دیگر

Gabriel Hanssen Kiss is currently an Associate Professor at the Department of Computer Science (IDI), Norwegian University of Science and Technology (NTNU), and Senior Engineer at the Operating Room of the Future, St Olavs Hospital. He holds a PhD in Engineering from K.U. Leuven, Belgium, with a focus on visualization and automated polyp detection in virtual colonoscopy, and a computer science engineer diploma from Technical University of Cluj-Napoca, Romania. Education: PhD in Engineering (K.U. Leuven), Computer Science Engineer (Technical University of Cluj-Napoca) Affiliations: NTNU (Associate Professor), St Olavs Hospital (Senior Engineer) His research focuses on medical image processing and visualization, extended reality (XR) systems, and ultrasound technology. Key subfields include volumetric data visualization, image registration/fusion, and XR applications in both medical and non-medical domains. Recent publications highlight AI-driven echocardiography, LiDAR-GNSS data fusion for localization, and mixed reality in surgical training. Collaborative work spans AI applications in transesophageal echocardiography for left ventricular function, 3D segmentation models, and augmented reality systems for medical education. He works with teams at NTNU and St Olavs Hospital, focusing on systems like the Operating Room of the Future (FOR).

• Machine Learning
• Semi-Supervised Learning
• Multi-Label Classification
• +۷ مورد دیگر

Jurica Levatić is a researcher at the Jožef Stefan Institute and the Jožef Stefan International Postgraduate School in Ljubljana, Slovenia. He is actively involved in research in machine learning and data mining, particularly in the development of semi-supervised learning methods for complex prediction tasks. His research focuses on advancing predictive modeling through interpretable tree-based methods such as Predictive Clustering Trees (PCTs), with applications in multi-label and hierarchical multi-label classification. His work integrates unlabeled data to improve model performance while maintaining interpretability, a critical aspect in intelligent systems. He also explores ensemble methods and feature weighting to enhance predictive accuracy. His recent publication in the International Journal of Intelligent Systems demonstrates strong contributions to semi-supervised learning frameworks, with extensive experimental validation across diverse domains including text, audio, biology, and ecology. The research shows significant performance gains over supervised baselines, particularly in low-label regimes. Scientific Awards: No awards mentioned in the provided text. Advising and Grants: No information available about students or advisees. No grants mentioned in the provided text. Labs and Teams: Jurica Levatić is affiliated with the Jožef Stefan Institute, a leading research institution in Slovenia, where he collaborates with experts like Sašo Džeroski and Dragi Kocev in the field of inductive machine learning and intelligent systems.

• Cardiorespiratory Medicine and Haematology
• Clinical Sciences
• Biomedical Engineering
• +۳ مورد دیگر

Dr. Giacomo Tarroni serves as an Honorary Research Fellow within the Department of Computing at the Faculty of Engineering, Imperial College London. His academic appointment positions him at the intersection of computer science, biomedical engineering, and clinical medicine, where he develops artificial intelligence solutions for critical healthcare challenges including cardiac imaging analysis and tumor segmentation through decentralized learning frameworks. His research portfolio spans multiple interdisciplinary domains: Medical Image Segmentation and Landmark Detection in cardiac CT/MRI Federated Learning for Privacy-Preserving Healthcare AI Unsupervised Anomaly Detection using Diffusion Models and Generative Techniques Cardiovascular Image Analysis and Disease Modeling Robust AI Algorithms for Heterogeneous Medical Data Synthetic Data Generation for Medical Imaging Challenges Dr. Tarroni's recent publications (2023-2025) reveal a strategic focus on solving real-world clinical problems through AI innovation. His leadership in the Federated Tumor Segmentation (FeTS) challenge demonstrates expertise in creating decentralized benchmarking systems for healthcare algorithms, while his work on diffusion-based anomaly detection (e.g., Disyre, Ensembled cold-diffusion) addresses the critical need for unsupervised methods in medical imaging. The consistent emphasis on data heterogeneity solutions and synthetic anomaly generation across his publications indicates a cohesive research trajectory aimed at making medical AI more deployable in diverse clinical settings.

• Machine Learning
• Differential Privacy
• Reinforcement Learning
• +۳ مورد دیگر

Yu-Xiang Wang is an Associate Professor at the Halıcıoğlu Data Science Institute and the Department of Computer Science and Engineering at UC San Diego. Previously, he was an Assistant Professor at UC Santa Barbara from 2018-2023, where he founded the S2ML lab and co-founded the Center for Responsible Machine Learning. He earned his Ph.D. from Carnegie Mellon University's Machine Learning Department and worked at Amazon AI before joining academia. Education: Ph.D. in Machine Learning, Carnegie Mellon University Research Focus: His work spans machine learning theory, differential privacy, reinforcement learning, and practical applications in healthcare and financial markets. His lab, S2ML, focuses on designing efficient ML algorithms with provable guarantees and addressing societal challenges like data privacy and abuse prevention. Recent Research Trends: His recent publications focus on neural network theory, differential privacy, reinforcement learning, and generative AI watermarking. Key themes include understanding the theoretical limits of deep learning, developing privacy-preserving algorithms, and ensuring the robustness of AI systems. Grants & Awards: NSF CAREER Award NSF SCALE MoDL Award NSF RI Award NSF III Award Students Advised: He has successfully advised 7 Ph.D. graduates including Ming Yin, Kaiqi Zhang, Dheeraj Baby, Rachel Redberg, Yuqing Zhu, Chong Liu, and Xuandong Li. Current Position: Actively recruiting motivated students and postdocs for his S2ML lab at UC San Diego.

• Machine Learning
• Cloud Computing
• Fault Diagnosis
• +۳ مورد دیگر

Chao Yin is a researcher at Shanghai University , Department of Computer Engineering and Science. His work spans multiple domains including Machine Learning, Cloud Computing, Fault Diagnosis, and Supply Chain Optimization. Key research areas: Machine Learning , Cloud Computing , Quantum Computing , Supply Chain Systems , Network Security Scientific Contributions (2024-2025): Developed heterogeneous graph neural networks for automotive supply chain analysis Created MSDF-VAE cloud-edge fault diagnosis framework using transfer learning Proposed quantum metrology methods with Heisenberg-limited precision Designed LARP pseudonym protocol for V2X communication Optimized fog computing resource scheduling with hybrid metaheuristics Prior Work (2012-2023): Contributed to fluid animation feature preservation from single images Developed label distribution learning for facial age estimation Designed erasure coding storage systems for big data Created multi-agent manufacturing networks in cloud environments

Jan Lellmannمشاهده پروفایل

استاد

• Variational Image Processing
• Optimization on Manifolds
• Image Registration
• +۷ مورد دیگر

Jan Lellmann is a Professor at the Institute of Mathematics and Image Computing of the University of Lübeck, with affiliations to Fraunhofer MEVIS . His research focuses on variational image processing , emphasizing the systematic formulation of prior knowledge into energy functions for improved accuracy and data efficiency. Applications span medical imaging , earth sciences , and biological data analysis . He develops non-smooth optimization methods for problems with combinatorial aspects like image segmentation . His recent work includes manifold-constrained optimization and quantum algorithms for imaging tasks. He has contributed software libraries like MFOPT (for manifold optimization) and COAL (for convex energy minimization). Scientific Awards: Best Student Paper Award at SSVM 2021 Honorable Mention at CVPR 2016 Recent Research Trends: Integration of quantum computing with classical image registration. Advancements in Riemannian geometry for protein dynamics and cryo-EM. Development of meta-learning frameworks for adaptive image alignment. Focus on non-smooth and higher-order regularization for sparse data reconstruction.

Antonio Vergariمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Machine Learning
• Probabilistic Modeling
• +۵ مورد دیگر

Antonio Vergari serves as a Reader (equivalent to Associate Professor) in the School of Informatics at the University of Edinburgh, affiliated with the Institute for Adaptive and Neural Computation (ANC). His research develops probabilistic machine learning systems that are provably reliable in real-world applications through the integration of complex reasoning, efficient inference, and neuro-symbolic learning paradigms. His primary research domains include Artificial Intelligence, Machine Learning, Probabilistic Modeling, and Neuro-symbolic AI, with specific expertise in probabilistic circuits, tensor networks, and constraint-satisfying architectures. Current investigations focus on unifying tensor factorization theory with circuit representations to overcome expressiveness limitations while maintaining computational tractability for complex reasoning tasks. Analysis of his publication trajectory reveals consistent advancement in reliable probabilistic modeling, particularly through circuit-based approaches that guarantee logical consistency in neural predictions. His work bridges theoretical foundations (e.g., expressiveness hierarchies) with practical implementations (e.g., Cirkit library), addressing critical gaps in benchmark complexity and scalable constraint satisfaction. Scientific recognition includes: ICLR 2024 Spotlight presentation (top 5% acceptance rate) NeurIPS 2023 Oral presentation (top 0.6% acceptance rate) NeurIPS 2021 Oral presentation (top 0.6% acceptance rate) Vergari leads the APRIL Lab, which actively contributes to open-source probabilistic modeling tools like Cirkit while organizing community initiatives such as the CoLoRAI workshop at AAAI-25. The lab maintains strong industry and academic collaborations focused on developing theoretically-grounded, deployable probabilistic systems.

• Computer Vision
• Machine Learning
• Healthcare AI
• +۷ مورد دیگر

Jianbo Jiao is an Associate Professor in Computer Vision and Machine Learning at the School of Computer Science, University of Birmingham, where he leads the MIx (Machine Intelligence + x) research group. Previously, he was a Postdoctoral Researcher in the Biomedical Image Analysis (BioMedIA) group and Visual Geometry Group (VGG) at the University of Oxford, working on the SeeBiByte and VisualAI projects. MSc from Peking University (2015) PhD in Computer Science from City University of Hong Kong (2018), supported by Hong Kong PhD Fellowship Scheme Visiting scholar at Beckman Institute, University of Illinois at Urbana-Champaign (2017-2018) Dr. Jiao's research focuses on Computer Vision, Machine Learning, Healthcare AI, and AI for Science. His work primarily investigates learning representations in open-world scenarios by exploring auxiliary information from multiple modalities (image/video, speech/audio, text/NLP, depth/stereo, gaze/saliency, motion) and reducing dependency on supervision through self-supervised, weakly-supervised, and semi-supervised learning approaches. His group has made significant contributions to multimodal learning, medical image analysis, and 3D vision. His recent publications demonstrate strong trends in multimodal learning, medical AI applications, and 3D computer vision. The research spans fundamental computer vision tasks like segmentation and scene understanding while addressing practical challenges in healthcare and scientific domains. Notably, there's significant focus on making vision systems more robust to distribution shifts and requiring less labeled data. Amazon Research Award recipient (2024) Royal Society Short Industry Fellow Best Paper Award at MICAD 2024 Best Paper Award at MICCAI 2024 ASMUS Workshop Best Presentation (Runner-Up) Award at MICCAI 2024 ASMUS Workshop Best Paper Award at ECCV 2022 Medical Computer Vision Workshop Multiple Outstanding Reviewer Awards from top conferences Dr. Jiao serves as Associate Editor for The Transactions on Machine Learning Research (TMLR) and IEEE Transactions on Circuits and Systems for Video Technology (T-CSVT), and has been actively contributing to the community as Area Chair for NeurIPS, ICML, ICLR, and ACM MM. His MIx group has secured multiple research grants including the Amazon Research Award for the PCo3D project on Physically Plausible Controllable 3D Generative Models. Current PhD students include Isaac Akintaro, Hao Ai, and Kangning Zhang. The MIx group maintains active collaborations with industry partners including Amazon and Meta, and has developed notable datasets such as 360+x for panoptic multi-modal scene understanding and DyMVHumans for dynamic human modeling. Their research bridges fundamental computer vision with practical applications in healthcare, physics, chemistry, and other scientific domains.

• Medical Image Analysis
• Deep Learning for Biomedical Applications
• Edge Computing and Real-Time Segmentation
• +۵ مورد دیگر

Jian Zhuang is a Professor at the Department of Artificial Intelligence, School of Computer Science and Engineering, University of Electronic Science and Technology of China. With over 30 years of academic contributions, he leads research at the intersection of medical image analysis , deep learning , and biomedical engineering . His work focuses on real-time medical imaging solutions for cardiac disease diagnosis and surgical planning. Research highlights include: Pioneering 3D CT and MRI segmentation frameworks for congenital heart disease Developing domain-adapted neural networks for clinical applications Advancing edge computing techniques for low-latency medical image processing Creating large-scale medical datasets like ImageCAS and ImageCHD His publications (90+) in venues such as Medical Image Analysis , IEEE Transactions , and MICCAI demonstrate technical leadership in medical AI and computer vision . Recognized with multiple awards including the MICCAI Best Paper Award and IEEE Access Outstanding Paper Award , he mentors a research group that has produced key tools for cardiac surgical telementoring and image-guided interventions .