جست‌وجوی استاد و پژوهشگر

Manfredo Atzoriمشاهده پروفایل

دانشیار

• Machine Learning
• Biomedical Data Analysis
• Computer Vision
• +۴ مورد دیگر

Manfredo Atzori serves as Associate Professor at the Department of Neuroscience, University of Padova, and has been a research scientist at the Institute of Information Systems of the University of Applied Sciences Western Switzerland (HES-SO Valais) since 2011. Education M.Sc. in Physics, University of Padova, 2006 Ph.D. in Bioengineering, University of Padova, 2009 Research Focus Dr. Atzori pioneers machine learning applications for biomedical multimodal data analysis. His work spans convolutional neural networks for surface electromyography (a field he helped establish globally in 2015), computer-aided cancer diagnosis in biopsies, and 3D-printed robotic prosthetic hands controlled via machine learning. He leads the Horizon 2020 ExaMode project for weakly-supervised knowledge discovery in digital pathology, integrating text and image analysis for medical diagnostics. Research Leadership As Scientific Coordinator of the Horizon 2020 ExaMode project (2019-present), he manages a seven-partner consortium advancing multimodal medical data analysis. He previously coordinated the Hasler Foundation's ProHand project for 3D-printed prosthetics and led the MeganePro Project (2016-2019), which investigated eye-hand coordination in robotic prostheses and neurocognitive effects of amputations using multimodal datasets. Since 2011, he has curated the Ninapro database—a globally utilized resource with thousands of users for robotic hand prosthesis research. Professional Recognition With over 80 peer-reviewed publications exceeding 2,000 citations, Dr. Atzori frequently presents as an invited speaker at international conferences. He serves on the editorial board of Scientific Data (Nature Publishing Group), reflecting his standing in computational biomedical research.

Paul Boniolمشاهده پروفایل

پژوهشگر

• Time Series Analytics
• Anomaly Detection
• Machine Learning
• +۲ مورد دیگر

Paul Boniol is a researcher at Inria, affiliated with the VALDA project-team—a collaboration between Inria Paris, École Normale Supérieure, and CNRS. His work focuses on time series analytics, anomaly detection, and machine learning applications. Ph.D. in Computer Science and Applied Mathematics (University of Paris, EDF R&D) Visiting Ph.D. at University of Chicago Education: Grenoble INP ENSIMAG Engineering School Research interests span: Unsupervised anomaly detection in large time series Time series management systems Machine learning for predictive maintenance Graph-based time series analysis Explainable AI for temporal data Recent publications emphasize advancements in: Weakly supervised anomaly localization Graph embedding techniques Model selection frameworks Interactive visualization tools Smart meter data analysis Scientific recognition: Paul Caseau Thesis Prize 2022 Lambdamu Congress Research-Industry Prize 2022 BDA & INFORSID Ph.D. Prizes 2022

Nicolas Duguéمشاهده پروفایل

استادیار

• Graph Mining
• Complex Networks
• Text Mining
• +۲ مورد دیگر

Nicolas Dugué is an Assistant Professor at the University of Le Mans, affiliated with the LIUM and EIAH teams. His research focuses on data mining, complex networks, and text mining, with applications in social network analysis, community detection, and interpretable embeddings. Education: PhD in Computer Science from LIFO (University of Orléans), Postdoc at LORIA (Nancy, France). His work spans graph mining, supervised learning, and clustering, particularly in social networks and scientific data analysis. Recent publications emphasize interpretable embeddings, community filtering, and modularity optimization in directed networks. He contributes to open-source tools like SINr for word and graph embeddings and explores interdisciplinary applications in medical research and scientometrics. Affiliated with LIUM (Laboratory of Computer Science of Le Mans) and EIAH (Education, Information, and Human-Computer Interaction) teams.

Gokberk Cinbisمشاهده پروفایل

دانشیار

• data-efficient machine learning
• few-shot learning
• zero-shot learning
• +۶ مورد دیگر

Gökberk Cinbiş is an Associate Professor at the Department of Computer Engineering, Middle East Technical University (METU), leading research in data-efficient machine learning with minimal supervision. His work spans zero-shot, few-shot, weakly-supervised, and self-supervised learning, with applications in vision-language integration and large-scale image/video understanding. PhD from Université de Grenoble (2014) M.A. from Boston University (2010) His research focuses on generative models, meta-learning, and robust computer vision systems. Recent work explores sparsity in parameter-efficient fine-tuning, token embeddings for vocabulary extension, and domain-aware LoRA components for personalization. Notable trends in his publications include: Advancements in zero-shot and few-shot learning Applications in remote sensing (SAR2ET for evapotranspiration) Meta-learning and hybrid augmentation techniques Sign language recognition and industrial vision benchmarks Scientific honors include: Science Academy - Young Scientist Award (BAGEP 2024) Google Faculty Research Award (2019-2020) Best PhD Thesis Prize by AFRIF (2015) Alper Atalay Best Student Paper Award (2016) He has supervised MSc students Gencer Sumbul and Berkan Demirel, secured TUBITAK grants on meta-learning and any-shot learning, and organized workshops on industrial inspection at CVPR and ECCV. Cinbiş leads METU ImageLab, focusing on computer vision and machine learning.

• Machine Learning
• Computer Vision
• Signal Processing
• +۶ مورد دیگر

Giacomo Boracchi is a researcher at the Polytechnic University of Milan , focusing on machine learning, computer vision, and signal processing. His work spans anomaly detection, change detection in data streams, 3D imaging, and biomedical applications. Key Collaborations : Diego Carrera, Luca Magri, Cesare Alippi Industries : Embedded systems, medical imaging, environmental monitoring Recent research explores adaptive Kalman filtering for battery estimation, zero-shot anomaly detection, and explainable AI for vision-language models. Publications highlight applications in waste sorting, histological data generation, and cardiac monitoring. His technical focus includes convolutional networks, ensemble learning, and domain adaptation. Boracchi's work bridges theoretical advancements with real-world systems in fraud detection, semiconductor manufacturing, and wearable health devices.

Jia Liuمشاهده پروفایل

پژوهشگر ارشد

• Bayesian statistics
• Spatial data analysis
• Image analysis
• +۳ مورد دیگر

Dr. Jia Liu serves as a Research Fellow at the Australian National University's Research School of Biology (Division of Ecology and Evolution) and concurrently holds a Technical Officer position in the Research School of Earth Sciences. Holding a PhD in Statistics, Dr. Liu conducts interdisciplinary research spanning statistical methodology, earth sciences, and computer vision applications. Education: PhD in Statistics Research focuses on Bayesian statistics, spatial data analysis, image analysis, geostatistics, and experimental design. Dr. Liu applies these methods to paleomagnetism, atmospheric science, and computer vision, with recent emphasis on uncertainty quantification and deep learning architectures for complex data analysis. Publication trends reveal dual trajectories: earth sciences applications (2020-2022) featuring directional statistics for paleomagnetic data and cloud physics modeling, alongside computer vision advancements (2021-2025) in 3D reconstruction, object detection, and segmentation. Core methodological contributions include novel approaches for spatiotemporal survey design and aleatoric uncertainty modeling. Awards: No major scientific awards documented Student advising activities and research grant funding details are not publicly available. The researcher maintains affiliations across multiple ANU schools but specific laboratory or team memberships were not specified in source materials.

Emanuele Principiمشاهده پروفایل

دانشیار

• Electrical Engineering
• Signal Processing
• Machine Learning
• +۴ مورد دیگر

Emanuele Principi serves as an Associate Professor in the Department of Information Engineering at the College of Engineering, Università Politecnica delle Marche (UNIVPM). His academic appointment falls under the scientific sector IIET-01/A - Elettrotecnica (Electrical Engineering). Based at Via Brecce Bianche 12 in Ancona, Italy, Professor Principi maintains regular office hours on Fridays from 15:00-18:00 via Microsoft Teams, and can be contacted at e.principi@univpm.it or by phone at 0712204626. Professor Principi's research spans electrical engineering with a strong focus on signal processing, machine learning applications, and energy systems. His work particularly emphasizes non-intrusive load monitoring (NILM), acoustic signal analysis, and renewable energy integration. He has developed innovative approaches combining deep learning with electrical engineering principles to address challenges in energy disaggregation, sound event classification, and smart grid technologies. His research bridges theoretical advancements with practical applications in sustainable energy systems and intelligent monitoring solutions. Analysis of Professor Principi's publication trends reveals a consistent research trajectory with increasing focus on machine learning applications for energy systems. His recent work (2023-2025) demonstrates growing sophistication in knowledge distillation techniques for non-intrusive load monitoring, with increasing attention to interpretability and edge computing implementations. He has also expanded his research into photovoltaic-thermal systems with thermoelectric generators, showing diversification into renewable energy conversion while maintaining his core expertise in electrical signal processing. Professor Principi has made significant contributions to the fields of non-intrusive load monitoring and acoustic signal processing through his extensive publication record. His work on knowledge distillation frameworks, weakly supervised learning approaches, and real-time signal processing systems has advanced methodologies in energy monitoring and sound event classification. Professor Principi actively advises students and researchers in electrical engineering, signal processing, and machine learning applications. His research projects likely involve collaborations with industry partners in energy technology and smart grid development, though specific grant details are not provided in the available information. He appears to lead or participate in research teams focused on developing practical implementations of his theoretical work, particularly in edge computing applications for energy monitoring. Professor Principi's laboratory or research group likely focuses on signal processing for energy systems and acoustic analysis, with equipment supporting electrical measurements, audio signal acquisition, and machine learning model development. His work suggests integration of hardware and software systems for real-world implementation of monitoring technologies.

Zhiyao Duanمشاهده پروفایل

استاد

• Computer Audition
• Audio-Visual Scene Understanding
• Machine Learning
• +۳ مورد دیگر

Zhiyao Duan is a Professor in the Department of Electrical and Computer Engineering and Computer Science at the University of Rochester, with affiliations to the Goergen Institute for Data Science. He holds a B.S. and M.S. from Tsinghua University and a Ph.D. from Northwestern University. Primary: Department of Electrical and Computer Engineering Secondary: Department of Computer Science Affiliated: Goergen Institute for Data Science His research focuses on Computer Audition, exploring audio-visual scene understanding, source separation, and human-computer collaborative music systems. He has received prestigious awards including an NSF CAREER Award and best paper honors at SMC 2017. His work is funded by NSF, NIH, Adobe, Kwai, and others. Recent publications emphasize audio-visual integration, speech synthesis, and deep learning architectures. He actively participates in academic service, serving on editorial boards and technical committees. The Audio Information Research (AIR) lab, which he leads, has hosted numerous students and collaborated with institutions like Northwestern University, Tsinghua University, and Stanford University. Scientific Awards: NSF CAREER Award Best Paper Award at SMC 2017 Best Paper Nomination at ISMIR 2017 Grants: NSF BIGDATA Grant No. 1741472 NSF Grant No. 1846174 for Human-Computer Collaborative Music Making University of Rochester AR/VR and Health Analytics Grants Students: Yapeng Tian Lele Chen Sefik Emre Eskimez Bochen Li Rui Lu Yujia Yan

• Computer Vision
• Panoramic and Spherical Image Processing
• Light Field Imaging
• +۷ مورد دیگر

Yike Ma is an active Associate Professor in the Department of Computer Science at the University of Science and Technology of China (USTC), School of Computer Science and Technology. With a prolific publication record spanning from 2011 to 2025, Ma has established expertise in computer vision with specializations in panoramic/spherical image processing, light field imaging, and applications in agricultural robotics and autonomous driving systems. Ma maintains a strong collaborative network, primarily with Feng Dai, Qiang Zhao, Yongdong Zhang, and Yucheng Zhang, producing numerous high-impact publications in top venues including CVPR, IJCAI, IEEE Transactions, and NeurIPS. University: University of Science and Technology of China (USTC) School: School of Computer Science and Technology Department: Department of Computer Science Research Focus: Computer Vision, Panoramic Imaging, Agricultural Robotics Ma's research interests center on advanced computer vision techniques, particularly for spherical and panoramic imagery, with significant contributions to oriented object detection, semantic segmentation, and light field processing. Recent work demonstrates increasing focus on agricultural robotics applications and autonomous driving systems, showing interdisciplinary expansion of core computer vision expertise. The research employs sophisticated deep learning approaches while addressing practical challenges in real-world applications. Analysis of Ma's 15 most recent publications reveals a clear progression toward practical applications of computer vision research, with growing emphasis on agricultural robotics (4 publications) and autonomous driving systems (3 publications), while maintaining strong foundational work in spherical/panoramic image processing (5 publications). The research demonstrates technical sophistication in handling boundary discontinuity problems, topology reasoning, and space-time perceptive clues, with increasing integration of large language models and generative AI techniques in recent work. While no specific awards are documented in the available publication records, Ma's consistent presence in top-tier conferences and journals including CVPR, IJCAI, and IEEE Transactions indicates recognition within the computer vision and AI research communities. The publication record shows steady output with increasing impact, particularly in the last five years. Ma's collaborative network is extensive, with primary collaborations through the University of Science and Technology of China. The research appears well-supported through projects in agricultural robotics and autonomous systems, though specific grant details aren't visible in the publication metadata. Ma has advised numerous graduate students as evidenced by authorship patterns where Ma appears as senior author on papers with junior researchers as first authors.

• Meta-learning
• Deep Learning
• Bayesian Deep Learning
• +۳ مورد دیگر

Boyan Gao is a Postdoctoral Researcher at the University of Oxford's Institute of Biomedical Engineering, part of the Engineering Science faculty. His research focuses on meta-learning, deep optimisation, and Bayesian deep learning, with applications in clinical machine learning. He is affiliated with the Computational Health Informatics (CHI) Lab under Prof. David Clifton, exploring optimiser and loss function learning for novel tasks. Education: PhD in Deep Learning and Machine Learning from the University of Edinburgh (supervised by Prof. Timothy Hospedales) Research Interests: Boyan's work bridges theoretical advancements in meta-learning with practical clinical applications. Key areas include gradient-based meta-learning, optimisation algorithm adaptation for unseen tasks, and Bayesian approaches in deep learning. His recent projects address challenges in medical image segmentation, physiological signal analysis, and scalable edge-based language models. Labs & Collaborations: He collaborates within the CHI Lab, focusing on clinical machine learning applications. His work intersects with digital health initiatives, leveraging AI for healthcare diagnostics and patient monitoring.

Lasse Riis Østergaardمشاهده پروفایل

دانشیار

• Medical Imaging
• Biomedical Engineering
• Image Processing and Computer Vision
• +۴ مورد دیگر

Lasse Riis Østergaard is an Associate Professor at the Department of Health Science and Technology, Aalborg University, affiliated with The Faculty of Medicine. His research focuses on medical imaging, AI applications in healthcare, and computational methods for analyzing medical images, particularly in CT and MRI. He leads projects such as 'Modelling and quantification of the cerebral cortex' and contributes to initiatives like the Danish Centre for Health Informatics and AI for the People. Key research areas include: Medical Image Analysis: Specializing in segmentation, artifact reduction, and quantitative measurements in CT/MRI AI in Surgery: Developing deep learning models for surgical action recognition and robotic skill assessment Clinical Applications: Investigating body composition via CT, lung function correlations, and placental vascular imaging He has authored over 120 publications and supervised 5 PhD students. Notable collaborations include work with the Virtual Brain project and NemoMed. Recent studies explore ethnic differences in body composition and AI-driven surgical training. Awards: None explicitly listed, but his work has received media attention including coverage in Surgical Endoscopy and Advances in Respiratory Medicine .

Matthew A. Shapiroمشاهده پروفایل

استاد

• Northeast Asian Innovation Systems
• Environmental Policy
• Energy Policy
• +۷ مورد دیگر

Matthew A. Shapiro is a Professor of Political Science at Illinois Institute of Technology’s Lewis College of Science and Letters and an Affiliate Professor at the Stuart School of Business. He holds a Ph.D. in Political Economy and Public Policy from the University of Southern California, an M.A. in Economics from the same institution, an M.A. in Korean Studies from Yonsei University, and a B.A. in Political Science from the University of California, San Diego. His research focuses on innovation systems, Northeast Asian transboundary environmental issues, politicized science, and social media’s role in political communication. He has been funded by the NSF, NOAA, and the EPA, among others, and held fellowships at Argonne National Laboratory and the East Asia Institute. Shapiro’s work bridges political science and technology policy, examining topics such as air pollution framing in Northeast Asia, filter bubbles in news recommendation algorithms, and equitable stormwater management in urban areas. His recent projects address energy policy, building electrification, and climate resilience. He has received the Fulbright U.S. Scholar award and the Woojung Scholarship. His teaching spans East Asian political economy, energy policy, and research methods in the social sciences. Key publications include analyses of news algorithm impacts, transboundary air pollution governance, and the Joint Center for Energy Storage Research. Shapiro’s grants and collaborations emphasize interdisciplinary solutions to global challenges like climate change and digital misinformation.

• Artificial Intelligence
• Evolutionary Algorithms
• Automatic Machine Learning
• +۴ مورد دیگر

Shengcai Liu is an Assistant Professor in the Department of Computer Science and Engineering at the Southern University of Science and Technology (SUSTech). Holding a PhD in Engineering, he has established himself as a researcher with over 10 publications as first or corresponding author in top artificial intelligence conferences and journals. His academic journey includes research positions at Singapore's Agency for Science, Technology and Research (A*STAR) before joining SUSTech's faculty. Education: PhD in Computer Science and Engineering, University of Science and Technology of China (2014-2020) Bachelor's in Computer Science and Engineering, University of Science and Technology of China (2010-2014) Dr. Liu's research centers on automatic algorithm design and evolutionary approaches to machine learning. He investigates how AI systems can autonomously construct efficient algorithms for complex optimization problems while maintaining robustness and reliability. His work bridges theoretical foundations with practical applications in combinatorial optimization, adversarial machine learning, and large language model development, with particular focus on making algorithm design processes more systematic and effective. His publication record demonstrates a clear trajectory from foundational work on algorithm portfolios to sophisticated integration of evolutionary methods with large language models. Recent work shows increasing focus on textual adversarial attacks, combinatorial optimization, and leveraging large language models as optimization tools themselves. This progression reflects both theoretical depth and practical relevance to current AI challenges. Professional Activities: Project Leader for Huawei-funded research on combinatorial optimization solvers (CNY 400,000) Member of IEEE, IEEE CIS, and AAAI Reviewer for top journals including TPAMI, TEVC, TCYB Conference reviewer for NeurIPS, ICML, AAAI, IJCAI Dr. Liu maintains active research collaborations through the Artificial Intelligence RAMS Technology Innovation Laboratory at SUSTech, where his team focuses on developing next-generation automatic machine learning systems with applications in optimization and security domains.

Hu Caoمشاهده پروفایل

پژوهشگر

• Autonomous Driving
• Robotic Grasping
• Medical Image Analysis
• +۵ مورد دیگر

Hu Cao is a postdoctoral research associate at the Chair of Robotics, Artificial Intelligence and Real-Time Systems (Prof. Alois Knoll) at the Technical University of Munich (TUM) . Holding a Ph.D. from TUM, his research bridges autonomous driving , robotic grasping , medical image analysis , and dense prediction (classification, detection, segmentation). Education : Ph.D. from TUM Hu's work explores: Autonomous Driving : Perception under adverse conditions, multi-sensor fusion, and risk-based safety models Robotic Grasping : Vision-language integration for 6D pose estimation Medical Imaging : Transformer-based segmentation techniques (e.g., Swin-Unet) His recent publications include 15+ works at top venues like CVPR , ICCV , IEEE TPAMI , and IEEE TIV , with 6052+ Google Scholar citations . Notably, Swin-Unet ranks among the top 3 most cited ECCV papers in 5 years, and his work on event-based autonomous driving perception was featured in IEEE Xplore Innovation Spotlight . Editorial roles include: Associate Editor for Visual Intelligence and Frontiers in Neurorobotics Editorial Board member of Artificial Intelligence and Autonomous Systems (AIAS) Topic Editor for Frontiers in Robotics and AI and Frontiers in Neuroscience He has reviewed for 20+ top journals (e.g., Nature Computational Science , IEEE TRO ) and served on program committees for NeurIPS , CVPR , ICCV , and MICCAI .

Asra Aslamمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۱۱ مورد دیگر

Dr. Asra Aslam is an incoming Assistant Professor of Data Science and AI at the University of Sheffield, UK, and currently serves as a Research Fellow at the University of Leeds, a Principal Investigator at the Alan Turing Institute, and a Visiting Researcher at Newcastle University. Her interdisciplinary work bridges artificial intelligence, computer vision, and healthcare data science. Ph.D. in Computer Vision and Machine Learning, University of Galway, Ireland (2021) M.Tech and B.Tech in Computer Science and Engineering, Aligarh Muslim University, India Former Assistant Professor at AMU, India Machine Learning Research Scientist at mindtrace.ai, Manchester, UK Her research focuses on Computer Vision , Deep Learning , Graph Neural Networks , and Health Data Sciences , with applications in multimorbidity, medical imaging, and smart cities. She develops AI tools for clinical decision support, including the DynAIRx project for medicines optimization in patients with multiple long-term conditions. Dr. Aslam's recent publications highlight trends in automated clinical codelist development , temporal graph neural networks for patient clustering , GAN-based medical data augmentation , and efficient fire detection models . Her work is published in top venues including CVPR, IEEE Access, Springer, and Elsevier journals. Women of the Future Award (Highly Commended), 2023 Education/Academic Leader of the Year, 2024 Rising Star of the Year, Women in Tech Excellence Awards, 2024 Hildegard Franke Early Career Award, 2024 Global Talent Award (Exceptional) by UKRI, 2023 IEEE/CVF, ICML, AISTATS Travel Awards She leads and co-leads major research grants including the NIHR Team Science Grant (£2.8M) , Alan Turing DSG (£80K) , and Horizon One: Global Academy Crucible . She supervises PhD and Master's students and mentors early-career researchers. Dr. Aslam is a strong advocate for diversity, serving as General Chair of Women in Computer Vision (WiCV) , AI Lead for Women in AI UK , and Early Career Researcher Representative across multiple institutions. She is actively involved in organizing workshops at CVPR, NeurIPS, and AIM-RSF, and serves on editorial boards of Discover Data (Springer Nature) and MDPI Journal of Imaging .