جست‌وجوی استاد و پژوهشگر

Christian Walter Peter Omlinمشاهده پروفایل

استاد

• Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۷ مورد دیگر

Christian Walter Peter Omlin is a Professor at the Department of Information and Communication Technology, University of Agder. He holds an honorary position at the University of South Africa and has held academic roles across institutions in South Africa, Cyprus, and Fiji. His expertise spans artificial intelligence, machine learning, and ethical AI. Omlin’s research emphasizes explainable AI, reinforcement learning, and applications in Industry 4.0, healthcare, and particle physics monitoring. He leads projects like the Telkom/Cisco Center for IP Computing and has contributed to virtue ethics in AI agent design. Education: PhD from Rensselaer Polytechnic Institute (1995), M.Eng from ETH Zurich (1987). Research Highlights: Developed affinity-based reinforcement learning (ab-RL) for interpretable AI agents. Advanced data quality monitoring (DQM) for CERN’s CMS detector using time-aware deep learning. Explored virtue ethics in AI through role-playing agents and ethical dilemma modeling. Teaching: Courses include Principles of AI, Algorithms, Urban Computing, and Digital Health. Grants/Advising: Led the Telkom/Cisco Center and the South African Innovation Fund’s “HearSEE” consortium. Supervised theses at multiple universities. Labs/Teams: Involved in CERN’s HCAL detector anomaly detection and dental radiology AI applications.

• Deep Learning
• Biomedical Engineering
• Molecular Biology
• +۳ مورد دیگر

Ihor Smal is a Researcher at Erasmus MC's Cell biology department, specializing in computational biology and deep learning applications in biomedical research. His work focuses on developing advanced image analysis tools for clinical and preclinical studies, including segmentation algorithms and trajectory analysis frameworks. Key research areas include molecular mobility patterns (e.g., BRCA2 protein behavior at DNA damage sites), 3D fluorescence microscopy analysis, and medical image registration techniques. Smal has contributed to open-source tools like TrackSegNet, advancing trajectory segmentation methodologies. His recent publications (2024) highlight trends in weakly supervised learning, diffusive state analysis, and universal CT image segmentation. Collaborations span interdisciplinary teams addressing genetic repair mechanisms and neuroimaging challenges.

Yue Wangمشاهده پروفایل

استادیار

• Text Data Mining
• Machine Learning
• Health Informatics
• +۳ مورد دیگر

Yue (Ray) Wang is an Assistant Professor at the School of Information and Library Science, University of North Carolina at Chapel Hill. He holds a PhD in Computer Science and Engineering from the University of Michigan, and prior degrees from Shanghai Jiao Tong University and Georgia Institute of Technology. His research focuses on text data mining, machine learning, and health informatics, with an emphasis on developing interactive and interpretable algorithms to reduce human effort in data analysis. He has contributed to clinical NLP, high-recall information retrieval, and computer-assisted qualitative analysis, winning awards such as the WSDM Best Paper Award (2016) and the Deborah Barreau Award for Teaching Excellence (2021). Dr. Wang’s work bridges computational methods with real-world applications, including environmental policymaking through extreme systematic reviews and NeuroBridge—a platform for neuroimaging data discovery. He teaches courses like INLS 509 (Information Retrieval) and INLS 690-270 (Data Mining). His recent research explores user-centered explainability in AI, leveraging crowdsourced experiments and in-lab studies to understand how users interact with machine predictions. He collaborates with organizations like the EPA on machine-assisted literature screening for environmental policy. Education: BS/BA in Information Security & English (Shanghai Jiao Tong University) MS in Computer Applied Technology (Shanghai Jiao Tong University) MS in Electrical and Computer Engineering (Georgia Tech) PhD in Computer Science and Engineering (University of Michigan) Awards: UNC Junior Faculty Development Award (2022) Outstanding Reviewer Award (WSDM 2022) Deborah Barreau Teaching Excellence Award (2021) WSDM Best Paper Award & Outstanding Reviewer (2016) Dow Distinguished Award (2015) Key Research Themes: User-centric AI interpretability Health informatics applications Large-scale data screening for policy Explainable retrieval models

• Computer Vision
• Deep Learning
• Biomedical Imaging
• +۴ مورد دیگر

Meng Xu is a Visiting Professor at the School of Electronic Engineering and Computer Science, Queen Mary University of London. Her research focuses on computer vision, deep learning, and biomedical imaging applications within robotics and healthcare domains. She contributes to advancing localization systems, image processing techniques, and neural network architectures for real-world challenges. Her work bridges theoretical advancements with practical implementations, such as end-to-end visual localization networks (e.g., Bev-locator) and unsupervised methods for biomedical image enhancement. She also explores multi-modal fusion strategies (e.g., MCAPR) and optimization techniques for efficient deep learning models. Key research directions include improving camera pose estimation accuracy, developing robust feature matching algorithms, and applying deep learning to body shape classification and human pose estimation. Her publications reflect a strong emphasis on solving technical challenges in both robotics and medical imaging through innovative computational approaches.

• Multi-modal Sensor Fusion
• Smart Surveillance
• 3D Reconstruction
• +۵ مورد دیگر

Egor Bondarau is an Associate Professor at Eindhoven University of Technology (TU/e), leading the Video Coding & Architectures group. His primary research focuses on multi-modal sensor fusion, smart surveillance systems, and photorealistic 3D reconstruction. He leads a research cluster on real-time data fusion from thermal, depth, laser, and RGB sensors, supervising five PhD students and multiple MSc students. Bondarau is a project leader in EU initiatives like PaSSANT, PS-CRIMSON, and APPS, addressing multi-camera surveillance challenges. Education: MSc in Robotics and Informatics from Belarus Polytechnic University (1997); PhD in Computer Science from TU/e (2009). Academic roles include teaching Computation I (BSc) and Computer Vision AI & 3D Data Analysis (MSc). Research interests span real-time computer vision, algorithm valorization (deployed at ST Microelectronics, ViNotion, etc.), and smart surveillance innovations. His work contributes to UN Sustainable Development Goals, particularly through traffic analysis and urban monitoring. Scientific Awards: ITEA Award of Excellence 2020, Best Lecturer EE Department 2017, ITEA 2024 Excellence Award. Advising & Grants: Supervised 21 academic works. Led 9 EU-funded projects, including SMART ITEA and Interreg initiatives. Labs/Teams: Coordinates TU/e’s internal multi-modal sensor fusion cluster and collaborates with global tech partners.

Maryam Tavakolمشاهده پروفایل

استادیار

• Uncertainty in Artificial Intelligence
• Machine Learning
• Reinforcement Learning
• +۳ مورد دیگر

Maryam Tavakol is an Assistant Professor at Eindhoven University of Technology, affiliated with the Uncertainty in AI group within the Mathematics and Computer Science department. Her expertise bridges artificial intelligence, machine learning, and computational modeling for sequential systems. PhD in Machine Learning, TU Darmstadt B.Sc. & M.Sc. in Computer Science, University of Tehran Her research focuses on uncertainty estimation , reinforcement learning , and transformer architectures applied to domains like drug-target interaction prediction, soccer analytics, and music generation. Recent work explores activation sparsity in LLMs and robust VAE frameworks. Key trends in her publications (2012–2024) span reinforcement learning , neural network architectures , and applications in healthcare and creative domains . Notable themes include contextual bandits, variational inference, and energy-efficient computing. Contributed to UN Sustainable Development Goals (Education/Academic Qualification) She has supervised multiple research projects and served as corresponding author in peer-reviewed conferences. Collaborations include TU Dortmund, Leuphana University, and industry internships at Criteo.

Paul Fieguthمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Image Processing
• +۷ مورد دیگر

Paul Fieguth is a Professor and Associate Vice President - Academic Operations at the University of Waterloo. He holds affiliations with the Full-time Faculty, Faculty of Mathematics, and the Intelligent and Autonomous Systems research group. His work focuses on interdisciplinary areas including machine learning, computer vision, medical imaging, and deep learning techniques for solving complex engineering and biological problems. He has contributed to advancements in photoacoustic remote sensing, autonomous systems, and large-scale biodiversity datasets like BIOSCAN-5M. His research bridges theoretical foundations (e.g., pattern recognition, inverse problems) with practical applications in robotics, medical diagnostics, and environmental monitoring. Education details are not explicitly provided in the text, but his professional roles suggest advanced training in computer science and engineering disciplines. His research interests span a wide range, including but not limited to: pattern recognition algorithms, deep learning architectures, remote sensing technologies, and computational methods for medical imaging. Recent work emphasizes innovations in rail defect detection, 3D reconstruction, and biodiversity assessment through multimodal datasets. Publications from 2022–2025 highlight contributions to fields like neural network optimization, uncertainty quantification, and generative adversarial networks for medical applications. While no specific awards are listed, his prolific publication record reflects recognition in academic circles. Advising and grants sections remain underdeveloped in the provided information, though his leadership roles suggest involvement in institutional research initiatives. He is a key member of teams advancing technologies such as PARS imaging and autonomous systems at the University of Waterloo.

Nilesh Madhuمشاهده پروفایل

استاد

• Machine Learning
• Audio & Speech Enhancement
• Automated Audio Scene Analysis
• +۴ مورد دیگر

Nilesh Madhu is a Professor for Audio and Speech Processing at Ghent University and imec, Belgium, where he leads research in the Internet Technology and Data Science Lab (IDLab). His academic journey began with a Dr.-Ing. degree (summa cum laude) from Ruhr-Universität Bochum, Germany in 2009, followed by a Marie-Curie postdoctoral fellowship at KU Leuven. Prior to his academic appointment, he served as Principal Scientist and team lead at NXP Semiconductors from 2011-2017, developing advanced audio algorithms for mobile devices. His research spans multiple domains within signal processing, with a particular focus on machine learning applications for audio and speech enhancement, automated audio scene analysis, and hearing prostheses. Professor Madhu's work bridges theoretical signal processing with practical implementations in communications, healthcare, and assistive technologies. Analysis of his recent publication record reveals a strong emphasis on deep learning approaches for speech enhancement, with particular attention to phase reconstruction, multichannel processing, and efficient model architectures. His research increasingly incorporates biomedical applications, particularly in cardiovascular monitoring using advanced sensing techniques like laser Doppler vibrometry. His laboratory work centers around the Internet Technology and Data Science Lab (IDLab) at Ghent University, where his team develops innovative signal processing algorithms with applications spanning mobile communications, hearing aids, and biomedical monitoring systems. The group maintains strong industry connections, evidenced by their focus on practical implementation constraints and real-world performance metrics.

Honglak Leeمشاهده پروفایل

استاد

• Machine Learning
• Deep Learning
• Representation Learning
• +۵ مورد دیگر

Honglak Lee is a Professor of Computer Science and Engineering at the University of Michigan, Ann Arbor, and Executive Vice President and Chief Scientist at LG AI Research. He holds a Ph.D. from Stanford University (2010) under Andrew Ng. His research focuses on deep learning, representation learning, and their applications in computer vision, robotics, and healthcare. Lee has served on the editorial boards of IEEE TPAMI and as an area chair for ICML, NIPS, and CVPR. Education: Ph.D. in Computer Science, Stanford University (2010); M.S. in Computer Science and Applied Physics (2006); B.S. in Physics and Computer Science, Seoul National University (2003). Research Interests: Machine learning, deep learning, representation learning, multimodal learning, reinforcement learning, and applications in healthcare and robotics. His work emphasizes scalable unsupervised feature learning, invariant feature extraction, and deep generative models. Key Contributions: Pioneered convolutional deep belief networks (ICML 2009), multimodal autoencoders (ICML 2011), and weakly supervised learning techniques. His work on disentangled representations (ICML 2014) and reinforcement learning algorithms (NIPS 2015) has been highly influential. Awards: Sloan Research Fellowship (2016), NSF CAREER Award (2015), AI's 10 to Watch (2013), and multiple best paper awards. Students and Advising: Advised over 50 students, including PhD graduates Xinchen Yan (Uber), Ruben Villegas (Adobe), and Junhyuk Oh (DeepMind). Current students include Lajanugen Logeswaran, Kibok Lee, and Sungryull Sohn.

• Machine Learning
• Planning with Machine Learning
• Weakly Supervised Learning
• +۳ مورد دیگر

Yingbin Bai is a Research Fellow at Australian National University's School of Computing, collaborating with Professor Sylvie Thiebaux. His research focuses on machine learning applications in planning, weakly supervised learning, and unsupervised representation learning. He holds a PhD supervised by Professors Tongliang Liu and Dadong Wang. His work emphasizes addressing challenges in noisy label environments, early stopping techniques, and biomedical image analysis. Key contributions include QUBIQ (uncertainty quantification in medical imaging) and frameworks for robust representation learning under imperfect data conditions. Publications span topics like symmetry breaking in learning systems, semantic shift mitigation in self-supervised learning, and analysis of biomedical image competition practices. Collaborations involve cross-disciplinary teams tackling both theoretical and applied machine learning problems.

Purang Abolmaesumiمشاهده پروفایل

استاد

• Medical Imaging
• Computer-Assisted Surgery
• Machine Learning
• +۲ مورد دیگر

Dr. Purang Abolmaesumi is Professor and Canada Research Chair in Biomedical Engineering at the University of British Columbia's Department of Electrical and Computer Engineering, with cross-appointments in Urologic Sciences and the School of Biomedical Engineering. His internationally recognized research program develops advanced techniques in medical imaging analysis, machine learning for healthcare, and computer-assisted interventions. His research focuses on developing AI-driven solutions for medical challenges, particularly in ultrasound-based diagnostics for cardiovascular conditions (aortic stenosis, left ventricular function) and cancer detection (prostate, endometrial). His team works on improving robustness in medical AI through uncertainty quantification, federated learning, and domain adaptation techniques. Current projects include real-time cancer detection in micro-ultrasound, automated echocardiogram analysis, and trustworthy AI frameworks for clinical deployment. Analysis of Dr. Abolmaesumi's recent publications reveals strong emphasis on improving clinical applicability of AI through: 1) Enhanced uncertainty quantification in diagnostic models 2) Development of federated learning approaches for multi-center studies 3) Novel methods for continual learning in healthcare 4) Integration of foundation models with medical domain knowledge 5) Conformal prediction frameworks for reliable classification. His work consistently translates technical innovations toward solving concrete clinical problems in cardiac and cancer diagnostics. His scientific contributions have been recognized through major awards including the Killam Faculty Research Prize, Killam Award for Excellence in Mentoring, and Canada Research Chair appointment. As a leading researcher in medical AI, he has supervised numerous graduate students and postdoctoral fellows who have advanced to academic and industry positions. Dr. Abolmaesumi directs research laboratories focused on medical image computing and AI for healthcare applications. His team collaborates extensively with clinical partners at Vancouver General Hospital and the Vancouver Prostate Centre to validate technologies in real clinical environments. Future research directions include developing real-time AI guidance systems for interventions and creating robust diagnostic frameworks deployable across diverse healthcare settings.

Chulwoo Packمشاهده پروفایل

استادیار

• Document AI
• Computer Vision
• Deep Learning
• +۶ مورد دیگر

Chulwoo Pack is an Assistant Professor in the McComish Department of Electrical Engineering and Computer Science at South Dakota State University. His research focuses on Document AI, Computer Vision, and Deep Learning, with applications in historical document analysis, semantic segmentation, and explainable AI. He holds a Ph.D. in Computer Science from the University of Nebraska-Lincoln (2023), an M.S. (2017), and B.S. (2015) in Computer Science from South Dakota State University. Key research areas include parameter-efficient fine-tuning, weakly-supervised learning, and adaptive image processing techniques for large document images. His work bridges computer science and digital humanities, addressing challenges in archival description, historical document restoration, and cultural heritage preservation. Notable collaborations include projects with the Library of Congress on digitization and data analytics. Professional roles include membership in the ACM, participation in the Graduate Program Development Committee, and contributions to the Faculty Search Committee. His technical expertise spans software engineering, robotic navigation simulators, and intelligent tutoring systems for API misuse correction. The Pack Research Site provides further details on ongoing projects and publications.

Marco Fumeroمشاهده پروفایل

پژوهشگر ارشد

• Geometric Deep Learning
• Geometry Processing
• Representation Learning
• +۲ مورد دیگر

Marco Fumero is a PostDoctoral Researcher at the Institute of Science and Technology Austria (ISTA), where he conducts foundational research at the intersection of geometry and artificial intelligence. Previously, he completed his Ph.D. in Computer Science at Sapienza University of Rome as a core member of the GLADIA research group under Professor Emanuele Rodolà's supervision, establishing a trajectory bridging theoretical geometry with practical deep learning applications. Ph.D. in Computer Science, Sapienza University of Rome Dr. Fumero's research program centers on exploiting geometric structures to revolutionize artificial intelligence systems, with primary focus on geometric deep learning, geometry processing, and representation learning. He pioneers methodologies for analyzing neural network latent spaces through spectral geometry and dynamical systems theory, developing frameworks that enable cross-model communication and zero-shot transfer. His work systematically addresses challenges in representation alignment, latent space dynamics, and disentangled feature extraction, with direct applications in 3D shape analysis, multimodal learning, and quantum-inspired computing. This research demonstrates exceptional theoretical rigor while maintaining strong connections to real-world problems in computer vision and scientific computing. His publication record reveals a dominant trend toward unifying geometric principles with deep learning architectures, particularly through spectral methods and functional map theory. The 2024-2025 publications showcase a coherent evolution from foundational latent space analysis (e.g., attractor dynamics in autoencoders) to practical frameworks for cross-model communication (e.g., cycle-consistent merging and semantic alignment). Key thematic threads include zero-shot capability development, invariance exploitation, and the translation of classical geometry processing techniques into neural network contexts. These contributions have established new paradigms for latent space manipulation across computer vision, graphics, and multimodal AI. Spotlight presentation at ICLR 2024 for "From Bricks to Bridges: Product of Invariances to Enhance Latent Space Communication" Multiple papers accepted at NeurIPS 2024 including "Latent Functional Maps" and "C2M3" During his doctoral training at Sapienza, Dr. Fumero actively mentored junior researchers within the GLADIA group, contributing to the development of next-generation geometric AI specialists through collaborative projects and technical guidance. His research has been supported by institutional funding from Sapienza University and ISTA, with potential backing from European research initiatives targeting foundational AI advances. Current work focuses on scaling geometric deep learning frameworks to complex multimodal scenarios while maintaining theoretical guarantees. Dr. Fumero maintains strong ties to the GLADIA research group at Sapienza University of Rome, which specializes in geometric learning and data analysis. At ISTA, he operates within a highly collaborative interdisciplinary environment that emphasizes theoretical computer science and its applications, contributing to the institute's mission of advancing frontier research through mathematical rigor and computational innovation.

• Computer Vision
• Deep Learning
• Object Detection
• +۳ مورد دیگر

Chunluan Zhou is a researcher at Nanyang Technological University (NTU), Singapore, with a PhD in Computer Vision and Deep Learning. His research focuses on object detection, pedestrian detection, transformer-based tracking, and occlusion reasoning. He has published extensively in top-tier conferences and journals such as ICCV, CVPR, ECCV, TIP, and TCSVT. Education: PhD from NTU under Prof. Junsong Yuan and Prof. Kai-Kuang Ma, M.Eng from Zhejiang University, and B.Eng from Harbin Institute of Technology. Research Interests: Computer Vision and Deep Learning, with emphasis on object detection, occlusion reasoning, transformer networks, and video analysis. His work addresses challenges like heavily occluded pedestrian detection, weakly supervised learning, and scene-debiasing action recognition. Professional Contributions: Active reviewer for top conferences (CVPR, ICCV, ECCV, NeurIPS) and journals (TIP, TCSVT, PR). His recent research includes scene-debiasing open-set action recognition (SOAR), cyclic self-training for object detection, and transformer-based visual tracking methods like AiATrack. Labs/Teams: Collaborates with Prof. Junsong Yuan's research group, focusing on computer vision and deep learning applications.

• Computer Vision
• Remote Sensing Systems
• Video Sports Analytics
• +۷ مورد دیگر

David A. Clausi is a Professor and University Research Chair at the University of Waterloo in the Department of Systems Design Engineering, Faculty of Engineering, with a distinguished career spanning computer vision, image processing, and pattern recognition. He previously served as Associate Dean - Research and External Partnerships (2019-2024) and leads the Vision and Image Processing (VIP) Research Group, whose work bridges academic research with commercial applications including the spinout company CREZ. His academic foundation was built entirely at the University of Waterloo: Doctorate in Systems Design Engineering (1996) Master's in Systems Design Engineering (1992) Bachelor's in Systems Design Engineering (1990) Professor Clausi's research pioneers AI-driven remote sensing for Arctic sea ice monitoring and video sports analytics in baseball/ice hockey. His group develops cutting-edge algorithms for sea ice classification, player tracking, and hyperspectral imaging, with strong emphasis on uncertainty quantification and weakly supervised learning techniques that solve real-world challenges in environmental monitoring and sports technology. Analysis of his 2023-2025 publications reveals dominant trends in sea ice analysis using SAR satellite imagery (particularly AI4Arctic Challenge datasets) and sports video analytics, with growing integration of Bayesian methods and transformer networks for enhanced accuracy in polar regions and athletic performance analysis. His exceptional contributions are recognized through: University Research Chair appointment (2024-2031) Triple Fellowship status (CAE, EIC, and Asia-Pacific AIA) CIPPRS Lifetime Achievement Award (2010) Five-time Outstanding Performance Award recipient Stanford University "Top 2% Scientist" designation As an active educator teaching SYDE 575 (Image Processing) and SYDE 121 (Digital Computation), he mentors graduate students under Sole-Supervisory Privilege Status. His research receives substantial grant support from federal agencies and industry partners, particularly for Arctic monitoring initiatives and sports technology commercialization through the VIP Research Group. The VIP Research Group maintains strategic partnerships with government agencies like the Canadian Ice Service and sports analytics firms, driving innovation in AI applications for climate resilience and athletic performance through cross-disciplinary collaboration between engineering, computer science, and domain specialists.