جستجوی استاد راهنمای دانشگاهی

• Machine Learning
• Computer Vision
• Human Behavior Analysis
• +۵ مورد دیگر

Dr. Harshala Gammulle is a Research Fellow at Queensland University of Technology (QUT), School of Electrical Engineering & Robotics. She holds a PhD in Computer Vision from QUT (2019), receiving the QUT Executive Dean's Commendation for Outstanding Doctoral Thesis. Her expertise spans machine learning, computer vision, and spatio-temporal modeling for human behavior understanding. She leads interdisciplinary projects with funding from DST Group, SmartSat CRC, QLD DESI, and others. Research focuses include: human action recognition, medical anomaly detection, satellite image analysis, and AI for environmental monitoring. Key projects involve quantum-classical hybrid ML for biomedical signal analysis, disaster forecasting via hyperspectral data, and autonomous combat vision systems. She has supervised PhD/MPhil candidates in ML and quantum hybrid ML. Education: PhD (Computer Vision, QUT 2019), BSc (University of Peradeniya, Sri Lanka). Awards: WiT Emerging Achiever Technology Award finalist (2021), University Award for Academic Excellence (2015). Teaching includes units like Digital Signals and Image Processing (EGH444), and Computing & Data for Engineers (EGB103). Current grants involve QLD DESI, SmartSat CRC, and Rheinmetall Defence Australia collaborations. Active in labs like SAIVT and QUT's Early Career Research schemes.

Rémi Flamaryمشاهده پروفایل

استاد

• Machine Learning
• Optimal Transport
• Domain Adaptation
• +۷ مورد دیگر

Rémi Flamary is a Professor in the Applied Mathematics department at École Polytechnique, France, and a member of the CMAP Laboratory. Previously, he held an Associate Professor position at Université Côte d'Azur in the Department of Electronics and Lagrange Laboratory. He completed his PhD at Rouen University under Alain Rakotomamonjy, focusing on statistical signal processing and optimization. His research interests span machine learning, optimal transport, domain adaptation, and their applications in biomedical engineering, energy, and remote sensing. He leads the development of the POT (Python Optimal Transport) library and contributes to projects like SKADA for domain adaptation. Education: PhD in Applied Mathematics, Rouen University (LITIS Laboratory) Research Focus: Flamary's work emphasizes leveraging optimal transport theory for machine learning tasks such as graph prediction, signal normalization, and cross-domain adaptation. His contributions include novel algorithms for unbalanced transport, semi-relaxed Gromov-Wasserstein distances, and end-to-end graph generation frameworks. He actively collaborates on applications in neuroscience, astronomy, and energy systems. Professional Activities: He has presented at NeurIPS and other top conferences, supervised PhD students like Cédric Vincent-Cuaz, and contributed to open-source software. His teaching includes courses on signal processing and machine learning at École Polytechnique.

Antoine Miechمشاهده پروفایل

پژوهشگر

• Computer Vision
• Machine Learning
• Natural Language Processing
• +۷ مورد دیگر

Antoine Miech is a Researcher at DeepMind's Vision Group , with prior affiliations at Inria and Ecole Normale Supérieure where he completed his computer vision Ph.D. under Ivan Laptev and Josef Sivic . He has collaborated with researchers from Facebook AI and Google during his academic career. Research Interests span video understanding, weakly-supervised machine learning, and multimodal analysis. His work focuses on: Text-video embedding Self-supervised video representation Action localization Anticipatory video modeling Scalable multimodal learning Scientific Contributions include: HowTo100M - A massive dataset of narrated instructional videos MIL-NCE - A novel loss function for video-text alignment MEE - A model for handling heterogeneous data Context Gating - Learnable pooling architecture Awards & Recognition : Google Ph.D. Fellowship (2018) Technical Leadership : Created the LOUPE TensorFlow toolbox for feature pooling and maintained annotated video dataset catalogs. Organized the Data Science Game competition (2016-2017).

Wolfgang Nejdlمشاهده پروفایل

استاد

• Search and information retrieval
• Information systems and databases
• Semantic Web and Web 2.0
• +۴ مورد دیگر

Wolfgang Nejdl is a Full Professor of Computer Science at Leibniz Universität Hannover since 1995 and the Head of the L3S Research Center since 2001. His research focuses on Web Science, search and information retrieval, semantic web technologies, peer-to-peer infrastructures, databases, technology-enhanced learning, and artificial intelligence. Education: M.Sc. (1984) and Ph.D. (1988) in Computer Science from Vienna University of Technology. Previous Positions: Assistant Professor in Vienna (1988–1992), Associate Professor at RWTH Aachen (1992–1995), and visiting professor/researcher at Xerox PARC, Stanford, University of Illinois at Urbana-Champaign, EPFL Lausanne, and PUC Rio. His research spans foundational and applied Web technologies, including social networks, trust and reputation, Web infrastructure, digital libraries, semantic web, collaborative filtering, and privacy-preserving systems. Recent projects like PHAROS, OKKAM, LiWA, and LivingKnowledge highlight his work in audio-visual search, web entities, web archive management, and diversity bias algorithms. Wolfgang Nejdl published over 230 scientific articles and held leadership roles as General Chair for AH'08 and PC Chair for WWW'09. He co-founded iSearch IT Solutions in 2006 to commercialize digital library and web engineering research from L3S projects. Scientific Awards: Founding member and head of the L3S Research Center The L3S Research Center, with a 2009 budget of €6 million (75% third-party funding), focuses on connecting the Web to real-world entities through research in Web Science, service computing, and security. Funding comes equally from the European Union and national/industry sources.

Steven Korevaarمشاهده پروفایل

مدرس

• Deep learning
• Computer vision
• Medical Imaging
• +۱ مورد دیگر

Steven Korevaar is a Lecturer and holder of an Early Career Development Fellowship (ECDF) at the School of Computing Technologies, RMIT University. His research focuses on deep learning and computer vision, particularly in domain generalization, medical imaging analysis, and algorithmic evaluation. He is actively involved in supervising research projects such as 'Learning optimal control of games and machines in real-time' and 'Learning Robust and Generalisable Models for Computer Vision Using Animation'. His publications emphasize medical imaging applications, exploring domain adaptation techniques to enhance model generalization. He has contributed to interdisciplinary work in health informatics and computer-aided diagnosis, addressing challenges in medical image classification and domain invariance. Steven is open to supervising Master's and PhD students in relevant fields. His research aligns with RMIT's commitment to innovation in computing technologies, with a focus on real-world applications in healthcare and machine learning.

• Computer Vision
• Biologically Motivated Vision
• Robot Vision Systems
• +۴ مورد دیگر

Paul Siebert is a Reader in Computing Science at the University of Glasgow, specializing in computer vision and robotics. He leads the Computer Vision and Graphics research group and teaches Digital Image Processing and Computer Systems. His research focuses on 3D vision systems, biologically inspired vision, and cognitive robot vision, with applications in clinical and media domains. He has pioneered commercial 3D surface scanning technology and collaborated with clinical groups such as Glasgow Dental School. Affiliations: University of Glasgow (Computing Science Department) Roles: Reader, Group Leader (Computer Vision and Graphics) Research interests include active binocular robot vision, 2D/3D sensing, and visual perception for robotics. Notable projects include work on driver attention monitoring, virtual character creation, and clinical anatomical imaging. Siebert previously directed the 3D-MATIC Faraday Partnership and served as Chief Executive of the Turing Institute, developing commercial vision systems. Publications span over 140 works, emphasizing applications like rain removal algorithms, continual learning in robotics, and foveated imaging. His work integrates deep learning, biological vision models, and real-world robotics challenges. Awards and recognitions are not explicitly listed, but his contributions to 3D vision commercialization and robotics research highlight significant impact in the field.

• Artificial Intelligence
• Pattern Recognition
• Biometrics
• +۶ مورد دیگر

Venu Govindaraju is a SUNY Distinguished Professor of Computer Science and Engineering at the University at Buffalo, where he also serves as Vice President for Research and Economic Development. He founded the Center for Unified Biometrics and Sensors (CUBS) in 2003 and leads the National AI Institute for Exceptional Education, funded by a $20M NSF grant. His research focuses on artificial intelligence, pattern recognition, biometrics, and handwriting analysis, with notable contributions to postal automation, cybersecurity, and early screening for dyslexia/dysgraphia in children. Govindaraju holds IEEE and ACM Fellowships, and was named Person of the Year by the Council of Heritage and Arts of India in 2024. He has advised 47 doctoral students and pioneered foundational AI technologies impacting global industries. Research interests include: AI-driven education tools, handwriting recognition systems, document analysis, and cross-domain biometric technologies. His work bridges AI with humanities through projects like the Marianne Moore Digital Archive. Recent initiatives focus on leveraging AI to address speech-language pathologies and enhance K-12 education accessibility. Leadership roles include co-chair of SUNY’s AI Task Group and advisor to the SUNY-IBM AI Research Alliance. He is a vocal advocate for responsible AI policy, advising lawmakers at White House events and contributing to legislative briefings. His innovations are highlighted in Empire AI, New York’s $400M computing consortium.

• Generalization in deep learning
• Sparse estimation
• Portfolio optimization using reinforcement learning
• +۶ مورد دیگر

LIU Peng is an Assistant Professor of Quantitative Finance (Practice) at the Lee Kong Chian School of Business, Singapore Management University. He holds a Ph.D. in Statistics and Data Science (Part-time) from the National University of Singapore (2021), an M.S. in Business Analytics (2015), and a B.Eng. in Electronic Science and Technology (2012). Prior to his academic role, he worked as a Manager at Standard Chartered Bank (2019–2022) and in analytics roles at Marina Bay Sands and IBM. Education: Ph.D. (NUS), M.S. (NUS), B.Eng. (Beijing Technology and Business University) His research focuses on generalization in deep learning, sparse estimation, portfolio optimization via reinforcement learning, financial text mining, risk management, and Bayesian optimization. His work bridges theoretical advancements with practical applications in quantitative finance and data science. Notable contributions include studies on explainable neural networks, Bayesian optimization frameworks for portfolio management, and risk analytics integrating human decision-making. His recent articles emphasize model risk assessment, cost-aware optimization, and financial data analysis. Awards: Best Ph.D. Graduate Research Award (NUS, 2020), Google TensorFlow Developer Certificate (2020–2023) He teaches courses in quantitative finance, machine learning, and risk management, and has secured grants including the Research Capability Building Fund (2023–2025). His research aligns with strategic priorities in digital transformation and financial innovation.

Mathieu Mercadierمشاهده پروفایل

استادیار

• Business Analytics
• Machine Learning
• Statistics
• +۴ مورد دیگر

Dr. Mathieu Mercadier is an Assistant Professor of Business Analytics at Dublin City University Business School, Ireland. He serves as Programme Chair for the MSc and Graduate Diploma in Business Analytics. His research focuses on Business Analytics, Machine Learning, Financial Risk Management, and Sustainable Finance. Education: PhD in Economics from Université de Limoges, France, specializing in Machine Learning applied to Banking and Finance. He has ten years of industry experience as a Market Finance Consultant. Research interests include applying Machine Learning to banking risk, sustainable finance, and quantitative finance. His work spans statistical modeling for financial stability, algorithmic risk assessment, and ESG fund evaluation. Key trends in his articles involve quantum-enhanced machine learning for stock forecasting, systemic risk measurement, and pandemic impact analysis. No scientific awards listed. Advising and grants: No formal grants or student advisees mentioned. Active in curriculum development for business analytics programs. Engaged in international conferences and seminars. Labs/Teams: Not explicitly stated in provided data.

• Fault-Tolerant Computing
• Storage Systems
• Erasure Coding
• +۳ مورد دیگر

James S. Plank is a Professor in the Department of Electrical Engineering and Computer Science at the University of Tennessee. He holds a PhD from Princeton University (1993) and has been at UT since 1993. His research focuses on fault-tolerant computing, erasure coding, distributed systems, and neuromorphic computing. He teaches programming courses from introductory to graduate levels and has won multiple teaching awards, including seven departmental awards, the College of Arts and Sciences Senior Faculty Teaching Award, and the Chancellor’s Citation for Excellence in Teaching. Plank is a member of the IEEE Computer Society and has contributed to open-source software like JGraph and Jerasure. His recent research emphasizes neuromorphic computing systems, including projects like NeuroPong and RISP Neuroprocessor. He collaborates with industry and academia on storage systems, checkpointing, and hardware-software co-design. Plank advises numerous graduate and undergraduate students, evident in his annual summer student gallery. He has secured grants such as the NSF-funded "Ground-roaming autonomous neuromorphic targeter" (2020). His lab, Neuromorphic UT, explores applications in control systems, vision, and robotics. Plank’s contributions to erasure coding and storage reliability include seminal works like the RAID-6 Liberation Code and SD codes for mixed failure modes.

Iman Nematollahiمشاهده پروفایل

پژوهشگر ارشد

• Robotics
• Robot Learning
• Reinforcement Learning
• +۵ مورد دیگر

Iman Nematollahi is a PostDoc in Robot Learning at the University of Freiburg under Prof. Dr. Abhinav Valada, having completed his PhD under Prof. Dr. Wolfram Burgard. He holds a MSc in Embedded Systems from the University of Freiburg and a BSc in Electrical Engineering from Shahid Beheshti University. His research focuses on robot learning, world models, and reinforcement learning, particularly in enabling robots to understand physics through world models and adapt skills in unstructured environments. Education: PostDoc in Robot Learning, University of Freiburg (2025–Present) PhD in Robot Learning, University of Freiburg (2019–2024) MSc in Embedded Systems, University of Freiburg (2015–2018) BSc in Electrical Engineering, Shahid Beheshti University (2010–2015) Research Interests: Robot manipulation, world models, reinforcement learning, computer vision, and self-supervised learning. His work emphasizes intuitive physics understanding, skill generalization, and sample-efficient policy improvement. Key Articles: Recent work includes LUMOS (language-conditioned imitation learning), Bayesian optimization for policy refinement, and 3D video prediction (T3VIP). These contributions bridge theory and real-world robotic applications, emphasizing long-horizon tasks and cross-environment adaptation. Awards & Grants: No explicit awards mentioned. His research has been supported through projects like OML (Organic Machine Learning). Teaching: Taught Deep Learning Lab (2020–2022) and Introduction to Mobile Robotics (2019).

Haibo Heمشاهده پروفایل

استاد

• Computational Intelligence
• Neural Networks
• Reinforcement Learning
• +۵ مورد دیگر

Dr. Haibo He is the Robert Haas Endowed Professor in the Department of Electrical, Computer, and Biomedical Engineering at the University of Rhode Island (URI). As an IEEE Fellow and NSF CAREER awardee, his research focuses on computational intelligence, neural networks, and reinforcement learning with applications to smart grids and microgrid systems. Ph.D. in Electrical Engineering, Ohio University, 2006 M.S. in Electrical Engineering, Huazhong University of Science and Technology, 2002 B.S. in Electrical Engineering, Huazhong University of Science and Technology, 1999 His research interests include: Computational Intelligence Adaptive Dynamic Programming Reinforcement Learning Deep Learning for Power Systems Distributed Control in Microgrids Imbalanced Data Learning Recent research trends from publications (2018-2025) show a focus on: Multi-agent reinforcement learning for energy systems Digital twin frameworks for grid security Event-triggered control mechanisms Finite-time convergence algorithms Cyber-attack resilient control systems Evolutionary computation in power networks Awards: IEEE Fellow (2018) NSF CAREER Award (2017) Dr. He leads the Computational Intelligence and Self-Adaptive Systems (CISA) Laboratory at URI, which conducts fundamental research on computational intelligence methods with applications to power systems, data mining, and neural networks.

• Indoor Localization
• Wireless Networks
• Pervasive Computing
• +۱۱ مورد دیگر

Shueng-Han Gary Chan is a faculty member in the Department of Computer Science and Engineering at the Hong Kong University of Science and Technology (HKUST), within the College of Engineering. He is actively engaged in research and mentoring, with a strong publication record in mobile computing, indoor localization, and AI for pervasive systems. His research focuses on indoor localization using Wi-Fi, geomagnetic, and inertial signals , sensor fusion , crowd counting with deep learning , domain adaptation , and efficient mobile AI systems . His work bridges theoretical innovation with real-world deployment, as seen in systems for missing person search and indoor navigation. Recent publications (2023–2025) show a consistent trend toward self-supervised and domain-agnostic learning , efficient model design for mobile devices , and robust signal fusion in noisy environments . His team leverages transformer architectures, graph neural networks, and novel optimization techniques to solve real-world challenges in urban and indoor spaces. He has advised numerous graduate students, including Jierun Chen, Zhuoxuan Peng, and Tianlang He, who have contributed as first authors to joint publications. His collaborations span institutions and include work on large-scale system deployments and mobile AI. He leads a research group focused on mobile and pervasive computing , with projects involving IoT-based contact tracing, indoor navigation (e.g., DeepNavi, SiFu), and real-time localization systems. The team emphasizes practical deployment and system robustness.

• Machine Learning
• Representation Learning
• Geometric Deep Learning
• +۴ مورد دیگر

Siamak Ravanbakhsh is an Associate Professor at McGill University's School of Computer Science and a Canada CIFAR AI Chair at Mila. His research focuses on machine learning, particularly representation learning with an emphasis on geometry, symmetry, and probabilistic inference. He has held academic positions at the University of British Columbia and was a postdoctoral fellow at Carnegie Mellon University. Education: B.Sc. in Computer Science, Sharif University of Technology M.Sc. and Ph.D. in Computer Science, University of Alberta (supervised by Russ Greiner) Postdoctoral Fellowship at Carnegie Mellon University (with Barnabás Póczos and Jeff Schneider) His research interests span geometric deep learning, equivariant networks, reinforcement learning, and AI for scientific applications. Notable contributions include work on symmetry-aware models, diffusion processes, and equivariant representation learning. Publications highlight advancements in causal abstraction, diffusion-based anomaly detection, and equivariant architectures for crystals and hierarchical structures. His work often bridges theory and application, emphasizing symmetry principles. Advising & Grants: Supervised over 20 graduate students and postdocs, including recent PhD graduates Daniel Levy and Mehran Shakerinava Active in mentoring M.Sc. and internship students He contributes to academic leadership roles at Mila and McGill, fostering interdisciplinary collaborations in AI research.

Julia Schnabelمشاهده پروفایل

استاد

• Medical Image Processing
• Machine Learning
• Image Reconstruction
• +۴ مورد دیگر

Prof. Dr. Julia Schnabel is the TUM Liesel Beckmann Distinguished Professor and Helmholtz Distinguished Professor at TUM's TUM School of Computation, Information and Technology. Her research focuses on computational imaging and AI in medicine, including medical image processing, machine learning, motion modeling, and quantitative imaging. She holds IEEE, Ellis, and MICCAI Society fellowships, and has pioneered work in image reconstruction, artifact correction, and AI-based diagnostics. Educations: Bachelor/Master from TU Berlin (1993) PhD from University College London (1998) Postdocs at UMC Utrecht, King's College London, and UCL Her research interests span medical AI, deep learning for medical imaging, and clinical evaluation methodologies. Key contributions include frameworks for motion artifact correction in MRI, physics-informed neural networks, and benchmark datasets like NOVA for anomaly detection in brain MRI. She has authored over 100 publications, with recent work advancing unsupervised anomaly detection and federated learning in healthcare. Prof. Schnabel leads interdisciplinary projects at TUM and Helmholtz Zentrum München, focusing on AI-driven solutions for diagnostic and therapeutic challenges. Her labs develop tools for real-time cardiac imaging, histopathology segmentation, and trustworthy AI guidelines (FUTURE-AI initiative).