جستجوی استاد راهنمای دانشگاهی

Minhyuk Sungمشاهده پروفایل

دانشیار

• Computer Vision
• Machine Learning
• Generative AI
• +۳ مورد دیگر

Minhyuk Sung is an Associate Professor in the School of Computing at KAIST, where he leads the KAIST Visual AI Group. He is also affiliated with the Graduate School of AI and the Metaverse Program. Previously, he worked as a research scientist at Adobe Research. He received his Ph.D. from Stanford University under the supervision of Leonidas Guibas and completed his M.S. and B.S. degrees at KAIST. Dr. Sung's research focuses on generating, manipulating, and analyzing various visual data, including images, videos, and 3D data. His work primarily centers around diffusion models, flow models, and other generative AI techniques applied to visual content. He has made significant contributions to the fields of 3D vision, text-to-image generation, and multi-modal AI systems. His recent publications demonstrate a strong trend toward improving the quality, efficiency, and controllability of generative models, with particular emphasis on 3D-aware generation, synchronization techniques, and grounding visual content with linguistic descriptions. His work spans multiple top-tier conferences including NeurIPS, CVPR, ICCV, and SIGGRAPH. Asiapgraphics Researcher Award (2024) Dr. Sung actively mentors students and recruits new members for his research group. He serves as an area chair for conferences like 3DV 2026 and frequently gives talks at major institutions including NVIDIA, Google, Stanford, and various universities across Asia. His research has attracted significant attention in both academic and industrial circles. He teaches advanced courses including Diffusion and Flow Models (CS492(C)), Machine Learning for 3D Data (CS479), and Diffusion Models and Their Applications (CS492(D)), demonstrating his commitment to training the next generation of AI researchers.

Daniel Junمشاهده پروفایل

استاد

• Structural Analysis
• Structural Mechanics
• Finite Element Methods
• +۵ مورد دیگر

Daniel Jun is a Professor of Structural Mechanics & Structural Analysis at Ruhr West University of Applied Sciences (Hochschule Ruhr West) in Mülheim an der Ruhr, Germany, where he also serves as Dean of Faculty 3 and Head of the Institute of Civil Engineering. With over a decade of experience in academia since joining the university in 2012, Professor Jun has established himself as a leading expert in structural engineering, particularly in the areas of cooling tower design, power plant construction, and reinforced concrete structures. Professor Jun's academic journey began at Ruhr-Universität Bochum, where he earned his Dipl.-Ing. (Master's degree) in Civil Engineering with a specialization in Structural Engineering in 1997. He continued his studies at the same institution, completing his Dr.-Ing. (PhD) in 2002 with a dissertation titled "Adaptive Simulationsstrategien für nichtlineare Finite-Element-Simulationen von Schalentragwerken" (Adaptive Simulation Strategies for Nonlinear Finite Element Simulations of Shell Structures). 2002: Dr.-Ing., Ruhr-Universität Bochum 1992-1997: Dipl.-Ing. in Civil Engineering, Ruhr-Universität Bochum Professor Jun's research spans structural analysis, structural mechanics, and computational methods with particular emphasis on cooling tower stability, power plant construction, and reinforced concrete structures. His recent work increasingly addresses sustainability aspects in construction, including carbon emission analysis. His publications consistently focus on practical engineering challenges, especially in power generation infrastructure, where he has contributed significantly to cooling tower design, maintenance, and lifecycle management standards. Over his career, Professor Jun has supervised numerous Bachelor's and Master's theses covering diverse topics from structural reinforcement techniques to AI applications in civil engineering. His professional activities include serving as a consulting engineer registered with Ingenieurkammer-Bau NRW and active participation in industry working groups. Member of the VDI advisory board "Structural Analysis" Deputy Chairman of the Structural Engineering Committee Consulting Engineer registered with Ingenieurkammer-Bau NRW Active participant in VGB Power Tech working groups Member of Bauforumstahl e.V. (Steel Construction Forum) Professor Jun teaches Structural Mechanics I & II, Structural Analysis I & II, and Numerical Methods in Engineering. His teaching combines theoretical foundations with practical applications, preparing students for real-world engineering challenges in construction and infrastructure development.

Baskar Ganapathysubramanianمشاهده پروفایل

استاد

• Computational physics
• Computational mechanics (fluid mechanics and heat transfer)
• Stochastic analysis
• +۹ مورد دیگر

Baskar Ganapathysubramanian is the Joseph C. and Elizabeth Anderlik Professor in Engineering and Professor of Mechanical Engineering at Iowa State University. His research focuses on computational mechanics, stochastic analysis, and AI-driven solutions for complex systems in agriculture and materials science. He holds an Affiliate Professor appointment and leads interdisciplinary projects at the intersection of engineering and data science. Education: Ph.D. and M.S. in Mechanical and Aerospace Engineering from Cornell University (2008, 2006); B.Tech. in Mechanical Engineering from Indian Institute of Technology Madras (2003). Research Interests: Scientific Machine Learning (SciML) for flow simulations and materials design AI in agriculture: precision phenotyping, crop modeling, and pest detection Advanced computational methods: adaptive mesh refinement, physics-informed neural networks Additive manufacturing cybersecurity and process optimization Key Research Themes: His work integrates high-performance computing with AI to address challenges in plant phenomics, organic semiconductor materials, and multiphase flow dynamics. Recent projects include TerraIncognita (species discovery), AgriField3D (maize datasets), and AIIRA (AI for resilient agriculture). Collaborations and Infrastructure: Active in the AI Institute for Resilient Agriculture (AIIRA), developing digital twins for crop systems. Co-developed tools like GRATE2.0 for polymer analysis and FlowBench for fluid dynamics benchmarking.

Michael Gaeblerمشاهده پروفایل

پژوهشگر

• Mind-Body Interaction
• Neuroscience
• Virtual Reality
• +۵ مورد دیگر

Dr. Michael Gaebler is a Researcher and Group Leader at the Max Planck Institute for Human Cognitive and Brain Sciences in Leipzig, Germany. His Mind-Body-Emotion Group investigates brain-body interactions in health and disease using advanced technologies like virtual reality (VR), neuroimaging, and psychophysiological methods. Key research topics include emotional arousal decoding, brain-heart coupling, and stress physiology. His work bridges cognitive neuroscience, clinical applications, and immersive technologies, with a focus on translational research in neurorehabilitation and mental health. Education: Dr. rer. nat. in Psychology (2010-2013), Humboldt-Universität zu Berlin & Charité - Universitätsmedizin Berlin MSc Brain & Mind Sciences (2006-2008), University College London & École Normale Supérieure Paris BSc Cognitive Science (2002-2006), Universität Osnabrück Research Interests: The group employs lab-based and real-world methodologies to study mental processes across lifespan and emotional states. Current projects explore: Physiological responses in VR environments Heart-brain interactions during emotional experiences Neuroimaging biomarkers for stress and aging Applications of AI in neuroscience data interpretation Key Tools: AffectTracker: Real-time emotion monitoring in VR Excite-O-Meter: Heart activity integration in VR experiments OpenVirtualObjects: Standardized 3D object library for VR research Grants & Collaborations: Active collaborations with Max Planck Dahlem Campus of Cognition and Leipzig Research Centre for Civilization Diseases (LIFE). Funded projects include EU Horizon and German Research Foundation (DFG) grants.

Stefanos Zafeiriouمشاهده پروفایل

استاد

• Computer Vision
• 3D Facial Modeling
• Machine Learning
• +۲ مورد دیگر

Stefanos Zafeiriou is Professor of Machine Learning and Computer Vision at Imperial College London, holding an EPSRC Early Career Fellowship. With over 12K citations and h-index 54, he leads research in 3D facial modeling, affective computing, and geometric deep learning. His group focuses on: High-fidelity 3D face/head reconstruction Neural talking head generation Facial behavior analysis Polynomial neural architectures Zafeiriou has organized major vision challenges (CVPR/ICCV), serves on editorial boards of TPAMI/IJCV, and co-founded startups Facesoft and Ariel AI. His students have received prestigious fellowships from Google, Intel, and Qualcomm.

Thomas Gärtnerمشاهده پروفایل

استاد

• Machine Learning
• Data Mining
• Graph Neural Networks
• +۵ مورد دیگر

Thomas Gärtner is a Full Professor of Machine Learning at TU Wien (Technical University of Vienna), leading the Machine Learning Research Unit (E 194-06). He previously held professorships at the University of Nottingham and the University of Bonn. His research focuses on efficient and effective machine learning algorithms, particularly in structured data domains like graphs and chemical compounds. Key areas include kernel methods, active learning, and graph neural networks. He is involved in projects such as NanoX (Austrian Science Fund) and StruDL (Vienna Science Fund), addressing challenges in structured data learning and trustworthy AI. Education: PhD from University of Bonn (2005), MSc from University of Bristol (2000), Diplom from University of Cooperative Education (1999). Research Interests: Machine Learning, Data Mining, Kernel Methods, Chemoinformatics, Graph Neural Networks, Active Learning, and Constructive Machine Learning. His work emphasizes real-world applications in chemistry and computer games. Grants & Awards: Emmy Noether Grant (2010), Best Paper Award (2016), ICLR Area Chair (2021), and recognition for contributions to EPSRC peer review (2020). Teaching: Offers courses like 'Introduction to Machine Learning' and 'Theoretical Foundations of Machine Learning.' Supervises PhD and diploma students in advanced topics such as graph algorithms and neural network distillation. Lab/Teams: Heads the Machine Learning Research Unit and collaborates with the competence center ML2R (Rhine-Ruhr region). Active in organizing ECML PKDD conferences and workshops on constructive machine learning.

Sylvain Calinonمشاهده پروفایل

مدرس

• Robotics
• Robot Learning
• Human-Robot Collaboration
• +۴ مورد دیگر

Sylvain Calinon is a Senior Research Scientist at the Idiap Research Institute and a Lecturer at the Ecole Polytechnique Fédérale de Lausanne (EPFL). He specializes in robot learning, human-robot collaboration, and optimal control. He holds a PhD from EPFL (2007) and has led research teams at IIT (2009–2014) and Idiap (2014–present). His work focuses on enabling robots to learn skills from demonstrations and interactions, emphasizing geometric and model-based approaches. Affiliations: Idiap Research Institute (Senior Scientist), EPFL (Lecturer in Microengineering) Research Interests: Robot learning, manipulation planning, optimal control, human-robot interaction, and industrial applications. Calinon's research has been supported by grants from the European Commission, Swiss National Science Foundation, and others. He has received multiple awards, including the ABB Award and Best Paper Awards at ICRA, IROS, and RSS. He has advised over 20 PhD students and contributes to editorial roles in top journals like IEEE Transactions on Robotics. His current projects include the H2020 MEMMO (memory of motion) and COLLABORATE (human-robot co-production systems). He also leads initiatives in geriatronics and industrial cobotics, emphasizing real-world applications.

Dorina Thanouمشاهده پروفایل

پژوهشگر

• Data Science
• Machine Learning
• Graph Signal Processing
• +۳ مورد دیگر

Dorina Thanou is a senior researcher and lecturer at École Polytechnique Fédérale de Lausanne (EPFL), leading strategic research initiatives on AI for Health and Science under the EPFL AI Center. Her work spans graph signal processing , machine learning , and AI for biomedical applications . She holds affiliations with multiple EPFL units, including the Signal Processing Laboratory 4 (STI IEM LTS4), the Chair of Mathematical Data Science (SB MATH MDS1), and the AI Center (VPA-AVP-CP AICENTER). Education: Diploma in Electrical and Computer Engineering (2008), University of Patras, Greece M.Sc. in Communication Systems (2010), EPFL Ph.D. in Electrical Engineering (2016), EPFL Her research focuses on interpretable and robust machine learning models for biomedicine , leveraging graph-based signal processing to analyze complex data in healthcare. Her publication trends emphasize graph representation learning , dynamic 3D data compression , and biomedical applications , particularly in cardiology and neuropathology. Scientific Awards: ELLIS Scholar in Geometric Deep Learning (2021) Best Student Paper Award, IEEE ICASSP (2015) Top 10% Paper Award, IEEE ICIP (2015) Best Paper Award, Picture Coding Symposium (2016) IEEE Senior Member (2023) She mentors PhD students Monteiro Lança Madeira Manuel and Ögüt Sevda and has led interdisciplinary collaborations through the Swiss Data Science Center (2016-2020) and Center for Intelligent Systems (2020-2024). Her labs include the Signal Processing Laboratory 4 , Mathematical Data Science Lab , and the AI Center .

Jean Hyaejin Ohمشاهده پروفایل

استاد پژوهش

• Artificial Intelligence
• Robotics
• Human-Robot Interaction
• +۷ مورد دیگر

Jean Hyaejin Oh is an Associate Research Professor at Carnegie Mellon University's Robotics Institute, where she leads the Bot Intelligence Group. Her research focuses on developing persistent robots capable of coexisting with humans through advances in AI, language understanding, multimodal perception, and navigation systems. She investigates how robots can interpret verbal commands, describe environments, generate semantic navigation plans, and explain their actions using natural language. Her work spans autonomous systems, human-robot interaction, computer vision, and machine learning, with emphasis on translating information between vision, language, and planning. Current projects include: DARPA ALIAS program (co-PI): Developing cockpit automation through semantic perception and pilot observation learning ARL RCTA program (PI): Intelligence architecture for language-guided robot navigation US DoD-Korea MOTIE collaboration (co-PI): Disaster response robotics using social media data Awards include: Best Cognitive Robotics Paper Award at ICRA 2015 She advises 10 PhD students and 1 master's student, with research outputs spanning robotic art, trajectory prediction, 3D vision, embodied AI, and autonomous exploration. Her recent publications demonstrate strong interdisciplinary focus across robotics, computer vision, and human-centered AI.

Matthias Ehrhardtمشاهده پروفایل

دانشگاهی

• Inverse Problems
• Optimization
• Machine Learning
• +۳ مورد دیگر

Matthias Ehrhardt is a Reader in the Department of Mathematical Sciences at the University of Bath, affiliated with multiple research centers including the Centre for Mathematics and Algorithms for Data (MAD), Centre for Nonlinear Mechanics, Bath Institute for the Augmented Human, and Centre for Bioengineering & Biomedical Technologies (CBio). PhD in Medical Imaging from University College London (2012-2015) MSc in Industrial Mathematics from Universität Bremen (2006-2012) Dr. Ehrhardt's research focuses on the mathematical foundations of imaging technologies, with particular expertise in inverse problems and optimization techniques applied to medical imaging. His work bridges theoretical mathematics with practical applications in biomedical imaging, developing novel computational methods for image reconstruction. He has established himself as a key researcher in the field of inverse problems, with contributions spanning regularization theory, stochastic optimization, and deep learning approaches to imaging science. His recent publications (2025) demonstrate a clear trajectory toward integrating advanced mathematical techniques with machine learning frameworks, particularly in bilevel optimization approaches for medical imaging applications. The research shows strong emphasis on geometric integration methods, stochastic optimization for large-scale problems, and novel approaches to image reconstruction that maintain mathematical rigor while improving computational efficiency. Dr. Ehrhardt serves as an Associate Editor for SIAM Journal on Imaging Sciences and has organized major conferences including 'Computational Techniques and Imaging Innovations in the Age of AI' and 'Deep Learning and Inverse Problems'. He currently leads multiple research projects with substantial funding from EPSRC and other organizations, focusing on deep learning applications to biomedical imaging. His research group operates within the Centre for Mathematics and Algorithms for Data (MAD), collaborating closely with biomedical researchers at the Royal United Hospitals Bath NHS Foundation Trust and international partners on cutting-edge imaging technologies for clinical applications.

• Robotics
• Motion Planning
• Multi-robot Systems
• +۳ مورد دیگر

Liding Zhang is a Ph.D. candidate and researcher at the Chair of Robotics, Artificial Intelligence and Real-Time Systems at the Technical University of Munich (TUM), supervised by Prof. Alois Knoll. His work focuses on advanced robotics with emphasis on motion planning and multi-robot systems. His educational background includes: Master’s degree in Mechanical Engineering and Automation Technology from Technical University of Clausthal (2022) Bachelor’s degree in Mechanical Engineering from Rhine-Waal University of Applied Sciences (2020) Zhang's research centers on sampling-based asymptotically optimal motion planning, high-DOF multi-robot manipulation, coordinated control of mobile robot fleets, and real-time performance optimization. His work explores geometric reasoning in complex robotic systems, addressing challenges in manipulation, navigation, and control through innovative algorithmic approaches that balance computational efficiency with solution quality. Key contributions include adaptive sampling techniques and optimization frameworks for dynamic environments. His publication record (2023-2025) demonstrates consistent contributions to top robotics venues including ICRA, IROS, and IEEE Transactions. The body of work reveals a cohesive research trajectory advancing sampling-based planning through innovations in informed search strategies, adaptive batch processing, and physics-inspired optimization. Recurring themes include handling high-dimensional configuration spaces, ensuring real-time performance, and developing robust solutions for multi-robot coordination and deformable object manipulation. Zhang actively contributes to the robotics community as a reviewer for major conferences (ICRA, IROS, Humanoids) and journals (RA-L, T-ASE, T-Mech), and chaired the Robot Motion Planning IV session at IROS 2024. As a researcher, he participates in nationally and EU-funded projects including Bavarian State Project KI.FABRIK. He mentors bachelor's and master's students on topics spanning LLM-based risk mapping, task and motion planning, and multi-robot coordination, with several theses resulting in peer-reviewed publications. He operates within TUM's Robotics, AI and Real-Time Systems group, which pioneers real-time control systems and advanced planning algorithms for industrial and service robotics applications, maintaining strong industry collaborations and state-of-the-art experimental facilities.

Raymond A Yehمشاهده پروفایل

استادیار

• Computer Vision
• Machine Learning
• Artificial Intelligence
• +۳ مورد دیگر

Raymond A. Yeh is an Assistant Professor in the Department of Computer Science at Purdue University since Fall 2022. Previously, he was a Research Assistant Professor at Toyota Technological Institute at Chicago (TTIC) and completed his PhD in Electrical Engineering at the University of Illinois at Urbana-Champaign (UIUC) in 2021. His research focuses on machine learning and computer vision, particularly in developing algorithms for effective and explainable models across audio, vision, language, and multi-agent systems. Education: Ph.D., Electrical Engineering, UIUC (2021) M.S., Electrical Engineering, UIUC (2016) B.S., Electrical Engineering, UIUC (2014) Research Interests: His work bridges machine learning and computer vision, emphasizing equivariance in neural networks, robustness, and scalable algorithms. Key areas include: Generative models (diffusion models, inpainting) Equivariant deep learning architectures Multi-modal reasoning (vision-language) 3D reconstruction and simulation Recent Contributions: Recent work includes model immunization techniques, scale-equivariant networks, and novel datasets like Tree-D Fusion. His publications span top venues (CVPR, NeurIPS, ECCV) with 4,551 total citations (h-index 18 as of 2025). Awards: Google PhD Fellowship (2018) Best Paper Runner-up at CVPR Workshop (2024) National Science Foundation (NSF) Grant (2024) Purdue Seed for Success Award (2024) Teaching: Courses include Introduction to AI, Computer Vision with Deep Learning, and Foundations of Deep Learning. Student evaluations consistently score above 4.5/5.0. Labs: Leads the Purdue Vision and Learning Lab, focusing on advancing AI through robust, interpretable models with practical real-world applications.

• Machine Learning
• Domain Adaptation
• Representation Learning
• +۴ مورد دیگر

Han Zhao is an Assistant Professor at the Siebel School of Computing and Data Science with a joint appointment in Electrical and Computer Engineering at the University of Illinois. His research spans machine learning foundations, focusing on domain adaptation, fairness in AI, multi-objective optimization, and transfer learning. His work bridges theoretical foundations with practical applications in computer vision and natural language processing. Research directions include: Domain generalization and adaptation techniques Explainable and fair machine learning Scalable algorithms for data attribution Multi-objective deep learning optimization Geospatial foundation models Zhao's recent publications demonstrate consistent focus on improving model robustness and interpretability. His 2024-2025 work addresses fundamental challenges in generalization, influence estimation, and efficient optimization, with applications ranging from graph neural networks to urban mobility analysis.

Michael Bronsteinمشاهده پروفایل

استاد

• Machine Learning
• Computer Vision
• Geometry Processing
• +۹ مورد دیگر

Michael Bronstein is a Professor in the Department of Computing at Imperial College London (since 2018), affiliated with the Faculty of Engineering. He holds additional roles as Visiting Professor at Tel Aviv University and Professor at USI Lugano, Switzerland. His expertise spans geometric methods in machine learning, data science, computer vision, graphics, and biomedicine. Education: PhD (Computer Science, Technion, 2007), MSc (Computer Science, Technion, 2005), BSc (Electrical Engineering, Technion, 2002). He has held visiting positions at Stanford, Harvard, MIT, and TUM. Research focuses on geometric deep learning, with applications in non-Euclidean data structures. Key projects include Project CETI, aiming to decode sperm whale communication. Industrial roles include Head of Graph Learning Research at Twitter, Principal Engineer at Intel, and leadership in startups like Fabula AI (acquired by Twitter) and Invision (acquired by Intel). Awards include the Royal Academy Silver Medal, Dalle Molle Prize, and multiple ERC grants. He is a Fellow of IEEE, IAPR, ELLIS, BCS, and a member of Academia Europaea.

Kevin Verbeekمشاهده پروفایل

استادیار

• Computational Geometry
• Graph Drawing
• Information Visualization
• +۳ مورد دیگر

Kevin Verbeek is an Assistant Professor in the Applied Geometric Algorithms group at Eindhoven University of Technology (TU/e), affiliated with the EAISI Foundational research institute. His research focuses on computational geometry, information visualization, and algorithms for cartographic challenges. He holds a PhD from TU/e (2012) and conducted postdoctoral research at UC Santa Barbara (2012–2014). He coordinates the TU/e Honors Academy's 'Competitive Programming and Problem Solving' track and is a member of the Eindhoven Young Academy of Engineering. Education: PhD in Applied Geometric Algorithms, TU Eindhoven (2012) Postdoctoral Researcher, UC Santa Barbara (2012–2014) Research Interests: Algorithmic stability for dynamic data visualization Geometric algorithms for social networks and cartography Topological data analysis and computational topology Braided river network modeling Key Projects: NWO Veni Award-funded project on stable geometric algorithms (2015–present) GLAMMap: Interactive geo-visualization for cultural heritage metadata Teaching & Outreach: Coordinator of Honors tracks in Competitive Programming Instructor for courses like 'Heuristic Algorithms' and 'Computer Science Research Project' Awards: NWO Veni Award for 'Stable Geometric Algorithms' (2015) Labs/Teams: Active in EAISI Foundational, focusing on algorithmic spatial data analysis and foundational AI research.