جستجوی استاد راهنمای دانشگاهی

Saghi Hajisharifمشاهده پروفایل

پژوهشگر

• Visual Machine Learning
• HDR Imaging
• Photo-realistic Rendering
• +۵ مورد دیگر

Saghi Hajisharif is a Researcher at Linköping University's Department of Science and Technology (ITN), affiliated with the Media and Information Technology (MIT) group. She holds a PhD in Visualization and Media Technology from Linköping University (2020), an MSc in Advanced Computer Graphics (2013), and a BSc in Computer Science from Amirkabir University (2009). Her work focuses on computational imaging, visual machine learning, HDR imaging, and light field technologies. She is a core member of the Computer Graphics and Image Processing research group. Research interests include sparse representation learning for computational imaging, synthetic data ethics, BRDF material modeling, and algorithmic fairness in AI. Her contributions span interdisciplinary projects recognized in IVA’s 100 List (2024), highlighting societal impact potential. She has co-authored influential studies on topics such as FROST-BRDF sampling techniques and metadata standards for GenAI synthetic data. Her articles reflect expertise in computer vision, graphics, and AI ethics, with key contributions to light field imaging, GAN fairness, and material modeling surveys. The IVA’s 100 List recognition underscores her innovative work’s societal relevance. She collaborates across disciplines to advance imaging technologies and ethical AI practices.

Patrick J.F. Groenenمشاهده پروفایل

استاد

• Data Science
• Multidimensional Scaling
• Optimization Algorithms
• +۵ مورد دیگر

Patrick J.F. Groenen is a full Professor of Statistics at the Erasmus School of Economics (ESE), Erasmus University Rotterdam , and currently serves as its Dean. His academic career spans leadership roles, including Director of the Econometric Institute (2014-2020) and President of the International Association for Statistical Computing (2015-2017). He has held visiting positions at Stanford University and contributed extensively to multidimensional scaling (MDS), data science , and statistical genetics . His research focuses on numerical algorithms for MDS, support vector machines , and optimization techniques applied to diverse fields. He is a founding editor of the open-access journal Journal of Data Science, Statistics, and Visualisation and has authored major textbooks including Modern Multidimensional Scaling and Applied Multidimensional Scaling and Unfolding . His work appears in top journals like Nature Genetics , Psychometrika , and Journal of Machine Learning Research . Recent publications highlight advancements in convex clustering , genomic prediction , and robust statistical methods . He has developed influential R packages such as GenSVM , SVMMaj , and smacof for multiclass classification and MDS software. His supervisory record includes 14 PhD students across economic psychology, genetics, and data science. Academic activities include editorial roles at Advances in Data Analysis and Classification and Psychometrika , alongside organizing international conferences like CARME 2007 on correspondence analysis. His contributions to nonlinear biplots and response style modeling have advanced visual data interpretation in social sciences.

Mayank Chadhaمشاهده پروفایل

استادیار مدعو

• Structural Health Monitoring
• Applied Mechanics
• Machine Learning
• +۷ مورد دیگر

Mayank Chadha is an Assistant Adjunct Professor in the Department of Structural Engineering at the University of California, San Diego. He holds a Ph.D. and M.S. in Structural Engineering from UC San Diego and a B.E. in Civil Engineering from PSG College of Technology, Anna University. His research focuses on integrating applied mechanics, machine learning, and Bayesian methodologies to address challenges in structural health monitoring (SHM), risk analysis, and optimal sensor design. He also explores the application of physics-constrained machine learning in hydrology and battery degradation modeling. Education: 2019: Ph.D. in Structural Engineering, UC San Diego 2023: Postdoctoral Fellowship, UC San Diego 2014: B.E. in Civil Engineering, PSG College of Technology Dr. Chadha’s research interests include structural health monitoring, applied mechanics, uncertainty quantification, and the development of decision-making frameworks for engineering systems. He has pioneered work on Bayesian model optimization, sensor placement strategies, and value-of-information analysis. His interdisciplinary approach bridges theoretical mechanics with practical data-driven solutions for modern engineering problems. His recent work emphasizes hybrid physics-based machine learning models for streamflow forecasting and battery RUL prediction, demonstrating the fusion of computational efficiency and physical principles. He has authored numerous articles on optimal sensor design, VoI metrics, and risk-informed decision-making in SHM systems. Scientific Awards: Gold medal for CBSE 12th exams (2010) Central Government Fellowship (2014) Gold medal for Civil Engineering proficiency (2014) In addition to academia, Dr. Chadha co-founded LogSpi LP, a quantitative investment hedge fund. He teaches courses on mechanics and signal processing, emphasizing foundational engineering principles and their real-world applications. His research also extends to the development of higher-order geometrically exact beam theories, with applications in computer graphics and shape sensing. He actively collaborates with the U.S. Army Corps of Engineers on projects involving inland waterway infrastructure and risk-based sensor design.

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

Ioannis Maniadis Metaxas is a lecturer at Queen Mary University of London's School of Electronic Engineering and Computer Science. He teaches undergraduate and postgraduate courses including Artificial Intelligence, Machine Learning, and Machine Learning for Visual Data Analysis. His research focuses on: Unsupervised representation learning Computer vision systems Large vision-language models Deep clustering techniques Generative models for data augmentation E-commerce personalization systems Recent publications demonstrate consistent focus on efficient learning methods for visual data, particularly investigating unsupervised pretraining, cluster optimization, and model fine-tuning approaches. His work shows progression from foundational 3D data augmentation to cutting-edge vision-language model optimization.

Bram C.M. Cappersمشاهده پروفایل

مدرس

• Visual Analytics
• Cybersecurity
• Data Science
• +۳ مورد دیگر

Bram C.M. Cappers is a University Lecturer in the Department of Mathematics and Computer Science at Eindhoven University of Technology. His work focuses on visual analytics, cybersecurity, and predictive analytics applied to dynamic heterogeneous networks. He holds a Master’s (2014) and PhD (2018) in Computer Science from TU/e, with doctoral research on security visual analytics for cyber threat analysis. Education: Bachelor in Software, Algorithms, Control Systems (Eindhoven University of Technology, 2012) Master in Software, Algorithms, Control Systems (Eindhoven University of Technology, 2014) PhD in Computer Graphics (Interactive Visualization of Event Logs for Cybersecurity, 2018) Research Interests: Developing predictive models for energy networks, visual analytics tools for cybersecurity incident response, and anomaly detection in complex systems. His work extends to applying these techniques in healthcare, finance, and energy sectors through startups like CodeNext21 and TIBO Energy. Key Contributions: Eventpad: Award-winning visual analytics framework for malware analysis and network intrusion detection Predictive analytics for VoIP fraud prevention (KPN partnership) Co-founder of two ventures commercializing academic research Awards: ICT.Open Award (2018) for traffic data anomaly detection Elsevier’s 30 onder de 30 (2020) for innovation leadership European Venture Program Best Executive Summary (2018) Grants & Activities: Received €150K Demonstrator grant (2018) Contributed to SECREDAS EU project (2018-2021) Speaker at Blackhat USA 2018 and multiple invited talks on cybersecurity

Charlotte Gerritsenمشاهده پروفایل

دانشیار

• Artificial Intelligence
• Sentiment Analysis
• Agent-Based Modeling
• +۷ مورد دیگر

Dr. Charlotte Gerritsen is an Associate Professor in Artificial Intelligence at the Faculty of Science, Vrije Universiteit Amsterdam (VU), and an affiliated member of the Network Institute. Her research focuses on agent-based modeling of social and emotional dynamics, emotion contagion in virtual environments, and applications of AI in crowd management, mental health, and criminology. She has published extensively on topics such as sentiment analysis, gamification, and the ethical implications of AI in public safety. Her work integrates computational methods with social science theories, addressing challenges like real-time violence detection, avatar-mediated emotion recognition, and serious gaming for resilience training. She has supervised 5 PhD theses and teaches courses on AI and Law, Artificial Intelligence, and Socially Aware Computing. Her research project, 'Integrating sentiment analysis in real-time crowd management,' highlights her commitment to bridging technology with societal needs. Education: PhD in Artificial Intelligence (implied by title 'dr.') Affiliations: Faculty of Science, Network Institute Key Research Areas: Emotion Contagion, Agent-Based Modeling, Crowd Behavior, Gamification Her articles analyze topics like emotion contagion in crowds, AI-driven crowd control, and virtual training systems. While no specific awards are listed, her prolific publication record underscores her academic impact. She advises students on criminological and computational social science projects and collaborates internationally in criminology, healthcare, and AI ethics.

• Scientific Visualization
• Data Analysis
• Medical Applications
• +۶ مورد دیگر

Ingrid Hotz is a Professor in Scientific Visualization at Linköping University, affiliated with the Department of Science and Technology (ITN) and the Center for Medical Image Science and Visualization (CMIV). She holds a Master's in Theoretical Physics from Ludwig Maximilian University (Munich) and a PhD in Computer Science from the University of Kaiserslautern. Her research focuses on data analysis and scientific visualization, spanning applications in fluid dynamics, medical imaging, and large-scale simulations. She has led research groups at the Zuse Institute Berlin (2006–2013) and the German Aerospace Center (DLR, 2013–2015). Her work integrates methods from computer graphics, computational geometry, and topology. Key research areas include multi-field visualization, topological data analysis, and scalable systems for ocean data exploration. She has contributed to software tools like VIAMD and pyParaOcean. Notable collaborations include material science research (e.g., beryllonitrene synthesis) and large-scale conference organization (Eurographics 2020, attracting 23,000 participants). Her research bridges theoretical foundations with practical applications in medicine, engineering, and environmental science. Publications highlight advancements in visualization techniques for molecular dynamics, medical imaging, and climate modeling. She actively participates in interdisciplinary projects, such as predicting liver steatosis dynamics and developing frameworks for analyzing brain activity via fMRI data. Her work emphasizes bridging gaps between computational methods and real-world scientific challenges.

Cunjian Chenمشاهده پروفایل

دانشگاهی

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

Cunjian Chen is an Adjunct Lecturer at the Department of Data Science & AI, Monash University, and a Research Fellow at Monash University Suzhou. He holds a PhD in Computer Science from West Virginia University and serves as an Associate Editor for journals like Neural Processing Letters and IET Image Processing. His research focuses on computer vision, deep learning, adversarial machine learning, and cross-spectral biometric systems. Notable contributions include advancements in thermal-to-visible face recognition, medical image segmentation, and robust adversarial defense mechanisms. Dr. Chen has received the Outstanding Area Chairs award at ICME 2021. He actively contributes to academic governance roles, including Tutorial Chair for IJCB and Area Chair for ICME, ICIP, ICASSP, IJCNN, and FG. He is currently accepting PhD and research assistant students in computer vision and deep learning, emphasizing innovative applications in security and healthcare. His research collaborations span global institutions, with recent work addressing challenges in cross-spectral imaging, medical AI, and adversarial robustness. Professional activities include patent engagements with the Monash Suzhou Research Institute. For further details, visit his personal website or Google Scholar profile .

Ilya Shpitserمشاهده پروفایل

دانشیار

• Causal Inference
• Missing Data
• Graphical Models
• +۵ مورد دیگر

Ilya Shpitser is a John C. Malone Associate Professor in the Department of Computer Science at Johns Hopkins University (JHU), within the Whiting School of Engineering. He is also a member of the Malone Center for Engineering in Healthcare and the Data Science and AI Institute. His research focuses on causal inference, missing data, graphical models, algorithmic fairness, and semi-parametric inference, with applications in healthcare, public health, and criminal justice. Shpitser holds a BA in Computer Science and Mathematics from UC Berkeley (1999), and MS (2004) and PhD (2008) in Computer Science from UCLA. Postdoctoral work included Harvard University and the University of Southampton. He has received awards such as the Causality in Statistics Education Award (2017) and the NSF CAREER Award (2020). His work addresses disparities in algorithmic bias, causal pathway analysis, and high-dimensional observational data. Key contributions include developing causal inference methods for missing data and fairness, as well as software tools like Ananke. He serves as an associate editor for the Journal of Causal Inference and the American Journal of Epidemiology. His research is funded by NSF, NIH, DARPA, and the Office of Naval Research. Shpitser advises numerous PhD students, many of whom have transitioned to academic and industry roles. His interdisciplinary collaborations span biostatistics, computer science, and public health, emphasizing real-world applications of causal methods.

• Machine Learning
• Artificial Intelligence
• Probabilistic Modelling
• +۱۰ مورد دیگر

Dr. Robert Lieck is an Assistant Professor in the Department of Computer Science at Durham University. He holds a PhD from the Machine Learning and Robotics Lab (now Learning and Intelligent Systems Lab) in Stuttgart/Berlin, Germany, and completed a postdoctoral position at the Digital and Cognitive Musicology Lab at EPFL, Switzerland. His research focuses on interdisciplinary applications of machine learning and artificial intelligence, with particular emphasis on cognitive modelling, music cognition, and ethical AI. Education: PhD in Machine Learning and Robotics, Stuttgart/Berlin, Germany (2012–2017) MSc Physics and Philosophy, Freie Universität Berlin Research Interests: Probabilistic Modelling (Bayesian inference, graphical models) Neuro-Symbolic Modelling (differentiable parsing algorithms) Structure Learning (feature discovery, hierarchical systems) Applications in music analysis, medical imaging, and autonomous systems Ethical implications of AI in policy and legislation Publications: Recent work includes advancements in deep reinforcement learning for diabetes management, recursive Bayesian networks, and computational models of musical expectancy. His research bridges theoretical AI with practical applications in musicology and healthcare. Students: Supervising four postgraduate students: Ishaq Ibrahim, Megan Finch, Ningxiang Xie, Xiaotang Zhang Labs: Active contributor to the Digital and Cognitive Musicology Lab (EPFL) and Durham's Computer Science research groups.

Liming Chenمشاهده پروفایل

استاد

• Computer Vision
• Pattern Recognition
• 3D Face Analysis
• +۲ مورد دیگر

Liming Chen is a Full Professor and Director of the Department Mathématiques - Informatique at École Centrale de Lyon, Université de Lyon. As a member of the Laboratoire d'Informatique en Image et Systèmes d'Information (LIRIS, UMR 5205), his research spans computer vision, pattern recognition, and multimedia computing. His extensive research portfolio includes 3D face analysis, image/video categorization, affect analysis, and biometric systems. He has led significant projects such as ANR 3D Face Analyzer, ANR Videosense, ANR Omnia, and ANR FAR3D, focusing on advanced recognition systems and multimodal analysis. Professor Chen supervises multiple PhD students and has developed open-source tools for 3D model processing. His work has received recognition through best performance awards at international competitions including SHREC 2011 (3D face recognition) and ImageCLEF 2011 (photo annotation).

• Data Visualization
• Machine Learning
• Neural Networks
• +۵ مورد دیگر

Professor Masa Takatsuka is a faculty member in the School of Computer Science at The University of Sydney, holding the rank of Professor. He serves as Associate Head of School (Postgraduate). His research focuses on data visualization, machine learning, and human-computer interaction, with notable contributions in spectral clustering, neural networks, and collaborative systems. He teaches courses such as COMP5048 Visual Analytics, COMP5116 Design of Networks & Distributed Systems, and others. Research interests include: Data visualization techniques (e.g., SOM-based methods) Graph neural networks and spectral clustering algorithms Interactive systems for large displays and collaborative environments Geospatial analysis and visualization tools like GeoVISTA Studio Recent publications span topics like random projection forests in graph convolutional networks (2023), topology-aware clustering (2018), and crowd-powered evaluation visualization (2017). His work emphasizes practical applications in domains like medical data analysis and social media sentiment exploration. He has secured grants including ARC Linkage Projects (2011, 2010) and industry partnerships. Current research students include Michael ASHSHIDDIQ PODBURY working on topological clustering methods. Labs/teams: Collaborations involve UnderworldGUI (geodynamic modeling tools) and projects leveraging self-organizing maps (SOM) for multidimensional data analysis.

• Computer Vision
• Computer Graphics
• Machine Learning
• +۵ مورد دیگر

Umberto Castellani is a Full Professor at the Department of Computer Science, University of Verona. His research spans computer vision, computer graphics, and machine learning, with applications in biomedical analysis and cultural industries. Research Focus 3D acquisition and registration Geometric deep learning and spectral methods Neuroimaging (diffusion MRI, fNIRS, EEG) Augmented/virtual reality and human-machine interaction Key Projects INDICE - Digital Innovations for Cultural Industries (2025) VVV - Virtual simulation of natural ecosystems (2023) SARAS - Robotic surgical assistant (2018) Collaborations with game industry and biomedical companies Laboratory : Member of Vision, Image Processing & Sound (VIPS) Lab.

Nuria Pelechano Gomezمشاهده پروفایل

استاد

• Character Animation
• Virtual Humans
• Immersive Environments
• +۳ مورد دیگر

Nuria Pelechano Gomez is a Professor at the Department of Computer Science, Polytechnic University of Catalonia (UPC), affiliated with the Barcelona School of Informatics (FIB). She leads research in the ViRVIG group (Visualització, Realitat Virtual i Interacció Gràfica), focusing on character animation, virtual humans, and crowd simulation. Her work emphasizes immersive environments and real-time rendering applications. Education: PhD in Computer Science from UPC (thesis: 'Real-Time Avatar Animation Synthesis in Virtual Reality' by Yun, H. 2023). Research interests include motion capture, crowd behavior modeling, and VR/AR applications. Recent work explores sparse data reconstruction, avatar customization, and disease spread simulations. She has contributed to over 140 publications, including high-impact journals like ACM Transactions on Graphics and Computer Graphics Forum. Awards include 'Best Teaching Assistant' in 2005 and 2006. Active in academic governance, she serves on program committees for top conferences (e.g., Eurographics, SIGGRAPH Motion in Games). Her projects (e.g., MPACT, XR4ED) are funded by national and EU grants. Labs/Teams: ViRVIG Group at UPC, collaborating with international institutions on projects like CAVAST (Crowd Animation, Visualization, and Simulation Testbed).

• Design Theory
• Critical Pedagogy
• Gender Studies in Design
• +۳ مورد دیگر

Dr. Robyn Cook is a Senior Lecturer in the Department of Graphic Design at Falmouth University. She holds a DPhil in Visual Art from the University of Pretoria (2017) and has extensive academic leadership experience, including Course Leader roles at Falmouth and the University of Johannesburg. Her research focuses on the intersection of power dynamics and design, exploring how design operates as a 'hyperobject' within societal systems. She runs The Office for Ulterior Research, blending design research and publishing. Her qualifications include a FHEA from Advance HE (2020), MA and BA(Hons) in Fine Art from Wits University, and a National Diploma in Graphic Design. She has secured significant research grants including a £57k UKRI/GCRF grant (2018-2021) and a £32k NRF Thuthuka grant (2014-2016). Currently supervising 6 PhD/Master's students, her teaching spans MA Communication Design and BA Graphic Design, emphasizing critical pedagogy and design ethics. Research interests include gender inequality in design education, post-humanist design futures, and the role of design in social change. Her work critiques neoliberal educational structures and advocates for participatory, activist-oriented practices. The Office for Ulterior Research serves as both a research repository and experimental publishing platform focusing on 'other-than-human' representations. Key articles like Unequal Stories series address systemic inequities in design, while Speculating on the future of graphic design explores AI's impact. Her Last Unstitute project challenges traditional academic structures through experimental learning environments.