Find Academic Supervisors

• Haptic Perception
• Multisensory Integration
• Material Properties
• +4 more

Dr. Anna Metzger is a Lecturer in Psychology at Bournemouth University since 2021. She holds a BSc in Biology from Hanover, MSc in Cognitive Science from Osnabrück, and a Dr. rer. nat. in Psychology from Justus-Liebig University Giessen. Her research focuses on haptic and visual perception, particularly object shape/material property encoding through active touch and multisensory integration. She uses motion/eye-tracking, computational modeling, and virtual/augmented reality. Research Interests include: Exploration strategies in tactile perception Unsupervised learning of material properties Neural correlates of haptic perception Computational modeling of tactile saliency Her work has been recognized with a Best Paper Award (2018) from the Eurohaptics Society. Current grants explore haptic texture synthesis and 3D shape perception. She teaches courses on cognitive neuroscience, experimental design, and statistics at both undergraduate and postgraduate levels. Grants : Synthesising Haptic Textures through Unsupervised Learning (British Academy, 2024-) Perception of objects’ 3D shape (Royal Society, 2024-) She collaborates internationally and serves as Associate Editor for IEEE Transactions on Haptics and Eurohaptics Conference Proceedings .

Xiaoyu ChenView profile

Assistant Professor

• Machine Learning
• Advanced Manufacturing
• Cyber Manufacturing Systems
• +5 more

Xiaoyu Chen serves as an Assistant Professor in the Department of Industrial and Systems Engineering within the School of Engineering and Applied Sciences at the University at Buffalo. His academic foundation includes a PhD in Industrial and Systems Engineering (2021) and an MEng in Computer Science (2021) from Virginia Tech, complemented by a BS in Optical Information Science and Technology from Beijing Institute of Technology (2015). Dr. Chen's research spans machine learning methodology for advanced manufacturing, networked data analytics in cyber manufacturing systems, statistical methods for Bayesian deep learning, smart connected health applications for organ transplantation and perioperative medicine, and human behavior/performance modeling. His work bridges industrial engineering with cutting-edge AI techniques to solve complex problems in manufacturing and healthcare systems. Analysis of recent publications reveals a strong focus on developing interpretable and context-aware AI systems for manufacturing and clinical applications. Key trends include multimodal learning frameworks, distributed data processing for fog manufacturing, human-AI collaboration paradigms, and medical diagnostics through novel data modalities like breath analysis. Dr. Chen actively teaches core engineering courses including Probability & Statistics for Engineers (IE 360/560), Simulation (IE 541), and Predictive Analytics (Deep Learning) (IE 463/663), demonstrating commitment to both foundational and emerging topics in industrial engineering education.

• Bayesian Statistics
• Time Series Analysis
• Econometrics
• +3 more

Gregor Kastner is Professor and Deputy Head of the Institute of Statistics at the University of Klagenfurt. His research focuses on Bayesian statistics, time series analysis, econometrics, and computational methods, with applications in finance, economics, and environmental modeling. He develops statistical software including packages for stochastic volatility modeling in R. Kastner's methodological work centers on Bayesian inference for high-dimensional problems, developing efficient computational algorithms for complex models. His applied research examines volatility dynamics in financial markets, macroeconomic forecasting, and spatial analysis of economic indicators. Recent projects include Bayesian nonparametric clustering for evaluating agricultural subsidies in Europe, sparse vector autoregressions for high-dimensional forecasting, and stochastic volatility models for commodity markets. He maintains active collaborations across economics, finance, and environmental science disciplines.

Yiming XuView profile

Assistant Professor

• Mathematics of Data Science
• Random Sampling and Approximation
• Statistical Ranking
• +3 more

Yiming Xu is an Assistant Professor in the Department of Mathematics at the University of Kentucky. He holds a Ph.D. from the University of Utah, completed a postdoc at the University of Waterloo, and worked as a Quantitative Analyst at Wells Fargo Bank. His research focuses on the mathematics of data science, including random sampling, statistical ranking, optimization-based uncertainty quantification, and multifidelity methods. Xu emphasizes problem-driven innovation and collaborates across disciplines. Education: Ph.D. in Mathematics, University of Utah, 2022 Postdoctoral Fellowship, University of Waterloo Quantitative Analyst, Wells Fargo Bank Research interests span computational statistics, numerical analysis, and machine learning applications. His work bridges theoretical foundations and practical data-driven problems, often integrating optimization and probabilistic techniques. Recent contributions include advancements in gradient descent for network analysis, multifidelity distribution estimation, and archetypal analysis using optimal transport. Publications reflect a trajectory in stochastic methods, statistical learning, and computational mathematics, with applications in PDE solvers and uncertainty quantification. Awards: None explicitly listed. Advising and grants: Actively seeks motivated students for collaborative projects. Teaching includes courses on matrix theory, data science, and numerical methods.

• 3D Computer Vision
• Shape Modeling
• Virtual Humans
• +4 more

Victoria Fernandez Abrevaya is a post-doctoral researcher at the Max Planck Institute for Intelligent Systems (Perceiving Systems department) in Germany. She holds a PhD from Inria Grenoble (France) under Professors Edmond Boyer and Stefanie Wuhrer, and a MSc in Computer Science from the University of Buenos Aires, Argentina. Her work focuses on 3D reconstruction and understanding of humans from 2D data, with emphasis on facial animation, neural rendering, and generative models. Research interests: 3D computer vision and shape modeling Neural rendering techniques Diffusion models for motion and appearance synthesis Biometric fairness in face analysis Real-time face capture systems Geometry-constrained multi-human rendering Recent work explores occluded face expression reconstruction (OFEr 2025), interactive dynamics modeling (InterDyn 2025), and latent realignment for motion diffusion (Lead 2025). Her SPARK system (2025) enables real-time monocular face capture through self-supervised learning. Prior contributions include FLAME (2023), a popular 3D face model framework, and work on multilinear autoencoders for dynamic facial analysis (2018). She co-developed ImAvatar (2022), an implicit morphable head avatar system from videos.

• Transportation Systems
• Behavioral Modeling
• Machine Learning
• +5 more

Rico Krueger is an Associate Professor at the Department of Technology, Management and Economics within the Transport Division at the Technical University of Denmark (DTU). His research focuses on developing models and technologies at the intersection of behavioral modeling, machine learning, and simulation to improve human-centric and sustainable transport systems. Key areas include travel demand forecasting, multimodal systems, and emerging technologies like virtual reality for understanding human decision-making. Education: Ph.D. in Civil and Environmental Engineering from UNSW Sydney (Australia), followed by postdoctoral research at École Polytechnique Fédérale de Lausanne (EPFL), Switzerland. He holds a prestigious European Research Council Starting Grant (2024–2029) for his project IMMERSION , exploring human decision-making through choice and process data integration. Research emphasizes interdisciplinary approaches, combining data science with behavioral theories to address challenges in urban mobility, sustainability, and public health. His work bridges theoretical advancements with practical applications, such as optimizing policy interventions during pandemics and enhancing ride-sourcing systems. Awards: ERC Starting Grant for IMMERSION project. Grants: Focus on behavioral modeling and pandemic-related transport policies. Labs/Teams: Active in DTU’s Intelligent Transport Systems Section, leading interdisciplinary projects on mobility and health.

Tarek M. TahaView profile

Professor

• Artificial Intelligence
• Neuromorphic Computing
• High Performance Computing
• +4 more

Dr. Tarek M. Taha is a Professor in the Department of Electrical and Computer Engineering at the University of Dayton. He leads the Parallel Cognitive Systems Laboratory, focusing on AI algorithms, neuromorphic computing, and high-performance systems. His research spans deep learning applications (e.g., medical imaging, cybersecurity), memristor-based architectures, and FPGA acceleration. Education: Ph.D., M.S., and B.S. in Electrical Engineering from Georgia Institute of Technology (2002, 1998, 1996), and a B.A. in Pre-Engineering from DePauw University (1996). Active collaborations include AFRL, NSA, Sandia National Labs, and multiple universities. Research Interests: Neuromorphic computing, memristor circuits, AI hardware acceleration, parallel algorithms, and cybersecurity. Key contributions include neuromorphic spiking processors (e.g., Intel Loihi), memristor device modeling, and low-power neuromorphic systems for real-time applications. Awards: NSF CAREER Award (2007), IEEE Dayton Bio-Engineering Award (2018), and multiple best-paper recognitions. Over 150 publications in top-tier journals/conferences. Advising: Supervised over 30 graduate students (listed in full description) and collaborates on grants from AFOSR, NSA, and industry. Labs include the Parallel Cognitive Systems Lab with state-of-the-art neuromorphic hardware and memristor fabrication capabilities.

Paraskevopoulos StefanosView profile

Professor

• Environmental Education
• Special Education
• Natural Resource Management
• +4 more

Professor Paraskevopoulos Stefanos holds an academic position at the University of Thessaly, focusing on Environmental Education and related interdisciplinary areas. His research spans special education methodologies, natural resource management in ecologically sensitive regions, and accessibility of environmental spaces for marginalized groups. He has collaborated extensively on projects involving virtual environments for educational purposes and behavioral studies related to environmental awareness. His work emphasizes practical applications of ecological principles through innovative teaching methods, particularly for students with disabilities. Key contributions include studies on public perception of marine conservation, policy frameworks for sustainable tourism in protected areas, and pedagogical strategies for integrating environmental science into special education curricula. Research interests include cross-cultural environmental psychology, the role of technology in ecological education, and systemic approaches to resource allocation challenges. His publications frequently bridge social sciences with environmental management, addressing both theoretical and applied aspects of human-environment interaction.

Hsiao-Chun WuView profile

Professor

• Statistical Learning
• Digital Signal Processing
• Wireless Communications
• +9 more

Hsiao-Chun Wu is a Professor and holder of the Michel B. Voorhies Professorship at Louisiana State University (LSU), affiliated with the Division of Electrical & Computer Engineering within the School of Electrical Engineering and Computer Science. He earned his Ph.D. in 1999 from the University of Florida. His research focuses on statistical learning, embedded algorithms, digital signal processing, and wireless communications. Key areas include optimization techniques for detection and estimation, image/speech processing, and networked systems design. He has contributed to advancements in sensor deployment strategies, tensor-based signal processing, and machine learning applications for posture recognition and multimedia classification. Notable technical interests span computational photography, robust multichannel decorrelation, and energy-efficient data collection in wireless sensor networks. His work often integrates tensor analysis and graph convolutional networks to address challenges in high-dimensional data processing. Dr. Wu's academic contributions include pioneering methods for indoor line-of-sight coverage optimization and developing novel modulation recognition techniques. His research also addresses industrial IoT performance optimization and biomedical applications such as noninvasive activity recognition using mmWave radar.

Angel ChangView profile

Research Fellow

• Computer Vision
• Machine Learning
• 3D Scene Understanding
• +3 more

Angel Chang is a Hans Fischer Fellow at the Technical University of Munich (TUM), hosted by Prof. Matthias Nießner in the Visual Computing Focus Group. She holds affiliations with Simon Fraser University and Eloquent Labs. Her research bridges natural language processing with 3D scene understanding, focusing on grounding textual descriptions to 3D data through innovations like ScanNet and ShapeNet. Key contributions include dense captioning, object localization in RGB-D scans, and unified vision-language models. Her work addresses challenges in 3D scene reconstruction, CAD model alignment, and interactive environments. Notable projects include SAPIEN for simulated part-based interactions and PartNet, a benchmark for hierarchical 3D object understanding. Chang's research aims to advance AI systems that interpret and generate complex 3D environments using multimodal data. Publications span top venues like CVPR and ACM Transactions on Graphics, demonstrating impact in computer vision, robotics, and machine learning. She collaborates across institutions to develop datasets and frameworks that drive progress in digital scene modeling and human-AI collaboration.

• Probability Theory
• Multivariate Statistics
• Gamma Distributions
• +2 more

Philippe Bernardoff is an Associate Professor at the University of Pau and the Pays de l'Adour. His research focuses on advanced probabilistic models, including multivariate gamma distributions, negative multinomial laws, and Laplace transform applications in statistical dependence structures. His work bridges theoretical probability and practical applications in areas like polarimetric image processing. Bernardoff's research interests center on developing statistical methodologies for complex multivariate systems, with emphases on distribution theory, simulation algorithms, and copula-based dependence modeling. His publications consistently explore the mathematical frontiers of infinitely divisible distributions and their computational implementations. His recent articles demonstrate a strong focus on advancing simulation techniques for gamma distributions and expanding the theoretical understanding of Laplace transforms in multivariate contexts. This work has implications for high-dimensional data analysis and stochastic modeling.

• Fluid Mechanics
• Coastal Sciences and Engineering
• Numerical Modeling
• +15 more

Cihan Bayındır is an Associate Professor at Istanbul Technical University's Faculty of Civil Engineering, Department of Civil Engineering, specializing in fluid mechanics, coastal engineering, and computational methods. His research integrates advanced mathematical techniques with practical engineering applications across multiple disciplines. His primary research areas include Fluid Mechanics, Coastal Sciences and Engineering, Numerical Modeling, Fluid Physics, Acoustics and Vibrations, Nonlinear Dynamics, and Quantum Hydrodynamics. Bayındır's work demonstrates a strong interdisciplinary approach, bridging traditional civil engineering with cutting-edge computational methods and quantum phenomena. His research has significant applications in coastal protection, renewable energy systems, disaster management, and infrastructure resilience. Analysis of his recent publications reveals a clear trend toward applying artificial intelligence and machine learning techniques to traditional civil engineering challenges. His work increasingly focuses on using compressive sensing, deep learning (particularly LSTM networks), and fuzzy logic systems to solve complex problems in wave dynamics, vibration analysis, and tsunami prediction. The integration of quantum computing concepts with classical fluid mechanics represents another distinctive aspect of his research portfolio. ITU Doctoral Special Award-2024-Advisor (2025) ITU 2023 Academic Performance Award (2024) Turkish Academy of Sciences (TUBA) Outstanding Young Scientist Award (GEBİP) (2022) Elsevier Frontiers Article Award (2020) Kaleidoscope Award (American Physical Society, 2016) Bayındır leads multiple research projects funded by ITU's Scientific Research Projects Unit (BAP), including studies on dam break wave propagation using fractional equations, optimization of wave energy concentrators with AI methods, and three-dimensional vibration control in marine structures. His work involves collaborations with international institutions, including previous engagement with CERN on accelerator design. He maintains an active research laboratory focused on hydraulics and coastal engineering, with specialized equipment for wave and vibration analysis in the Hydraulics Laboratory HL 216 at ITU.

• Machine Learning
• Computational Biology
• Drug Discovery
• +3 more

Yan Zhang is a Visiting Professor in the Department of Psychiatry at the Yale School of Medicine, Yale University. His research focuses on the intersection of machine learning and computational biology, particularly in drug discovery, protein structure prediction, and generative AI models. He has contributed to advancements in diffusion models, causal language modeling, and bioinformatics tools. Primary Appointment: Psychiatry Affiliation: Yale School of Medicine Research interests include applying AI to biomedical problems, developing novel algorithms for molecular modeling, and creating benchmark datasets for protein analysis. His work integrates mathematical theory with practical software development, as seen in frameworks like Torchdrug. Recent publications emphasize diffusion models for protein side-chain packing, causal language integration in generative systems, and multi-task learning for protein sequence understanding. These contributions highlight his dual focus on theoretical innovation and real-world biomedical applications. No scientific awards or grants are explicitly mentioned in the provided materials. However, his active research portfolio suggests ongoing collaborative projects in drug discovery and AI-driven medical technologies.

• Machine Learning
• Gaussian Processes
• Bayesian Inference
• +3 more

Paul Chang is a Visiting Professor in the Department of Computer Science at Aalto University, associated with the Professorship Solin A. He holds a Doctor of Technology (Tekn. toht.) degree in Computer Science from Aalto University, awarded in December 2024. His research focuses on machine learning, Gaussian processes, Bayesian inference, and sequential learning, with applications in neural networks and optimization. His work contributes to UN Sustainable Development Goals related to education and innovation through advancements in probabilistic modeling and computational methods. Chang's academic background includes a doctoral thesis titled Rethinking Inference in Gaussian Processes: A Dual Parameterization Approach (2024), exploring novel methods for improving efficiency and scalability in Gaussian process models. His research has been published in leading conferences such as ICML and ICLR, with notable contributions to sequential learning algorithms, Bayesian optimization, and memory-based approaches in Gaussian processes. Key research interests include dual parameterization techniques, sparse representations in neural networks, and the integration of simulation-based inference with diffusion models. His work bridges theoretical advancements in machine learning with practical applications in optimization, active learning, and temporal data analysis. Collaborations span international institutions, reflecting his global academic engagement. Chang's publications highlight advancements in function-space parameterization, memory-augmented Gaussian processes, and amortized probabilistic conditioning. These contributions address challenges in scalable Bayesian methods and sequential decision-making systems. His research emphasizes computational efficiency, robustness, and applicability to real-world problems in machine learning.

Francesco PantaloneView profile

Lecturer

• Computational Statistics
• Official Statistics
• Sampling Theory
• +3 more

Francesco Pantalone is a Lecturer in Official Statistics at the University of Southampton, affiliated with the Statistical Sciences Research Institute (S3RI) and the Methodological Innovation group. His research focuses on survey methodology, official statistics, and environmental surveys, with particular attention to sampling design impacts and variance estimation in complex surveys. He teaches courses including STAT1003, STAT2007, STAT6125, and RESM6007, covering quantitative methods and data analytics. His recent work includes advancements in spatial sampling algorithms, M-quantile regression applications, and census coverage estimation. Supervising PhD student Jillian Delaney, his contributions span both methodological development and practical applications in official statistics.