Find Academic Supervisors

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.

Mark Andrew PostView profile

Senior Lecturer

• Robotics
• Autonomous Systems
• Space Robotics
• +7 more

Dr. Mark A. Post is a Senior Lecturer in Robotics and Autonomous Systems at the University of York's School of Physics, Engineering and Technology. He has over 61 peer-reviewed publications and £1M+ in research funding as PI, including Horizon 2020 and Horizon Europe grants. His work spans space robotics , autonomous systems , and bio-inspired designs . B.A.Sc. Electrical Engineering (University of Toronto, 2004) M.Sc. Space Science (York University, 2008) Ph.D. Space Engineering (York University, 2014) Research focuses on autonomous planetary rovers , nanosatellites , and bio-inspired robots with robust probabilistic reasoning, modular sensor fusion, and adaptive control systems. Key projects include: STARFAB : Space warehouse ecosystem (Horizon Europe, 2024–2026) MOSAR : Modular satellite reconfiguration (Horizon 2020, €3.999M) RoboFish : Underwater inspection AUV (Supergen ORE Hub funding) Awarded three EU grants and multiple STFC/Innovate UK projects, he supervises PhD students in modular robotics , probabilistic programming , and autonomous systems .

Nikela PapadopoulouView profile

Lecturer

• High-Performance Computing (HPC)
• Energy Efficiency
• Sustainable Computing
• +3 more

Nikela Papadopoulou is a Lecturer (Assistant Professor) in Low Carbon and Sustainable Computing at the University of Glasgow's School of Computing Science. She is affiliated with the GLAsgow Systems Section and focuses on optimizing high-performance computing (HPC) systems for reduced environmental impact, including performance modeling, application optimization, and energy-efficient resource management. Her work also explores co-design strategies for machine learning workloads. Education: PhD in Electrical and Computer Engineering from National Technical University of Athens (NTUA). Postdoctoral research at Chalmers University of Technology (Sweden) and NTUA, contributing to European projects like ACTiCLOUD, EuroEXA, and HiDALGO. Member of ACM and HiPEAC. Research Interests: HPC systems optimization, energy efficiency in computing, sustainable computing practices, machine learning co-design, and parallel algorithm development. Teaching: Currently teaches Systems Programming (COMPSCI4081) and Internet Technology (COMPSCI5012) at the MSc level. Advising: Supervises Shuxuan Li on FPGA-based compiler transformations for sequence models. Active in grant-funded projects related to HPC and sustainable computing. Labs/Teams: Active contributor to the GLAsgow Systems Section, focusing on HPC and low-carbon computing innovations.

David MonniauxView profile

Researcher

• Software Verification
• Computability Theory
• Computational Complexity
• +7 more

David Monniaux is a senior researcher (Researcher) at the CNRS, based at VERIMAG laboratory, a joint research unit of CNRS and Université Grenoble Alpes. His work focuses on formal methods for software verification, particularly on proving the correctness of critical software used in civil aviation and industrial applications. He holds a PhD in Computer Science from Université Paris Dauphine (2001) and a Habilitation from Université Joseph Fourier, Grenoble (2009). Education: PhD in Computer Science, 2001, Université Paris Dauphine Accreditation to Supervise Research (Habilitation), 2009, Université Joseph Fourier, Grenoble Agrégation in Mathematics Research Projects: VERASCO (2012–2015): Integrated a static analyzer into the CompCert compiler and proved its correctness using Coq. STATOR (2012–2017): Advanced program invariant inference techniques using abstract interpretation and SMT-solving. ASOPT (2009–2012): Static analysis algorithms leveraging optimization and game theory. Key Contributions: Developed the Astrée static analyzer for proving absence of runtime errors in C programs. Pioneered work on verified compilers and formal verification of optimizations. Contributed to tools like Mjollnir for quantifier elimination and the Verimag Polyhedra Library. Grants & Awards: ERC Consolidator Grant (STATOR project). ANR funding for VERASCO. Labs & Teams: Leading research at VERIMAG in static analysis and formal methods. Collaborations with Absint GmbH for industry applications of Astrée.

Anne Marie SvaneView profile

Associate Professor

• Topological Data Analysis
• Probability Theory
• Mathematical Statistics
• +3 more

Anne Marie Svane is an Associate Professor at the Department of Mathematical Sciences, Aalborg University, specializing in topological data analysis, stochastic geometry, and probability theory. Her research bridges theoretical mathematics with applied statistics, focusing on geometric functionals and computational methods. Education: PhD in Mathematics from Aarhus University. Her recent work explores cobordism obstructions, Gibbs processes, and applications in climate science and medical research. Key contributions include: Advancing kernel persistence methods for topological data analysis. Developing central limit theorems for spatial point processes. Modeling moisture dynamics in building physics with climate data. Investigating the interplay between digital algorithms and geometric structures. She collaborates on interdisciplinary projects like AI-Aalborg Intelligence and contributes to educational initiatives such as Girls' Day in Science.

Junghee ChoiView profile

Assistant Professor

• Educational Policy
• Higher Education Finance
• College Admissions
• +3 more

Dr. Junghee Choi is an Assistant Professor in the Department of Educational Leadership & Policy Studies at the University of Oklahoma's Jeannine Rainbolt College of Education. She specializes in educational policy, finance, and organizational studies, with a focus on college access, admissions equity, government funding mechanisms, and comparative international education systems. Her work critically examines how policy interventions influence social stratification and institutional behavior. Ph.D. from Pennsylvania State University Research interests emphasize: - Policy design and implementation impacts - Resource allocation in higher education - Racial/economic equity in admissions - Global education systems comparisons Editorial roles include Research in Higher Education and Innovative Higher Education journal boards. Current projects explore performance-based funding, test-optional admissions, and gender-inclusive data practices. No advisees or grants explicitly listed in available materials. Office: Collings Hall 204 | Phone: (405) 325-4202

Torben KnudsenView profile

Associate Professor

• Wind Turbine Engineering
• Process Control
• Control Systems
• +6 more

Torben Knudsen is an Associate Professor at the Department of Electronic Systems, Aalborg University, Denmark. He is affiliated with the Automation & Control Learning and Decisions Lab and holds positions in The Technical Faculty of IT and Design. His research focuses on control systems, wind turbine engineering, stochastic processes, and biomedical applications such as diabetes modeling. Education: Advanced degrees in engineering, though specific details are not explicitly listed. Research Interests: Wind turbine dynamics, offshore wind farm control, stochastic modeling of biomedical systems, fault detection, and optimization techniques. His work integrates control theory with renewable energy systems, particularly addressing challenges in wind turbine aeroelasticity and floating offshore wind farms. Recent projects include the development of health-aware simulation platforms (FOWLTY) and insulin dose optimization algorithms for type 2 diabetes. Key Projects: ICONIC (2023-2027) : Focuses on intelligent control of industrial processes. PRESTIGE (2020-2023) : Explored predictive manufacturing and quality control. Modelling and Control of Type 2 Diabetes (2019-2024) : Developed stochastic models for glucose-insulin dynamics. Knudsen has received the Best Paper Award (2015) for contributions to predictive manufacturing. His research also extends to pandemic modeling, including work on SARS-CoV-2 variant outbreaks and hospital preparedness during epidemics. Advising & Grants: Active in interdisciplinary collaborations, including funding from EU projects like Aeolus and OFFWIND. He contributes to grants focusing on wind farm control, diabetes modeling, and stochastic systems. Labs & Teams: Leads the Automation & Control Learning and Decisions Lab, collaborating with international teams on energy systems and biomedical engineering challenges.