Find Academic Supervisors

Andreas ZischgView profile

Professor

• Flood Risk Assessment
• Climate Change Adaptation
• Extreme Hydrological Events
• +3 more

Prof. Dr. Andreas Zischg is a leading academic at the Institute of Geography , University of Bern, serving as Group Leader for Human-Environment-Systems Modelling and Co-Director of the Mobiliar Lab for Natural Risks. With a focus on flood risk assessment and climate change adaptation , his research integrates coupled component models and GIS-based geospatial analysis to address extreme hydrological events and their societal impacts. Key Research Areas: Flood risk dynamics, climate resilience, surface water flood forecasting, and spatiotemporal vulnerability analysis Leadership Roles: Mobiliar Lab for Natural Risks (Co-Director), Human-Environment-Systems Modelling Group (Leader) Recent publications highlight his work on probabilistic flood warnings , property-level adaptation measures , and human mobility during disasters . His 2025 work explores surrogate models for national flood forecasting, while 2024 studies analyze road network disruptions, climate change storylines, and systematic flood risk monitoring frameworks. The Mobiliar Lab collaborations demonstrate his commitment to transdisciplinary research , combining hydrometeorological data with social science methodologies. His team develops visualization tools for risk communication and storyline approaches to climate change impact analysis, particularly in alpine environments. Current methodological innovations include deep-learning damage prediction and mobile phone data analysis for disaster response. These projects emphasize both technical model development and practical implementation for Swiss emergency management and policy frameworks.

• Formal Methods
• Software Dependability
• Software Security
• +7 more

Panagiotis Katsaros is a Professor at the Department of Informatics, Aristotle University of Thessaloniki. His research focuses on Formal Methods, Software Dependability & Security, and Internet of Things (IoT) systems. 1992: Dipl. in Mathematics, Aristotle University 1993: M.Sc in Software Engineering, Aston University 2002: PhD in Informatics, Aristotle University His work integrates model-based design with probabilistic verification to address challenges in embedded systems , IoT , and cloud environments . He has pioneered methodologies for correctness-by-construction and security-aware elasticity in distributed systems. Recent publications (2015-2019) highlight trends in multi-core embedded systems , probabilistic model checking , and logical error detection . His research bridges formal verification with practical system design . Scientific Recognition Best Paper Award at 17th Panhellenic Conference on Informatics (2013) Best Paper Award at 15th Panhellenic Conference on Informatics (2011) He has supervised PhD students Stylianos Basagiannis and Anakreon Mentis , and contributes to the Dependability & Security Group (DSG A.U.Th) . His work impacts spacecraft design , cloud security , and transaction processing systems .

Eric LecolinetView profile

Professor

• Human-Computer Interaction
• Touch and Tactile Interfaces
• Gesture-Based Interaction
• +5 more

Professor Eric Lecolinet is a faculty member at Télécom Paris, Institut Polytechnique de Paris, leading the DIVA (Design, Interaction, Visualization & Applications) research team within the LTCI laboratory. He is also President of the French-speaking Human-Machine Interaction Association ( AFIHM ). Education & Qualifications He earned his Habilitation à diriger des recherches (HDR) in 2018 from Université Paris-Saclay, specializing in Computer Science and Human-Computer Interaction, with a thesis entitled “Making Interaction Gestural: Expressivity and Memorability of Gestures” . Research Interests His research spans: Touch and tactile interfaces, including artificial skin and multi-modal feedback. Graphical and gestural user interfaces for mobile, wearable, and immersive systems. Virtual and augmented reality interaction techniques, with emphasis on safety and memory effects. Interactive visualization and novel display platforms, notably the Digiscope high-performance visualization network. Publications & Trends Over the past five years, Professor Lecolinet’s work has concentrated on: Understanding and designing haptic interfaces that leverage artificial skin (Skin-On) and pneumatic feedback for affective communication. Exploring security and ethical implications of perceptual manipulations in VR, including memory distortions and physical risks. Developing compact gesture vocabularies for small surfaces and confined spaces, combining marking menus, finger-count shortcuts, and multi-modal feedback. Scientific Awards & Honors No specific awards are listed in the provided material, but he is recognized through leadership roles: President of AFIHM, member of the LTCI laboratory council, and scientific head of Télécom Paris Fablab. Teaching & Advising He directs the Master Interaction, Graphics & Design (IGD) at IP Paris and co-directs the 3D & Interactive Systems engineering track (IGR). He teaches courses on: Human-Computer Interaction Interactive 2D/3D and mobile application development Programming paradigms (C/C++, Qt, Swing) Current PhD students include Yang Liu and Elise Bonnail . Labs & Teams He leads the DIVA group (≈25 researchers) and oversees the PIXLS and IRIS platforms within the Equipex+ Continuum and former Digiscope networks. He also manages the Télécom Paris Fablab , a digital fabrication laboratory serving both research and education.

Pauline BarmbyView profile

Professor

• Galactic and Stellar Processes
• Big Data and analytics
• Observational Astrophysics
• +2 more

Pauline Barmby is a Professor in the Department of Physics & Astronomy at Western University's Faculty of Science. She is an observational astrophysicist specializing in stars and star formation in nearby galaxies. Barmby joined Western University in 2007 after serving as a staff scientist at the Smithsonian Astrophysical Observatory, where she worked on the Spitzer Space Telescope's IRAC camera. She is also a Western Space Investigator and has made significant contributions to Canadian astronomy through her role as co-chair of the panel that developed the Canadian Astronomical Society's 2020 Long Range Plan. Professor Barmby's research focuses on understanding how stars, gas, dust, dark matter, and black holes in galaxies fit together. Her work involves developing new tools and techniques to combine data from ground-and space-based telescopes. Her primary research domains include Planetary Science & Astronomy, with specific interests in Galactic and Stellar Processes and Big Data analytics. Barmby's approach integrates multi-wavelength observations with advanced data analysis methods to study galaxy formation and evolution. She has pioneered techniques for combining data from Gaia, NED, and SIMBAD to create comprehensive views of galactic structures. An analysis of Barmby's recent publications reveals a strong focus on nearby galaxy surveys, particularly using data from missions like Gaia, Spitzer, and upcoming facilities like the Rubin Observatory and SKA. Her work often involves combining multiple data sources to create comprehensive galactic maps. There's a clear trend toward using machine learning techniques for galactic component mapping and source classification, reflecting her interest in Big Data analytics. Her research spans observational astronomy, astrostatistics, and data-intensive science, with particular attention to low surface brightness galaxies and spiral structures like UGC 2885. Among her notable achievements, Professor Barmby served as co-chair of the panel that developed the Canadian Astronomical Society's 2020 Long Range Plan, a significant contribution to shaping the future of Canadian astronomy. Her work has also influenced policy, as evidenced by her contribution to the "Report on Mega-Constellations to the Government of Canada and the Canadian Space Agency" (2021). She has been instrumental in major projects like TONGS (Treasury of Nearby Galaxy Surveys) and JINGLE (JCMT Legacy Survey). While specific details about her advising activities aren't provided in the source material, Professor Barmby's leadership role in developing Canada's astronomy roadmap suggests substantial involvement in mentoring and research supervision. Her work with major telescopes and space missions indicates she likely leads significant research collaborations and grant-funded projects focused on galaxy evolution and observational techniques. Professor Barmby's research involves collaborative teams working on large-scale galaxy surveys. Her focus on data integration across multiple wavelengths suggests her team develops specialized software tools for astronomical data analysis, particularly for handling the "Big Data" challenges inherent in modern observational astronomy. She maintains active involvement with major observational facilities including the James Clerk Maxwell Telescope (JCMT), Spitzer Space Telescope legacy data, and future missions like the Roman Space Telescope and SKA.

• High-dimensional time-series analysis
• Statistical modeling under non-stationarity
• Sparse dynamic factor models
• +3 more

Dr Alex Gibberd is a Senior Lecturer (equivalent to Associate Professor) in Statistics within the School of Mathematical Sciences at Lancaster University. He has been a faculty member since 2018, following postdoctoral research at Imperial College London and a PhD in Statistics from University College London (UCL) in 2017. He also holds an MPhys in Astrophysics from the University of St Andrews (2012). Education: PhD in Statistics, University College London (2017) MPhys in Astrophysics, University of St Andrews (2012) Research Interests: Dr Gibberd's research focuses on high-dimensional time-series analysis, with methodological contributions in statistical modeling under non-stationarity and high-dimensionality. His work spans both theoretical developments and practical applications, particularly in neuroscience and finance. Key areas include: Sparse dynamic factor models and regularized estimation techniques Spectral analysis and locally-stationary wavelet models Optimization algorithms for model selection in high-dimensional settings Applications in brain connectivity analysis and economic forecasting Research Themes: His recent publications demonstrate a strong focus on developing interpretable statistical methods for complex systems. These include advances in principal component analysis with joint rank and covariance estimation, sparse dynamic factor models, and regularized spectral estimation for high-dimensional point processes. The applications range from neural connectivity modeling to energy efficiency policy analysis. Grants & Funding: Research in Paris Grant (2024) Support of Collaborative Research with Dr S. Roy at University of Bath (2021) Model Selection for High-Dimensional Temporal Disaggregation in Official Statistics (2021-2023) Reducing End Use Energy Demand in Commercial Settings Through Digital Innovation (2021-2025) Wavelet Methods for Dependency Analysis in Multivariate Time Series (2020) STOR-i: Information Fusion for Non-homogeneous Panel and Time-series Data (2019-2025) PhD Supervision & Students: Dr Gibberd supervises PhD students at the intersection of high-dimensional statistics and time-series analysis. Current students include Carla Pinkney (STOR-i CDT), Ziyan Zhao, and Kai Zheng (Centre for Marketing Analytics & Forecasting). Research Affiliations: STOR-i Centre for Doctoral Training Centre for Marketing Analytics & Forecasting Changepoints and Time Series Research Group Data Science Institute - Foundations Social and Economic Statistics Group

• Quantum Chemistry
• Computational Chemistry
• Solvation Modeling
• +3 more

Roberto Di Remigio Eikas is an Associate Professor in the Department of Chemistry at UiT The Arctic University of Norway. His research focuses on quantum chemistry, computational methods, and solvation modeling. Key research areas: Quantum Chemistry, Relativistic Electronic Structure Theory, Multiwavelet Frameworks, and Solvation Modeling Major contributions to open-source software development (VAMPyR, PCMSolver, MRChem) Collaborator in Nordic Quantum Chemistry Library Ecosystem His recent work explores multiwavelet-based quantum chemical methods, relativistic effects in solution-phase calculations, and stochastic approaches to coupled cluster theory. Publications span high-impact journals like Journal of Chemical Theory and Computation and Journal of Chemical Physics , highlighting innovative numerical techniques and software implementations.

• Formal Methods in Software Engineering
• Programming Languages and Formal Logic
• Trustworthy Distributed Systems
• +9 more

Dr. Boris Düdder is an Associate Professor at the Department of Computer Science (DIKU), University of Copenhagen, focusing on Formal Methods, Programming Languages, and Trustworthy Distributed Systems. He leads the Software Engineering & Formal Methods research group and serves as Deputy Head of Department for Teaching, advocating for neurodiversity and inclusion. Primary Affiliation: Department of Computer Science, University of Copenhagen External Lectureship: Copenhagen Business School (CBS), University of Electronic Science and Technology of China (UESTC) Honorary Research Fellowship: Institute for Intelligent Production Systems, Qingdao, China His research bridges formal computer science foundations with industrial applications, including automated program generation for adaptive systems (Combinatory Logic Synthesizer), verifiable AI, blockchain for healthcare IT, and secure data ecosystems. Key projects involve Smart Factories, Data Markets, and Ethical AI assessment via Z-Inspection. Scientific recognition includes the 2022 SSIP Excellence In Service Innovation Award for Z-Inspection. He has secured international grants in blockchain, FinTech, and secure data sharing, operating in multinational consortia. Contact: boris.d@di.ku.dk | Phone: +4593565748 | Website: Research Profile

Aneta NeumannView profile

Researcher

• Artificial Intelligence
• Machine Learning
• Evolutionary Computation
• +7 more

Aneta Neumann is a Researcher at the School of Computer and Mathematical Sciences within the University of Adelaide. Her work bridges bio-inspired computation with machine learning and computational creativity , focusing on dynamic and stochastic multi-objective optimization for real-world applications in mining, renewable energy, cybersecurity, and public health. Education : B.Sc. in Computer Science from Christian-Albrechts-University of Kiel, Germany; Ph.D. from University of Adelaide, Australia. Her research explores evolutionary diversity optimization to generate innovative solutions for complex problems like the Traveling Thief Problem , Chance-Constrained Knapsack , and Time-Use Planning . She investigates theoretical foundations through runtime analysis and applies these insights to industrial software integration in mining and energy sectors. Recent work emphasizes AI-based optimization trends , particularly in quantum computing benchmarks (e.g., Maximum Cut) and health outcomes via time-use scheduling. Her publications span top venues like GECCO , AAAI , NeurIPS , and Algorithmica . Scientific Awards : ACM-W Scholarship (2018), Hans-Juergen and Marianna Ohff Research Grant, Best Paper Award at GECCO 2024, multiple Best Paper Nominations at GECCO (2019, 2021, 2022). She co-organizes key conferences ( AI-OPT 2025 , EMO 2025 ) and serves as track co-chair for Genetic Algorithms at GECCO. Her teaching includes the Big Data Fundamentals course in the University of Adelaide's MicroMasters program.

• Computer Science
• Fundamental Components
• Physical Computing
• +3 more

Florian Wörister is a researcher in the Faculty of Computer Science, actively contributing to computer science education through the Research Group Education, Didactics and Entertainment Computing. His work focuses on developing innovative block-based programming environments to teach complex low-level computing concepts, making them accessible to learners through visual interfaces. His educational background includes a Bachelor of Science (BSc) and a Diplom-Ingenieur (Dipl.-Ing.), a German engineering degree equivalent to a Master of Science. This foundation supports his research in bridging theoretical computer science with practical educational tools. Wörister's research centers on fundamental components of computer science, with specific expertise in physical computing, JavaScript libraries, and programming education. He investigates how block-based interfaces can demystify low-level computing concepts like assembler, effectively connecting visual programming paradigms with traditional machine-level languages. His work extends to data management challenges, particularly in persistent identification of dynamic data subsets for research reproducibility. Recent publications reveal a strong emphasis on educational technology for computer science pedagogy, demonstrating how block-based environments lower barriers to learning low-level computing. His 2024 Koli Calling publication shows significant research impact with 3 citations and 5 reader captures, while his 2021 data management work addresses critical infrastructure needs for dynamic scientific datasets. No scientific awards were documented in the provided materials. Information regarding student advising, grant funding, or formal mentorship roles is not specified in available sources. As a core member of the Research Group Education, Didactics and Entertainment Computing, Wörister contributes to advancing computational thinking pedagogy and exploring intersections between educational methodologies and entertainment computing applications.

R. Gregory FranksView profile

Associate Professor

• Performance Modelling
• Distributed Software
• Queueing Networks
• +4 more

Dr. R. Gregory Franks is an Associate Professor in the Department of Systems and Computer Engineering at Carleton University. He holds a Ph.D. from Carleton and is a registered Professional Engineer (P.Eng.). His research focuses on performance modeling of distributed systems, operating systems design, and internet protocol routing. Education: Ph.D. in Systems and Computer Engineering, Carleton University Research Focus: Dr. Franks specializes in performance modeling techniques including layered queueing networks, simulation methods, and Petri Nets. His work addresses distributed software performance, reverse engineering challenges, and operating system optimization. Current investigations include peer-to-peer protocol analysis, system optimization using layered models, and trace-based performance model generation for complex systems. Publication Trends: His research output consistently addresses performance modeling innovations, with recent emphasis on layered queueing network optimizations, automated trace analysis, SIP application server performance, and autonomic software engineering. Work demonstrates strong applications in telecommunications, distributed systems, and parallel computing. Supervision and Labs: Directs the RADS Lab (Real-time and Distributed Systems) and has supervised multiple graduate students in performance engineering. Current projects involve collaborations with industry partners including AT&T and CITO, focusing on internet telephony systems and distributed component architectures.

Ronny BergmannView profile

Associate Professor

• Nonsmooth Optimization on Manifolds
• Proximal Methods
• Manifold-Valued Image Processing
• +7 more

Ronny Bergmann is an Associate Professor (Førsteamanuensis) at the Department of Mathematical Sciences at the Norwegian University of Science and Technology (NTNU) in Trondheim, Norway, where he has been employed since March 2021. Previously, he served as a postdoctoral researcher and deputy head of the Numerical Analysis of Partial Differential Equations group at Chemnitz University of Technology, Germany, and earlier worked within the Image Processing Group at Kaiserslautern University of Technology where he completed his habilitation in early 2018. His doctoral research was conducted at the University of Lübeck under supervision of Jürgen Prestin. Bergmann's research primarily focuses on nonsmooth optimization on manifolds , with particular emphasis on proximal methods and their applications in manifold-valued image processing. His work extends to optimization methods on manifolds and high-dimensional power manifolds, graph-based approaches for manifold-valued data processing that combine local and nonlocal methods, and anisotropic periodic translation invariant spaces with Fourier-based algorithms. His research bridges theoretical mathematics with practical computational applications in image processing and data analysis. His recent publications demonstrate significant contributions to manifold optimization theory and applications, with a strong emphasis on developing computationally efficient algorithms for manifold-valued data. The publications span topics including Riemannian proximal gradient methods, Newton's methods on manifolds, total variation techniques for mesh processing, and geometric approaches to Kalman filtering. His work often involves developing practical implementations, such as the Manopt.jl and Manifolds.jl packages in Julia, which have demonstrated substantial performance improvements over existing tools. Bergmann's research has been published in top-tier journals including SIAM Journal on Scientific Computing, Journal of Optimization Theory and Applications, and SIAM Journal on Optimization, reflecting the high quality and impact of his contributions to the field of manifold optimization and its applications. As both a theoretical mathematician and computational scientist, Bergmann has made significant contributions to advancing the state-of-the-art in optimization on manifolds, particularly in developing methods that maintain intrinsic geometric properties while achieving computational efficiency. His work has applications across multiple domains including image processing, computer graphics, robotics, and data science.

Salvador Garcia MunozView profile

Visiting Professor

• Chemical Engineering
• Pharmaceutical Engineering
• Process Analytics
• +5 more

Salvador Garcia Munoz serves as a Visiting Professor in the Department of Chemical Engineering at Imperial College London's Faculty of Engineering. His expertise spans pharmaceutical engineering, process analytics, and chemometrics, with a strong focus on applying multivariate data analysis methods to chemical and pharmaceutical processes. Education: PhD in Chemical Engineering from McMaster University, Canada MS in Chemical Engineering from Monterrey Tech, Mexico BS in Chemical and Computer Systems Engineering from Monterrey Tech, Mexico Professor Garcia Munoz specializes in multivariate data analytics for process understanding and control, particularly in pharmaceutical manufacturing. His work bridges the gap between theoretical chemical engineering principles and practical industrial applications. He has developed expertise in Principal Components Analysis (PCA), Partial Least Squares (PLS), process monitoring, and optimization techniques. His research focuses on creating data-driven models for complex systems where data is abundant, correlated, and noisy, with direct applications in continuous manufacturing, quality by design, and real-time release testing in the pharmaceutical industry. His publication record demonstrates a strong trend toward integrating advanced statistical methods with chemical engineering principles to solve pharmaceutical manufacturing challenges. The majority of his recent work focuses on process analytics, model-based design, and optimization for pharmaceutical processes, with particular emphasis on continuous manufacturing, design space definition, and quality control. His articles reveal expertise in applying chemometrics to pharmaceutical unit operations including granulation, dissolution, and powder flow systems. Professor Garcia Munoz actively contributes to the academic community through teaching specialized courses on process analytics and multivariate methods. He is also a key contributor to open-source scientific software, notably as one of the authors of KIPET (Kinetic Parameter Estimation Toolkit), which is used for parameter estimation in chemical reaction systems. His work with PYOMO (Python-based Optimization Modeling Objects) demonstrates his commitment to developing accessible computational tools for the engineering community. His research group collaborates extensively with industry partners, particularly in the pharmaceutical sector, to develop practical solutions for manufacturing challenges. The focus on real-world applications is evident in his publications, which often include case studies using data from actual industrial processes. His work bridges the gap between academic research and industrial implementation, making significant contributions to the advancement of pharmaceutical manufacturing technologies.

Stefan HinzView profile

Professor

• Remote Sensing
• Photogrammetry
• InSAR
• +5 more

Prof. Dr.-Ing. Stefan Hinz is a Professor at the Karlsruhe Institute of Technology (KIT), serving as Spokesperson for the Topic Data Science in Climate and Environmental Research within the KIT Climate and Environment Center. He is based at the Institute for Photogrammetry and Remote Sensing (IPF) in Karlsruhe, Germany, where his work bridges advanced remote sensing methodologies with critical environmental and climate challenges. His research spans Remote Sensing , Photogrammetry , and Environmental Monitoring , with specialized expertise in InSAR, GNSS, and deep learning applications. Key focus areas include land subsidence analysis (particularly in the Mekong Delta), infrastructure safety monitoring for dams and bridges, atmospheric dust density estimation, and climate adaptation strategies through spatial planning. His interdisciplinary approach integrates geospatial data science with practical environmental solutions. Recent publications reveal a pronounced shift toward AI-driven remote sensing, with deep learning techniques enhancing traditional methods like SAR interferometry. His work emphasizes real-world applications in geohazard monitoring, environmental protection, and climate resilience, often targeting vulnerable regions like the Mekong Delta. This trend reflects a broader movement toward computational innovation in earth observation science. Prof. Hinz leads strategic initiatives within the KIT Climate and Environment Center, coordinating data science efforts for climate research and environmental protection. His role involves fostering cross-disciplinary collaboration to address sustainability challenges through advanced remote sensing technologies and spatial data analysis.

Greg KulczyckiView profile

Associate Professor

• Digital Education
• Software Engineering
• Formal Methods
• +3 more

Greg Kulczycki is an Associate Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University (Virginia Tech) , part of the College of Engineering . His research focuses on digital education , formal methods in software development, and bridging industry-academia collaboration in software engineering education. He earned his Ph.D. in Computer Science from Clemson University in 2004. Key research interests include: Formal verification of software components and systems Web services and transactional service composition Software reuse and productivity enhancement Industrial practices in web application development Recent publications emphasize formal methods for error detection, education-industry partnerships, and optimizing mobile network performance. His work often intersects computational linguistics (e.g., term extraction) and data-centric service design. No scientific awards or student advising information is listed in the available data.

NIKOLAOS SAPIDISView profile

Professor

• Mechanical Design
• Computer-Aided Design (CAD)
• Computer-Aided Engineering (CAE)
• +7 more

Nikolaos Sapidis is a Professor at the Department of Mechanical Engineering, University of Western Macedonia, Greece, leading the 'Division of Constructions and Materials'. His career spans over three decades with previous roles at the University of the Aegean and multiple academic/research institutions including the National Technical University of Athens and Technical University of Catalonia. He earned a Ph.D. in Mechanical and Aerospace Sciences from the University of Rochester, USA, and holds degrees in Applied Mathematics (University of Utah) and Naval Architecture (National Technical University of Athens). His research focuses on Computational Design and Analysis , including CAD/CAE integration, geometric modeling of curves/surfaces, virtual engineering, and finite-element methodologies. Notable areas include solid modeling, point-cloud-based CAD, reverse engineering, and virtual reality applications in mechanical systems. He has authored over 80 publications and actively contributes to editorial boards of journals like Computer-Aided Design and Computer-Aided Design and Applications . His work emphasizes practical industrial applications, with research implemented in systems by companies like General Motors and Mercedes-Benz. Key contributions include algorithms for shape preservation in curves/surfaces, automated assembly design frameworks, and finite-element model optimization for large-scale systems. He directs the Laboratory of Mechanical Systems , advancing interdisciplinary research in mechanical design and virtual engineering.