Find Academic Supervisors

• Dependability Engineering
• Performability Analysis
• Cloud Computing
• +7 more

Ermeson Carneiro de Andrade is a Professor at the Department of Systems and Computer Engineering within the Center of Informatics at the Federal University of Pernambuco (UFPE) in Brazil. His research focuses on dependability engineering, performability analysis, and fault tolerance in distributed and embedded systems. Over his career spanning more than 15 years, he has established himself as a prominent researcher in the field of system reliability through numerous publications in top-tier journals and conferences. Dr. Andrade's research interests primarily center on the analysis and modeling of system dependability, with particular expertise in UAV-based monitoring systems, cloud computing environments, and IoT architectures. His work bridges theoretical modeling with practical applications, particularly in environmental monitoring, disaster recovery solutions, and mission-critical systems. He has made significant contributions to understanding software aging phenomena in various computing environments and developing performability-aware solutions for real-time systems. The analysis of his recent publications reveals a strong focus on UAV systems for environmental monitoring, particularly deforestation detection, with increasing attention to weather impacts and vehicle density-aware traffic monitoring. His research demonstrates a consistent pattern of applying stochastic modeling techniques to solve practical problems in distributed systems, with recent work expanding into NoSQL database performance, satellite constellation dependability, and the performance-interpretability trade-offs in machine learning models. This evolution shows his ability to adapt to emerging technologies while maintaining core expertise in system reliability. Dr. Andrade has been actively involved in mentoring students and collaborating with researchers across Brazil and internationally. His work often involves interdisciplinary teams addressing complex system challenges. While specific awards aren't detailed in the available publication records, his consistent output in high-impact venues demonstrates recognition within the dependability engineering community. His laboratory work appears to focus on system modeling and analysis, with particular emphasis on experimental validation through simulation and real-world testing. Current projects suggest involvement in UAV-based monitoring systems for environmental applications, with strong connections to public sector institutions in Pernambuco state.

Vlad-Costin AndreiView profile

Researcher

• Integrated Sensing and Communications (ISAC)
• Digital Twinning
• Neuromorphic Computing
• +5 more

Vlad-Costin Andrei is a Researcher at the Chair of Theoretical Information Technology , Technical University of Munich (TUM), specializing in wireless communication systems and digital twinning. He joined the ACES Lab (TUM's Chair of Theoretical Information Technology) in late 2021 after 3.5 years in the aerospace and defense industry. Research Focus: Joint Communications and Sensing (6G), Neuromorphic PHY Layer, Digital Twins, MIMO-OFDM Resilience Projects: 6G-life, 6G Future Lab Affiliation: ACES Lab, TUM His work bridges theoretical foundations with practical implementations, including demonstrations of digital twinning platforms and sensing-assisted receivers. Recent publications emphasize anti-jamming frameworks, federated learning over wireless networks, and trajectory optimization for UAV-enabled ISAC systems. Scientific Awards: Best Paper Award, IEEE Symposium on Joint Communications and Sensing (2023) His research is supported by third-party grants such as BMBF's 6G-life, DFG's Gottfried Wilhelm Leibniz Prize, and multiple collaborative projects.

Thomas NaglerView profile

Professor

• Mathematical Statistics
• Machine Learning
• High-dimensional Dependence Models
• +3 more

Thomas Nagler is a Professor at the Department of Statistics, Faculty of Mathematics, Computer Science and Statistics at Ludwig Maximilian University of Munich (LMU Munich). He also serves as a principal investigator at the Munich Center for Machine Learning (MCML), where he leads research at the intersection of mathematical statistics and machine learning. Nagler received his academic training at Technical University of Munich (TU Munich), earning a BSc in Mathematics (2009-2012), followed by an MSc in Mathematical Finance (2012-2014), and ultimately a PhD in Mathematical Statistics (2014-2018). Prior to his current position at LMU Munich, he held assistant professor positions at TU Delft (2021-2022) and Leiden University (2019-2021). Professor Nagler's research focuses on developing novel statistical methods with theoretical guarantees and scalable algorithms. His work spans high-dimensional dependence modeling, particularly using vine copulas, statistical machine learning, time series and functional data analysis, and statistical computing. He emphasizes creating methods that can be practically implemented and applied to solve real-world problems across diverse domains. An analysis of Nagler's recent publications reveals a strong emphasis on vine copula methodology, uncertainty quantification in machine learning, and applications to climate science and epidemiology. His work bridges theoretical statistics with practical implementation, often resulting in open-source software tools that make advanced statistical methods accessible to practitioners. The interdisciplinary nature of his research is evident in collaborations spanning climate modeling, healthcare, and finance. While specific awards are not detailed in the available information, Nagler's research impact is evident through his significant contributions to statistical methodology and his active engagement with the research community through open-source software development. As a principal investigator at MCML and Professor at LMU Munich, Nagler leads a research group focused on advancing statistical methodology for complex data analysis. His GitHub profile indicates active collaboration with students and researchers, with several followers from LMU Munich and other institutions. His research program appears to be well-funded through the MCML and university resources, supporting both methodological development and application-focused projects. Nagler maintains strong ties with the computational statistics community through his leadership of the VineCopula and pyvinecopulib projects, which provide essential tools for dependence modeling. His work with the Munich Center for Machine Learning positions him at the forefront of interdisciplinary research combining statistical theory with practical machine learning applications.

• Digitized Building Maintenance
• Building Information Modeling (BIM)
• Automation and Robotics in Construction
• +3 more

Dr. Hendrik Morgenstern serves as a Postdoc and Senior Engineer at RWTH Aachen University's Chair and Institute of Construction Management, Digital Engineering and Robotics in Construction (ICoM), where he advances digital solutions for building maintenance and construction robotics. His work focuses on integrating Building Information Modeling (BIM) with diagnostic data to optimize infrastructure lifecycle management. Morgenstern earned his B.Sc. and M.Sc. in Civil Engineering with specialization in Functional and Structural Engineering from Karlsruhe Institute of Technology (KIT), complemented by studies in Sustainable Development. He completed his Dr.-Ing. doctorate at RWTH Aachen in 2023 with research on automated maintenance planning using BIM-enriched diagnostic data. His research program centers on digitized building maintenance, BIM applications for existing structures, and robotics automation in construction. Key contributions include predictive maintenance frameworks using Bayesian inference, geopolymer material development for structural repair, and point cloud integration for as-built modeling. He emphasizes resource efficiency and data-driven decision-making across all projects. Analysis of his 15 publications (2021-2025) reveals three dominant trends: (1) Convergence of BIM with non-destructive diagnostics for predictive maintenance, (2) Development of smart materials like temperature-stable geopolymers for crack injection, and (3) Integration of robotics and AI for automated facility management. His work consistently bridges civil engineering fundamentals with computer science innovations. Morgenstern actively contributes to major research initiatives including the RoboTUNN project (awarded bauma Innovation Award 2025 for tunneling robotics) and the BIM4People consortium focused on digital transformation in construction. His work demonstrates strong industry collaboration through projects with German construction firms and participation in standards development.

• Wireless Communications
• Information Theory
• UAV Networks
• +3 more

Dr. Yulin Hu serves as a Visiting Professor at RWTH Aachen University, holding the Chair of Information Theory and Data Analytics. His research program bridges theoretical foundations with practical implementations in next-generation wireless systems, with particular emphasis on UAV-aided networks and information-theoretic approaches to communication challenges. His core research interests span multiple interconnected domains: Wireless Communications (especially finite blocklength regimes) Information Theory applications in network design UAV trajectory optimization and network integration Wireless power transfer with nonlinear energy harvesting Edge computing and distributed learning systems Data analytics for network performance optimization Analysis of Dr. Hu's 2025 publication record reveals a concentrated research thrust on UAV trajectory design, where he develops joint optimization frameworks addressing energy efficiency, security, and reliability constraints. His work consistently integrates information-theoretic principles—particularly finite blocklength analysis—to solve practical challenges in ultra-reliable low-latency communications (URLLC) and wireless power transfer. A distinctive feature of his approach is the fusion of deep reinforcement learning with traditional optimization methods for dynamic network scenarios, including no-fly zone constraints and covert operations. While no specific scientific awards are documented in the available materials, his prolific output across top-tier venues demonstrates significant scholarly impact. Details regarding graduate student mentoring and research funding mechanisms remain unspecified in the current documentation. The Chair of Information Theory and Data Analytics, which Dr. Hu leads, functions as a specialized research unit focused on theoretical rigor and algorithmic innovation for wireless systems, though specific laboratory infrastructure or team composition details are not provided.

Artur CzumajView profile

Professor

• Theoretical Computer Science
• Randomized Algorithms
• Parallel and Distributed Computing
• +4 more

Artur Czumaj is a Professor of Computer Science and Director of the Center for Discrete Mathematics and its Applications (DIMAP) at the University of Warwick, United Kingdom. He is a member of the Global Faculty at the University of Cologne and serves as President of the European Association for Theoretical Computer Science (EATCS). PhD and Habilitation from University of Paderborn Recipient of the IBM Award and Fellow of EATCS His research focuses on theoretical computer science, particularly in the design of randomized algorithms, analysis of large data, and applications to parallel/distributed computing, property testing, sublinear algorithms, optimization algorithms, and algorithmic game theory. His work has been funded by major institutions including EPSRC, NSF, Royal Society, European Union, Simons Foundation, and IBM. The 15 most recent publications reflect expertise in graph algorithms, distributed systems, approximation techniques, and algorithmic complexity. His research spans foundational algorithm design to practical applications in large-scale data processing. Fellow of the European Association for Theoretical Computer Science (EATCS) IBM Award recipient Czumaj has held leadership roles in prominent conferences, including Program Committee Chair for STOC, ICALP, SODA, and HALG. His academic career is supported by grants from multiple international research councils and organizations.

Srishti YadavView profile

Research Fellow

• AI and Society
• Machine Learning
• Computer Science
• +3 more

Srishti Yadav is a Research Fellow at the University of Copenhagen and University of Amsterdam , affiliated with the Pioneer Centre for AI and ILLC respectively. She is advised by Dr. Serge Belongie and Dr. Ekaterina Shutova . Education: M.Sc. (Research-Track) in Computing Science, Simon Fraser University , Canada Research Interests: AI and Society Cross-Cultural Competency in Multimodal Models AI Safety and Evaluation Frameworks Model Interpretability and Dataset Creation Scientific Awards: ELLIS PhD Fellowship Advising & Community: Board Member, Women in Computer Vision (WiCV) Advisor for WiCV@ICCV2023 and WiCV@CVPR 2021 Chaired workshops at CVPR 2024, CVPR 2023, CVPR 2020 Labs & Teams: Belongie Lab (University of Copenhagen) Shutova Lab (University of Amsterdam) Collaborator at MILA Biodiversity Monitoring Project

Lukas HermannView profile

Researcher

• Robot Learning
• Deep Reinforcement Learning
• Self-Supervised Learning
• +3 more

Lukas Hermann is a PhD researcher at the Autonomous Intelligent Systems group within the Department of Computer Science at the University of Freiburg. He collaborates with Prof. Dr. Wolfram Burgard and Prof. Thomas Brox, focusing on robot learning and autonomous systems . University of Freiburg: 2011–2019 (B.Sc. and M.Sc. in Computer Science) Current role: Researcher in Robotics & AI (since 2020) Research Interests: Robot Learning Deep Reinforcement Learning Self-Supervised Learning Language-Conditioned Policy Learning Sim-to-Reality Transfer Article Trends: Lukas's publications focus on language-driven robotic imitation learning, adaptive curriculum generation for sim-to-real transfer, and unsupervised dynamics modeling. His work addresses challenges in autonomous manipulation, visual servoing, and data-efficient policy learning using optical flow and structured representations. Projects: CALVIN: Benchmark for language-conditioned long-horizon tasks FlowControl: Optical flow-based visual servoing Bike Navigation in Rome: Safe/easy route planning Adaptive Curriculum Generation for Sim-to-Real Vision-Based Robotic Manipulation with Natural Policy Gradients

• Artificial Intelligence
• Machine Learning
• Computer Vision
• +5 more

Bo Wang is an active academic researcher primarily affiliated with multiple Chinese institutions, with strong connections to Tsinghua University, Beijing Jiaotong University, and other leading Chinese universities. His research spans artificial intelligence, machine learning, computer vision, medical image analysis, and intelligent control systems, demonstrating significant interdisciplinary work across computer science, engineering, and biomedical applications. Primary institutional affiliation: School of Computer Science and Technology at multiple Chinese universities Active research areas: AI/ML applications in healthcare, computer vision, federated learning, and intelligent control systems Extensive publication record across top-tier venues in multiple disciplines Wang's research interests focus on the intersection of artificial intelligence and practical applications. His work demonstrates strong expertise in developing novel machine learning architectures for medical image analysis, including applications in CT imaging, MRI, and sperm tracking. He has made significant contributions to federated learning approaches for large language models, sliding mode control systems, and molecular optimization frameworks. His research consistently bridges theoretical advances with practical implementations across healthcare, manufacturing, and environmental monitoring domains. Analysis of Wang's recent publications reveals a strong trend toward interdisciplinary AI applications, particularly in medical imaging and bioinformatics. His work on VAE-GANMDA for microbe-drug association prediction, ACE-QSM for accelerating MRI acquisition, and text-guided molecular optimization demonstrates innovative approaches at the intersection of AI and life sciences. Wang also maintains active research in industrial applications including digital twin technology for energy systems and robust scheduling approaches for multi-factory production. Notable research contributions include: FLFT: A Large-Scale Pre-Training Model Distributed Fine-Tuning Method with Federated Learning VAE-GANMDA: Microbe-drug association prediction model ACE-QSM: Accelerating quantitative susceptibility mapping using diffusion models Digital twin-empowered power consumption prediction systems Wang actively collaborates with researchers across China and internationally, with publications spanning computer science, engineering, medical imaging, and environmental science journals. His work demonstrates strong technical depth across multiple AI methodologies while maintaining focus on practical applications that address real-world challenges in healthcare, manufacturing, and environmental monitoring.

• Artificial Intelligence
• Information Systems
• Blockchain Technology
• +3 more

Oliver Hinz is a prominent researcher in the field of information systems and digital technologies, focusing on areas such as artificial intelligence ethics, blockchain applications, cybersecurity, and digital responsibility. His work bridges technical innovation with societal impact, addressing challenges in corporate governance, healthcare informatics, and user-centric technology design. Research interests: AI governance, blockchain tokenization, digital democracy, and ethical technology adoption Co-authored over 160 publications in leading journals like Information Systems Research and MIS Quarterly Key collaborations with institutions like TU Munich and Tilburg University Recent work emphasizes explainable AI frameworks, disaster management through social media analytics, and the socio-economic implications of digital platforms.

• Network Protocols Engineering
• Cybersecurity
• Distributed Systems
• +3 more

Klaus Wehrle is a Professor of Computer Science and Head of the Communication and Distributed Systems Group (Informatik 4) at RWTH Aachen University. He holds a diploma (1999) and doctorate (2002) from the University of Karlsruhe (now KIT). His research focuses on network protocols, cybersecurity, and distributed systems, funded by DFG, ERC, EU, and industry. He leads the DFG Priority Program on Cyber-Physical Networking and holds ERC grants. Awards include Südwestmetall and FZI Dissertation Prizes. He serves on IEEE, ACM, and DFG committees. His work spans protocol engineering, network simulation, and reliable communication software. He advises the Department of Computer Science at RWTH and collaborates on projects like In-Network Computing and Industrial IoT security. His research addresses industrial systems, smart grids, and privacy-preserving data sharing.

• Variational Calculus
• Partial Differential Equations
• Large Deviations Theory
• +3 more

Dr. Michiel Renger is a researcher at the Department of Mathematics, Technische Universität München (TUM), within the School of Computation, Information and Technology. His research focuses on variational calculus, partial differential equations, large deviations theory, non-equilibrium thermodynamics, and chemical reaction networks. He has contributed to advancing the understanding of macroscopic fluctuation theory, gradient flows, and their applications in stochastic systems. Teaching responsibilities include courses on higher mathematics for engineering students at TUM and specialized lectures on large deviations and convex analysis at TU Berlin. His work bridges theoretical mathematics with applications in physics and engineering, emphasizing interdisciplinary approaches. Renger’s publications span peer-reviewed journals in mathematics and physics, with a focus on rigorous probabilistic and analytical methods. He holds a PhD in Mathematics from Technische Universiteit Eindhoven (2013) and has collaborated on projects in collaboration engineering, addressing challenges in collaborative modeling and organizational design. His research also extends to applied problems like node counting in wireless networks and statistical consulting for industry.

• Biomedical Engineering
• Medical Statistics
• Ophthalmology
• +3 more

Prof. Konstantin Kotliar is a Professor at Aachen University of Applied Sciences, specializing in Biomedical Engineering and Medical Statistics. He teaches courses in Mathematics, Advanced Medical Statistics, and Space Physiology. His research focuses on retinal vessel analysis to study microvascular dysfunction in diseases such as Fabry disease, diabetes, and Alzheimer’s. Kotliar’s work bridges ophthalmology, neuroscience, and cardiovascular science, with a particular interest in developing non-invasive diagnostic tools for early disease detection. His studies often involve collaborations with global health initiatives and clinical trials, such as the EndoAfrica-NWU Study. He has contributed to understanding neurovascular coupling mechanisms and their implications for neurological disorders. Research interests include retinal vessel dynamics, cardiovascular risk stratification, and the application of statistical methods in medical diagnostics. Kotliar’s methodologies span from biomechanical modeling to machine learning-driven image analysis, emphasizing translational research with clinical relevance. His work has implications for improving therapies and preventive measures in chronic diseases.

Armando Walter ColomboView profile

Professor

• Cyber-Physical Systems
• Industrial Digitalization
• Industry 4.0
• +3 more

Prof. Dr. Armando Walter Colombo is a Professor at the Department of Technology, Electrical Engineering and Informatics at the University of Applied Sciences Emden/Leer. He leads the Institute I2AR as Scientific Director and coordinates the DAAD/DAHZ Binational Master in Industrial Informatics with the Universidad Tecnológica Nacional-FRRe in Argentina. His research focuses on Cyber-Physical Systems (CPS), Industrial Digitalization, Industry 4.0, and Smart Manufacturing. Key areas include asset administration shells, IoT integration, and sustainable industrial automation. He holds IEEE Fellow status and is a Distinguished Lecturer for the IEEE Systems Council. He has pioneered educational frameworks like T-CHAT and contributed to standards alignment (RAMI4.0, IEEE Industrial Agents). Recent publications emphasize Industry 4.0 compliance, digital twins, and AI in logistics. Responsibilities: DAAD Master Coordinator, Institute I2AR Director, and International Relations Officer. Awards: IEEE Fellow, Distinguished Lecturer (IEEE Systems Council). Grants & Partnerships: DAAD-funded binational programs, EU-funded PERFoRM projects. His work bridges academia and industry through platforms like the ICPS-based Digital Factory Lab, addressing SME digitalization and sustainable automation.

Göran KauermannView profile

Professor

• Nonparametric Models
• Semi- and nonparametric analysis
• Generalized Linear Models
• +2 more

Prof. Dr. Göran Kauermann is a Full Professor of Statistics at the Ludwig-Maximilians-University Munich , holding the Chair of Applied Statistics in Social Sciences, Economics and Business . His research spans nonparametric models, generalized linear models, and network data analysis, with applications in economics, epidemiology, and data science. Education: Diplom in Economic Mathematics (1991, TU Berlin), PhD in Statistics (1994), Habilitation (Venia Legendi) in Statistics (2000) Kauermann’s research interests focus on penalized regression , network analysis , and statistical modeling in economics, social sciences, and public health. Recent work explores label uncertainty in machine learning , spatio-temporal conflict diffusion , and dynamic network models for economic and social data. Scientific trends in his publications include penalized splines for nonlinear modeling, network flow estimation in social and economic contexts, and label variation analysis in machine learning. His collaborations span climate zone classification , Covid-19 mortality modeling , and smart city parking analytics . Scientific Awards: Bruce Russett Award (2020) for political network research Leadership Roles: He served as Dean of the Faculty of Mathematics, Informatics and Statistics (2019–2021), Speaker of the Elite Master Program in Data Science (2016–2026), and Chair of the German Statistical Society (2005–2013). He also held editorial roles in journals like AStA Advances in Statistical Analysis and Statistical Modelling .