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Edon KelmendiView profile

Lecturer

• Dynamical Systems
• Logics in Computer Science
• Game Theory
• +3 more

Dr. Edon Kelmendi is a Lecturer in Computer Science at Queen Mary University of London. He holds a doctorate from Laboratoire Bordelais de Recherche en Informatique and completed postdoctoral research at the University of Warsaw, University of Oxford, and Max Planck Institute. His research focuses on theoretical computer science, particularly dynamical systems, logics in CS, and game theory. Kelmendi's work examines algorithmic verification of dynamical systems, automata theory, and game-theoretic models. His recent publications demonstrate strong focus on linear dynamical systems and injective piecewise affine maps. He received the Distinguished Paper Award at LICS 2022 for his computational contributions to linear recurrence sequences.

Isabel Valera MartinezView profile

Professor

• Machine Learning
• Artificial Intelligence
• Data Science
• +4 more

Isabel Valera Martínez is a full Professor of Machine Learning at the Department of Computer Science, Saarland University, and an Adjunct Faculty at the Max Planck Institute for Software Systems (MPI-SWS). She holds a PhD in Machine Learning from the University Carlos III of Madrid and has held prestigious fellowships including the Humboldt Postdoctoral Fellowship and the Minerva Fast Track Fellowship from the Max Planck Society. Her research focuses on developing interpretable, robust, and fair machine learning methods, with applications in healthcare, social systems, and consequential decision-making like hiring and loan approvals. Education: PhD in Machine Learning (2014) and MSc in Multimedia and Communications (2012) from University Carlos III of Madrid; Master’s work at Leibniz University Hannover (2009); Telecommunications Engineering from the Technical University of Cartagena (2009). Research Interests: Fairness in AI, Bayesian nonparametric models, robust machine learning, and applications in healthcare and social systems. She co-leads the ELLIS Robust Machine Learning program and the Saarbrücken AI & Machine Learning (SAM) Unit. Key Publications: Contributions to fair classification frameworks, automatic discovery of data types, and modeling psychiatric comorbidity. Notable venues include AAAI, NIPS/NeurIPS, JMLR, and ICML. Awards: Humboldt Fellowship, Minerva Fellowship, Best Paper Honorable Mention at WWW 2017. Labs & Collaborations: Active in the ELLIS Society and MPI-SWS, focusing on interdisciplinary AI applications.

Fuyuan ZhangView profile

Researcher

• Artificial Intelligence
• Cybersecurity
• Formal Methods
• +7 more

Fuyuan Zhang is a Postdoctoral Researcher at the Max Planck Institute for Software Systems, specializing in advanced software testing methodologies and formal verification techniques. His research focuses on improving the reliability and security of AI systems, quantum computing frameworks, and concurrent systems through innovative testing criteria, adversarial attacks, and compositional reasoning. Key areas of expertise include: Large Language Model (LLM) testing and validation Quantum program analysis and security Adversarial machine learning and neural network robustness Formal verification of concurrent and cyber-physical systems Automated bug detection in complex software systems His work bridges theoretical foundations with practical applications, addressing critical challenges in AI safety, quantum software reliability, and system-wide security certification.

• Database Systems
• Data Engineering
• Distributed Systems
• +4 more

Tilmann Rabl is a Professor affiliated with the Hasso Plattner Institute (HPI) at the University of Potsdam, Germany. His research focuses on database systems, distributed computing, and scalable data processing. He leads projects exploring serverless cloud infrastructure, stream processing, and machine learning integration with databases. Key areas of research include optimizing GPU-based data processing, developing benchmarks like TPCx-IoT and TPCx-AI, and advancing techniques for distributed systems, including RDMA and NVLink-based architectures. His work emphasizes practical systems, such as Skyrise (serverless data processing), Rhino (distributed state management), and PROTEUS (scalable machine learning). Rabl has contributed to foundational tools like BlockJoin for matrix partitioning and has explored performance trade-offs in persistent memory and CXL device memory. His collaborative projects address challenges in real-time data analytics, sensor data coherence, and interoperable data science workflows.

Jan-Hendrik HeinrichsView profile

Researcher

• Neuroethics
• AI Ethics
• Philosophy of Mind
• +1 more

PD Dr. Jan-Hendrik Heinrichs is a researcher at the Institute of Neuroscience and Medicine (Brain and Behavior - INM-7) at Research Center Jülich. His work explores philosophical and ethical dimensions of neuroscience and artificial intelligence. Research Focus His research examines neuroethical frameworks, moral responsibility in technology development, and philosophical aspects of cognitive enhancement. Recent publications analyze ethical boundaries of neurotechnologies and AI applications. Publication Trends Recent work addresses conceptual challenges in moral responsibility, ethical implications of neuroprosthetics, and limitations of cognitive scaffolding. Publications consistently bridge philosophy, neuroscience, and technology ethics.

• Network Science
• Social Influence Modeling
• Control Systems
• +5 more

Chiara Ravazzi is an accomplished researcher at Politecnico di Torino specializing in network science, social influence modeling, and control systems. Her academic career spans over 15 years with consistent high-impact publications in top-tier venues including IEEE journals and conferences. Dr. Ravazzi's research focuses on understanding complex network dynamics with applications spanning social networks, wireless communications, and pandemic modeling. Her work bridges theoretical network analysis with practical implementations, particularly examining how information and influence propagate through networks. She has developed novel methodologies for analyzing social influence, optimizing network resources, and predicting behavioral patterns in complex systems. Analysis of her recent publications (2023-2025) reveals three major research thrusts: (1) Social network dynamics and influence modeling, particularly examining innovation diffusion and popularity mechanisms; (2) Optimization of communication networks with applications to 5G/6G systems; and (3) Network-based approaches to public health challenges like pandemic intervention planning. Her interdisciplinary work demonstrates strong methodological rigor while addressing contemporary real-world problems. Dr. Ravazzi maintains extensive collaborations with leading researchers including Fabrizio Dabbene, Enrico Magli, Paolo Frasca, and Valentina Breschi. Her publication record shows consistent growth in both quantity and impact, with numerous first-author and senior-author contributions across multiple domains. She serves on program committees for major conferences in control systems and network science.

• Blockchain Technology
• Distributed Systems
• Permissioned Blockchains
• +3 more

Artem Barger is a researcher specializing in blockchain technology, distributed systems, and database optimization. With affiliations primarily in blockchain development and academic research, he has contributed extensively to Hyperledger Fabric enhancements and decentralized information systems. Research Interests Optimizing state databases for blockchain platforms Byzantine Fault Tolerance in distributed networks Permissioned blockchain architectures Tokenization of real-world assets AI applications in soft skills evaluation Recent Publications Barger's work focuses on improving blockchain scalability and security through techniques like certification blocks, Patricia Merkle tries, and verifiable randomness. He has also explored tokenization applications in charity and energy sectors.

• Machine Learning
• Computer Science
• Medical Informatics
• +7 more

Yan Li is a researcher with extensive contributions across interdisciplinary domains including Machine Learning , Signal Processing , and Environmental Science . Affiliated with institutions such as the University of Southern Queensland , Hebei University , and Shandong University , Li's work spans applications in Medical Informatics , Remote Sensing , and Operations Research . Recent publications highlight expertise in Deep Learning (e.g., hyperspectral classification, image fusion), Stochastic Modeling (e.g., chemotaxis models), and Federated Learning (e.g., vertical federated fuzzy clustering). Collaborative projects include 3D Reconstruction , Smart Grid Security , and Landslide Monitoring using satellite data. Li's 2025 work demonstrates a focus on Medical Imaging (segmentation algorithms with dual-frequency decoupling), AI in Education (ChatGPT adoption), and Industrial IoT (GPU-accelerated vessel trajectory visualization). While no explicit academic rank is provided, their prolific publication record suggests a Researcher role.

• Artificial Intelligence
• Machine Learning
• Internet of Things (IoT)
• +5 more

G. Thippa Reddy is a prolific researcher with a focus on advanced technologies such as artificial intelligence, machine learning, and blockchain, particularly in healthcare, IoT, and cybersecurity domains. His work spans interdisciplinary areas including federated learning, edge computing, and smart city infrastructure. He has collaborated extensively with researchers like Praveen Kumar Reddy Maddikunta, Gautam Srivastava, and Mamoun Alazab, producing over 150 publications in high-impact journals like IEEE Access, IEEE Internet Things Journal, and IEEE Transactions on Industrial Informatics. His research emphasizes practical applications of AI in real-world scenarios, such as privacy-preserving medical systems, secure UAV networks, and inclusive education for individuals with disabilities. He explores cutting-edge topics like the Metaverse's role in Industry 5.0, blockchain-enhanced security frameworks, and the integration of large language models into intelligent transportation systems. Key contributions include frameworks for federated learning in healthcare, optimized routing protocols for underwater communications, and explainable AI (XAI) methods for industrial automation. His work often addresses challenges in scalability, privacy, and ethical deployment of emerging technologies.

• Speech Recognition
• Natural Language Processing
• Machine Learning
• +3 more

Tien Ping Tan is an experienced researcher specializing in speech recognition , natural language processing , and machine learning applications. With a PhD in Automatic Speech Recognition for Non-Native Speakers from Joseph Fourier University (2008), his career spans two decades of impactful contributions across multiple domains.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +7 more

Sio Kei Im is an active researcher with a focus on computer science, machine learning, and human-computer interaction. His recent work spans multiple domains including image processing, quantum computing, and virtual reality. Publications address advanced data augmentation (LogicMix), multi-modal quantum watermarking (MMQW), and efficient neural decoding algorithms (TRHyper). Research interests include time series optimization, dialogue summarization, and haptics in VR environments. Collaborations with experts in linguistics, electrical engineering, and software development indicate interdisciplinary expertise. Key contributions involve adaptive algorithms for AI model protection, speaker recognition systems, and real-time 3D rendering techniques.

• Machine Learning
• Image Retrieval
• Data Clustering
• +3 more

Mohammed Lamine Kherfi is a researcher affiliated with Université de Ouargla, Algeria. His work focuses on machine learning, image retrieval, and data clustering with applications in computer vision and optimization. He has collaborated extensively with researchers like Oussama Aiadi, Mebarka Allaoui, and Djemel Ziou. His research bridges theoretical advancements in machine learning with practical applications in areas such as fruit classification, semantic image retrieval, and deep learning models. Key research areas include optimization algorithms (e.g., PSO integration with t-SNE), multi-view learning, and Bayesian methods for image representation. He has contributed to improving clustering techniques, feature extraction, and the development of lightweight neural network architectures. His work often emphasizes efficient and energy-aware solutions for real-world problems. Over 30 publications span prestigious venues like Expert Systems with Applications, IEEE Access, and Multimed Tools Appl. His collaborative network includes institutions in Algeria and international partners, reflecting a global impact in computational intelligence and computer vision.

• Smart Grids
• Power System Cybersecurity
• Cyber-Physical Systems
• +7 more

Peter Palensky is a leading researcher in smart grids, power system cybersecurity, and cyber-physical systems. His recent work focuses on digital twins for power systems, quantum computing applications in energy analysis, and secure blockchain frameworks for distributed energy resources. He collaborates extensively with institutions across Europe, particularly in Dutch and Mongolian grid stability projects. Research areas include Smart grid resilience against cyber attacks Quantum-enhanced power flow analysis Electric vehicle grid integration (V2G) Machine learning for energy systems optimization High-voltage direct current (HVDC) security His publications emphasize practical implementations, such as hardware-in-the-loop testing for photovoltaic systems and real-time simulation models for energy storage. Recent articles explore large-scale synthetic data generation for grid analysis, dynamic tariff impacts on EV charging, and advanced control strategies for offshore MMC grids.

Philip WotschackView profile

Researcher

• Social Inequality
• Labor Markets
• Digitalization
• +3 more

Dr. Philip Wotschack is Head of the Research Group 'Working with Artificial Intelligence' at the Weizenbaum-Institut for the Networked Society. He has held various academic roles including Researcher at WZB Berlin Social Science Center and the University of Groningen. His research focuses on social inequality, labor markets, digitalization, and algorithmic management. He earned his Ph.D. in Sociology from the University of Groningen (2009). Key research areas include: Impact of AI-driven algorithmic management in workplaces Continuing training practices for low-skilled workers Workplace digitalization and automation Gender and social inequality in training access Recent projects include the EU-funded INCODING project analyzing collective bargaining's role in algorithmic management governance, and experimental studies on human-technology interaction in automated systems. His work integrates institutional theory perspectives with empirical analyses of organizational practices. He has led projects funded by the German Research Foundation and European Commission. Publications span topics like labor market segmentation, workplace automation ethics, and skill development policies. He has authored/co-authored books on work-life balance and labor market institutions.

Andrew TordaView profile

Professor

• Protein Structure Prediction
• Computational Biology
• Bioinformatics
• +3 more

Prof. Andrew Torda leads the Biomolecular Modeling research group at the University of Hamburg's Center for Bioinformatics (ZBH). He holds a professorship and focuses on computational methods in structural biology. His work includes developing force fields for protein structure prediction, substitution matrices for sequence alignment, and threading algorithms like 'wurst.' He joined the University of Hamburg in 2002 after positions at the Australian National University and ETH Zurich. Research Interests: Low-resolution force fields for protein modeling Scoring functions and protein sequence optimization Lattice models for free energy estimation Protein sequence-structure alignment methods Software Projects: 'wurst' threading code with a web server for structural predictions Optimized amino acid substitution matrices Genome simulation tools (collaboration with Jan Wigger) Teaching & Collaboration: Teaches bioinformatics modules and mentors students on software development projects. His group actively engages in computational methods for molecular design and systems biology.