Dongqi Han is an Assistant Professor at the School of Cyberspace Security, Beijing University of Posts and Telecommunications (BUPT), where he joined in July 2024. He earned his Ph.D. in Computer Science from Tsinghua University in 2024, advised by Prof. Zhiliang Wang, with a joint Ph.D. at Nanyang Technological University under Prof. Yang Liu. His research spans AI/LLM + Security, Traffic Analysis, and Network Security. Education Ph.D., Tsinghua University (2019-2024) CSC Visiting Ph.D., Nanyang Technological University (2022-2023) Bachelor's, Jilin University (2015-2019) Research Focus: • Trustworthy AI for cybersecurity • Anomaly detection in network traffic • Network architecture security analysis Scientific Awards: IRTF Applied Networking Research Prize (2024) Distinguished Artifact Award at CCS'24 Outstanding graduate of Tsinghua University (2024) Qihang Award (2-claass) from Tsinghua University (2024) Grants & Collaborations: Participated in key R&D programs, State Grid projects, and collaborations with Alibaba and Qi-anxin Group.
Peter Jax is a Full Professor at RWTH Aachen University , leading the Chair of Communication Systems . His research focuses on speech and audio processing , with expertise in active noise control , spatial audio , and machine learning applications for acoustic systems. Diploma in Electrical Engineering (1997), RWTH Aachen University PhD (2002), RWTH Aachen University Research areas include binaural direction-of-arrival estimation , MIMO acoustic system identification , and adaptive filtering for consumer and medical audio applications. Recent articles highlight innovations in ambisonics upscaling , noise control for UAVs , and data-driven uncertainty modeling in headphones. Scientific honors : Distinguished Member of Technicolor Fellowship Network (2010) Johann-Philipp-Reis Preis Borchers Medal E-Plus Award for Best Dissertation With over 25 patents in speech/audio processing and leadership roles in industry (Deutsche Thomson OHG, 2005–2015), he bridges academic research and industrial innovation.
Dr. Sandra Diaz Pier is a Scientific Lead at the Jülich Supercomputing Centre (JSC) within the Jülich Research Centre , Germany. Specializing in computational neuroscience , high performance computing (HPC) , and machine learning , she bridges neuroscience and advanced computational methods through her research. Education: B.Sc. in Electronic Systems Engineering, Mexico M.Sc. in Computer Science (focus: machine learning, quantum computing), Mexico Second M.Sc. in Electrical Engineering, Ontario, Canada Ph.D. in Computer Science, Germany (2021) Her research focuses on modeling and simulating brain dynamics and plasticity at multiple scales, leveraging HPC to accelerate large-scale neural network simulations. She actively contributes to EU projects like the Human Brain Project (HBP) , Virtual Brain Cloud , and EBRAINS 2.0 , emphasizing infrastructure development and educational training. Her work includes open-source tools such as the NEST simulator , The Virtual Brain , and L2L , enabling efficient parameter exploration and multiscale co-simulation frameworks. The 15 most recent publications highlight her interdisciplinary approach, spanning topics from quantum computing in biomolecular simulations to neural plasticity algorithms and cloud-based brain modeling . These articles reflect her expertise in integrating machine learning , multi-scale simulation , and HPC infrastructure for neuroscience challenges, including seizure propagation, Parkinson’s disease progression, and swarm intelligence in spiking networks. She leads technical coordination in projects like EBRAINS and serves as a task leader in the HBP infrastructure work package , while also organizing workshops and hackathons for open-source tools. Her role involves supporting domain scientists through methodological research and workflow optimization for brain simulations.
Professor Hendrik Dietz holds the Chair of Biomolecular Nanotechnology at the Technical University of Munich (TUM) , affiliated with the TUM School of Natural Sciences and the Munich Institute of Robotics and Machine Intelligence . His research focuses on constructing synthetic molecular devices and machines through DNA origami and self-assembly principles. Research Themes DNA origami for programmable nanodevices Self-assembly inspired by natural molecular systems Molecular visualization with cryo-EM Applications in medicine and synthetic biology Key Article Trends : Dietz's work explores DNA-based rotary motors, virus-trapping shells, and bio-inspired vesicle production. His recent articles highlight integrations of DNA origami with electrochemical sensing, deep learning, and transmembrane transport systems. Scientific Awards ERC Consolidator Grant (2016) Gottfried Wilhelm Leibniz Prize (2015) Hoechst Lecturer Scholarship (2012) Arnold Sommerfeld Award (2010) ERC Starting Grant (2010) Collaborations and Grants : He receives funding from the Deutsche Forschungsgemeinschaft (DFG) via the Excellence Clusters CIPSM and NIM, SFB863, and the Leibniz Prize program, as well as the European Research Council. Dietz collaborates with institutions like Harvard Medical School and the Max Planck School Matter to Life.
Marcel Kollovieh is a researcher at the Technical University of Munich (TUM), affiliated with the TUM School of Computation, Information and Technology and the Department of Computer Science . He is part of the Data Analytics and Machine Learning (DAML) Lab under the mentorship of Prof. Dr. Stephan Günnemann. Education B.Sc. and M.Sc. in Informatics from TUM Research Interests : Marcel focuses on generative models (including variational autoencoders, diffusion models, and score-based models), graphs , and time series , with additional work on hierarchical structures , robustness , and Bayesian learning . His publications span conferences like ICML , ICLR , and NeurIPS , alongside journals such as TMLR . Themes include token merging , flow matching , and adversarial robustness in temporal data, alongside probabilistic clustering and diffusion models . Lab and Team : Marcel works within the DAML Lab at TUM.
Oliver G. Ernst is a Professor of Numerical Analysis at Technische Universität Chemnitz . His research focuses on Numerical Analysis , Uncertainty Quantification , and Inverse Problems , with applications in Thermo-Hydro-Mechanical (THM) processes , Electromagnetics , and Stochastic Partial Differential Equations . He is associated with the Numerical Analysis group at TU Chemnitz. Key Research Areas : Efficient numerical methods for PDEs Krylov subspace techniques Stochastic finite element methods Multi-physics modeling Geoscientific applications Recent Publications (2025-2010): THM simulations under uncertainty Neural network PDE solvers Bayesian inversion frameworks Rational Krylov algorithms Deflated restarting strategies Collaborations : TU Bergakademie Freiberg University of Manchester Technical University of Munich University of Maryland University of Geneva Software Development : Contributor to OpenGeoSys platform Developer of FEMALY MATLAB library Academic Recognition : h-index 32, i10-index 66, with over 4423 citations since 2020.
Lorenz Linhardt is a Researcher and PhD candidate at the Machine Learning Group of Technical University of Berlin, affiliated with the Berlin Institute for the Foundations of Learning and Data (BIFOLD). His research focuses on robustness of deep neural networks, spurious correlations, and representation learning, with applications in explainable AI and medical domains. Educational Background: M.Sc. in Computer Science, 2019 – ETH Zürich B.Sc. in Computer Science, 2016 – University of Vienna Research Interests: Robust Machine Learning : Addressing vulnerabilities in neural networks caused by spurious correlations and adversarial examples. Human Alignment : Bridging gaps between AI outputs and human judgments via representation learning and similarity metrics. Medical Applications : Leveraging machine learning for counterfactual inference in healthcare and dose-response modeling. Article Trends: Recent work emphasizes alignment between human judgments and AI systems, with studies on latent diffusion models, adversarial robustness, and forensic analysis of malware classifiers. Earlier contributions span astronomy surveys and decision tree optimization. Labs/Teams: Active in BIFOLD and the TU Berlin Machine Learning Group, focusing on foundational AI research.
Qianru Sun is a Joint Research Fellow jointly appointed at National University of Singapore (NUS) and the Max Planck Institute for Informatics (MPI-INF) since April 2018. Previously she was a Post-doc Researcher at MPI-INF (2016–2018) supported by the prestigious Lise Meitner Award Fellowship. Education: Ph.D. in Computer Science, Peking University (PKU), 2010–2016 (top 10%) B.S. in Electronic Engineering, Nanjing University of Posts and Telecommunications (NUPT), 2006–2010 (rank 1/77) Exchange student, The University of Tokyo, Oct 2014 – Jan 2015 Research Interests: Dr Sun’s research lies at the intersection of computer vision and machine learning , with a strong focus on few-shot learning , meta-learning , transfer learning , image generation and image classification . She designs algorithms that enable machines to learn new visual concepts with minimal supervision, explores adversarial robustness, and develops transferable representations for cross-domain tasks. Scientific Awards & Honors: Lise Meitner Award Fellowship, Max Planck Institute for Informatics (2016) Professional Service & Community Contributions: Dr Sun actively contributes to the research community through program-committee memberships and editorial roles: Program Committee: ICCV 2019, CVPR 2018, ICCV 2017 Workshop PC: CV-COPS (CVPR 2018), CEFRL (ECCV 2018), WiCV (CVPR 2019, ECCV 2018) Editorial Board: CAAI Transactions on Intelligence Technology Reviewer: IEEE TPAMI, TMM, TCSVT, IJCV Invited speaker: Columbia University (DVMM), CVPR ODAR Workshop, Joint Lecture Series (MPI & Saarland University) Tutorial presenter: ICMR 2018 (Japan)
Rizwan Qureshi is an active researcher and academic specializing in artificial intelligence, machine learning, and their applications in medical imaging and bioinformatics. With a robust publication record spanning from 2017 to 2025, he has established himself as a significant contributor to the fields of computer vision and biomedical AI. His research interests focus on Artificial Intelligence , Machine Learning , Medical Imaging , Computer Vision , and Biomedical Engineering . Qureshi's work demonstrates particular expertise in object detection systems (especially YOLO variants), medical image segmentation, vision-language models, and applications of AI to healthcare problems including lung cancer research and diabetic retinopathy detection. Analysis of his recent publications (2023-2025) reveals a strong trend toward medical applications of AI, with approximately 60% of his work focusing on healthcare-related problems. His research shows increasing emphasis on model robustness, explainability, and handling distribution shifts in real-world applications. The publications span top venues including IEEE Access, IEEE Transactions on Medical Imaging, CVPR, and BIBM. Qureshi maintains extensive collaborations with researchers across multiple institutions, with frequent co-authorship with Hong Yan, Tanvir Alam, Jia Wu, and Sheheryar Khan. His work demonstrates both technical depth in machine learning methodologies and practical application to significant healthcare challenges. While specific details about his academic advising are not evident from the publication record alone, his numerous publications with multiple co-authors suggest active participation in research teams and likely supervision of graduate students. His work shows consistent funding support through publication in reputable journals and conferences.
Dr. Johannes Twiefel is a Researcher at the Knowledge Technology Research Group within the Department of Informatics at the University of Hamburg. His work focuses on speech recognition, language understanding, and brain-inspired models using echo state networks. He leads the LemonSpeech project, funded by the German Federal Ministry for Economic Affairs and Energy, which aims to develop German automatic speech recognition (ASR) systems for local hardware deployment. His research spans noise robustness in ASR, robotic interaction, and multimodal learning. Education: Doctor rerum naturalium (Dr. rer. nat.) in Computer Science, University of Hamburg (2020) Master's degree in Computer Science, University of Hamburg (2014) Research Interests: Reservoir Computing and Echo State Networks Machine Learning and Neural Networks Speech Recognition and Language Understanding Human-Robot Interaction Robotics and Multimodal Systems Grants and Projects: EXIST Scholarship (2021–present) for LemonSpeech DOCKS Project (post-processing ASR hypotheses) Labs/Teams: Active in the Knowledge Technology Research Group, collaborating on neuro-inspired architectures and ASR systems.
Sven Schewe is a Professor in the Department of Computer Science at the University of Liverpool, affiliated with the School of Electrical Engineering, Electronics and Computer Science. He leads the AI Section and is a founding member and former leader of the Verification Group. He also has secondary affiliations with the Algorithms, Complexity Theory and Optimisation Group and the Institute for Risk and Uncertainty. Research Interests: His research centers on automata theory and game theory, particularly their applications in the verification and synthesis of reactive and safety-critical systems. He investigates infinite-duration games, automata over infinite words and trees, and develops algorithms and tools for automated verification, synthesis, and learning of optimal control strategies. His work extends to reinforcement learning with formal guarantees, cyber-physical systems, and AI safety. Recent Research Trends: His recent publications demonstrate a strong integration of formal methods with machine learning, particularly in adversarial training, neural network robustness, and model-free reinforcement learning under omega-regular objectives. He also applies formal reasoning to interdisciplinary domains such as chemical space exploration and materials science. Scientific Awards: Finalist for the ERCIM Cor Baayen Award 2010 Dr. Eduard Martin Preis 2009 GI Dissertation Award 2008 Advising and Grants: He actively supervises numerous PhD students and postdoctoral researchers. He is Principal Investigator (PI) or Co-Investigator (CI) on multiple major grants, including EPSRC Programme Grants, Royal Society Fellowships, and Horizon Europe projects. His funded research spans topics such as game theory, verification, synthesis, reinforcement learning, and risk analysis. He has hosted visiting researchers and collaborated internationally with institutions in Germany, France, India, Taiwan, and the US. Labs and Teams: He co-founded and led the Verification Group and previously led the AI Section at the University of Liverpool. These groups focus on formal methods, automata, games, and their applications in AI and safety-critical systems.
Daxin Tian is a prominent professor at Beihang University's School of Transportation Science and Engineering, specializing in intelligent transportation systems and vehicular networks. With over 170 publications spanning from 2006 to 2025, his research has significantly contributed to the advancement of connected and autonomous vehicle technologies. His work appears consistently in top-tier IEEE journals including IEEE Transactions on Intelligent Transportation Systems, IEEE Transactions on Intelligent Vehicles, and IEEE Internet of Things Journal, establishing him as a leading authority in the field. Professor Tian's research interests encompass several critical areas in modern transportation technology: Connected and Autonomous Vehicle Systems Vehicular Networking and Communication Protocols Vehicle Platooning and Cooperative Driving Algorithms Edge Computing Applications for Transportation Computer Vision for Autonomous Driving Perception Traffic Flow Optimization and Prediction Models Resource Allocation in Vehicular Networks His recent publications demonstrate an increasing sophistication in addressing complex multi-vehicle scenarios while maintaining practical considerations like communication reliability, energy efficiency, and safety constraints. The research trajectory shows a clear evolution from foundational networking and control problems toward more integrated AI-driven solutions that combine computer vision, natural language processing, and advanced control theory for next-generation transportation systems. Professor Tian maintains extensive international collaborations, particularly with researchers at Canadian institutions including Victor C. M. Leung's group, while leading a substantial research team at Beihang University. His work frequently bridges theoretical advances with practical transportation challenges, resulting in numerous high-impact publications that address real-world implementation barriers in intelligent transportation systems.
Fabian Gans is a Researcher at the Max Planck Institute for Biogeochemistry, affiliated with the Department Biogeochemical Integration led by Prof. Dr. M. Reichstein. He leads the Scalable Spatiotemporal Data Structures and Analytics (SSDSA) research group and is actively involved in the Empirical Inference of the Earth System group under Dr. Miguel D. Mahecha, as well as the Energy and Earth System group under Dr. A. Kleidon. His work is central to advancing data-driven methodologies in Earth system science. His research focuses on Earth system dynamics, particularly through the development and application of Earth System Data Cubes (ESDCs), which integrate multivariate spatiotemporal datasets for robust analysis. He employs machine learning, remote sensing, and hybrid modeling to study carbon and water fluxes, climate extremes, and ecosystem responses. His work bridges observational data with modeling frameworks to improve understanding of biosphere-atmosphere interactions. The 15 most recent publications highlight a strong trend toward data integration, scalability, and the use of artificial intelligence in Earth sciences. Key themes include the FLUXCOM framework for upscaling carbon fluxes, the development of Earth System Data Cubes, analysis of compound climate extremes, and hybrid modeling approaches. His research consistently emphasizes open science, reproducibility, and the need for integrated data platforms to tackle global environmental challenges. Scientific Awards: No awards listed in the provided text. Advising and Grants: No formal advisees or students are listed. No specific grants or funding sources are mentioned, though his involvement in large collaborative projects like FLUXCOM and Earth System Data Cubes suggests participation in significant research initiatives. Labs and Teams: Fabian Gans leads the Scalable Spatiotemporal Data Structures and Analytics (SSDSA) group and is a key member of the Empirical Inference of the Earth System team. He is also involved in the DeepESDL platform, an open collaborative environment for Earth system research, indicating leadership in developing research infrastructure and fostering interdisciplinary collaboration.
Prof. Rolf Findeisen is a Professor in the Department of Control and Cyber-Physical Systems (CCPS) at Technische Universität Darmstadt. His work focuses on advancing control theory and its applications in cyber-physical systems, autonomous systems, and energy storage systems. Key areas include model predictive control (MPC), battery management systems, machine learning integration into control frameworks, and optimization of crystallization processes. He leads research on safety-critical systems, data-driven control methods, and interdisciplinary applications in robotics and biotechnology. His research spans theoretical advancements in MPC stability, stochastic control, and Gaussian process modeling, alongside practical implementations in autonomous vehicles, lithium-ion battery systems, and bioprocess optimization. Notable contributions include frameworks like HILO-MPC for integrating machine learning with control systems, and methodologies for safe exploration in autonomous navigation. Prof. Findeisen's publications emphasize energy-efficient trajectory planning, fault detection in battery systems, and real-time optimization of manufacturing processes. His work bridges academic theory with industrial applications, addressing challenges in scalability, safety, and computational efficiency. His lab collaborates on national projects like IN-Fly-Tec and INFLIGHT, focusing on innovative flight control systems and sensor technologies. Current research trends include hybrid intelligent optimization, cybergenetic control of microbial systems, and safe reinforcement learning for control systems.
Sebastian Thiery serves as a Professor in Manufacturing Engineering at Leuphana University of Lüneburg, specifically holding a Ph.D. Professorship for Manufacturing – Innovative Manufacturing. His research focuses on advanced manufacturing processes with particular emphasis on sheet metal forming technologies. Thiery's primary research interests include Incremental Sheet Forming with Active Medium (IFAM) , Deep Drawing Processes , Process Control and Optimization , and the application of Artificial Neural Networks in manufacturing systems. His work bridges theoretical modeling with practical industrial applications, particularly in metal forming operations where geometrical accuracy and process robustness are critical concerns. Analysis of his publication record reveals a clear research trajectory focused on improving manufacturing processes through innovative control strategies. His recent work emphasizes the integration of machine learning techniques with traditional manufacturing processes, particularly using neural networks for friction compensation and draw-in prediction. The publications demonstrate increasing sophistication in process control methodologies, moving from basic IFAM process development to sophisticated closed-loop control systems that incorporate real-time monitoring and adaptive adjustments. Thiery actively collaborates with researchers including Mazhar Zein El Abdine, Jens Heger, and Noomane Ben Khalifa, suggesting participation in a dedicated research group or laboratory focused on advanced manufacturing processes. His work appears to be supported by research grants, including funding from the German Research Foundation (DFG) as indicated in one of his publications.
