Sarath Menon is a computational materials scientist at Ruhr-University Bochum and Max-Planck-Institut für Eisenforschung GmbH. His work focuses on atomistic simulations, machine learning interatomic potentials, and thermodynamic property calculations. He contributes to open-source software like pyiron and pace. Doctor of Engineering, Mechanical Engineering (2021) Master of Science, Materials Science and Simulation (2018) Bachelor of Technology, Mechanical Engineering (2012) His research centers on developing machine learning potentials for thermodynamic modeling, with applications in phase diagrams and nucleation studies. He employs methods like transition path sampling and hyperdynamics. Menon teaches Python programming, electronic structure methods, and atomistic simulation techniques. He has organized workshops on reproducible workflows and quantum mechanics in solid-state physics. Key software contributions include: pyscal : Structural analysis tool for atomic environments pace : High-performance Atomic Cluster Expansion implementation calphy : Free energy calculation library atomRDF : Ontology-based structure manipulation
Nikolas Zöller is a research scientist at the Max Planck Institute for Human Development 's Adaptive Rationality department. His work focuses on collective intelligence, human-AI collaboration, and computational modeling of social systems. He combines methods from physics, computer science, and psychology to study decision-making processes in both natural and artificial collectives. Ph.D. candidate at Constructor University Bremen (2020-2024) Research Associate at Potsdam University of Applied Sciences (2015-2022) Master of Science in Physics from Freie Universität Berlin (2014) Bachelor of Science in Physics from Freie Universität Berlin (2010) His research spans: Collective intelligence frameworks Human-AI collaborative diagnostics Agent-based modeling of social networks Technological co-diffusion patterns Thermodynamic optimization in physical systems Recent publications highlight his work on: Diagnostic accuracy in human-AI collectives GitHub collaboration topology Affect control theory in group dynamics Co-diffusion of complementary technologies Scientific recognition includes: European Press Prize (2022) as part of a team Springer Best Masters Award (2014)
Shweta Suran is a Postdoctoral Fellow at the Max Planck Institute for Human Development (Berlin, Germany) with affiliations as an External Researcher at Tallinn University of Technology (School of Information Technologies, Department of Software Science) and IT University of Copenhagen (Collective Intelligence Research Group). She previously held roles at TalTech, The Open University (UK), and Krishna Engineering College. Doctor of Philosophy (2022) in Computer Science, Tallinn University of Technology Research Master's Degree (2014) in Knowledge Engineering, SRM Institute of Science and Technology Her research focuses on Collective Intelligence, Collective Behaviour, Social Network Analysis, Data Mining, and Digital Image Processing. She explores how individuals interact with online content, the impact of conversational affordances on deliberation quality, and correlations between social media preferences, media choices, and socio-economic/political factors. Her recent publications include work on a digital collaborative platform for the silver economy (2023), frameworks for collective intelligence (2020), pathfinding in medical imaging (2014), and AI applications in spatial data infrastructure (2019-2021). She co-supervised Sijo Arakkal Peious (2019 Master's thesis). Marie Skłodowska-Curie Postdoctoral Fellowship (2024) Seal of Excellence from European Commission (2023) SAP Fellowship Award (2016) Gold Medal at SRM University (2014) She contributed to projects like the EU Green Deal-aligned Waterway Safety Management program (2020-2021) and cost-effective 3D spatial data infrastructure (2019-2021), with funding totaling €1,996,000. Her work bridges ICT, collective behavior, and societal challenges.
Prof. Dr. Tobias Lasser is an Adjunct Professor at the Technical University of Munich (TUM) since 2024, leading the Computational Imaging and Inverse Problems research group. He holds affiliations with the TUM School of Computation, Information and Technology and the Munich Institute of Biomedical Engineering. His academic career includes a PhD (2011) and habilitation (2017) in Computer Science from TUM, along with prior roles as a Postdoctoral Fellow and Akademischer Rat at TUM's Chair for Computer Aided Medical Procedures. His research focuses on computational imaging , inverse problems in tomography , and clinical decision support systems . Key areas include X-ray phase-contrast/dark-field imaging, light field microscopy, and multi-modal medical data analysis. Notable contributions include advancements in sparse-view CT reconstruction, artifact-free deconvolution techniques, and AI-driven diagnostic tools. Recent work emphasizes integrating deep learning with traditional imaging modalities, such as encoder-decoder architectures for anomaly detection and attention-based models for skin lesion classification. His team also explores robotic sample holders for advanced CT setups and open-source frameworks like elsa for tomographic reconstruction. Educations: Diplom-Informatiker (2006), Diplom-Mathematiker (2008), Dr. rer. nat. (2011, summa cum laude), Habilitation (2017) Awards: IEEE editorial award (2023), Best Poster (2021), Supervisory Excellence (2021), Teaching Award (2021) Labs/Teams: Munich Institute of Biomedical Engineering, Computational Imaging Group (TUM)
Philip Diederich is a researcher at the Chair of Communication Networks at Technical University Munich (TUM), where he has been since 2021. He holds an M.Sc. in Electrical and Computer Engineering from TUM (2021) and a B.Sc. from the same institution (2019). His research focuses on real-time networking in diverse environments, including data centers, industrial networks, and wide area networks. Key interests include deterministic networks with hard/soft real-time guarantees, software-defined networking (SDN), and the integration of real-time systems with best-effort networks. Recent work explores topology effects on real-time network performance, resilience in time-sensitive networks, and affordable latency measurement setups. He collaborates on projects like integrating deterministic networking with 5G and developing hybrid models for TSN resource allocation. Philip co-advises student theses on topics like end-to-end scheduling in large-scale deterministic networks and has contributed to open research directions in network security and protocol synthesis.
Hasan Yagiz Özkan is a Scientific Staff member at the Chair of Communication Networks at Technische Universität München. His research focuses on networked control systems, software-defined networking, cybersecurity, and real-time communication protocols. He contributes to projects such as the ERC Network Flexibility initiative and the 6G Future Lab Bavaria. Recent work includes advancements in task-oriented scheduling for networked control systems and intrusion detection using machine learning. His publications span topics like bug resolution prediction in softwarized networks and age-of-information-aware frameworks. He has collaborated on papers addressing network reliability, software-defined radio implementations, and contrastive pretraining for cybersecurity. His inactive status may pertain to a specific role, but he remains active in academic research and publications.
Saeed Amizadeh is a prominent researcher specializing in artificial intelligence and machine learning, currently affiliated with Microsoft's research division. His work spans multiple domains of AI including speech processing, natural language understanding, computer vision, and time series analysis, with a particular focus on developing novel frameworks that bridge symbolic reasoning with neural approaches. Over the past decade, he has established himself as a significant contributor to the field through publications in top-tier conferences including ICASSP, ICLR, AAAI, KDD, and IJCAI. Dr. Amizadeh's research interests primarily center around advancing the theoretical foundations and practical applications of machine learning systems. His work on neuro-symbolic visual reasoning has contributed to understanding how to effectively disentangle visual perception from logical reasoning in AI systems. He has made significant contributions to differentiable programming, particularly in making classical machine learning pipelines fully differentiable, which enables end-to-end optimization of complex ML workflows. His research on speech enhancement using GANs represents cutting-edge work in audio processing, while his time series anomaly detection frameworks have practical applications in numerous industry settings. Analysis of his publication trends reveals a consistent trajectory from theoretical machine learning foundations toward increasingly applied research with practical industrial relevance. His early work (2010-2015) focused on fundamental algorithms and probabilistic modeling approaches, while his more recent publications (2019-2025) demonstrate a shift toward practical AI systems with direct applications in speech processing, audio separation, and enterprise machine learning. A notable theme throughout his career is the development of frameworks that enable more efficient, scalable, and interpretable AI systems. As a key contributor to Microsoft's ML.NET framework, Dr. Amizadeh has played an important role in developing tools that make machine learning more accessible to enterprise developers. His collaborative work spans academia and industry, with notable partnerships with researchers from various institutions as well as within Microsoft Research.
Frank Noé is a W2 Professor (Associate Professor equivalent) of Mathematical Modeling in the Life Sciences at the Free University of Berlin, with promotion to W3 pending. He concurrently serves as an Adjunct Professor in the Department of Chemistry at Rice University. His research develops computational frameworks for molecular kinetics, integrating machine learning with biophysical simulation to decode complex biomolecular processes. Academic qualifications: Dr. rer. nat. in Informatics and Biophysics, Universität Heidelberg (2006, summa cum laude) Master of Science in Computing, Cork Institute of Technology, Ireland (2000-2002) Diplom Ingenieur (BA) in Electrical Engineering, Berufsakademie Stuttgart (1996-1999) Noé pioneers the application of deep learning to molecular simulation, notably through Boltzmann generators and VAMPnets. His work bridges statistical mechanics, biophysics, and data science to model protein dynamics, ligand binding, and cellular signal transmission at unprecedented timescales. This interdisciplinary approach has established new paradigms for analyzing molecular kinetics. Recent publications demonstrate a decisive shift toward AI-driven methodologies, where neural networks extract kinetic features from molecular trajectories. This enables atomistic exploration of processes previously inaccessible to simulation, such as millisecond-scale protein folding and membrane receptor dynamics. Major honors: Simons Fellow at IPAM (2019) ACS Early-Career Award in Theoretical Chemistry (2019) ERC Consolidator Grant (2017) ERC Starting Grant (2012) Eliteprogramm für Postdoktoranden fellowship (2007) Research leadership: Principal Investigator, DFG Collaborative Research Center 958 "Scaffolding of Membranes" (2011-present) Principal Investigator, DFG Collaborative Research Center 740 "From Molecules to Modules" (2009-present) Faculty member, International Max-Planck Graduate School for Computational Biology Organizer, International Workshop on Molecular Kinetics He directs theoretical research within the SFB/TRR 186 consortium on molecular switches, developing open-source software for kinetic analysis while mentoring the next generation of computational biophysicists through DFG-funded graduate programs.
Sara Nazari is a Researcher at the Leibniz Center for Tropical Marine Research (ZMT) , affiliated with the Working Group: Submarine Groundwater Discharge . She integrates interdisciplinary methodologies to address climate change, land use, and socio-economic impacts on water resources. Education: MSc in Water Resources Management and Engineering, Isfahan University of Technology (2015–2018) Her research emphasizes: Groundwater modeling Integrated and adaptive water resources management Sustainability analysis Big-Data analysis Conflict resolution theories Her publications focus on global groundwater recharge trends, computational models, and conflict resolution frameworks. She developed open-source tools for hydrological assessments and contributed datasets on global recharge patterns. She is based at Fahrenheitstr. 6, Bremen, Germany, and can be reached at sara.nazari@leibniz-zmt.de .
Dr. Shaukat Ali is a leading researcher at Simula Research Laboratory (Certus Software V&V Center, Norway), with a PhD from the University of Oslo . His work bridges quantum software engineering and cyber-physical systems (CPS) validation, focusing on digital twins, autonomous vehicles, and healthcare IoT applications. Collaborates with institutions like South Dakota State University, COMSATS University, and Mohammad Ali Jinnah University Co-developed tools: QuCAT (Quantum combinatorial testing), DeepScenario (autonomous driving datasets), and EvoCLINICAL (medical digital twin evolution) Research spans quantum software testing (IBM quantum computers), uncertainty-aware validation of medical devices, and search-based optimization in CPS. His 2025 work introduces quantum circuit mutants, uncertainty-wise test oracles, and foundational models for digital twin creation. Publications in journals like ACM Transactions on Software Engineering and conferences including ICSE , GECCO , and IEEE Quantum Software reflect his interdisciplinary approach. Current projects address quantum software robustness, LLM-driven scenario realism, and safe adaptation in robotics.
Ghang Lee is a Professor at the Department of Architecture and Architectural Engineering of Yonsei University in Seoul, South Korea. He directs the Building Informatics Group, focusing on Building Information Modeling (BIM) , AI applications in construction , and automated code compliance checking . His work bridges human expertise with artificial intelligence, advancing digital transformation in the built environment. Developed systems: Tower Crane Navigation System (TCVS), IoT-based exit signs, and NADIA (speech-to-BIM tool) Co-author of the internationally recognized BIM Handbook Recipient of Silver Medal (President of Korea) and multiple Outstanding Research Achievement Awards Research Interests span BIM, information interoperability, quality management, and AI-driven construction technologies. His recent work emphasizes: NLP for defect detection and classification Domain-specific corpus embeddings Human-AI collaboration in design Smart construction and digital twinning Building informatics standardization Visual-language models for defect analysis Scientific Awards include Silver Medal (President of Korea), Stanford/Elsevier Top 2% Scholar recognition, and multiple Outstanding Conference Paper Awards from KICEM, AIK, and KAIARI. He also holds Yonsei Distinguished Teaching Awards and Ministry of Land, Infrastructure, and Transport commendations.
Robert Schweppe is a Researcher at the Department of Computational Hydrosystems , Helmholtz Centre for Environmental Research (UFZ) in Leipzig, Germany. He has held this position since 2017, following prior roles as Project Engineer at HYDRON GmbH (2014-2017) and Research Assistant at UFZ (2013). Expertise in hydrological modeling using mHM, ECLand, LARSIM Developed pyflow configuration tools and MPR software for environmental data processing Key contributor to 4DHyro project integrating Earth Observation data with hydrological models His research interests span hydrological modeling , drought forecasting , machine learning applications , and climate change impacts on water systems. His 2022 publications on parameter regionalization and floodplain modeling demonstrate methodological innovations in environmental data analysis. Notable scientific recognitions include the Deutschlandstipendium scholarship (2012-2013) and Helmholtz Field Study Fellowship (2022). He collaborates extensively with European institutions on compound environmental risks and smart monitoring technologies .
Dr.-Ing. Michael Selzer is a Group Leader in Research Data Management at the Karlsruhe Institute of Technology (KIT), specifically within the Institute of Nanotechnology. He leads the Kadi4Mat project, focusing on FAIR (Findable, Accessible, Interoperable, Reusable) research data infrastructure for materials science. His work bridges computational modeling and digital research workflows. Research Interests: Michael Selzer specializes in computational materials science , with a strong emphasis on phase-field modeling for simulating microstructure evolution, fracture mechanics, and multiphase systems. His research extends to materials informatics , digital workflows , and research data management , particularly in the context of battery materials, porous media, and solid-state systems. He integrates machine learning and data science to analyze and optimize materials properties. Publication Trends: His recent publications (2023–2025) highlight a growing focus on FAIR data infrastructure (e.g., Kadi4Mat, KadiStudio), large language models in battery science (LISA), low-code simulation platforms (MUSICODE), and reproducibility in bioprinting . These reflect a strategic shift toward digitalization and automation in materials research, while maintaining a strong foundation in physics-based modeling. Scientific Contributions: Developed and advanced phase-field models for grain growth, crack propagation, and interfacial phenomena. Contributed to the development of Kadi4Mat, a research data infrastructure for materials science. Integrated machine learning and AI into materials characterization and battery research. Published extensively in journals such as Acta Materialia , Computational Materials Science , and Scientific Data . Advising and Grants: While no direct students are listed, his leadership role in Kadi4Mat suggests mentorship and team supervision. He has likely secured funding for digital infrastructure and computational materials projects, evidenced by sustained publication output and collaborative work with major institutions. His research is highly collaborative, involving teams across KIT and international partners. Labs and Teams: He leads the Research Data Management group under the Kadi4Mat initiative at KIT. This team focuses on developing digital tools for materials research, including electronic lab notebooks (KadiWeb), workflow automation (KadiStudio), and ontology-based data integration. The group operates at the intersection of computational science, data engineering, and materials discovery.
Andy Schürr is a Full Professor at the Institute for Data Technology within the Department of Electrical Engineering and Communication Technology at Technische Universität Darmstadt. He holds a Dr. rer. nat. in Computer Science from RWTH Aachen and has held academic positions at institutions including the University of the German Armed Forces Munich and Queen’s University (Canada). His research focuses on model-based software engineering, embedded systems, graph transformation systems, and P2P technologies. Professional Activities: Co-organizer of >20 conferences/workshops Programme Committee Member for >130 conferences Editorial board member of Software & System Modeling (since 2004) Member of steering committees for GI Conference on Modeling, ICGT, and FASE Research Interests: Model-driven development of embedded systems Visual model transformation and specification languages Integration of system engineering tools Graph transformation applications in networking and real-time systems Awards & Recognition: Listed among the most cited computer scientists globally. Grants & Funding: Active in securing research funding for model-based engineering tools, graph transformation applications, and real-time systems development. Labs/Teams: Leads TU Darmstadt’s Real-Time Systems Group , focusing on model-driven prototyping and adaptive communication systems.
Prof. Dr.-Ing. Matthias Hollick is a Full Professor of Computer Science at the Technical University of Darmstadt, leading the Secure Mobile Networking Lab (SEEMOO). He is co-affiliated with the Electrical Engineering and Information Technology Department. His research focuses on Security, QoS, and Resilience in Mobile Systems, Privacy in Cyber-physical Systems, and Cross-layer Optimization in Wireless Networks. He holds leadership roles as the Speaker of the emergenCITY Research Center (Hessian LOEWE initiative) and Deputy Speaker of the DFG Doctoral School on Privacy and Trust for Mobile Users. His work has been published at top venues including ACM MobiCom, ACM IMWUT, IEEE S&P, and USENIX Security, earning over 15 best paper/demonstration awards. Key contributions include protocols for secure IoT communication, privacy-preserving authentication (e.g., PrivateDrop), and resilient networking solutions for disaster scenarios (e.g., RESCUE framework). His lab’s open-source tools, such as FreeSpeaker and BTLEmap, advance smart home and Bluetooth security research. Scientific recognition includes awards at ACM MobiCom, ACM IMWUT, and IEEE DOCSS. His research bridges academia and industry, addressing real-world challenges like satellite communication security, 5G vulnerabilities, and anti-stalking protections via devices like AirTags. Current work emphasizes 6G resilience, UWB security, and decentralized crisis communication systems.
