Lara Kösters is a Doctoral Researcher at the Max Planck Institute for Biogeochemistry in Jena, Germany, affiliated with the Biogeochemical Integration department and the Biod.AI.versity Observation & Integration research group. Her work bridges computational methods with biodiversity and biogeochemical research. Ph.D. student (2021–present), Max Planck Institute for Biogeochemistry, Jena, Germany Research assistant (2020–2021), Humboldt-Universität zu Berlin M.Sc. in Life Science (2018–2020), University of Münster B.Sc. in Life Science (2015–2018), University of Münster Her research focuses on Artificial Intelligence in biodiversity studies , biogeochemical integration , and computational biology . She develops AI-driven tools for plant identification and ecological data management, as evidenced by her publications on deep learning for morphometrics, DNA barcoding frameworks, and parasitic plant research. Lara’s recent work involves geometric morphometrics , taxonomic affinity modeling , and DNA barcoding , reflecting a trend toward data science and environmental informatics in her publications.
Jisu Kim is an Assistant Professor at Utrecht University in the Netherlands, affiliated with the Faculty of Social Sciences and the Department of Sociology and Inequality. She was previously an Affiliate Researcher at the Max Planck Institute for Demographic Research (MPIDR), where she worked in the Laboratory of Migration and Mobility as part of the Digital and Computational Demography group. Research Interests: Her work lies at the intersection of demography, data science, and computational social science. She specializes in using big data—especially digital trace data from social media platforms like Twitter—to study international migration, social integration, gender differences in migration, cultural assimilation, and public sentiment around migration. She applies advanced computational methods including network analysis, natural language processing, and machine learning to analyze large-scale datasets for demographic insights. The recent articles highlight a consistent focus on leveraging social media data to understand migration dynamics, integration patterns, and public discourse. Her publications span computational demography, migration policy, gender and migration, and digital polarization, reflecting a strong interdisciplinary approach. She frequently publishes in top journals and conference proceedings in both demography and computational social science. Scientific Awards and Honors: DGD Best Paper Award for 'Gender Differences in the Migration Process: A Narrative Literature Review' Seed Money Award in Migration and Societal Change, Utrecht University Best Poster Award at Complenet 2023 1st Prize for Flash Talk at 'New data for the new challenges of population and society' Advising and Grants: While no formal students are listed, she actively mentors through programs like the Population and Social Data Science Summer Incubator. She has secured competitive funding such as the Seed Money Award and has been involved in EU-funded projects like PREMIUM. She regularly organizes workshops and training sessions, indicating strong engagement in academic leadership and community building. Labs and Teams: She was a key member of the Laboratory of Migration and Mobility at MPIDR and contributed to the Digital and Computational Demography group led by Emilio Zagheni. She has organized multiple editions of the International Workshop on Migration and Mobility Research in the Digital Era (MIMODE), demonstrating leadership in advancing methodological innovation in the field.
Cristina Pop is a researcher affiliated with Babes-Bolyai University (Department of Clinical Psychology and Psychotherapy) and has contributed to interdisciplinary research spanning computer science , energy systems , and health informatics . Her work integrates machine learning and bio-inspired optimization algorithms to address challenges in renewable energy prediction , smart grid management , and health monitoring for seniors .
Guido Montúfar is Assistant Professor at the University of California, Los Angeles (UCLA) in the Departments of Mathematics and Statistics, and Research Group Leader at the Max Planck Institute for Mathematics in the Sciences (MPI MIS). His work bridges mathematical machine learning, deep learning theory, and information geometry. Current affiliations: UCLA (since 2017) and MPI MIS (since 2018) ERC Starting Grant on Deep Learning Theory Research interests focus on the interplay between model capacity, optimization landscapes , and generalization in deep learning, combining tools from algebraic geometry , optimal transport , and information theory to analyze neural network behavior. Recent publications address tropical geometry of neural networks , algebraic optimization in reinforcement learning , and information-theoretic approaches to data representation . His work reveals connections between policy gradient methods and Wasserstein gradient flows . Scientific grants: ERC Starting Grant, DFG SPP 2298, NSF CAREER
Ariane Hanemaayer is an Associate Professor at Brandon University (2016–present) and a former Visiting Scholar at institutions including Egenis (University of Exeter, 2022–23), CRASSH (University of Cambridge, 2019–21), and the Max Planck Institute for the History of Science (2021). She earned her PhD in Sociology (Theory and Culture specialization) from the University of Alberta in 2014. Education: PhD in Sociology, University of Alberta (2014) Postdoctoral Fellowship: Killam Post-Doctoral Fellowship at Dalhousie University (2015–16) Her research examines intersections between medical knowledge , healthcare systems, algorithmic technologies, and medical governance. Current work focuses on pain management and opioid regulation in Canada, the UK, and the US, funded by a SSHRC Insight Grant. Recent publications include books on evidence-based medicine and AI critiques, alongside collaborative presentations at institutions like Humboldt University, the University of Cambridge, and the Max Planck Institute. Her work bridges theory, policy analysis, and public engagement. Scientific Awards : Killam Post-Doctoral Fellowship Ariane collaborates with international teams, including at the Biennial Meeting of the International Society for the History, Philosophy, and Social Studies of Biology (2023), and contributes to digital humanities initiatives linked to the Max Planck Institute’s research communication strategies.
Steve Riddle is a Research Professor with extensive contributions to software engineering, systems of systems (SoS) modeling, and requirements engineering. His work spans formal methods, traceability frameworks, and safety-critical system design across institutions and projects. Active in software traceability and requirements engineering Key contributor to SoS architectural modeling Collaborator in formal methods education initiatives His research focuses on traceability in software systems , contract-based analysis of SoS , and protective wrapping for off-the-shelf components . He explores how project metrics predict change-proneness and develops frameworks for competency-based computing education. Recent publications analyze team-based capstone projects in computing education and instructor perspectives on software engineering pedagogy. His work bridges theoretical approaches (like VDM++) with industry-aligned problem-solving .
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)
Prof. Dr. Orestis Papakyriakopoulos is a faculty member at the Technical University of Munich (TUM), holding the position of Professor of Societal Computing within the School of Social Sciences and Technology. His research focuses on equitable socio-algorithmic ecosystems, AI ethics, and the societal implications of data-driven technologies. He holds a Dipl. Ing. in Civil Engineering from the National Technical University of Athens, an MA in Philosophy of Science and Technology from TUM, and a PhD in Computer Science from TUM. Before joining TUM in 2024, he was a visiting scholar at MIT Media Lab, conducted postdoctoral research at Princeton University, and worked as an AI research scientist at Sony. His research interests include algorithmic bias mitigation, political communication on social media, and the ethical challenges of AI systems. He has extensively studied platforms like TikTok, Reddit, and YouTube, analyzing how algorithms shape political discourse and influence public opinion. Key themes in his recent work include cross-national attitudes toward facial analysis AI, the sociotechnical alignment of AI with human values, and the design of responsible data curation frameworks. His publications often bridge technical innovations with critical societal analysis. Prof. Papakyriakopoulos has contributed to policy debates on AI ethics and digital governance, particularly in Europe. He advises on transparency in political advertising and has collaborated on initiatives to combat misinformation, including studies on vaccine perceptions during the early phases of the U.S. rollout.
Shadi Albarqouni is a W2 Professor of Computational Imaging Research at the University Hospital Bonn, AI Young Investigator Group Leader at Helmholtz Munich, and Senior Research Affiliate at TU Munich. His roles include leading research in medical imaging, computational procedures, and artificial intelligence in healthcare. He holds a PhD in Informatics from Technical University Munich (2017) under Prof. Nassir Navab, with prior academic experience as a lecturer and postdoctoral researcher. Education: PhD Informatics (2013-2017), Technical University Munich M.Sc. Electrical Engineering (2005-2010), Islamic University of Gaza B.Sc. Electrical Engineering (2001-2005), Islamic University of Gaza Research Interests: Medical Image Analysis (e.g., segmentation, anomaly detection) Deep Learning applications in healthcare Generative models and computer vision Surgical robotics and augmented reality Federated learning for medical data Publications: Over 30 peer-reviewed articles focusing on medical imaging, including works on GANs for skin lesion synthesis, federated learning algorithms, and MRI anomaly detection. Recent trends emphasize interdisciplinary approaches combining AI with clinical challenges. Awards: Best Paper Award at MIAR 2016 Reviewer Commendation at MICCAI 2018 DAAD PRIME Fellowship Advising/Grants: Leads research groups focusing on AI in healthcare and collaborates internationally. Manages projects on federated learning and medical data challenges. His lab at University Hospital Bonn focuses on computational imaging and surgical data science. Labs/Teams: Director of labs at University Hospital Bonn (Department of Diagnostic and Interventional Radiology), Helmholtz Munich, and TU Munich. Active in interdisciplinary teams like RobUSt (Robotics and Ultrasound) and NARVIS (Navigation and Visualization).
Dr. Christoph Hennersperger is a Co-Founder and CTO of OneProjects, an Irish-German MedTech startup focused on cardiac imaging and data-driven healthcare solutions. He is affiliated as a senior research scientist and lecturer at the Chair of Computer Science Applications in Medicine (Prof. Navab) at Technical University of Munich (TUM). His research integrates medical device development, computational sonography, and robotics in surgical applications. He has directed the MedInnovate fellowship program and led the EU Horizon2020 EDEN2020 project. Education: Electrical Engineering (Information Technology) from TUM (2006–2011). Professional History includes roles as a Research Fellow at Trinity College Dublin (2016–2019), Research Manager at Klinikum Rechts der Isar (2016–2018), and Fellow at BioInnovate Ireland (2015–2016). He has supervised over 20 MSc/BSc theses on topics like Ultrasound-Guided Interventions and Surgical Robotics. Teaching: He has lectured since 2014 on courses including Computer Aided Medical Procedures , Medical Augmented Reality , and MedInnovate . Research focuses on 3D ultrasound imaging, robotic interventions, and AI-driven medical solutions. Current projects include RoBildOR (robotics for multimodal imaging) and SUPRA (real-time ultrasound processing). Key innovations include the SegThy Dataset and Leg-3D-US Dataset for medical imaging, and developments in 3D ICE imaging for cardiac interventions. His work bridges hardware, software, and clinical needs, emphasizing collaborative team-driven healthcare innovation.
Véronique Helfer is a Senior Scientist at the Leibniz Center for Tropical Marine Research (ZMT) GmbH, working in Programme Area 4 – Ecosystem Co-Design towards a sustainable Anthropocene and the Working Group Mangrove Ecology. Her research focuses on mangrove ecosystems, their protection, restoration, and resilience to global change. Using interdisciplinary approaches, she investigates factors driving microbial, faunal, and floral community composition in mangroves and how these translate into ecosystem services. Her work spans molecular biology (eDNA metabarcoding, landscape genetics), chemical ecology, functional ecology, and species distribution modeling. Recent publications highlight her expertise in mangrove canopy gaps, Blue Carbon verification frameworks, and interdisciplinary research. She has contributed to stakeholder initiatives like the sea4soCiety project and the Floating Mangroves energy solutions publication. Her educational background includes a 2010 PhD in Conservation Genetics from the University of Lausanne (supervised by Fumagalli L), with prior educational brochures on evolutionary biology and alpine ecosystems.
William H. Sanders is a Professor at the University of Illinois at Urbana-Champaign with over 35 years of research experience in cybersecurity, dependable computing, and performance evaluation. His work bridges theoretical modeling with practical security applications for critical infrastructure systems, particularly power grids and industrial control systems. Dr. Sanders' research focuses on developing quantitative methods for security assessment, with emphasis on cyber-physical systems. He is best known for his leadership in developing the Möbius modeling framework, a comprehensive tool for performance and dependability evaluation. His work spans security metrics, intrusion detection, and resilience mechanisms for smart grid infrastructure. His publication record shows consistent scholarly output through 2023, with recent work focusing on security argumentation frameworks, advanced metering infrastructure security, and cyber-physical state estimation. The research demonstrates a clear progression from foundational modeling techniques to applied security solutions for critical infrastructure protection. Dr. Sanders has collaborated extensively with researchers across multiple institutions, with frequent co-authors including Michel Cukier, Tod Courtney, and Kaustubh Joshi. His work appears regularly in top security and dependability venues including IEEE Transactions on Dependable and Secure Computing, DSN, and QEST. His research has practical implications for securing national critical infrastructure, particularly the electrical power grid. Recent projects focus on detecting electricity theft, securing advanced metering infrastructure, and developing game-theoretic approaches to intrusion response. He has made significant contributions to establishing security metrics as a rigorous scientific discipline within cybersecurity.
Marco Viviani is a researcher affiliated with University of Milano-Bicocca (Italy) and University of Torino (Candiolo, Italy), focusing on information retrieval , health informatics , and social media credibility . His work spans multi-dimensional relevance assessment generative AI applications misinformation detection privacy-aware systems Research highlights include ROMCIR workshops on reducing online misinformation, Web2Vec for structural content analysis, and LLM reasoning capabilities evaluation. Key collaborations with Gabriella Pasi, Rishabh Upadhyay, and Marinella Petrocchi appear across 103 publications from 2004-2025. His work integrates machine learning and knowledge graphs for healthcare applications, with recent focus on blockchain social media dynamics and privacy-utility trade-offs . He contributes to ECIR WI/IAT CLEF eHealth MDAI conferences and journals like IEEE Access and Frontiers in Artificial Intelligence .
Sharad Kumar Gupta is a Guest Scientist at the Helmholtz-Centre for Environmental Research - UFZ in Leipzig, Germany, and a Scientist at the Center for Advanced Systems Understanding (CASUS) in Görlitz. His research focuses on remote sensing , environmental informatics , and geospatial data analysis with applications in ecological modelling , UAV technology , and environmental risk assessment . Education Ph.D. in Remote Sensing (2015) - Indian Institute of Technology Mandi M.Tech in Geoinformatics (2014) - NIT Bhopal B.Tech in Computer Science (2011) - Uttar Pradesh Technical University Research Highlights Developed Drone4Tree cloud platform for UAV-based tree canopy detection Specialized in hyperspectral data scaling and agricultural stress monitoring Published extensively on environmental data integration , geophysical inversion , and urban green infrastructure Affiliations Dept. Monitoring and Exploration Technologies, UFZ Dept. Earth Systems Research, CASUS (HZDR) Key Publications address landslide susceptibility mapping , UAV-based environmental monitoring , machine learning applications in agriculture, and multi-method data integration for subsurface analysis. His work appears in journals like EGU General Assembly , Permafrost Periglacial Processes , and Environmental Earth Sciences .
Dr. Maximilian Lange is an Academic Staff researcher in the Department of Remote Sensing at the Helmholtz Centre for Environmental Research - UFZ in Leipzig, Germany. His work focuses on applying remote sensing, artificial intelligence, and modeling techniques to monitor vegetation dynamics, land-use, and land-use intensity. As part of the Remote Sensing Team led by Prof. Dr. Jian Peng, he contributes to several research programs under UFZ's 2021-2027 strategic framework. Dr. Lange's research interests center on the derivation of vegetation dynamics, land-use and land-use intensity with remote sensing, artificial intelligence and modeling techniques. He specializes in organizing big data from remote sensing datasets and developing software tools for environmental analysis. His work on phenological data analysis through R packages like phenex and phenmod has established him as a key contributor to environmental informatics. His research bridges the gap between satellite data acquisition and practical environmental monitoring applications, particularly in forest condition assessment and grassland management. Analysis of Dr. Lange's publication record reveals a strong focus on practical applications of remote sensing data for environmental monitoring. His work demonstrates expertise in time series analysis of satellite data, particularly using Sentinel-2 imagery, with applications spanning forest condition monitoring, grassland management, and phenological studies. Recent publications show an increasing integration of machine learning techniques with traditional remote sensing approaches, reflecting the evolving nature of environmental data science. Dr. Lange has been actively involved in several significant research projects including the Forest Condition Monitor (2021-2024), which provides national-scale depiction of forest condition using satellite data, and Project iForest (2024), which integrates citizen science data into tree species classification methods. His earlier work on validating Sentinel-2 products has contributed to the robust application of these data for environmental monitoring across Germany.
