Jonathan T. Barron is a Researcher at Google DeepMind in San Francisco, specializing in Computer Vision , Neural Rendering , and 3D Scene Reconstruction . He earned his PhD at UC Berkeley under Jitendra Malik and has pioneered advancements in NeRF (Neural Radiance Fields) and diffusion-based 3D generation. Research Interests : Computer Vision, Deep Learning, Generative AI, Image Processing, and 3D Reconstruction via Radiance Fields. His work includes Bolt3D for rapid 3D scene generation, CAT3D/CAT4D for text-to-3D/4D, and Zip-NeRF for anti-aliased radiance fields. He has also developed real-time rendering frameworks like SMERF and NeRF-Casting for reflections. Scientific awards: PAMI Young Researcher Award He has served as Area Chair for CVPR, ICCV, and NeurIPS, and his research is widely adopted in applications like Google's Lens Blur , Portrait Mode , and Jump VR .
Prof. Dr.-Ing. Ahmad Osman is a Professor at the Saarland University of Applied Sciences (htw saar), specializing in Test Technologies and Test Methods within the Faculty of Engineering. He also holds an Adjunct Professor position at Laval University in Quebec, Canada, in the Department of Electrical Engineering and Computer Science. His research focuses on Artificial Intelligence applications in Signal and Image Processing for Non-destructive Testing (NDT) , with extensive work on Deep Learning , 3D Ultrasound Tomography , and Sensor Data Fusion in industrial contexts. Engineering Artificial Intelligence Signal Processing Image Processing Non-destructive Testing Quality Control Augmented Reality Osman leads the AutomaTiQ research group and serves as Head of the Algorithms/Signal and Data Processing Department at Fraunhofer IZFP . His recent publications (2017–2022) emphasize Deep Learning for defect detection in CFRP , Terahertz Imaging for artwork diagnostics, and Acoustic Sensors for agricultural quality control. He has organized international conferences on Structural Health Monitoring and contributed to Springer books on NDT technologies. His projects include ComforTex-AI (2024) and development of 3D positioners for ultrasound measurements. Collaborations span institutions in Germany, Canada, Italy, and Brazil, with advisory roles in the German Society for NDT and technical committees for conferences in Montreal and Egypt.
Prof. Dr. Bernd Lucke is a full professor at the University of Hamburg , affiliated with the Faculty of Business, Economics and Social Sciences and Department of Economics . He holds the Chair for Economic Growth and Business Cycles and has been active in research, teaching, and public policy discourse. Research Interests : Macroeconomics, Monetary Policy, European Economic Policy, Business Cycles, Econometrics, Development Policy, and Public Finance. Publications : His recent work (2025–2020) focuses on synthetic control methods for EU monetary analysis, ECB policy critiques, debt sustainability, digital currency legislation, and expropriation impacts on FDI. Earlier works span econometric testing of Ricardian equivalence, productivity shocks, and growth modeling. Media Contributions : Regular political commentary in outlets like Cicero and Frankfurter Allgemeine Zeitung , often critiquing ECB decisions, EU fiscal integration, and inflation dynamics. Contact : bernd.lucke@uni-hamburg.de
Prof. Felix Krahmer is a TUM Tenure Track Assistant Professor of Optimization and Data Analysis at the Department of Mathematics, Technische Universität München (TUM), part of the School of Computation, Information and Technology. Previously, he held a Junior Professorship (W1) for Mathematical Data Analysis at the University of Göttingen (2012–2015). His research focuses on mathematical foundations of data science, including compressed sensing, signal quantization, uncertainty quantification, and optimization algorithms. He has led an Emmy Noether research group and contributed to the ZeMat interdisciplinary project. Education : PhD in Mathematics (2009), New York University, advisors: Percy Deift & Sinan Güntürk MSc in Mathematics (2007), New York University BSc in Mathematics (2004), Jacobs University Bremen Research Interests : Krahmer’s work bridges theoretical mathematics and applied data science. Key areas include compressed sensing algorithms, high-dimensional signal reconstruction, quantization theory for analog-to-digital conversion, and optimization methods for inverse problems. He also explores applications in imaging (e.g., MRI reconstruction) and stochastic processes. Key Contributions : His research on phase retrieval, sigma-delta quantization, and compressed sensing recovery has advanced signal processing techniques. Recent efforts focus on uncertainty quantification for high-dimensional inverse problems and the mathematical analysis of consensus-based optimization. Grants & Awards : Emmy Noether Research Group Grant (DFG) Funding from BMBF (ZeMat project) Teaching & Outreach : Krahmer has taught advanced courses on compressed sensing, functional analysis, and random matrix theory. He co-organized workshops on probabilistic techniques and clinical risk prediction, emphasizing interdisciplinary collaboration. Labs/Teams : Member of the TUM Data Science Research Group, focusing on optimization, imaging, and uncertainty quantification. Active in the Munich Center for Quantum Science and Technology (MCQST).
Prof. Dr.-Ing. Weihan Li is a Junior Professor at RWTH Aachen University, specializing in Artificial Intelligence and Digitalization for Batteries. He is affiliated with the Institute for Power Electronics and Electrical Drives (ISEA) and the Center for Ageing, Reliability, and Lifetime Prediction of Electrochemical and Power Electronic Systems (CARL). His research bridges informatics, electrochemistry, and power electronics to advance battery technology through AI. B.Sc. in Automotive Engineering (Tongji University, 2014) M.Sc. in Automotive Engineering and Transport (RWTH Aachen, 2017) Ph.D. in Electrical Engineering and Information Technology (RWTH Aachen, 2021, summa cum laude) Prof. Li’s research focuses on AI-driven battery modeling, diagnostics, and optimization. Key areas include digital twin technology, electrochemical parameterization, and lifetime prediction using field data. He explores multi-scale kinetic processes, thermal management, and mechanical-electrochemical coupling effects in battery systems. The articles listed reflect his leadership in AI-powered battery analytics, spanning degradation prediction, fast charging, failure mode analysis, and grid-scale storage. His work emphasizes both theoretical innovation (e.g., diffusion models, physics-informed neural networks) and practical applications (e.g., second-life battery screening, automotive integration). Clarivate Highly Cited Researcher 2024 BMBF BattFutur Research Group (€2M+) German Thesis Award (Körber Foundation) Reichart Prize vgbe Innovation Prize Battery Young Research Award Umbrella Award RWTH Innovation Award Prof. Li leads an interdisciplinary research group with over €6 million in grants from BMBF, BMWK, BMDV, European Commission, and industry partners. His teams focus on battery informatics, AI-driven diagnostics, and digitalization of testing processes at CARL and ISEA.
David W. Hogg is Professor of Physics and Data Science in the Center for Cosmology and Particle Physics in the Department of Physics at New York University. He serves as Senior Research Scientist in the Astronomical Data Group in the Center for Computational Astrophysics of the Flatiron Institute and maintains an affiliation with the Max-Planck-Institut für Astronomie in Heidelberg. His primary research focuses on observational cosmology, particularly approaches that use galaxies to infer physical properties of the Universe. He also conducts significant research on stellar kinematics in the Milky Way and the measurement and discovery of exoplanets. Across all domains, Hogg develops engineering systems and statistical methodologies that enable large-scale astrophysical projects for both his research group and the broader community. Recent work demonstrates expertise in robust statistical methods, particularly dimensionality reduction techniques like Robust-HMF. His research bridges theoretical statistics with practical applications in major astronomical surveys including Gaia, SDSS-V, and SPHEREx. He frequently explores connections between Bayesian and frequentist approaches to astronomical data analysis, with recent work on nuisance parameter integration, anomaly detection, and robust matrix factorization. Research supported by NYU, NASA, NSF, Moore Foundation, Sloan Foundation Additional support from Max Planck Society, Humboldt Foundation, ERC, Simons Foundation Hogg is actively involved in major astronomical projects including Astrometry.net, Gaia, and SDSS, with long-term comprehensive goals of analyzing all galaxies, stars, and astronomical images. His work emphasizes open science principles, reproducible research practices, and the development of publicly accessible tools for the astronomical community.
Prof. Melanie Schienle is a Professor and Chair of Statistical Methods and Econometrics at the Department of Economics and Management, Karlsruhe Institute of Technology (KIT). She also holds a professorship in the Department of Mathematics at KIT since 2021. Her expertise spans statistical methods, econometrics, financial risk analysis, and forecasting. She leads the HKMetrics Network and the RespiNow Hub for respiratory disease forecasting. She serves as a Senior Fellow at the Rimini Center for Economic Analysis (RCEA), a steering committee member of the German Economic Association, and a member of the University Research Council at KIT. Education: Ph.D. (Dr. rer. pol.) in Economics from Mannheim University (2008), summa cum laude; Diploma in Mathematics (University of Karlsruhe, 2003) with a minor in theoretical physics. She has held academic positions at Leibniz University Hannover (2012–2015) and Humboldt University of Berlin (2008–2012). Research interests focus on financial networks, systemic risk, time series analysis, and machine learning applications in economics. She co-leads projects on nowcasting and forecasting, including collaborative efforts during the pandemic to predict hospitalizations. Her work integrates advanced statistical techniques with real-world policy implications. Prof. Schienle is an Associate Editor for the International Journal of Forecasting and Journal of Time Series Analysis . She has authored over 50 peer-reviewed publications and contributed to high-impact journals like Nature Communications and Journal of Business & Economic Statistics . She leads the Institute of Statistics at KIT and chairs the MathSEE initiative for interdisciplinary mathematical applications.
Elisabeth Schilling serves as Associate Professor of Social Sciences at the University of Applied Sciences for Police and Public Administration North Rhine-Westphalia (HSPV NRW), holding a W2 professorship since December 2009. She previously taught at the Cologne department until 2012 and now works at the Bielefeld location. Her academic appointments include habilitation in Sociology at the University of Erfurt (2022) and guest professorship at Georg-August-Universität Göttingen (2015). Schilling's institutional affiliations extend to the Max-Weber-Kolleg at the University of Erfurt and the Institute for Diversity Research at Göttingen. Her education includes a PhD in Sociology from Heinrich-Heine-University Düsseldorf (2005, magna cum laude), with dissertation titled "Die Zukunft der Zeit: Vergleich von Zeitvorstellungen in Russland und Deutschland im Zeichen der Globalisierung." Additional academic training includes specialized sociology studies at RWTH Aachen (2000-2001) and University of California at Davis (2000), where she achieved top 5% standing in her cohort. Schilling's research fundamentally examines how time structures shape social experience across multiple domains. Her work explores temporal dimensions of migration, particularly how Ukrainian refugees construct future perspectives amid trauma. She investigates gendered time inequalities in public administration careers, analyzing how interrupted career paths disproportionately affect women. Her scholarship reveals how bureaucratic systems administer time through scheduling practices that reinforce social inequalities. Schilling develops methodological approaches for studying temporal diversity through qualitative time practice assessments, bridging classical life course theory with biographical subjectivity. Her publication trajectory shows consistent focus on time, migration, and biography since 2005, with recent work increasingly addressing refugee experiences and temporal aspects of public administration. The articles reveal methodological sophistication in qualitative time research, with growing emphasis on practical applications for public sector management. Schilling's work demonstrates strong interdisciplinary connections between sociology, gender studies, and public administration. Her current research portfolio includes multiple funded projects examining time perspectives among Ukrainian refugees (2023-2024), integration of Ukrainian refugees (2022-2023), and time management during remote work periods (2020-2021). These projects reflect her ability to address timely social issues through her temporal lens while maintaining theoretical rigor. Schilling maintains active research collaborations including the Time Perspective Network around Philip Zimbardo and the Max-Weber-Kolleg. Her editorial work includes special issues of BIOS journal and edited volumes with Springer VS. She directs research groups focusing on time structures as inequality-reproducing classification systems, demonstrating sustained scholarly impact in her field.
Dr. Raimon Tolosana Delgado is a Research Fellow at the Helmholtz-Zentrum Dresden-Rossendorf (HZDR), affiliated with the Helmholtz Institute Freiberg for Resource Technology. He leads research in predictive geometallurgy and statistical analysis of mineral resources, focusing on translating geological data into processing insights. His research integrates geostatistics , compositional data analysis (CoDa) , and machine learning to model ore behavior and resource potential. Key areas include: Predictive geometallurgy for forecasting ore/waste behavior Bayesian statistics for parameter estimation and uncertainty analysis Development of R-based tools (e.g., compositions and gmGeostats packages) for mineral data analysis Particle-based process modelling for mineral separation optimization Recent publications emphasize machine learning integration (e.g., neural networks for geophysical tensor fields), tailings reprocessing (3D geostatistical assessment of resource potential), and advanced statistical methods for compositional data. A consistent trend involves enhancing predictive accuracy in mineral processing through multi-source data fusion. Dr. Tolosana Delgado coordinates the development of technology platforms for geometallurgical data analysis, including databases and interfaces for industrial applications. His work bridges ore geology, mineral processing, and metallurgy to optimize resource efficiency.
Sandra Keiper is a Lecturer at the Institute of Mathematics within Faculty II - Mathematics and Natural Sciences at Technical University of Berlin. She has held academic positions since at least 2011, including roles as Tutor, Assistant, and Lecturer, with teaching responsibilities in Analysis, Linear Algebra, and Partial Differential Equations for both mathematicians and engineers. Research interests include: Compressed Sensing and Sparse Signal Recovery Numerical Linear Algebra with applications to high-dimensional data Wavelet and curvelet transforms for geometric multiscale analysis Approximation theory for finite-valued and cartoon-like functions Deep learning and graph approximation techniques Professional activities : Active in teaching since 2011 (Analysis I-III, Functional Analysis, Integral Transforms) Supervising theses since 2015 on topics like Compressed Sensing and Deep Learning Invited lectures at Caltech, ETH Zurich, and Alan Turing Institute Research stays at Hausdorff Institute, ETH Zurich, and Duke University
Krisztina Kis-Katos serves as Professor for International Economic Policy at the University of Göttingen's Department of Economics, a position she assumed in 2016. She holds prominent leadership roles including Chairwoman of the Standing Field Committee of Development Economics of the German Economic Association (Verein für Socialpolitik) and Chairwoman of the Scientific Advisory Board of the RWI Leibniz Institute for Economic Research. Her institutional affiliations extend to research fellowships at IZA and RWI, along with editorial positions at the Journal of Labour Market Research, European Journal of Political Economy, Bulletin of Indonesian Economic Studies, and Journal of Development Studies. Professor Kis-Katos earned her Economics education in Szeged and Konstanz, attended the Swiss Doctoral Program at the Study Center Gerzensee, and received her doctoral degree from the University of Freiburg in 2010. Her scholarly work spans applied development economics and political economy with particular focus on how (de-)globalization and macroeconomic processes affect social and economic outcomes including labor markets, firm performance, land use change, deforestation, and conflict. Her research portfolio reveals consistent thematic threads across recent publications: the intersection of environmental concerns with economic development (particularly deforestation and palm oil in Indonesia), the gendered impacts of trade liberalization, the socioeconomic effects of pandemics like COVID-19, and the complex relationship between governance, corruption, and economic outcomes. Methodologically, her work combines rigorous econometric approaches with innovative data sources including satellite imagery and high-frequency power usage data. Teaching prize for the best doctoral course in RTG 1723, University of Göttingen (2019) Teaching prize of the Student Union of Economics of the University of Freiburg (2014) BMZ/GIZ Public Policy Award (2013) Friedrich-August-von-Hayek-award (2011) Excellence award of the KfW Development Bank (2011) Professor Kis-Katos leads multiple significant research initiatives including the BMZ-DEval funded evaluation of Madagascar's forest restoration program, the DFG-funded Thailand-Vietnam Socioeconomic Panel, and the DFG Research Training Group on Sustainable Food Systems. Her advisory role extends to supervising doctoral candidates through these projects and previously serving as spokesperson for the Research Training Group 1723 on Globalization and Development. Her substantial grant portfolio demonstrates strong research leadership across international collaborations involving institutions in Germany, Indonesia, Thailand, Vietnam, and the United States. Her work connects closely with the Collaborative Research Centre 990 on Ecological and Socioeconomic Functions of Tropical Lowland Rainforest Transformation Systems in Sumatra, Indonesia, reflecting her deep engagement with environmental-economic research questions in Southeast Asia. She also contributes to interdisciplinary teams through projects like PlanetHealth examining global land-use impacts of the COVID-19 pandemic.
Dr. Marcell K. Peters is a Senior Academic Councillor at the Chair of Animal Ecology and Tropical Biology (Zoology III) at the University of Bremen. His research focuses on biodiversity patterns, ecosystem functioning, and climate-land use interactions in tropical and montane environments, with extensive fieldwork in East Africa and the Amazon. He leads projects under DFG and EU funding, including the UPSCALE initiative. Habilitation in Zoology (University of Würzburg, 2018) PhD in Biology (University of Bonn, 2008) Diploma in Biology (RWTH Aachen & University of Bonn, 2003) Research spans multi-taxa community ecology, army ants and ant-following birds, DNA barcoding applications, and climate change impacts on pollination networks. Google Scholar highlights recent work on climate-agriculture interactions in sub-Saharan Africa, trait-based community assembly, and network resilience in biodiversity hotspots. His publications emphasize elevational gradients, disturbance ecology, and functional diversity across Mount Kilimanjaro studies. Current affiliations include the DFG Research Unit Kilimanjaro and EU-funded UPSCALE project. He employs advanced methods like airborne LiDAR for biodiversity prediction and investigates nutrient use by ant communities across continents.
Yan Zhang is a scientific leader at Meshcapade and a guest lecturer at ETH Zurich's Computer Vision and Learning Group (VLG). He previously served as a postdoctoral researcher at ETH Zurich (2020-2023) and research intern at Max Planck Institute for Intelligent Systems (2018-2020). His research focuses on generative human foundation models, human motion and behavior synthesis, 3D human perception, and applications in AR/VR, embodied AI, and interactive avatars. He has pioneered methods for scene-conditioned motion generation, contact-aware reconstruction, and egocentric interaction modeling. His recent publications (2025-2020) span Real-time motor models for avatars (PRIMAL, ICCV'25) Diffusion architectures for motion (RoHM, CVPR'24) Scene-population algorithms (Odysseus, CVPR'22) Physics-aware reconstruction (EgoHMR, ICCV'23) Whole-body grasping models (SAGA, ECCV'22) Multi-modal datasets (EgoBody, ECCV'22) Scientific recognition includes the Qualcomm Innovative Fellowship Europe 2023 . He organized workshops at CVPR'25, ECCV'24, and ECCV'22, and served on senior program committees (AAAI'26) and area chairs (CVPR'25). As co-supervisor, he mentored student projects on diffusion-based hand motion capture, 3D pose estimation, body-scene interaction, and mixed reality navigation at ETH Zurich (2020-2023). His work bridges computer vision, machine learning, and computer graphics to advance human-centric AI systems.
Johannes Soeding is a Research Group Leader in the Computational Biology department at the Max Planck Institute for Multidisciplinary Sciences in Göttingen, Germany. His work bridges physics, bioinformatics, and molecular biology, focusing on computational methods for biological data analysis. His research interests include computational biology, protein structure and function prediction, metagenomics, transcriptional regulation, and statistical genomics. He develops widely used software tools such as HH-suite, HHpred, MMseqs2, and Foldseek for protein sequence and structure analysis. The recent publications demonstrate a strong focus on high-throughput biological data, particularly in protein structure search (e.g., Foldseek), metagenomic gene discovery (e.g., MetaEuk), and regulatory genomics. His work combines algorithm development with deep biological insights, often published in top-tier journals like Nature Biotechnology , Science , and Nature Methods . He has been involved in significant methodological advances in sequence clustering, contact prediction, and eQTL analysis, showing a consistent trend toward scalable, data-driven approaches in genomics and proteomics. Soeding has contributed to major projects in gene regulatory networks and RNA biology, often in collaboration with experimental groups. His leadership in developing open, efficient bioinformatics tools has had a broad impact on the scientific community. He is affiliated with several graduate programs including IMPRS Physics of Biological and Complex Systems, Biomolecules: Structure - Function - Dynamics, and Genome Science, indicating active participation in training the next generation of scientists.
Philippe Ciblat is a Professor at TELECOM Paris Tech, affiliated with the Department of Signal Processing and Communications. His research spans signal processing, wireless communications, and machine learning applications in networking. He has collaborated extensively with institutions like the University of Paris-Saclay and international researchers in areas such as cooperative communication protocols, resource allocation, and coding theory. Research Interests: Machine learning for signal processing, wireless channel modeling (Rician fading), lattice decoding, caching strategies, and distributed optimization. Notable Work: Pioneered transformer-based packet scheduling, neural network approaches to lattice decoding, and effective capacity analysis in fading channels. His contributions include over 170 publications in top venues (IEEE Trans. Signal Process., IEEE Trans. Wireless Commun.) and collaborations with industry partners on practical implementations like cache-aided polar coding. He has advised multiple researchers in distributed systems and wireless resource management.
