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Gheorghe CraciunView profile

Professor

• Mathematical Biology
• Dynamical Systems
• Chemical Reaction Networks
• +3 more

Gheorghe Craciun is a Professor in the Department of Mathematics and the Department of Biomolecular Chemistry at the University of Wisconsin-Madison. His research focuses on mathematical and computational models in biology and medicine, particularly dynamical systems models of biological interaction networks. He has been a visiting researcher at the Max Planck Institute for Mathematics in the Sciences during the 2019-2020 academic year and has organized the Madison Workshops on Mathematics of Reaction Networks. Craciun's primary research interests include Mathematical Biology, Dynamical Systems, Chemical Reaction Networks, Computational Biology, Systems Biology, and Algebraic Geometry. He investigates systems of differential equations with polynomial right-hand sides, which are common in biochemical reaction networks, ecological interactions, and epidemiological models. His work often involves proving global stability, analyzing multistability, and characterizing steady states using tools from algebraic geometry and combinatorics. Recent publications demonstrate his focus on toric differential inclusions, endotactic networks, and the global attractor conjecture, extending to applications in biochemical networks and discrete Boltzmann equations. His extensive publication record reveals a strong trend toward algebraic and geometric methods for analyzing complex biological networks, with significant contributions to reaction network theory, stability analysis, and parameter characterization. Craciun's work bridges abstract mathematical concepts with practical applications in biochemistry, ecology, and medicine, including modeling vitellogenin production in trout and peptide mass distributions. He has collaborated extensively with international researchers including Alicia Dickenstein, Anne Shiu, Bernd Sturmfels, Casian Pantea, and Miruna-Stefana Sorea. In education, Craciun teaches graduate courses such as Math 703 and mentors students through the Madison Math Circle and Putnam Club, while organizing specialized workshops that foster collaboration in reaction network theory.

• Nonlinear Dynamics
• Time Series Analysis
• Statistical Physics
• +5 more

Prof. Dr. Holger Kantz serves as Head of the research unit "Nonlinear Dynamics and Time Series analysis" at the Max Planck Institute for the Physics of Complex Systems in Dresden, Germany. He also holds an Adjunct Professorship (Honorprofessor) in Statistical Physics at the Institute of Theoretical Physics within the Department of Physics at the Technical University Dresden. Dr. Kantz's research spans multiple disciplines within nonlinear dynamics and statistical physics. His work focuses on time series analysis, nonlinear dynamics, stochastic processes, and complex systems. He has made significant contributions to understanding anomalous diffusion, extreme events prediction, and the statistical properties of chaotic systems. His research has applications in atmospheric science, climate modeling, power grid dynamics, and biological systems. Analysis of Dr. Kantz's recent publications reveals a strong interdisciplinary approach connecting statistical physics with climate science, energy systems, and scientometrics. His work demonstrates sophisticated applications of stochastic modeling to real-world complex systems, with particular attention to anomalous diffusion processes, extreme events, and predictability limits in chaotic systems. The publications show increasing methodological sophistication in handling nonstationary time series and developing predictive models for rare events. Dr. Kantz leads a research group focused on nonlinear dynamics and time series analysis at the Max Planck Institute. His work has significant implications for understanding and predicting complex phenomena across multiple scientific domains, from climate dynamics to power grid stability, with practical applications in risk assessment and system reliability.

Frederik TilmannView profile

Professor

• Seismology
• Geophysics
• Earthquake Analysis
• +3 more

Prof. Dr. Frederik Tilmann is a leading seismologist at the GFZ German Research Centre for Geosciences (Section 2.4 Seismology) and a professor at the Freie Universität Berlin . His work focuses on seismic waveform analysis to understand geodynamic processes in subduction zones and continental collisions. Current affiliations: Head of Seismology Section, GFZ Potsdam University Professor, Freie Universität Berlin Research interests include: Earthquake source characterization Seismic tomography methods Mantle dynamics and lithospheric deformation Machine learning applications in seismic data analysis Volcano-seismic monitoring Ocean bottom seismology techniques Recent publications highlight advancements in: Full waveform inversion for mantle dynamics Machine learning for seismic phase picking Anisotropy studies in Alpine and Himalayan regions Subduction zone microseismicity analysis Volcano-induced landslide detection Scientific awards include: Feodor-Lynen Fellowship (Humboldt Foundation) Trinity Hall College Staff Fellowship Multiple citations in high-impact journals Collaborative work spans global seismic infrastructure projects like SMART cables, the Collaborative Seismic Earth Model, and the AlpArray network. His methodology innovations in shear wave splitting and depth phase picking have become standards in computational seismology.

Ralf MerzView profile

Professor

• Catchment Hydrology
• Flood Risk Assessment
• Climate Change Impact on Water Resources
• +3 more

Prof. Dr. Ralf Merz serves as Head of the Department of Catchment Hydrology at the Helmholtz Centre for Environmental Research (UFZ) and holds a Full Professorship in Catchment Hydrology at Martin-Luther University Halle-Wittenberg since 2011. His career bridges hydrological modeling, flood risk assessment, and water quality analysis across diverse climates from Central Asia to Europe. MSc in Civil Engineering (Technical University of Karlsruhe, 1997) PhD in Hydrology (Vienna University of Technology, 2002) Habilitation in Hydrology (Vienna University of Technology, 2009) Research Interests span comparative hydrology, flood generation mechanisms, climate change impacts on water resources, and nitrate dynamics in river systems. His work emphasizes process-based understanding of runoff events and regional flood modeling through innovative approaches like the PHEV distribution framework. Scientific Contributions include over 100 publications (2003-2025) on: Flood frequency analysis in changing climates Groundwater recharge in arid regions Hydrochemical response to droughts Remote sensing applications for groundwater studies Multi-response calibration of hydrological models Key projects involve MOSES observatory development, TRACER research school, and Pamir Mountains glaciological studies. Recognitions : APART research grant (Austrian Academy of Sciences, 2006) Leadership extends to directing the Catchment Hydrology department and participating in European hydrological networks like the Bode Hydrological Observatory and TERENO infrastructure. His methodological advancements include flood time-scale analysis and event runoff coefficient regionalization.

• Gravitational Wave Data Analysis
• Cosmological Parameter Estimation
• LISA Data Analysis
• +3 more

Dr. Jonathan Gair is a Group Leader in the Astrophysical and Cosmological Relativity Division at the Max Planck Institute for Gravitational Physics (Albert Einstein Institute) in Potsdam, Germany. Previously, he served as Professor of Astrostatistics at the University of Edinburgh (2018-2019) and as Reader (Associate Professor) in Statistics at the same institution (2015-2018). Dr. Gair's research focuses on gravitational wave data analysis and its applications to cosmology and fundamental physics. His work spans multiple areas of gravitational wave astronomy, with particular emphasis on: Developing and applying new methodologies for gravitational wave data analysis Using gravitational wave observations to derive cosmological parameters, particularly the Hubble constant Developing data analysis tools for the LISA space-based gravitational wave detector Exploring the scientific potential of gravitational wave observations for testing general relativity Creating computationally efficient techniques for parameter inference in gravitational wave astronomy Dr. Gair plays a leading role within the LIGO/Virgo collaboration in deriving cosmological constraints from gravitational wave observations. He currently chairs the LISA Science Group, overseeing the development of data analysis tools for the planned ESA-led LISA mission. His research has significantly contributed to our understanding of how gravitational wave observations can serve as "standard sirens" for measuring cosmic distances and probing the expansion history of the universe. Dr. Gair's work involves both theoretical development and practical application of data analysis techniques. He has developed methods for handling selection effects in rate estimation of gravitational wave events, techniques for mapping gravitational wave backgrounds using methods adapted from cosmic microwave background analysis, and approaches for incorporating model uncertainties into gravitational wave parameter estimation.

Diogo Carbonera LuvizonView profile

Researcher

• Computer Vision
• Computer Graphics
• Deep Learning
• +4 more

Diogo Carbonera Luvizon is a Researcher at the Max-Planck-Institut für Informatik (MPI-INF) in Saarbrücken, Germany, and a member of the Visual Computing and Artificial Intelligence (VIA) Research Center. He holds a PhD in Computer Vision and Machine Learning from CY Cergy Paris University (2019), and Bachelor's and Master's degrees in Engineering and Applied Computing from UTFPR, Brazil. His research focuses on solving complex problems in Computer Vision, Computer Graphics, and Deep Learning, particularly in human modeling and real-time systems. Education: PhD (2019) - CY Cergy Paris University; M.Sc. (2015) - UTFPR; B.Sc. (2011) - UTFPR. Research interests include 3D human pose estimation, action recognition, multitask learning, and novel view synthesis. He has contributed to patents on multiplane image generation (Samsung) and vehicle speed measurement systems. His work has been recognized with awards like the Best Paper Honorable Mention at GCPR-VMV 2022 and Best Presentation Award at ETIS Lab (2018). He has developed open-source tools, including the deephar repository for human action recognition and pose estimation. His current affiliations include MPI-INF and the VIA Research Center, a partnership between MPI-INF and Google.

Mariya TonevaView profile

Researcher

• Machine Learning
• Natural Language Processing
• Neuroscience
• +5 more

Mariya Toneva is a tenure-track faculty member at the Max Planck Institute for Software Systems , conducting groundbreaking research at the intersection of Machine Learning , Natural Language Processing , and Neuroscience . She leads the Bridging AI and Neuroscience (BrAIN) group , focusing on computational models that align AI systems with human brain processes. Her work aims to enhance both AI capabilities and neuroscience understanding through this cross-disciplinary approach. Actively recruiting postdocs, PhDs, and research interns in areas like code/text representation, brain-AI alignment, and neuroimaging data analysis Collaborator on NIH-funded projects using fMRI and neuropixel data Research Themes : Her group explores neural mechanisms of language processing, event segmentation in narratives, memory reactivation via music, and effective human-AI collaboration frameworks. Key methods include LLM analysis, cross-modal similarity metrics, and naturalistic task-based fMRI studies. Key Publications (2024-2025): Brain-tuned speech models (INTERSPEECH 2025) Cognitive event boundaries in LLMs (Behavioral Research Methods 2025) Music-induced memory reactivation (biorxiv 2024) LLM-brain alignment reasons (EMNLP 2024) Advising : Mentors PhD candidates Omer Moussa (speech processing), Camila Kolling (representational similarity), and Gabriele Merlin (LLM alignment). Collaborates with institutions like MIT, NYU, and ETH Zurich.

Thomas WildView profile

Professor

• Manycore system on chip (SoC) architectures
• Network processor (NPU) architectures
• On-chip communication architectures
• +3 more

Dr.-Ing. Thomas Wild serves as an Academic Director at the Technical University of Munich (TUM), working within the TUM School of Computation, Information and Technology at the Chair of Integrated Systems. He maintains an active research and teaching role at the institution, with his office located in Building N1 (Theresienstr. 90), Room N2136 in Munich, Germany. Dr. Wild's research focuses on advanced computing architectures, with particular emphasis on manycore system on chip (SoC) architectures, network processor (NPU) architectures, on-chip communication architectures including networks on chip (NoC), and system level design methodologies. His work bridges theoretical research with practical implementation, often exploring design space exploration techniques to optimize system performance. The evolution of his research over two decades demonstrates a consistent focus on improving communication architectures and system-level design for embedded and high-performance computing platforms. His recent publications (2023-2025) reveal a growing integration of machine learning techniques with traditional hardware design, particularly in optimizing power-performance tradeoffs in embedded systems. There's a clear trend toward hardware-software co-design approaches, with significant work on SmartNICs, Linux system optimization, and network processing acceleration. His research shows strong interdisciplinary connections between computer architecture, networking, and machine learning. EUROPRACTICE representative for TUM city campus, facilitating access to commercial EDA tools for academic purposes Active collaborator with Professor Andreas Herkersdorf and other researchers at TUM Focus on practical implementations with FPGA-based prototyping and real system modifications Dr. Wild teaches several hardware design courses including VHDL Lab, SystemC Lab, and HW/SW Codesign, contributing to the education of next-generation computer engineers. His teaching directly complements his research in system design and hardware acceleration, providing students with hands-on experience in cutting-edge technologies.

Carsten NowakView profile

Research Professor

• Conservation Genetics
• Wildlife Monitoring
• Environmental DNA (eDNA) Analysis
• +5 more

Dr. Carsten Nowak serves as Head of Conservation Genetics at the Senckenberg Research Institute and Natural History Museum Frankfurt, where he leads the national reference center for genetic analyses of wolves and lynx. His work bridges cutting-edge molecular methods with practical conservation applications, focusing on wildlife monitoring and species preservation. Since 2010, his laboratory has been responsible for central examination of all samples collected through Germany's nationwide wolf and lynx monitoring programs. Nowak's research interests center on developing highly sensitive molecular marker systems for wildlife monitoring, with particular expertise in environmental DNA (eDNA) methods and SNP chips optimized for genotyping forensic and non-invasively collected environmental samples. His work addresses critical questions in conservation biology, including inbreeding, genetic impoverishment, population origins, and human environmental impacts on genetic population structures of native species. He investigates how genetic methods can clarify previously unanswered questions in nature conservation, where data limitations often hinder efficient and targeted conservation measures. His extensive publication record (2023-2025) reveals a strong focus on wolf population dynamics across Europe, hybridization detection methodologies, and conservation genomics of endangered species like the garden dormouse. Nowak's research demonstrates sophisticated approaches to tracking animal movements across national borders, assessing genetic diversity in recovering populations, and developing practical tools for non-invasive wildlife monitoring. His work on wolf-dog hybridization has particular policy relevance as wolf populations expand across Europe. As a public-facing scientist, Nowak regularly engages with policymakers and the public through presentations, media appearances, and citizen science projects. He has participated in high-profile events including briefings for Federal Environment Minister Svenja Schulze and numerous public lectures on wildlife conservation topics. His leadership extends to coordinating international research collaborations, including the CEwolf Consortium and various transboundary monitoring networks. Nowak directs the Center for Wildlife Genetics in Gelnhausen, where his team conducts DNA-based analyses of predator-prey interactions, livestock depredation incidents, and wildlife population monitoring. His laboratory serves as the national reference center for genetic analyses of wolves and lynx in Germany, processing samples from across the country to distinguish individuals, determine relatedness, and monitor population health. The center also develops molecular methods for detecting wolf-dog hybrids and other conservation genetics applications.

Daniel WilhelmView profile

Professor

• Econometrics
• Statistics
• Nonparametric Methods
• +4 more

Daniel Wilhelm is a Professor of Statistics and Econometrics at LMU Munich, with a courtesy appointment in the Department of Economics. His research focuses on econometric theory, nonparametric methods, measurement error modeling, and statistical inference. He leads the Statistics and Econometrics Group at LMU and holds affiliations with the Centre for Microdata Methods and Practice (CeMMAP), Institute for Fiscal Studies (IFS), and the Centre for Research and Analysis of Migration (CReAM). Wilhelm’s work includes groundbreaking contributions to NPIV estimation, robust statistical testing, and the development of R and Stata packages for rank inference and econometric analysis. His recent publications address topics like rank-based inference, measurement error detection, and high-dimensional independence testing. He organizes academic events such as the Munich Econometrics Seminar and the LMU-Todai Econometrics Workshop. His research emphasizes methodological rigor and practical applications, with a focus on improving statistical techniques for social science and policy analysis.

Moritz PetersenView profile

Associate Professor

• Sustainable Supply Chain Management
• Circular Economy
• Decarbonization
• +3 more

Prof. Dr. Moritz Petersen is an Associate Professor of Sustainable Supply Chain Practice and Co-Director of the Center for Sustainable Logistics and Supply Chains (CSLS) at Kühne Logistics University (KLU) in Hamburg, Germany. His academic career began at Hamburg University of Technology, where he completed his doctoral studies in 2017 under Prof. Dr. Wolfgang Kersten, and he has been at KLU since 2016. Research Focus: Decarbonization of logistics, Circular Economy, Blockchain applications in supply chains, and sustainable product development Teaching: Courses on Supply Chain Sustainability, Circular Product Development, and Lean Logistics Operations across KLU’s Bachelor to MBA programs Outreach: Co-hosts the podcast “Das Gleiche in Grün?!”, collaborates on educational children’s books about logistics, and frequently speaks at industry events like the Deutscher Logistik-Kongress Research Trends from his 15 most recent articles center on: Blockchain technology’s role in sustainable logistics Decarbonization strategies across maritime and road freight Circular economy implementation through product design and waste stream analysis Behavioral and organizational barriers to sustainability Public-private partnerships in European logistics Information sharing as a CE enabler Scientific Awards: 2020 Best Paper Award, International Journal of Operations & Production Management (IJOPM) Research Projects include: CREAToR: EU Horizon 2020 project on polymer recycling GATE: German Ministry of Economy-funded GHG emissions data exchange HANSEBLOC: Blockchain applications in logistics ChainLog: Blockchain use cases Development of Logistics Competence Assessment Toolkit

Andrea BastoniView profile

Research Fellow

• Real-Time Systems
• Cyber-Physical Systems
• Operating System Design
• +5 more

Dr. Andrea Bastoni is a Postdoctoral Researcher and Research Fellow at the Chair of Cyber-Physical Systems in Production Engineering at Technical University of Munich (TUM), Faculty of Mechanical Engineering. He is also the CTO and co-founder of Minerva Systems , developing operating system solutions for AI-ready embedded applications. His expertise spans real-time operating systems, cyber-physical systems, and predictable system design for heterogeneous platforms. His research focuses on enhancing predictability of memory hierarchies in complex SoCs through techniques like memory bandwidth regulation and cache partitioning. This work has industrial applications in safety-critical domains such as avionics and railways, where he contributes to certifiable hypervisors and operating systems. As former Software Architect of the PikeOS hypervisor at SYSGO GmbH (2012-2020), he specialized in DO-178C, IEC 61508, and EN 50128 standards. His academic background includes a Ph.D. in Computer Engineering from the University of Rome Tor Vergata (2007-2011), where he developed LITMUS^RT as part of UNC's Real-Time Systems Group during a visiting researcher period (2009-2010). His publications reflect ongoing work on Multicore Real-Time Scheduling , Mixed-Criticality Task Isolation, and Arm DynamIQ shared unit analysis. He actively participates in program committees for conferences like RTSS, DSN, and DATE.

• Computer Science
• Artificial Intelligence
• Machine Learning
• +12 more

Hao Liu is a researcher affiliated with institutions like Chinese Academy of Sciences , Beihang University , and Stanford University . His work spans Computer Science , Artificial Intelligence , and Robotics . Key affiliations: National Space Science Center (Beijing), School of Astronautics (Beihang), Key Laboratory of Pervasive Computing (Tsinghua) Research interests include Machine Learning , Image Processing , Graph Neural Networks , and Wireless Communication Optimization His recent publications focus on: Advanced control systems for fuzzy models Medical imaging via hyperspectral analysis Transformer-based approaches in NLP and vision Quantum-safe and edge computing protocols

• Database Systems
• Distributed Databases
• Transaction Processing
• +5 more

Daniel J. Abadi is a prominent researcher in database systems at Yale University. With over two decades of impactful research, he has made significant contributions to the fields of distributed databases, transaction processing, and column-oriented database systems. His work bridges theoretical foundations with practical implementations that have influenced both academia and industry. Dr. Abadi's research primarily focuses on database system architecture, with particular emphasis on: Distributed and geo-replicated database systems High-performance transaction processing Column-oriented and analytical database systems Stream processing and real-time analytics Cloud and serverless database technologies Integration of machine learning with database systems His recent work shows a continued focus on addressing scalability challenges in modern database systems, with particular attention to multi-region transaction processing, automated data management, and the integration of machine learning techniques. The trend in his publications indicates a strong emphasis on practical, deployable systems that solve real-world problems faced by industry. Dr. Abadi has been instrumental in several major research initiatives and reports that have shaped the direction of database research, including the Seattle Report and the Cambridge Report on Database Research. Throughout his career, Dr. Abadi has mentored numerous students and collaborated extensively with leading researchers in the field. His work has received significant recognition through widespread citations and adoption of his ideas in both academic and industrial database systems.

Cornelia WrzusView profile

Professor

• Personality Development
• Socio-Emotional Development
• Aging
• +3 more

Cornelia Wrzus is a Professor at Ruprecht-Karls-Universität Heidelberg, leading the Psychological Aging Research group. Her work focuses on socio-emotional and personality development across adulthood and old age, with an emphasis on longitudinal studies and real-time data collection methods like Ecological Momentary Assessments (EMA). Email: cornelia.wrzus@psychologie.uni-heidelberg.de Phone: +49 6221 54-8110 Office: Room 032, Bergheimer Straße 20, 69115 Heidelberg Her research explores how personality traits interact with social situations and life events, using frameworks such as TESSERA to model developmental processes. Current projects are detailed on her lab’s website, with publications accessible via ResearchGate and Google Scholar. Wrzus’s recent articles highlight interdisciplinary methodologies and the role of compliance in EMA studies, aging-related stress reactivity, and the coupling of daily life processes with trait development. Her work spans personality psychology, social science, and behavioral research. She maintains an open-science approach through her OSF profile, which hosts preregistrations, materials, and scripts for ongoing projects.