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Rainer K. SilbereisenView profile

Research Professor

• Human Development
• Developmental Psychology
• Dynamic Interactionism
• +12 more

Rainer K. Silbereisen is a Research Professor of Developmental Psychology at Friedrich Schiller University Jena , Director of the Center for Applied Developmental Science , and Adjunct Professor of Human Development at Pennsylvania State University . He has held faculty positions at Technical University Berlin, Justus Liebig University Giessen, and Penn State, alongside leadership roles at the Gesis social science infrastructure company and the University of Haifa Board of Governors. Research Focus: Human development across lifespan, dynamic interactionism, social change impacts, acculturation of immigrants, entrepreneurship, and biobehavioral aspects of adolescence. Applications: Prevention of maladjustment, promotion of positive development, and social policy relevance. International Collaborations: Comparative studies in Germany, Poland, Israel, and post-Soviet states. Funding Sources: German National Science Foundation, W.T. Grant Foundation, Jacobs Foundation, and the German Federal Ministry of Education and Research. Scientific Recognition: Member of Academia Europaea and Erfurt Academy of Sciences Fellow of American Psychological Association and Association for Psychological Science 2012 ISSBD Distinguished Scientific Award 2014 EARA Lifetime Award Academic Leadership: Past-President of ISSBD, German Psychological Society, and IUPsyS. Editor of European Psychologist and American Psychologist Associate Editor. Directed large-scale networks on immigration and social change.

Peter HedströmView profile

Professor

• Materials Science
• Materials Design
• Phase Transformations
• +8 more

Peter Hedström is a Professor of Materials Science at the Department of Materials Science and Engineering, KTH Royal Institute of Technology. He leads the Hultgren Laboratory for Materials Characterization and directs the Center for X-rays in Swedish Materials Science (CeXS) and the Vinnova competence center NEXT. His research focuses on advanced materials characterization, structure-property relations, and materials design, particularly in metallic alloys, steels, ceramics, and composites. He co-founded companies Ferritico and Scatterin based on his research. Hedström’s work leverages large-scale infrastructure like synchrotron and neutron methods, with key projects including ENDUREIT for improving duplex stainless steels and Track-AM for additive manufacturing analysis. Education: PhD from Luleå University of Technology. Earlier roles at MEFOS/Swerim before joining KTH in 2008. Research Interests: Phase transformations, materials characterization (e.g., synchrotron/X-ray/neutron techniques), additive manufacturing, machine learning applications, and fatigue mechanics. His group explores topics like low-temperature embrittlement, microstructure-strength relationships, and cemented carbide sintering. Articles Trends: Recent work emphasizes in-situ observations of phase separation, precipitation kinetics, and microstructural stability under fatigue. Studies often integrate computational modeling with experimental methods, highlighting interdisciplinary approaches. Grants/Projects: Directs CeXS (hosting the Swedish beamline P21 at PETRA III) and NEXT. Active in EIT Raw Materials (ENDUREIT) and MMD initiatives. Supervises PhD/postdoc projects in neutron scattering, Mg-AM, and machine learning. Labs/Teams: Hultgren Laboratory, SwedNess graduate school, and collaborations with industrial partners like Ferritico.

Luyang ZhaoView profile

Assistant Professor

• Robotics
• Artificial Intelligence
• Soft Robotics
• +9 more

Luyang Zhao is an incoming tenure-track Assistant Professor in the Department of Electrical and Computer Engineering at Clemson University (starting August 2025). He earned his PhD in Computer Science and double undergraduate degrees in Computer Science and Mathematics from Dartmouth College and the University of Minnesota respectively. Academic Affiliation : Clemson University (Assistant Professor) Education : PhD in Computer Science (Dartmouth College), BS in Computer Science & Mathematics (University of Minnesota) His research focuses on Robotics , particularly soft robotics, modular systems, and bio-inspired designs. Key areas include: Large Language Models for robotic design automation Modular tensegrity systems for self-assembling structures Swarm coordination strategies Multi-environment adaptability (land/aquatic/aerial) Simulation tool integration for design optimization Recent publications highlight his work on SoftSnap modular platforms, LLM-driven swarm intelligence, and bioinspired dolphin robots. He received the Neukom Outstanding Graduate Research Prize for his contributions. Industry Experience : Research internships at Amazon Robotics and TuSimple Mentorship : Advised 6+ graduate/undergraduate researchers Open-Source Contributions : Developed SoftSnap platform for rapid prototyping Academic Service : Workshop co-organization (IROS 2023), peer reviewing (RA-L, ICRA, IROS, RoboSoft, BioRob)

Noel CressieView profile

Professor

• Spatial Statistics
• Environmental Informatics
• Bayesian Statistics
• +3 more

Noel Cressie is a Distinguished Professor of Statistics at the University of Wollongong (UOW), Australia, affiliated with the School of Mathematics and Applied Statistics and the National Institute for Applied Statistics Research Australia (NIASRA). He is also the Director of the Centre for Environmental Informatics (CEI). His academic journey includes a PhD from Princeton University (1975) and a B.Sc. with First Class Honours from the University of Western Australia (1972). His research focuses on spatial and spatio-temporal statistics, Bayesian methods, environmental informatics, and applications in climate science. Notable projects include work on atmospheric CO2 flux inversion (WOMBAT framework), Antarctic environmental research (SAEF initiative), and statistical remote sensing for NASA. He has secured over $20 million in research funding and authored four influential books, including Statistics for Spatial Data . Cressie has received prestigious awards such as the COPSS R.A. Fisher Award (2009), Pitman Medal (2014), and Fellowship of the Australian Academy of Science (2018). He leads interdisciplinary teams addressing global challenges like carbon cycle dynamics and biodiversity modeling. His contributions to statistical methodology and environmental science have been recognized through international collaborations and advisory roles.

Fariba KarimiView profile

Professor

• Algorithmic Fairness
• Computational Social Science
• Network Science
• +7 more

Fariba Karimi is a Professor of Social Data Science at Graz University of Technology and leads the Algorithmic Fairness research group at the Complexity Science Hub (CSH) in Vienna. Her work sits at the intersection of computational social science, network science, and AI ethics, with a strong focus on fairness, inequality, and bias in algorithmic systems. She holds a PhD in Physics and Computational Science from Umeå University, Sweden (2015), and was a Postdoctoral Researcher at GESIS – Leibniz Institute for Social Sciences, Germany. Her research combines large-scale data analysis, agent-based modeling, and network science to study how social structures and algorithms interact to shape disparities, particularly for underrepresented groups. Computational Social Science Algorithmic Fairness and Bias Network Science and Homophily Digital Humanism Urban Inequality and Implicit Bias Gender and Age Equity in Academia Her recent publications reveal a consistent focus on understanding and mitigating algorithmic and structural inequities. Themes include the visibility of minorities in networks, the impact of segregation on health and cognition, gender citation gaps in physics, and the development of fair ranking and recommendation systems. She employs interdisciplinary methods to analyze both digital and real-world social systems. Fariba Karimi has received several prestigious recognitions, including: Young Scientist Award from the German Physical Society (2023) ERC Starting Grant (2024) Nomination for the Hedy Lamarr Award (2021) She leads the 'Humanized Algorithms' project at CSH, funded by the ERC, and co-leads an EU Horizon project (MAMMOth) on multi-criteria fairness in AI. She advises PhD researchers such as Lisette Espín-Noboa and collaborates with institutions like the University of Mannheim and the Centre for Social Sciences. Her work contributes to both academic knowledge and practical solutions for fairer AI systems and more equitable societies.

Renaud LambiotteView profile

Professor

• Network Science
• Complex Systems
• Temporal Networks
• +7 more

Renaud Lambiotte is Professor of Networks and Nonlinear Systems at the Mathematical Institute, University of Oxford. He holds a PhD in Physics from Université libre de Bruxelles and has held research and faculty positions at ENS Lyon, Université de Liège, UCLouvain, Imperial College London, and the University of Namur. He is currently an active academic in applied mathematics and network science. His research focuses on complex systems, particularly dynamics on networks, temporal networks, and stochastic processes. He applies these to social and brain networks, data mining, and urban systems. His work bridges theoretical modeling and real-world data, emphasizing the structure and evolution of complex systems. His recent publications demonstrate strong trends in network theory, including hypergraphs, community detection, multidimensional dynamics, and data quality in network interventions. He also explores applications in urban air quality and gentrification, showing a commitment to socially relevant complex systems research. Scientific Awards: Prix Wernaers 2013 Prix Wernaers 2016 Prix Wernaers 2020 Verdickt-Rijdams 2016 de l'Académie royale de langue et de littérature françaises He is the co-founder of L’Arbre de Diane, a publishing initiative at the science-literature interface, which received multiple awards. He teaches advanced courses such as Differential Equations II and Networks. He is affiliated with the Machine Learning and Data Science and the Oxford Centre for Industrial and Applied Mathematics research groups. He has authored or co-edited key texts in the field, including A Guide to Temporal Networks and Modularity and Dynamics on Complex Networks , and has published around 130 peer-reviewed articles. His research is supported by ongoing collaborations and active publication output, indicating sustained academic leadership.

William AkotoView profile

Assistant Professor

• Cyber Security
• International Relations
• Political Economy
• +3 more

William Akoto is an Assistant Professor of Global Security in the Department of Foreign Policy & Global Security at the School of International Service, American University, and serves as Director of Research at the Center for Security, Innovation, and New Technology (CSINT). Previously, he taught at Fordham University, the University of South Carolina, and Nelson Mandela University in South Africa. Education: PhD in Political Science, University of South Carolina, 2019 Research Interests: Dr. Akoto's work centers on interstate cyber conflict dynamics , examining how states weaponize cyber capabilities for national security objectives. His research extends to the political economy of aid and trade , analyzing debt relief mechanisms and trade-sanction interactions, and military coups , investigating coup triggers and consequences through complex systems theory. He develops microfounded theoretical models to explore institutional and preference-based drivers of state behavior. Publication Trends: His 2020-2024 publications reveal a decisive shift toward cyber conflict research, with 80% of recent work focusing on state-sponsored cyber attacks, economic espionage, and cyber-proxy warfare. This builds on earlier expertise in trade-politics linkages (2013-2020), demonstrating consistent application of political economy frameworks across security domains. His methodology increasingly integrates complex systems science to analyze large-scale behavioral patterns. Scientific Awards: SIS Collaborative Research Award (June 2024) Outstanding Graduate Student Teacher Award (April 2019) Adam Smith Fellow, Mercatus Center (August 2016–July 2017) Thurmond-Atwater Fellowship (July 2015–May 2016) IAAR Research Fellow (August 2014–July 2015) Advising and Grants: Dr. Akoto has secured competitive research funding including the 2024 SIS Collaborative Research Award and Mercatus Center fellowship. His teaching excellence was recognized with the 2019 Outstanding Graduate Student Teacher Award. While specific advisees aren't documented, his course designs emphasize analytical skill development through constant feedback mechanisms, debate, and current events integration across undergraduate international relations curricula. Labs and Teams: As CSINT Director of Research, he leads interdisciplinary initiatives examining emerging technology-security intersections, with current focus on cyber warfare economics, satellite cybersecurity, and AI governance frameworks through structured collaboration between academic researchers and security practitioners.

Benjamin FehrmanView profile

Assistant Professor

• Stochastic Analysis
• Stochastic Partial Differential Equations
• Statistical Physics
• +5 more

Benjamin Fehrman is an Assistant Professor in the Department of Mathematics at Louisiana State University, specializing in stochastic analysis with a focus on stochastic partial differential equations and their applications to statistical physics. His research encompasses diffusion processes in random environments, stochastic homogenization, and randomized optimization algorithms in machine learning. His research interests center on the mathematical theory of stochastic partial differential equations, particularly those arising in statistical physics. Fehrman investigates fluctuating hydrodynamics, non-equilibrium systems, and the connection between interacting particle systems and their continuum limits. His work often involves developing well-posedness theory for challenging SPDEs with conservative noise structures and analyzing large-scale behavior in random media. Analysis of his recent publications reveals a strong focus on conservative stochastic PDEs and their connection to interacting particle systems, particularly the zero-range process and symmetric simple exclusion process. His research shows increasing attention to large deviation principles, kinetic formulations of skeleton equations, and the mathematical foundations of fluctuating hydrodynamics. The interdisciplinary nature of his work bridges probability theory, partial differential equations, and mathematical physics. Fehrman's research has been supported by prestigious grants including the National Science Foundation DMS-Probability Standard Grant 2348650, the Simons Foundation Travel Grant MPS-TSM-00007753, and the Louisiana Board of Regents RCS Grant 20130014386. He has supervised PhD students Andrea Clini (University of Oxford, 2020-2024) and Shyam Popat (University of Oxford, 2021-present), as well as postdoc Simone Floreani (University of Oxford, 2022-2023). Fehrman has also organized significant academic events including the "Interacting Particles, Fluctuating Systems, and SPDEs" workshop at the University of Oxford in June 2023, funded by an EPSRC Early Career Fellowship. His teaching portfolio includes advanced courses in stochastic analysis, stochastic differential equations, and stochastic homogenization at both Louisiana State University and the University of Oxford, where he previously held a position.

• Educational inequalities
• Social class inequalities in higher education access
• Ethnic inequalities in higher education access
• +4 more

Professor Vikki Boliver is a distinguished academic in the Department of Sociology at Durham University, specializing in educational inequalities and social mobility. Her research focuses on how social class and ethnic background influence access to higher status universities, with particular attention to quantitative methodologies and policy-relevant applications. Professor Boliver's research interests center on educational inequalities, with special emphasis on social class and ethnic disparities in access to prestigious universities. Her work examines social stratification and mobility across multiple generations, investigating how family background influences educational trajectories. She is particularly known for her expertise in quantitative research methods applied to large-scale educational datasets, producing policy-relevant findings that inform university admissions practices and widening participation initiatives. Analysis of Professor Boliver's recent publications reveals a consistent focus on educational stratification, with particular attention to contextual admissions, social mobility across generations, and ethnic disparities in higher education access. Her work spans methodological innovations in measuring educational inequality, critical examinations of meritocracy in university admissions, and cross-national comparisons of educational systems. The research demonstrates increasing attention to intersectional approaches examining how class, ethnicity, and other identity markers interact in shaping educational opportunities. Editorial Board member of Higher Education Policy British Journal of Sociology of Education editorial board member BSA Sociology journal editorial board member Professor Boliver actively supervises numerous PhD students, primarily focusing on educational inequalities, social mobility, and higher education policy. Her supervision portfolio includes both current and completed doctoral projects examining topics such as educational tracking, grammar school effects, ethnic disparities in university access, and contextual admissions practices. She frequently collaborates with other faculty members on supervision teams, contributing her expertise in quantitative methods and educational policy analysis to interdisciplinary research projects.

Johannes SoedingView profile

Researcher

• Computational Biology
• Bioinformatics
• Protein Structure Prediction
• +9 more

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.

• Genomics
• Yeast Genetics
• Chromosome Biology
• +7 more

Conrad Nieduszynski is a Senior Group Leader in the School of Biological Sciences at the University of East Anglia (UEA), where he leads a research group focused on yeast genomics and chromosome biology. His work bridges molecular biology, synthetic biology, and computational genomics, with a strong emphasis on fission yeast and pathogenic Candida species. His research interests include genome assembly , chromosome engineering , copy number variation , and telomere biology . He employs cutting-edge genomic technologies to understand genome structure, evolution, and function in model and pathogenic yeasts. His lab investigates both fundamental mechanisms and applied aspects, such as drug resistance in fungal pathogens. The recent articles highlight a strong trend in synthetic genomics (e.g., tRNA neochromosome construction) and high-resolution genome analysis (e.g., telomere-to-telomere assemblies). His work spans evolutionary biology, medical microbiology, and biotechnological innovation, reflecting a multidisciplinary approach to understanding genome dynamics. Scientific Impact: Publishes in high-impact journals including Cell and mBio Research referenced in patents and Wikipedia Widely shared in social media and covered by numerous news outlets Active collaborations across international research networks Dr. Nieduszynski advises a research team and collaborates extensively with leading scientists in genomics and synthetic biology. While specific grants are not listed, his publication record and high-profile work suggest active funding support. His research output is both prolific and influential, with applications in biotechnology and medicine. Laboratory and Collaborations: Leads an active research group at UEA Collaborates with experts in yeast genetics, synthetic biology, and infectious disease Engaged in large-scale genome projects and chromosome engineering initiatives

Minchen LiView profile

Assistant Professor

• Computer Graphics
• Machine Learning
• Robotics
• +4 more

Minchen Li Assistant Professor at Carnegie Mellon University's School of Computer Science (Computer Science Department). Formerly an Assistant Adjunct Professor at UCLA's Mathematics Department. Holds a Ph.D. from the University of Pennsylvania's SIG Center for Computer Graphics, followed by a postdoctoral position there. Research focuses on physics-based simulation, integrating numerical analysis, high-performance computing, and machine learning. Notable contributions include the IPC method for frictional contact simulation and large-scale material point methods. Education Ph.D., Computer and Information Science, University of Pennsylvania (2020) M.Sc., Computer Science, University of British Columbia (2018) B.Eng., Computer Science and Technology, Zhejiang University (2015) Research Interests Advances in physical simulation for visual computing, robotics, and manufacturing. Specializes in robust and efficient methods for solid/fluid dynamics, contact modeling, and GPU acceleration. Combines numerical analysis with machine learning to address challenges in simulation accuracy and versatility. Awards 2021 ACM SIGGRAPH Outstanding Doctoral Dissertation Award 2024 SCA Early Career Researcher Award Advising & Labs Leads the Simulation Intelligence Group (SIG) at CMU Graphics Lab. Advises PhD students Guying Lin, Juntian Zheng, Michael Liu, and Zhaofeng Luo. Collaborates with industry and academic partners on projects like VR-based modeling systems and scalable simulation frameworks.

Kaiyi JiView profile

Assistant Professor

• Large-scale optimization
• Machine learning
• Multi-task learning
• +6 more

Kaiyi Ji is an Assistant Professor in the Department of Computer Science and Engineering at the University at Buffalo, SUNY. He earned his PhD in Electrical and Computer Engineering from Ohio State University in 2021 and completed a postdoctoral fellowship at the University of Michigan. His research focuses on large-scale optimization, machine learning, and foundation models. PhD: Electrical and Computer Engineering, Ohio State University (2021) Postdoc: University of Michigan (2022) BSc: University of Science and Technology of China (2016) Research interests include bilevel optimization, multi-task learning, continual learning, and AI4Science, with applications in robotics and crystal property prediction. Recent work explores efficient algorithms for LLM training and optimization. His publications span top venues like ICLR, ICML, NeurIPS, and IEEE Transactions on Information Theory. He received the CSE Junior Faculty Research Award (2023) and NSF CAREER Award (2025). He advises PhD students and actively participates in departmental service as Associate Chair for Graduate Student Admissions and organizer of academic workshops.

Wolfgang HauptView profile

Researcher

• Urban Climate Governance
• Climate Change Adaptation and Mitigation
• Urban Resilience
• +12 more

Dr. Wolfgang Haupt is a researcher and Head of the Research Group 'Politics and Planning' at the Leibniz-Institut für Raumbezogene Sozialforschung (IRS) since December 2023. He holds a PhD in urban studies from the Gran Sasso Science Institute and Sant'Anna School of Advanced Studies and has a background in geography from Technische Universität Dresden. He is currently completing his habilitation at the Chair of Regional Planning, Brandenburg University of Technology Cottbus-Senftenberg, where he also teaches. His research focuses on urban climate governance, resilience, policy learning between cities, and institutional change. His research expertise spans urban climate governance, climate change adaptation and mitigation, urban resilience, policy diffusion, transnational municipal networks, cultural heritage in urban development, and institutional change. He has led and contributed to numerous research projects on local climate policy, city-to-city learning, and transformation pathways in German and European cities. His work emphasizes the role of smaller and structurally disadvantaged municipalities in climate action, challenging dominant narratives focused on large, well-resourced cities. The most recent publications reflect a strong trend in analyzing climate governance in medium-sized and smaller cities, the impact of social movements like Fridays for Future, the role of disruptive events (e.g., Tesla Gigafactory) in institutional change, and comparative studies of climate policy in European forerunner cities. His research increasingly integrates interdisciplinary and transdisciplinary approaches, examining the intersection of climate, migration, and infrastructure policies. Lead Project: Conflicts in Planning: Major Projects and their Potential for Institutional Change (2022–2025) Third-party funded project: Unlikely Climate Pioneers: Local Governments’ Net Zero Actions in Left Behind Places in the UK and Germany (2024–2026) Third-party funded project: Im Dialog mit Expert*innen aus der kommunalen Praxis: Was können wir aus der Migrations- und Klimakrise lernen? (2024–2025) Cross-departmental project: Disruption and Spatial Development (2022–2025) Habilitation project: Forerunner, Follower and Laggards: Governance of Climate Change in German Cities (2022–2025) Dr. Haupt regularly supervises and collaborates with early-career researchers and contributes to academic and public discourse through teaching, conference presentations, and expert policy dialogues. He is actively involved in transdisciplinary research, engaging with municipal practitioners and policymakers to bridge research and practice in urban planning and climate governance. He leads multiple research teams focusing on climate governance, institutional change, and urban resilience.

• Database Systems
• Stream Processing
• Distributed Data Management
• +5 more

Volker Markl is a Professor at Technische Universität Berlin in the Institute of Software Engineering and Theoretical Computer Science, with additional affiliations at the Berlin Institute for the Foundations of Learning and Data (BIFOLD) and the German Research Center for Artificial Intelligence (DFKI). His research spans database systems, stream processing, and distributed data management with significant contributions to both theoretical foundations and practical implementations. Markl's research interests focus on next-generation data management systems, particularly for streaming and IoT environments. His work addresses critical challenges in distributed query processing, system integration, and performance optimization. He has pioneered approaches for stream processing in volatile infrastructures and developed innovative techniques for GPU-accelerated database operations. His NebulaStream project represents a major contribution to distributed stream processing systems. His publication record demonstrates consistent impact across top database venues including VLDB, SIGMOD, and ICDE. Recent work shows increasing focus on machine learning integration with database systems, privacy-preserving query processing, and educational approaches for teaching large-scale data management. Markl has mentored numerous researchers who have become prominent in the database community, with frequent collaborators including Steffen Zeuch, Tilmann Rabl, and Philipp Grulich. His leadership extends to major research initiatives and collaborations across European institutions.