Dr. Marion Schrumpf is a Group Leader in the Soil Biogeochemistry research group at the Max Planck Institute for Biogeochemistry, affiliated with the Department of Biogeochemical Processes. Her work focuses on soil carbon dynamics, mineral-organic matter interactions, and climate change impacts on soil systems. Research areas: Soil biogeochemistry, carbon cycling, mineral-soil interactions, nutrient stoichiometry, microbial ecology, and climate modeling. Email: mschrumpf@... Phone: +49 3641 57-6182 Office: B2.015 Her recent publications address themes like mineral control over soil carbon stabilization, drought effects on soil processes, microbial stoichiometric adaptation, and the Jena Soil Model's role in simulating carbon-nutrient interactions. She leads efforts to disentangle the complex relationships between land use, mineralogy, and soil organic matter turnover across diverse ecosystems.
Shuhao Fu is a Program Postdoctoral Fellow at the Santa Fe Institute (SFI) researching the intersection of machine learning and cognitive science. He completed his Ph.D. in Psychology at UCLA under advisors Hongjing Lu and Ying Nian Wu, following a B.S. in Computer Science and Mathematics from Hong Kong University of Science and Technology. His research examines human-like relational reasoning in AI systems through cognitive modeling and computational approaches. Research focuses on: Bridging human-machine reasoning gaps via analogical mapping Developing explicit relational representations in vision models Structural cognitive modeling for compositional understanding Multimodal reasoning and scene interpretation Relational knowledge representation in biological and artificial systems Publication trends show concentrated work in computational cognitive science (2021-2025), with evolving focus from visual analogy fundamentals to applications in 3D recognition, social interaction modeling, and mental health diagnostics. Recent work demonstrates increased emphasis on transformer architectures, multimodal integration, and human-AI comparative studies. Professional experience includes research internships at Google X and Mineral.ai, with prior affiliation at Johns Hopkins University's CCVL lab under Alan Yuille. Currently serves as reviewer for ICML, ICCV, and Cognitive Science Society conferences.
Xinfeng Gao is a Professor of Mechanical & Aerospace Engineering at the University of Virginia, leading the CFD & Propulsion Laboratory. She specializes in high-performance computing (HPC) algorithms for fluid dynamics, combustion, and plasma systems. Her work integrates numerical methods, parallel computing, and data analytics to address complex engineering challenges. Prior to UVA, she held a professorship at Colorado State University from 2011 to 2023, establishing the CFD and Propulsion Lab there. She earned her PhD in Aerospace Engineering from the University of Toronto in 2008, followed by postdoctoral research at Lawrence Berkeley National Laboratory (LBNL). Her research focuses on three core areas: high-order CFD methods for high-speed flows, parallel adaptive algorithms for spatial and temporal domains, and HPC combined with data analytics for aerospace design optimizations. Applications include reduced-order models for turbulence, propulsion device innovation, and quantum computing for fluid simulations. She collaborates with national labs (LLNL, LBNL), aerospace industries (Boeing), and software companies to translate research into practical solutions. Her recent grants include the NSF Mid-Career Advancement Award (2022–2025) for CFD+DA integration in commercial tools and UVA’s RIG Award (2025–2026) for gas-surface material studies under extreme conditions. She teaches MAE 6720 (Computational Fluid Dynamics) and MAE 3420 (Computational Methods). Key awards include the 2023 University of Virginia Research Achievement Award and the 2022 NSF MCA Award. Her work emphasizes cross-disciplinary innovation, blending computational science with experimental validation through initiatives like the Gas-Surface-Materials RIG project, involving experts from MAE, MSE, Chemistry, and Physics.
Robert Jacob is a Professor of Computer Science at Tufts University's School of Engineering, where he leads research in human-computer interaction with particular focus on implicit brain-computer interfaces. His work bridges computer science, cognitive science, and interface design to create adaptive systems that respond to users' cognitive states without explicit input. His educational background includes a Ph.D. from Johns Hopkins University. Professional milestones include: ACM CHI Academy membership (2007) ACM Fellow designation (2016) Leadership roles as ACM SIGCHI Vice President and conference chair for CHI, UIST, and TEI Professor Jacob's research centers on implicit interaction techniques, particularly using fNIRS brain sensing to create adaptive interfaces. His work has evolved from foundational studies in reality-based interaction and tangible programming to current neuroadaptive systems that measure cognitive workload in real-time. This research spans domains including music learning, museum education, and general user interface adaptation. His publications reveal consistent focus on brain-computer interfaces since 2012, with increasing sophistication in physiological measurement and machine learning techniques. Recent work integrates multiple physiological signals beyond brain data to create comprehensive user state models. Major recognitions include: CHI 2016 Best Paper Award for music learning research CHI 2014 and 2012 Best Paper Honorable Mentions Keynote addresses at major conferences including Neuroadaptive Technology Conference (2017) Extensive media coverage in New Scientist, IEEE Computer, and Boston Globe Professor Jacob has mentored 17 PhD students who now hold faculty positions at institutions including Worcester Polytechnic Institute, Northwestern University, and Carleton University. His HCI Lab, located in the Joyce Cummings Center, receives funding from NSF and other sources supporting neuroadaptive interface research. Current projects focus on broadening implicit interaction to include multiple physiological measurements while maintaining user privacy and system transparency.
Prof. Judith R. Miller is a Professor in the Department of Mathematics and Statistics at Georgetown University, affiliated with the College. Her research integrates mathematical models, particularly partial differential equations (PDEs), to study evolutionary ecology, including the ecological genetics of invasive species and population dynamics under climate change. She specializes in analyzing nonlinear PDEs, integrodifference equations, and nonlinear waves to advance theoretical frameworks and ecological applications. Her work bridges applied mathematics and biology, focusing on species range evolution, trait variance dynamics, and invasion biology. Notable contributions include continuum models for species range shifts and studies on wave pinning in evolutionary systems. While no specific awards or grants are listed, her research highlights interdisciplinary collaboration between mathematics and environmental science. Prof. Miller’s academic contributions span over two decades, with publications addressing topics like mutation survival during invasions, spatially explicit quantitative trait models, and genetic differentiation in heterogeneous environments. Her work emphasizes the interplay between theoretical mathematics and real-world ecological challenges, particularly under climate change scenarios.
Professor Sergei Petrovskii is a Chair in Applied Mathematics at the University of Leicester's School of Computing and Mathematical Sciences. His research focuses on mathematical ecology, ecological modeling, and complex systems analysis, with a particular emphasis on climate change impacts, oxygen depletion in oceans, and ecological catastrophes. He has published over 150 peer-reviewed papers and four books, including influential work on global anoxia and mass extinction dynamics. As Editor-in-Chief of Ecological Complexity (2011–2021) and Section Editor-in-Chief of Mathematics ' Mathematical Biology section since 2020, he has significantly shaped interdisciplinary research agendas. His research interests span modeling ecological transients, population dynamics, and invasive species spread. Key contributions include frameworks for landscape decision-making, stochastic models of protest dynamics, and the MPDE conference series he founded. Despite no explicit mention of awards, his editorial roles and prolific publishing underscore his academic influence. His work integrates mathematical modeling with real-world challenges, addressing issues like oxygen minimum zones and the socioeconomic dimensions of climate change. Publications highlight his exploration of transient dynamics, regime shifts, and ecological responses to environmental change. His interdisciplinary approach bridges ecology, epidemiology, and social systems, evidenced by studies on protest dynamics and pandemic modeling. While no lab names are explicitly stated, his research often involves collaborative projects like the Landscape Decisions initiative and MPDE conferences.
Matti Minkkinen is a Docent at the Turku School of Economics (University of Turku) and a Postdoctoral Researcher in Information Systems Science at the Department of Management and Entrepreneurship. His work bridges futures studies with ethics, privacy, and socio-technical systems in digital transformation. Recent roles focus on responsible AI governance and foresight methodologies. University: University of Turku School: Turku School of Economics Department: Department of Management and Entrepreneurship His research explores how digital technologies reshape organizational practices, emphasizing Futures Consciousness as a human capacity. Key themes include responsible AI , privacy protection , and causal layered analysis in scenario planning. Publications highlight ethical governance frameworks and EU policy debates. Recent articles address generative AI ethics , ML system integration , and AI auditing across journals like Communications of the Association for Information Systems and Information and Management . Topics cluster around socio-technical systems, digital ethics, and institutional adaptation to AI. Teaching and editorial roles include co-curating student research collections at Finland Futures Research Centre. No explicit scientific awards are listed, but his work contributes to foresight theory and practice.
Professor Jordan Taylor is affiliated with Princeton University as a faculty member in the Department of Biomedical Engineering within the School of Engineering and Applied Science. His research focuses on unraveling computational processes in motor control and learning, with particular emphasis on interactions between explicit cognitive strategies and implicit motor adaptation during skill acquisition. Taylor leads the Intelligent Performance and Adaptation Laboratory , aiming to develop optimal training protocols for motor rehabilitation post-stroke or disease. Research Interests : Taylor investigates how humans learn motor skills through dual mechanisms of declarative strategy formation and implicit neural adaptation. His work explores the neural systems underlying these processes and their functional consequences, especially in pathological conditions like cerebellar degeneration. Current studies examine working memory constraints, reward modulation of implicit adaptation, and plan-based generalization of motor learning. Publication Trends : Recent articles analyze dual mechanisms in sensorimotor learning, reward-driven adaptation, and contextual influences on motor memory. His computational neuroscience approach combines behavioral experiments, neural imaging, and theoretical modeling to study cognitive-motor interactions across various tasks.
Natalie Davis is an Assistant Professor in Environmental Geography at the Faculty of Science, Vrije Universiteit Amsterdam, and affiliated with the Amsterdam Sustainability Institute. Her research focuses on equity and agency in sustainability transitions, particularly within food systems, employing agent-based modelling and network analysis. She holds a PhD in Environmental Science (Lancaster University, 2021), an MSc in Environmental Science (Lancaster University, 2018), and a BSc in Information Science (University of North Carolina at Chapel Hill, 2015). Her work contributes to UN Sustainable Development Goals, emphasizing sustainable consumption and food systems. Her research explores consumer behavior, food system dynamics, and network structures, with recent projects including a NWO-funded study on the Dutch food system transition. She has published on topics such as dietary patterns, Indigenous food networks, and resource distribution inequalities. Her methodologies emphasize transdisciplinary approaches to address systemic challenges in sustainability transitions. Labs/Teams: Institute for Environmental Studies (IVM) and Amsterdam Sustainability Institute (ASI).
Luís Moreira de Sousa is an Assistant Professor at the Department of Computer Engineering, Higher Technical Institute (Instituto Superior Técnico), University of Lisbon. His academic work bridges computer science and geography, with a focus on Geoinformatics rather than traditional GIS. He is affiliated with the Information and Decision Support Systems research unit and teaches Data Administration and Information Systems. His research spans several interconnected domains: spatial simulation, semantic web technologies for geospatial data, hexagonal grid systems, and resource depletion studies. Dr. de Sousa has developed innovative approaches to spatial simulation through his DSL3S (Domain Specific Language for Spatial Simulation Scenarios) and has contributed significantly to the Semantic Web through the GloSIS web ontology for soil data. His current major project is the book Spatial Linked Data Infrastructures , which bridges geospatial science and semantic web technologies. His publication record shows consistent contributions to spatial simulation tools, semantic web applications in geospatial domains, and resource depletion analysis. His work demonstrates a clear trajectory from practical spatial simulation tools toward more sophisticated semantic web applications for geospatial data. The recent focus on spatial linked data infrastructures represents the culmination of his interdisciplinary approach, combining computer science rigor with geospatial domain knowledge. Dr. de Sousa is a strong advocate for open source tools and maintains an active presence in the FOSS4G (Free and Open Source Software for Geospatial) community. His professional digital footprint includes Codeberg, Mastodon, ORCID, Google Scholar, LinkedIn, ResearchGate, and StackExchange. Outside his academic work, he maintains interests in resource depletion (having created the Portuguese Peak Oil website PicoDoPetroleo.net in 2005 and contributed to TheOilDrum), cycling (covering thousands of kilometers annually), music, and literature. His Goodreads profile shows an active engagement with science fiction, history, and science literature.
Professor Jarno Vanhatalo is a Professor of Statistics at the University of Helsinki, serving as vice director of the Research Center for Ecological Change. He is affiliated with both the Faculty of Biological and Environmental Sciences and the Faculty of Science, where he leads the Environmental and Ecological Statistics Group. His work bridges advanced statistical methodology with pressing ecological and environmental challenges. His research focuses on the development and application of statistical models for ecological and environmental data. Key areas include: Bayesian statistics and hierarchical modeling Gaussian processes and spatial statistics Species distribution modeling Ecological risk assessment Climate change impact analysis Biodiversity monitoring and conservation Professor Vanhatalo's publication record shows a consistent focus on integrating sophisticated statistical methods with ecological applications. His recent work demonstrates increasing emphasis on climate change impacts on biodiversity, spatially explicit modeling of ecological processes, and the development of Bayesian methods for uncertainty quantification in environmental predictions. His research spans terrestrial and marine ecosystems, with particular attention to Nordic and Baltic regions. Among his notable contributions are advancements in: Joint species distribution modeling Bayesian calibration of environmental models Spatiotemporal survey design Model-based variance partitioning in ecological studies Statistical approaches to Arctic risk management Professor Vanhatalo has received significant research funding, including an ERC grant for "Predective Understanding of the effects" (2024-2029) and leadership roles in multiple Academy of Finland and EU-funded projects focused on biodiversity, ecological change, and risk management in polar waters. He actively contributes to the academic community through: Supervising doctoral students in Wildlife Biology and Mathematics and Statistics programs Serving on editorial boards for journals including Conservation Biology and Ecology Letters Peer reviewing for numerous statistical and ecological journals Organizing workshops on statistical methods in ecology
Maria Grazia Alaimo is a Researcher in the Department of Earth and Marine Sciences at the University of Palermo . Her work focuses on environmental geochemistry, atmospheric pollution analysis, and biomonitoring using plants, lichens, and human biological matrices. She has conducted extensive studies on trace element distribution in urban and industrial areas of Sicily. Current affiliation: University of Palermo Academic rank: Researcher Research Interests: Her research spans trace element geochemistry , urban air quality , and human exposure to heavy metals . Key areas include: Atmospheric pollution dynamics Soil and plant interactions Biomonitoring techniques Industrial contamination effects Health risk assessment Recent publications demonstrate expertise in PM10/PM2.5 analysis , lichen biomonitoring , and heavy metal accumulation in mushrooms and human hair . While no explicit awards or students are listed in available data, her work contributes to environmental health and pollution mitigation strategies.
Jeffrey Zabel is a Professor of Economics and Director of the MS Program in Data Analytics at Tufts University's School of Arts and Sciences. He holds a PhD in Economics from UC San Diego (1987) and a BA in Mathematics from Swarthmore College (1979). His research focuses on applied micro-economics, particularly urban, housing, education, environmental, and labor economics, leveraging econometric methodologies. He has served as co-editor of the Journal of Housing Economics , associate editor at Regional Science and Urban Economics , and editorial board member at Real Estate Economics . His professional roles include Fellow at the Weimer School of Advanced Studies in Real Estate, Research Affiliate at NYU’s Institute for Education and Social Policy, and Board Member of the Boston Research Data Center (BRDC). Education: PhD in Economics, University of California, San Diego (1987) BA in Mathematics, Swarthmore College (1979) Research Interests: Urban and housing economics emphasize spatial market dynamics and policy impacts. His work on education economics explores school choice mechanisms and equity, while environmental studies assess natural resource valuation. Labor economics research examines job displacement and mobility. He also develops econometric tools for hedonic pricing and spatial analysis. Key Research Trends: Recent articles address property value analysis (e.g., ZTRAX applications), national park environmental impacts, and labor-market-housing interdependencies. His work bridges econometric innovation with policy-relevant insights in urban and environmental domains. Grants & Awards: Recent funding includes the Smith Richardson Foundation and Russell Sage Foundation grants for studies on worker adaptation and geographic mobility. While no explicit awards are listed, his editorial roles and fellowships reflect peer recognition in real estate and urban economics. Teaching & Advising: Teaches graduate/undergraduate courses in econometrics and data analytics. Advises students through independent studies and research assistantships, though specific advisee names are not documented here. Labs/Teams: Engaged in interdisciplinary collaborations through Tufts’ Data Analytics program and external partnerships like NYU’s Institute for Education and Social Policy, focusing on data-driven policy analysis.
Auguste Genovesio is a Research Director (DR INSERM) leading the Computational Bioimaging and Bioinformatics team at the Centre for Computational Biology within the École Normale Supérieure (ENS) in Paris. His work focuses on large-scale cellular morphology analysis, integrating machine learning, microscopy, and computational modeling to study cellular responses to perturbations. His team develops algorithms for analyzing high-dimensional biological data, with applications in drug discovery, functional genomics, and neuroscience. Education and Affiliations: Genovesio’s research is anchored at ENS and collaborates with institutions like Institut Curie, Collège de France, and ESPCI. His lab develops open-source tools such as PySpacell and ALFA , advancing spatial analysis and genomic data processing. Research Interests: His group combines deep learning, bioinformatics, and experimental biology to tackle challenges in cellular dynamics, morphological heterogeneity, and predictive modeling. Recent work includes applying diffusion models to reveal subtle phenotypes and optimizing microscopy image analysis pipelines. Key Projects: Cross-modal knowledge distillation for transcriptomics, latent diffusion models for small datasets, and super-resolution microscopy via StyleGAN regularization. Applications: Collaborations in drug screening, neurobiology (e.g., Drosophila memory studies), and cancer cell analysis. Publications: Over 50 peer-reviewed articles since 2007, including work in Nature Communications , Developmental Cell , and NeurIPS . Recent focus on generative AI for biological image analysis and self-supervised learning biases. Grants & Awards: While specific grants aren’t listed, his lab’s cutting-edge research suggests significant institutional and collaborative support. No explicit awards mentioned in texts. Labs/Teams: Director of the Computational Bioimaging group, part of the Functional Genomics section at ENS. Supervises PhD students and postdocs in AI-driven biology and computational microscopy.
Bruce Wiggins is an Associate Professor in Audio Engineering at the College of Science and Engineering. His research focuses on spatial audio technologies, including Ambisonics, binaural auralization, and 3D audio systems. Notable projects include the GASP guitar system and WHAM webcam-based head-tracked audio solutions. He has contributed to advancements in microphone array calibration, speaker array modeling, and virtual reality audio integration. His work bridges theory with practical applications in music technology and acoustic engineering. Education: PhD in Audio Engineering (2004). Research Interests: Ambisonics, spatial audio capture and reproduction, 3D audio for virtual reality, binaural rendering, and innovative musical instrument design. His work emphasizes practical implementations such as the GASP guitar system and calibration tools for low-cost microphone arrays. Article Trends: Recent publications address virtual stereo microphone techniques (2024), dynamic electrical systems (2024), and browser-based 3D audio (2023). Earlier work explores head-tracking algorithms (2016–2020) and acoustic modeling for domestic environments (2017). Grants/Advising: No explicit grants listed. Advising details unavailable but has collaborated with numerous researchers on projects like WHAM and GASP. Labs/Teams: Active in interdisciplinary teams developing spatial audio tools and instruments, including collaborations on virtual reality auralization and ambisonic guitar systems.
