Joergen Kornfeld is a researcher at the University of Cambridge, affiliated with the MRC Laboratory of Molecular Biology (LMB) in the Connectomics of Learned Behaviour group. His work focuses on understanding how learned behaviors are encoded in neural circuits through connectomic analysis. Institution: University of Cambridge Role: Connectomics Researcher Research Interests: • Connectomics and synaptic connectivity mapping • High-throughput 3D electron microscopy • Deep learning applications in neural network analysis • Behavioral memory storage mechanisms • Comparative neuroanatomy of learned behaviors • Computational modeling of neural circuits. Recent publications highlight his expertise in developing deep learning tools (e.g., DeepFocus, SyConn2) for connectomic reconstruction, with applications in zebra finch song learning and larval zebrafish neural circuits. His work bridges advanced imaging techniques, computational methods, and behavioral neuroscience. Techniques: High-throughput 3D electron microscopy, flood-filling networks Model Systems: Zebra finch, larval zebrafish
Prof. Dr. Matthias Rarey is a computer scientist and Professor at the University of Hamburg's Center for Bioinformatics. He holds a Ph.D. in Computer Science from the University of Bonn (1996) and has been leading the Algorithmic Molecular Design working group since 2002. His research focuses on molecular design algorithms, cheminformatics tools, and 3D bioinformatics. Co-founder of BioSolveIT GmbH Former cheminformatics group leader at Fraunhofer SCAI Former researcher at SmithKline Beecham and Roche Bioscience Head of Helmholtz Data Science Graduate School DASHH Director of Center for Data and Computing in Natural Science (CDCS) Research interests span algorithmic molecular design, cheminformatics, structure-based drug discovery, and machine learning applications in bioactivity prediction. His group developed widely used tools like FlexX, PoseView, and SpaceLight for molecular modeling and fragment space analysis. Recent publications focus on geometric pattern matching in protein-ligand interfaces, combinatorial fragment space encoding, adverse drug reaction network analysis, and efficient shape-based virtual screening. The work emphasizes scalable algorithms for billion-sized compound libraries and integration of machine learning with traditional cheminformatics approaches. Scientific awards include: GMD Award 1996 (Best Dissertation) GMD Award 2000 (Best Project) NRW Wissenschaftspreis 2002 Corwin Hansch Award 2005 Emerging Technologies Award 2011 Norddeutscher Wissenschaftspreis 2020 Academic leadership roles: Founding director of Center for Bioinformatics Co-founder of M.Sc. Bioinformatics and B.Sc. Computing in Science programs Chair of doctoral committee at Faculty of Computer Science Member of EMBL-EBI's Molecular and Cellular Structure advisory board Former Associate Editor of Journal of Chemical Information and Modeling
Giomara Lárraga Maldonado is a Postdoctoral Researcher at the Faculty of Information Technology within the University of Jyväskylä , Finland. She contributes to the Multiobjective Optimization Group and is affiliated with the Decision Analytics utilizing Causal Models and Multiobjective Optimization (DEMO) thematic research area. Research Focus: Interactive Multiobjective Optimization, Evolutionary Computation, Explainable AI Key Areas: Preference integration, Decomposition-based methods, Human-Computer Interaction for decision support Her recent work explores explainability frameworks (e.g., LIME integration), phase-specific algorithm configuration, and semantic distance studies for visualization. She collaborates with researchers like Kaisa Miettinen and Giovanni Misitano. She has contributed to conferences such as GECCO, PPSN, and AAMAS, with publications emphasizing open-access availability. The R-XIMO framework (2022) highlights her work on explainable systems.
Professor Alicia Rambaldi is Director of Research at the School of Economics, Faculty of Business, Economics and Law at the University of Queensland. She is also an Affiliate of the Centre for Efficiency and Productivity Analysis. Her academic career spans decades of research in econometric methodologies with applications to real-world economic problems. Professor Rambaldi's research interests focus on applied econometrics, time series econometrics, state-space models, and spatial time series models. She has made significant contributions to economic measurement, particularly in developing methodologies for computation of price indices for land and property, estimation with linked administrative data, and smoothing methodologies combining spatial and temporal information. Her work bridges theoretical econometrics with practical applications in housing markets, climate adaptation, and international economic comparisons. Her recent publications demonstrate a consistent focus on housing economics, with numerous papers on hedonic pricing models, property valuation, and the impact of environmental factors on real estate markets. She has also maintained a strong research program in international comparisons, purchasing power parity, and productivity analysis, often collaborating with leading researchers in these fields. Professor Rambaldi is actively involved in research supervision, currently advising on topics including language barriers faced by immigrants, distributive politics, and copula models. Her completed supervision includes significant work on purchasing power parities, development indexes, trade studies, and spatial analysis of tourism employment. Her current research projects include spatial time series models with applications to housing and land prices, transport demand modeling, and international comparisons. She has secured substantial funding from diverse sources including the Australian Research Council, Natural Hazards Research Australia, and government departments, demonstrating the applied relevance of her work. Professor Rambaldi leads an active research group within the Centre for Efficiency and Productivity Analysis, focusing on developing and applying advanced econometric techniques to address pressing economic measurement challenges. Her work often involves interdisciplinary collaboration with researchers in environmental science, urban planning, and transportation studies.
Prof. Dr. Axel-Cyrille Ngonga Ngomo is a Professor at the University of Paderborn , affiliated with the Faculty of Electrical Engineering, Computer Science and Mathematics and the Institute of Computer Science . He leads the Data Science group at the Heinz Nixdorf Institute and is a member of the Sonderforschungsbereich Transregio 318 (Constructing Explainability). His roles include heading the Informatik Rechnerbetrieb (IRB) team. Research Focus : Knowledge graphs, semantic web technologies, explainable AI, and distributed systems. Selected Projects : SAIL (Sustainable Life Cycle of Intelligent Sociotechnical Systems), TRR 318 (Constructing Explainability), Colide (Co-training for Industrial Data), 3DFed (Dynamic Data Distribution), and SFB 901 (On-The-Fly Computing). Contact : Email axel.ngonga@uni-paderborn.de , Office F1.225 (Fürstenallee 11) and TP6.3.106 (Technologiepark 6), Paderborn. Teaching : Courses include Seminar on Recent Advances in Knowledge Graphs, Project Groups on SPARQL Query Processing, Large Language Model Training, Retrieval Augmented Generation, and Foundations of Knowledge Graphs.
Walter Jetz is a Professor of Ecology and Evolutionary Biology and the School of the Environment at Yale University, where he directs the Center for Biodiversity and Global Change. He chairs the E.O. Wilson Biodiversity Foundation and co-chairs the GEO BON Species Population Working Group. His work focuses on biodiversity science, conservation, and global change ecology. Education: D.Phil. in Zoology (University of Oxford, 2002), M.Sc. in Integrative Bioscience (Oxford, 1997) Research interests include macroecology, species distribution modeling, and conservation science across spatial scales. His group develops tools like Map of Life , Wildlife Insights , and Half-Earth Project to address biodiversity monitoring and area-based conservation. Current projects explore climate change impacts on tropical ecosystems, movement ecology, and machine learning applications in biodiversity science. Recent publications focus on niche scaling, climate change vulnerability, deep learning for species distribution, and mountain biodiversity monitoring. Awards include being an ISI Highly Cited Researcher since 2014. Over 30 former students hold faculty positions globally. The lab promotes diversity, equity, and inclusion in science and collaborates with NASA, Microsoft, and the Gordon and Betty Moore Foundation.
Jens Kattge is an Independent Research Group Leader at the Max Planck Institute for Biogeochemistry in Jena, Germany, specializing in Functional Biogeography. His career spans roles from Research Associate (2002–2005) to Senior Scientist (2010–2012) and current group leadership since 2013. He focuses on plant functional traits, their role in terrestrial ecosystems, and their integration into Earth system models. He coordinates the global TRY Initiative , a plant trait database for biodiversity research, and contributes to the German Centre for Integrative Biodiversity Research (iDiv). His research spans functional biogeography, trait-climate relationships, ecosystem modeling, and biodiversity-ecosystem functioning. Key projects include developing the rtry R package for trait data preprocessing and analyzing global datasets like CoRRE Trait Data and sPlotOpen. Recent publications investigate leaf cuticle thickness, drought responses in tree species, trait controls on biomass, global biodiversity patterns, and the integration of citizen science with Earth observation data. His work emphasizes trait data representativeness and future-proofing ecological science.
Ben Domingue is an Associate Professor at Stanford University's Graduate School of Education and, by courtesy, in the Department of Sociology. His research bridges psychometrics, quantitative methods, and interdisciplinary applications in education, psychology, and social sciences. He leads the development of the Item Response Warehouse, a data resource for psychometrics research, and explores how statistical tools can better measure complex educational and psychological outcomes like reading ability and treatment effects. PhD in Education from the University of Colorado at Boulder (2012). MA and BS in Mathematics from the University of Texas at Austin (2006, 2001). His work focuses on advancing psychometric methodologies, including response time analysis, item-level treatment effects, and predictive accuracy metrics (e.g., InterModel Vigorish). He investigates how genetic and environmental factors interact with educational outcomes and social mobility, using large-scale datasets like the Health and Retirement Study and Add Health. Recent articles emphasize AI-driven psychometric tools, cross-cultural validation of medical assessments, and equity in educational testing. 2024–2025: Associate Professor, Stanford GSE. 2015–2022: Assistant Professor, Stanford GSE. Affiliated: Stanford Center for Longevity, Bio-X, Population Health Sciences. Scientific awards include the Jacobs Foundation Research Fellowship (2022–2024) and AERA Open Outstanding Reviewer (2018, 2019). His advising roles span doctoral and master’s students, with a focus on psychometrics and social-genomic research.
Paul Leadley is a Professor at the University of Paris-Saclay, France, where he directs the Population and Community Ecology group within the Ecology, Society and Evolution Laboratory (IDEES). His research focuses on global change impacts on terrestrial ecosystems, biodiversity, and ecosystem functioning through field experiments and mathematical modeling. His educational background includes a B.S. in Science from Pennsylvania State University (1981), an M.S. in Botany from North Carolina State University (1985), and a Ph.D. in Ecology from San Diego State University and UC Davis (1993). Prior to his professorship, he worked as a research technician at San Diego State University, Smithsonian Environmental Research Center, and New Mexico State University, followed by post-doctoral research at the University of Basel. Leadley investigates climate change and rising CO2 impacts on plant diversity, biodiversity-ecosystem functioning relationships, and nutrient competition between plants and soil microorganisms. His experimental work centers on California and temperate grasslands, examining fire, temperature, CO2, nitrogen deposition, and precipitation interactions. He develops multi-scale models from rhizosphere nutrient fluxes to regional climate change projections, collaborating with institutions like INRA, Stanford University, and Northern Arizona University. His research integrates field experiments with mathematical modeling to quantify uncertainties in global change projections. His recent publications (2008-2012) reveal consistent themes: climate change impacts on biodiversity via species distribution modeling, interactive effects of multiple global change drivers on soil nitrogen cycling, and development of biodiversity scenarios for policy. Key methodological approaches include multi-model comparisons, experimental manipulations of grassland ecosystems, and trait-based biodiversity assessments. Scientific Awards: No specific awards listed in the provided text. Leadley has directed five Ph.D. students: Alexandra Gastine, Romain Barnard, Xavier Raynaud, Audrey Niboyet, and Sandrine Fontaine. He has secured major research grants including QDiv (770 k€, 2005-2009), SCION (470 k€, 2010-2012), and HumboldtCES (450 k€, 2010-2012). Current projects focus on biodiversity modeling (MOBILIS), biome boundary shifts, and climate change impacts on forests. He participates in international assessment processes including IPBES and IPCC. He leads the Population and Community Ecology team at IDEES Laboratory, comprising approximately 30 researchers, engineers, technicians, and graduate students. The team operates experimental sites in California grasslands and collaborates with national networks including FRB, AllEnvi, and GIS Climat, Environnement, Société. Current initiatives include eco-evolutionary approaches to climate change impacts and development of adaptive forest management strategies.
Yihan Sun is an Assistant Professor at the University of California, Riverside (UCR) since January 2020. He earned his Ph.D. in Computer Science from Carnegie Mellon University (CMU) , advised by Guy Blelloch , and holds a Bachelor's degree in Computer Science from Tsinghua University . Research Interests: Yihan Sun focuses on the theory and practice of parallel computing , including Parallel algorithms and data structures Write-efficient algorithms for Non-Volatile Memory (NVM) Computational geometry (range trees, Delaunay triangulations) Graph algorithms (SSSP, SCC, cluster-based BFS) Concurrent and persistent data structures Multi-version concurrency control (MVCC) with garbage collection Applications in databases, transactional systems, and computational biology Recent Research Trends: His work on join-based parallel balanced trees has been foundational, supporting four balancing schemes (AVL, red-black, weight-balanced, treaps) and enabling efficient implementations in graph analytics, spatial queries, and dynamic programming. Recent publications focus on output-sensitive algorithms , scalable graph libraries (PASGAL) , and pedagogical approaches to teaching parallel algorithms. Teaching: He teaches CS260 (Parallel Algorithms) at UCR and has served as a guest lecturer for MIT 6.886 (Algorithm Engineering) and CMU 15-859 (Algorithms in the real world) . He also contributed to algorithm education through a tutorial at the ACM Symposium on Principles and Practice of Parallel Programming (PPoPP 2019) . Labs & Collaborations: Yihan is a core contributor to the PAM (Parallel Augmented Maps) library, which has been integrated into systems like Aspen (graph-streaming) and C-trees . He collaborates with teams at CMU-Parlay , PBBS , and Ligra , with his code available on Github for community feedback.
Julian Shun is an Associate Professor at the Massachusetts Institute of Technology (MIT) in the Department of Electrical Engineering and Computer Science (EECS) and a principal investigator in the Computer Science and Artificial Intelligence Laboratory (CSAIL). Previously, he was a Miller Research Fellow at UC Berkeley and earned his Ph.D. from Carnegie Mellon University under Guy Blelloch. His research focuses on parallel and high-performance computing, with emphasis on graph analytics, spatial/graph clustering, and dynamic algorithms. He designs algorithms with theoretical guarantees and empirical efficiency, along with high-level programming frameworks to simplify parallel code development. His work spans cache-oblivious, external-memory, and streaming graph algorithms, addressing scalability and performance across diverse computational architectures. Julien's recent publications highlight advancements in parallel graph traversal, dynamic connectivity, and approximation algorithms for centrality metrics. His research also explores domain-specific languages like GraphIt for graph analytics and frameworks such as Julienne for work-efficient bucketing. Scientific Awards: Miller Research Fellow at UC Berkeley He has taught graduate-level courses at MIT, including 6.506 (Algorithm Engineering) and 6.886 (Graph Analytics), emphasizing theoretical foundations, experimental analysis, and open-ended research projects.
Michael J. Cafarella is an Associate Professor in the Computer Science and Engineering department at the University of Michigan . His research focuses on databases, information extraction, data integration, and data mining, with applications in economics, social media analysis, and combating human trafficking. He leads the Software Systems Lab and Michigan Database Group . Scientific Awards NSF CAREER award Sloan Research Fellowship (2016) 2018 VLDB Ten-Year Best Paper award Research Impact : Cafarella co-founded the Hadoop open-source project and Lattice Data (acquired by Apple). His work on DeepDive and DARPA MEMEX was featured on 60 Minutes and in Scientific American . Funding from The Census Bureau, DARPA, Google, NSF, Yahoo!, General Electric, and Dow.
Miroslaw Staron is a Professor of Interaction Design and Software Engineering at Chalmers University of Technology. He maintains a unique 50/50 work arrangement, spending half his time on field research at Ericsson while holding his academic position. His research bridges academic theory with industrial practice through collaborations with major companies including Volvo Car Corporation and Volvo Information Technology. His research spans several key areas in software engineering: Software metrics and measurement systems in industry Model driven software development and empirical studies Defect prediction in software projects Requirements engineering in model-based development Applications of AI and machine learning in software engineering Automotive software development and security Staron's recent work demonstrates a strategic shift toward integrating AI technologies into software engineering processes, with particular focus on automotive applications. His publications from 2024-2025 reveal expertise in generative AI applications for code review automation, testing methodologies, and requirements engineering, showing how these technologies can transform traditional software development practices while addressing domain-specific challenges in automotive systems. Current research projects include: Kvantdatorer för framtidens mobilitetslösningar (2025-2027) Automatiserad och designoptimerad programvarukonstruktion/kodgenerering (2025-2029) Förvandla fordonsarkitektur med hjälp från AI (2021-2023) Arkitektonisk design och verifiering/validering av system med maskininlärning komponenter (2020-2024) With 78 publications documented in Chalmers' research database, Staron has established himself as a significant contributor to evidence-based software engineering research with strong industrial relevance.
Michael D. Frakes is the A. Kenneth Pye Distinguished Professor of Law at Duke Law School and Professor of Economics in the Duke Economics Department. He is also a Research Associate at the National Bureau of Economic Research and Co-Editor-in-Chief of the American Law and Economics Review since late 2023. Education: BS in Economics (MIT, 2001), JD (Harvard, 2005), PhD in Economics (MIT, 2009). Prior Affiliations: Cornell Law School, Northwestern University School of Law, Visiting Professor at Harvard and NYU. Frakes conducts empirical research at the intersection of health law, torts, and innovation policy. His work examines how legal and financial incentives influence healthcare delivery, racial disparities in health outcomes, and patent office dynamics. He has secured NIH R01 grants to study medical liability's impact on care quality and pharmaceutical patent databases. His recent publications focus on pharmaceutical patent strategies, medical malpractice reform, and racial concordance in healthcare. While no explicit awards are listed, his grants and editorial leadership highlight his influence in empirical law and economics.
Daniel Vallero is an Adjunct Professor in the Department of Civil and Environmental Engineering at Duke University . He holds a Ph.D. from Duke (2000) , an M.S. from the University of Kansas (1996) , and a B.A. from Southern Illinois University (1974) . His career spans environmental engineering, exposure assessment, and climate change adaptation, with affiliations including North Carolina Central University (2012-2013) as Associate Professor. Education B.A., Southern Illinois University, 1974 M.S., University of Kansas, 1996 Ph.D., Duke University, 2000 Key Research Areas Environmental systems science Air pollution modeling and control Hazardous waste bioremediation Climate change governance Exposure-based chemical prioritization Biogeochemical cycling under climate stress Publication Trends Focus on PFAS exposure pathways , climate adaptation strategies , and pollutant fate in ecosystems Recent work includes high-throughput exposure models and environmental justice in global warming Scientific Awards Federal Honor Awards (2025) from the U.S. EPA Jeffrey B. Taub Award (1999) at Duke University Notable Contributions Authored Air Pollution Calculations and Environmental Systems Science Developed exposure prioritization tools like Ex Priori Post-9/11 environmental contamination studies in New York City
