Paul Evans is a Professor in the Department of Materials Science & Engineering at the University of Wisconsin-Madison, College of Engineering. His research focuses on nanoscale materials synthesis, ultrafast dynamics, and advanced X-ray characterization techniques. PhD, Harvard University (2000) MS, Harvard University (1996) BS, Cornell University (1994) Evans investigates solid-phase epitaxy of complex oxides, strain imaging in acoustic devices, and optically driven phase transitions. His work combines experimental and computational approaches, including deep learning for diffraction data analysis. His recent publications highlight breakthroughs in nanoscale crystallization, ultrafast magnetization dynamics, and hybrid magnon-phonon systems. Awards include the Bascom Professorship and Vilas Mid-Career Award. Surface Science and Technology Bascom Professorship (2022) Vilas Associate Award (2019) Polygon Engineering Outstanding Instructor Award (2006) Evans teaches courses in materials structure, advanced X-ray methods, and thesis research. His lab enables scalable synthesis of perovskites and defect-minimized oxide heterostructures.
Trilce Estrada is an Associate Professor in the Department of Computer Science at the University of New Mexico (UNM), School of Engineering. She leads the Data Science Laboratory and is actively involved in research, teaching, and service. Her work focuses on solving data- and compute-intensive problems in science, health, and education, particularly in resource-constrained environments. Her research interests include: Machine Learning and scalable learning techniques Big Data analytics and distributed systems High Performance Computing and cyberinfrastructure In-situ data analytics for scientific workflows Pervasive healthcare using mobile and distributed systems Although no publication list is provided, her current research projects indicate a strong focus on distributed learning, in-situ analysis for molecular dynamics, cyberinfrastructure optimization, and robust science in high-throughput computing. These projects reflect interdisciplinary collaboration and a commitment to scalable, reproducible, and impactful computing solutions. Dr. Estrada has served in numerous leadership roles in major computing conferences, including as Program Co-Chair for IEEE Cluster 2022, Chair of the Mentor-Protégé Program at SC19, and Chair of the IPDPS PhD Forum and Student Program for multiple years. She has also been active in NSF panels and curriculum development, particularly in integrating Big Data into educational frameworks. She is committed to mentoring and improving diversity in computing, serving as faculty advisor for Women in Computing and CSGSA, and participating in outreach to attract underrepresented groups to computer science. She currently oversees a research group but is not accepting new students due to capacity. Independent study opportunities are available under strict eligibility criteria. Her lab, the Data Science Laboratory, supports interdisciplinary research in data-intensive domains. She is also involved in educational initiatives and curriculum development at UNM and nationally.
Prof. Dr. Hanna Meyer is a Professor of Remote Sensing and Spatial Modeling at the Institute of Landscape Ecology, University of Münster (WWU). She leads the Remote Sensing and Spatial Modeling Group and is actively involved in teaching and research in geospatial data science, machine learning, and environmental monitoring. Her work is supported by multiple national and international funding bodies including the DFG, EU Horizon Europe, and internal university grants. B.Sc. Geography, Philipps University Marburg (2007–2010) M.Sc. Environmental Geography, Philipps University Marburg (2010–2013) Ph.D., Philipps University Marburg (2014–2018) Her research focuses on machine learning methods for spatial data, optical remote sensing, environmental monitoring, and spatio-temporal modeling. She develops and applies advanced statistical and machine learning techniques to satellite and drone-based data for mapping ecological variables, land cover, and environmental change. Her work emphasizes methodological rigor, model transferability, and uncertainty quantification in spatial predictions. The recent publications reflect a strong trend in developing and validating machine learning models for environmental mapping, with applications in soil science, peatland hydrology, forest ecology, and polar climatology. She contributes both to theoretical advancements in spatial model validation and to practical software tools in R for geospatial analysis. She has secured competitive research funding for projects such as PRISM, Carbon4D, Uebersat, and BEyond, focusing on spatial pattern recognition, carbon modeling, AI model transferability, and biodiversity prediction. She teaches courses on remote sensing, spatial data analysis with R, and environmental modeling, and supervises students and early-career researchers. She collaborates widely with researchers across institutions and leads a dynamic research group including postdoctoral researchers and students. Her open-source contributions, particularly R packages like CAST and uavRst, support reproducible research in geospatial machine learning.
Prof. Dr. Susann Müller is Senior Scientist and Group Leader of the Flow Cytometry Working Group at the Department of Applied Microbial Ecology, Helmholtz Center for Environmental Research (UFZ) in Leipzig, Germany. Since 2011, she has held an Associate Professor position for Microbiology at Leipzig University’s Faculty of Life Sciences, bridging fundamental microbial ecology with environmental biotechnology applications through single-cell analytics. Education: 1985: Diploma in Biochemistry, Martin Luther University Halle-Wittenberg 1992: PhD, University of Halle-Wittenberg (Population dynamics of S. cerevisiae) 2003: Habilitation, Technical University Dresden (Multiparametric Cytometry) Her research pioneers microbial community flow cytometry to extract single-cell high-dimensional data, applying macroecological concepts to quantify stability metrics (resistance, resilience, displacement speed, elasticity) in engineered systems. Current focus includes bio-based circular economy initiatives: developing the carboxylate platform for sustainable chemical production and biological phosphate recovery from wastewater streams for resource valorization. Recent publications (2021-2025) reveal consistent innovation in flow cytometry applications, with emphasis on stability assessment in bioreactors, predator-prey dynamics in complex communities, and real-time monitoring of wastewater systems. She integrates ecological theory with multi-omics and data science to decode microbial assembly principles across environmental, agricultural, and industrial contexts. Professional roles: President, German Society of Cytometry (DGfZ, 2008-2010) Associate Editor, Microbiology for Cytometry Part A ISAC Educational Committee (2011-2012) and Scholars Program Committee (2013-2015) Current grants: PHOM project (SMWK InfraProNet 2024-2027): €449,160 for wastewater phosphorus recovery Z-PROJECT (DFG 2022-2025): €556,550 for bacterial biofilm analysis PROMICON (EU H2020 2021-2025): €200,000 for industrial microbiome consortia Moore Foundation (2020-2024): $23,000 for archaeal evolutionary tools Chinese Scholarship Council (2022-2026): Artificial community construction The Flow Cytometry Working Group under her leadership at UFZ develops standardized mock communities (Nature Protocols 2020), automated analysis tools (flowEMMi), and cytometric barcoding methods. It collaborates with Leipzig University, Technical University Dresden, and international partners including UC Santa Barbara, driving innovations in real-time environmental monitoring and wastewater treatment optimization.
Gaby Umbach is a Part-time Professor and Founding Director of GlobalStat at the European University Institute's Robert Schuman Centre for Advanced Studies . She serves as a non-resident Visiting Fellow for the European Parliament, Adjunct Professor at the Universities of Cologne and Innsbruck, and Board member of the Institute for European Politics Berlin. Her research focuses on knowledge-evidence-data interactions in governance, analyzing measuring/statistics as governance techniques , data literacy in policy-making, and transformations of politics through multilevel/anticipatory governance and sustainable development . She designed the GlobalStat database, linking it to the European Parliamentary Research Service and OECD. Scientific awards: Stiftung Demokratie Best PhD Thesis Award (2009) Key article trends include: global governance frameworks, data-driven EU policy innovations, open science impacts, and strategic foresight mechanisms. Her 2023-2024 publications emphasize economic indicators in international trade, policy evaluation methodologies, and database design for global governance.
Natasa Sladoje is a Professor in Computerized Image Analysis at the Department of Information Technology, Uppsala University. She is affiliated with the Vi3 and Image Analysis research group and leads the MIDA research group. Her work spans artificial intelligence, biomedical image analysis, deep learning, and algorithm development, with applications in medical imaging and life sciences. Her research focuses on developing advanced image analysis methods, particularly using machine and deep learning, to enable automated analysis of image data in science and everyday life. Key areas include medical image analysis, image registration, segmentation, pattern recognition, and discrete geometry. She applies these techniques to critical domains such as oral cancer detection, cytology, and multimodal imaging. The recent publications highlight a strong trend in AI-driven medical diagnostics, particularly in cancer detection using whole slide images, self-supervised learning for sparse instance detection, and contrastive learning for multimodal image registration. Her work also emphasizes reproducibility and benchmarking in bioimage analysis through frameworks like BIAFLOWS and public datasets like HISTOBREAST. She has no listed scientific awards in the provided text. Natasa Sladoje supervises research within the MIDA group and collaborates extensively on projects involving bioimage analysis, deep learning, and medical applications. While specific grant details are not mentioned, her leadership in collaborative frameworks and publication output suggests active involvement in funded research initiatives. She leads the MIDA (Medical Image Analysis) research group, which focuses on developing and applying novel image analysis tools for biomedical applications, particularly in cancer diagnostics and multimodal imaging.
Abolfazl Asudeh is an Associate Professor at the University of Illinois Chicago , affiliated with the Department of Computer Science and director of the Innovative Data Exploration Laboratory (InDeX Lab) . His work bridges data management, fairness, and AI. ACM and IEEE Senior Member VLDB Ambassador VLDB Endowment’s NSF Liaison Associate Editor for IEEE TKDE Research Interests focus on Algorithmic Fairness and Data-centric Responsible AI , with applications to ranking systems, LLMs, social networks, and misinformation detection. His work leverages Approximation Algorithms , Computational Geometry , and Randomized Methods to build efficient, fair systems. Scientific Awards : 2021: Google Research Scholar Award 2021: Communications of the ACM Research Highlight 2019: ACM SIGMOD Research Highlight 2020: VLDB Journal Special Issue on Best of VLDB 2017: ACM SIGMOD Most Reproducible Paper Grants include NSF IIS-2348919 (2024-2027) for fairness-aware data structures and NSF IIS-2107290 (2021-2024) for collaborative fairness research. Labs & Collaborations : Leads InDeX Lab with interdisciplinary teams, collaborating with institutions like University of Michigan, University of Texas at Arlington, and industry partners including Google and ACM.
Yann Renisio is a CNRS Research Fellow at the Center for Research on Social Inequalities (CRIS) at Sciences Po, a position he has held since October 2021. He is also an affiliated researcher at the Department of Sociology of Education and Culture at Uppsala University. His work focuses on understanding social structures and inequalities through rigorous sociological research. Dr. Renisio earned his PhD in sociology from EHESS in 2017. Prior to his current position, he held postdoctoral positions at Sciences Po, Uppsala University, and the Collège de France, demonstrating his international research experience and academic mobility across prestigious institutions. Yann Renisio's research spans multiple interconnected areas within sociology. His primary interests include social stratification, higher education systems, kinship networks, and the sociology of science. He investigates how social inequalities are reproduced and transformed through educational pathways and professional specialization, with particular attention to methodological innovations in social science research. His current projects examine higher education trajectories, professional specialization in medicine, practice-report gaps in survey data, and kinship networks. His methodological approach often integrates digital traces with traditional research methods to provide more comprehensive insights into social phenomena. Dr. Renisio's publication record reveals a strong focus on methodological innovation and the sociology of knowledge. His recent work explores the integration of digital trace data with survey methodologies, topic modeling of scientific disciplines, and the social organization of academic knowledge. Across his publications, he consistently examines how social structures shape individual opportunities and choices, with particular attention to educational and professional trajectories. His research bridges theoretical sociology with empirical analysis of contemporary social issues, particularly in understanding how digital practices intersect with traditional social stratification patterns. Dr. Renisio is actively involved in significant research projects including ANR RECORDS (2019-2023), which investigates music streaming practices and their relationship to social stratification, and ANR MEDSPE (2021), which examines the choice of medical specialties and locations among French doctors. These projects demonstrate his commitment to understanding how social structures influence individual choices in contemporary society through both traditional and digital methodologies. As a CNRS Research Fellow, Dr. Renisio contributes to the vibrant research community at CRIS, collaborating with scholars across disciplines and institutions. His work exemplifies the interdisciplinary approach needed to address complex social inequalities in modern societies, particularly through innovative methodological approaches that bridge digital and traditional research paradigms.
Dr. Yi Ting Chua is a Research Fellow at the University of Cambridge's Department of Computer Science and Technology, affiliated with the Cambridge Cybercrime Centre. She holds a PhD in Criminal Justice from Michigan State University (2019), with prior collaborative work under Dr. T. Holt and Dr. O. Smirnova. Current research bridges computer science, criminology, and gender studies in cybercrime contexts Key methodological approach: Social network analysis of online communities Her article trends ( 2013-2020 ) show interdisciplinary focus on: Cybercrime market economics (price analysis, revenue estimation) Radicalization dynamics in far-right forums Gender roles in online criminal subcultures Framework development for unintended cybersecurity consequences Notable scientific contributions include: 2020 Best Paper (STAST) for cybersecurity framework research 2019 Best Paper (APWG eCrime) for unintended harms analysis Active in stakeholder engagement projects related to: Intimate partner abuse victim support Far-right forum monitoring Cybercrime dataset standardization
Dr. Alastair Kay is a Lecturer in the Department of Mathematics at Royal Holloway, University of London. His research focuses on theoretical quantum computation, quantum information theory, and quantum cryptography, particularly addressing challenges in quantum state transfer, error correction, and networked quantum systems. He holds a PhD from the University of Cambridge under Prof. Artur Ekert and a physics degree from Keble College, University of Oxford. His research spans topics such as Quantum state transfer protocols using engineered Hamiltonians Quantum error correction mechanisms for experimental systems Entanglement properties in graph states and spin networks Applications of quantum computing in cryptography and information theory The articles listed reflect his work in quantum information science, computational physics, and theoretical cryptography. Key trends include advancements in fault-tolerant quantum communication, optimization of spin chain dynamics, and foundational studies in quantum correlations and nonlocality. Alastair actively develops software tools like quantikz for quantum circuit diagrams and ConTeXi for LaTeX equation integration in Microsoft Office. He also emphasizes open science principles and reproducibility in quantum research through personal commentary and collaboration with his fiancée, a Panton Fellow in open research practices.
Christos Ouzounis is a Professor of Bioinformatics at the Department of Informatics, Aristotle University of Thessaloniki , with a career spanning institutions including the European Bioinformatics Institute , King's College London , and University of Toronto . His work bridges Computational Biology , Digital Biology , and Metagenomics , focusing on large-scale data analysis, machine learning applications, and functional annotation of proteins. Education : BSc in Biological Sciences (1986), MSc in Biological Computation (1987), and DPhil in Computational Chemistry (1993) Key Roles : Director of the Bioinformatics Centre at King's College London (2007-2010), Research Director at IDEP-EKETA (2014-2020) His research interests include low-complexity protein sequences , Covid-19 seasonality patterns linked to UV radiation, and metagenomic analysis of urban microbiomes in cultural heritage sites. Current projects involve machine learning models for microbial coexistence networks, ontological classification of biomedical literature, and bioinformatics tool development . Publications highlight trends in archaeal genomics , functional dark matter in metagenomics, and epidemiological modelling . Notable collaborations include work on BioTextQuest v2.0 for concept discovery and MjCyc for metabolic pathway analysis.
Sarah Neuwirth is a tenured Professor for Computer Science at Johannes Gutenberg University Mainz (JGU) and a Visiting Researcher at the Jülich Supercomputing Centre. She manages JGU's High Performance Computing (HPC) division, coordinates regional/national HPC activities, and represents JGU in NHR, Gauss-Allianz, and HPC committees. Education : PhD (Dr. rer. nat.) in Computer Science (2018), Heidelberg University Diplom in Computer Science (2012), University of Mannheim Bachelor of Science in Computer Science (2010), University of Mannheim Research Interests : Parallel File and Storage Systems Modular Supercomputing (resource disaggregation/virtualization) Performance Engineering High Performance Computing Networking Reproducible Benchmarking Parallel I/O Publications Trends : Her work focuses on HPC performance modeling, parallel I/O optimization, modular supercomputing, network characterization, and reproducible benchmarks. Key themes include resource disaggregation, automated workflows, and data-intensive distributed applications. Scientific Awards : 2023 PRACE Ada Lovelace Award for HPC ZONTA Science Award 2019 Grants & Leadership : She leads the High Performance Computing division at JGU, participated in European DEEP projects, and serves on SC conference committees.
Associate Professor Kai-Hsiang Chuang is a Principal Research Fellow at the School of Biomedical Sciences within the Faculty of Health, Medicine and Behavioural Sciences at the University of Queensland. He is also affiliated with the Queensland Brain Institute and the Centre for Advanced Imaging. His research focuses on understanding brain networks, developing advanced imaging techniques, and translating these findings to improve diagnosis and intervention for neurological disorders. Dr. Chuang received his Ph.D. in electrical and biomedical engineering from the National Taiwan University, Taiwan, in 2001. His doctoral research focused on improving the detection of brain activity using functional magnetic resonance imaging (fMRI). Ph.D. in Electrical and Biomedical Engineering, National Taiwan University (2001) Dr. Chuang's research spans multiple areas of brain imaging and neuroscience. His primary focus is on functional brain mapping , where he develops in vivo imaging techniques including functional MRI and multimodal integration with optogenetics, calcium imaging, and electrophysiology. He applies these techniques in both humans and animal models to improve understanding and intervention of brain function, disease processes, and treatment effects. Another key area is brain networks in learning, memory, and dementia . His work explores how brain network wiring and activity underpin cognition and behavior, with particular focus on understanding the causal relationship between brain network activity and memory formation. He develops techniques to modulate behavior by manipulating brain network activity. More recently, Dr. Chuang has expanded into brain waste clearance research, studying the brain's fluid drainage system that clears waste and toxic molecules like amyloid plaques. His lab is developing imaging techniques to track this system's function and understand its regulatory mechanisms, which could provide new treatment targets for dementia. Analysis of Dr. Chuang's recent publications reveals a strong focus on advancing functional MRI techniques for brain network analysis, particularly in rodent models. His work consistently bridges basic neuroscience with clinical applications, especially in understanding memory formation and dementia. A notable trend is the development of multimodal approaches that combine fMRI with optogenetics, calcium imaging, and electrophysiology to establish causal relationships in brain networks. His research increasingly addresses the translation of preclinical findings to human applications, with growing emphasis on Alzheimer's disease mechanisms and potential interventions. Dr. Chuang serves on the editorial boards of multiple prestigious journals including Frontiers in Neuroscience: Brain Imaging Methods , Imaging Neuroscience , and Scientific Reports , reflecting his standing in the field. Editorial Board Member, Frontiers in Neuroscience: Brain Imaging Methods Editorial Board Member, Imaging Neuroscience Editorial Board Member, Scientific Reports Dr. Chuang is actively involved in research supervision, currently serving as Principal Advisor for one PhD student working on "Developing imaging and neuro-technologies for decoding memory formation" and Associate Advisor for two other PhD projects. He has successfully completed supervision of three PhD students on topics related to resting-state networks, memory consolidation, and functional MRI. ARC Discovery Projects (2024-2028): "Decoding the brain network of memory formation" ARC Training Centre for Innovation in Biomedical Imaging Technology (2017-2024) NHMRC-NIH BRAIN Initiative Collaborative Research Grants (2016-2023) Universities Australia - Germany Joint Research Co-operation Scheme (2017-2018) Mater Medical Research Institute Limited grant for mindfulness-based cognitive therapy research (2017-2020) Dr. Chuang leads the Functional and Molecular Neuroimaging Group at the Queensland Brain Institute. His laboratory focuses on understanding the functional connectome of the brain and developing functional and molecular imaging techniques to study brain connectivity associated with behavior. The group has developed various MRI techniques to track neuronal connections, map large-scale brain synchrony, and quantify cerebral blood flow and metabolism in vivo. His research team collaborates extensively with other experts at UQ and internationally, including collaborations with Associate Professor Darryl Eyles, Professor Jürgen Götz, Professor Tianzi Jiang, Dr. Fatima Nasrallah, Professor Linda J. Richards, Professor Pankaj Sah, Professor Elizabeth Coulson, Dr. Patricio Opazo, Professor Feng Liu, and Professor Markus Barth.
Dr. Matthew Barclay is a Principal Research Fellow (Statistician) in Cancer Healthcare Epidemiology at University College London's Behavioural Science and Health department. With expertise spanning medical statistics, epidemiology, and health services research, his work focuses on analyzing cancer registry data, primary care electronic health records, and healthcare claims data to improve understanding of cancer diagnosis and treatment pathways. Dr. Barclay's educational background includes: PhD in "The design of composite indicators of healthcare quality: a multi-method analysis" from University of Cambridge (2021) MSc in Statistics with Medical Applications from University of Sheffield (2015) MMath in Mathematics from Durham University (2011) Dr. Barclay's research spans the entire cancer diagnostic and management pathway, with particular focus on risk of cancer in primary care patients, cancer characteristics at diagnosis (including socio-demographic variations in staging), treatment patterns, and short-term outcomes. His methodological expertise lies in applied statistics, particularly in cohort design within electronic health record datasets and developing consistent data resources for research. His work frequently employs cancer registry data, primary care records, and healthcare claims to address critical questions in cancer epidemiology and healthcare quality. Analysis of Dr. Barclay's recent publications reveals a strong focus on cancer diagnosis pathways, particularly how symptoms present in primary care lead to cancer diagnosis. His work spans multiple cancer types but has particular emphasis on lung, colorectal, and breast cancers. Methodologically, his research combines epidemiological approaches with advanced statistical techniques applied to large-scale datasets including UK Biobank, cancer registries, and primary care records. International comparative studies through the International Cancer Benchmarking Partnership represent another significant strand of his recent work. Dr. Barclay advises postgraduate and PhD students on topics related to his research interests in cancer epidemiology and health services research. His collaborative network spans multiple institutions and countries, reflecting the international nature of his research on cancer diagnosis and treatment pathways.
Dr. Susan M. Sereika is a Professor in the Department of Health and Community Systems at the University of Pittsburgh School of Nursing . She holds secondary appointments in the Department of Biostatistics and Health Data Science and the Department of Epidemiology in the School of Public Health, as well as affiliations with the Clinical and Translational Science Institute and UPMC Hillman Cancer Center's Biostatistical Facility. Associate Dean for Computing and Information Technology (School of Nursing) Faculty Statistician (Office of Research Scholarship) Active in Senate Computing & IT Committee (Pitt, 1992-present) Her statistical expertise focuses on: Longitudinal data analysis for intensive monitoring (Aardex MEMS, Fitbit, actigraphy) Latent variable methods (group-based trajectory, dual/multi-trajectory analyses) Dyadic analysis Model assessment She collaborates on weight loss, lifestyle self-management, symptom management, and regimen adherence research. Academic Contributions include: PhD-level courses: Advanced Quantitative Methods Seminar (NUR 3290) Independent study supervision (NUR 3060) Statistical mentorship for honors/masters/doctoral committees Consulting for NIH reviews and journal editorial boards
