Aleksander Madsen is a Researcher at the University of Oslo (UiO), affiliated with the Department of Sociology and Human Geography under the Faculty of Social Sciences. His primary research interests include social stratification, immigrant assimilation in labor markets, welfare state policies, and statistical methods. He holds a PhD from Oslo Metropolitan University and an MA from UiO. Education: PhD in Sociology, Centre for the Study of Professions, Oslo Metropolitan University (2014–2018) MA in Sociology, University of Oslo (2013) His research explores labor market integration of immigrants, gender disparities in male-dominated workplaces, and the impact of education-occupation mismatch on health outcomes. He has conducted longitudinal studies on long-term sickness absence and labor market trajectories using sequence analysis. Dr. Madsen has held postdoctoral and research positions at UiO and NIFU, and was a visiting scholar at Stanford University's SCANCOR (2024) funded by the Fulbright Foundation. His work spans interdisciplinary collaborations, including projects on workplace diversity, ethnic stratification, and policy evaluations. Awards: U.S. - Norway Fulbright Foundation Grant (2023–2024) His research group affiliations include the Social Inequalities and Population Dynamics team and the ORGMIGRANT project, investigating work organizations and immigrant assimilation. Recent publications address residential networks among immigrants, workplace ethnic diversity, and women’s attrition from male-dominated sectors.
Daniele Caramani is a Part-time Professor at the Robert Schuman Centre for Advanced Studies at the European University Institute, where he holds the Ernst B. Haas Chair in European Governance and Politics and co-directs the European Governance and Politics Programme. He also serves as a professor and Head of Department at the University of Zurich, holding the Chair of Comparative Politics. His academic journey began with BA and MA degrees from the University of Geneva, followed by a PhD from the European University Institute. Dr. Caramani's research focuses on comparative politics, European integration, and globalization, with particular emphasis on political cleavages, electoral systems, and democratization processes. His work bridges historical analysis with contemporary political challenges, examining how territorial and functional divisions shape political landscapes across different eras and regions. His approach combines theoretical innovation with empirical rigor, drawing connections between 19th century mass democratization and current debates about European integration and global governance. Caramani's publications reveal a consistent trajectory of increasingly complex analyses, moving from national political systems to European integration and then to global comparative frameworks. His work demonstrates strong methodological expertise in comparative and quantitative analysis, with a particular focus on large cross-national datasets and historical electoral results. The evolution of his research shows a clear progression from national-level analysis to transnational and global comparative frameworks. Stein Rokkan Prize for Comparative Social Science Research (for The Nationalization of Politics ) APSA Lijphart-Przeworski-Verba Dataset Award (for Constituency-Level Elections Archive) As an educator and academic leader, Caramani has supervised numerous students and edited influential textbooks in comparative politics. His leadership extends to directing major research projects, including an ERC Advanced Grant on global cleavages across world regions. At the University of Zurich, he serves as Head of Department, demonstrating administrative leadership alongside his research and teaching responsibilities. His Constituency-Level Elections Archive (CLEA) represents a significant contribution to the field, providing essential data for comparative electoral research.
Marta Kwiatkowska is a Professor of Computing Systems at the University of Oxford and a Fellow of Trinity College. Her research focuses on probabilistic verification , quantitative model checking , and formal methods for complex systems including autonomous robots, medical devices, and biological systems. She leads the development of the PRISM and PRISM-games probabilistic model checkers. Key research areas: Probabilistic systems, formal verification, autonomous robotics, medical device analysis, systems biology Grants: ERC Advanced Grant VERIWARE, EPSRC Programme Grant Mobile Autonomy Awards: 2024 ETAPS Test-of-Time Tool Award for PRISM Students: Current and former advisees in topics spanning formal methods, robotics, and quantitative verification The PRISM-games extension enables verification of stochastic multi-player games with applications in network protocols, autonomous systems, and game theory. Her work bridges theory, algorithms, and practical implementation, with real-world applications in ubiquitous computing and nanotechnology.
Professor Matthias Mann is a world-leading scientist serving as Director of the Proteomics and Signal Transduction department at the Max Planck Institute of Biochemistry in Martinsried, Germany, and Director of the Proteomics department at the Novo Nordisk Foundation Center for Protein Research, Faculty of Health Sciences, University of Copenhagen, Denmark. With an h-index exceeding 277 and over 350,000 citations, he is recognized as the highest cited German researcher and one of the most influential scientists globally in proteomics. His educational background includes: Ph.D. in Chemical Engineering from Yale University (1988) Master's Degree in Physics from Georg August University Göttingen (1984) Bachelor's of Arts in Mathematics from Georg August University Göttingen (1982) Professor Mann's research focuses on advancing mass spectrometry-based proteomics to understand biological systems at the protein level. His work spans technological developments in mass spectrometry, bioinformatics and computational analysis, signal transduction and posttranslational modifications, and clinical proteomics applications for disease diagnosis and treatment. The Mann lab has pioneered groundbreaking methods like SILAC for quantitative proteomics and MaxQuant for proteome data analysis. Their vision is to translate proteomics knowledge into clinical practice for predictive, diagnostic, and preventive medicine, with recent work focusing on AI-guided platforms for analyzing proteomes from minimal tissue samples. Analysis of Professor Mann's recent publications reveals a strong trend toward clinical applications of proteomics, particularly in cancer research, metabolic diseases, and neurodegenerative disorders. His work increasingly integrates spatial proteomics, single-cell resolution techniques, and artificial intelligence approaches to uncover disease mechanisms and identify potential biomarkers, with a clear shift from basic technology development toward direct clinical applications and personalized medicine. Professor Mann has received numerous prestigious awards throughout his career: 2025: Elected member of the American National Academy of Sciences 2024: Dr. H.P. Heineken Award for Biochemistry and Biophysics 2023: Otto Warburg Medal 2019: Nominated member of the Bavarian Academy of Sciences 2013: Elected member of Leopoldina German National Academy of Sciences 2012: Körber European Science Award, Louis-Jeantet Foundation Prize for Medicine, Ernst Schering Prize, and Leibniz Prize Professor Mann leads a highly collaborative research team involved in multiple international networks including the Bill & Melinda Gates Foundation, Michael J. Fox Foundation for Parkinson's Research, CLINSPECT-M, and Munich Heart Alliance. His lab has mentored numerous successful researchers, with several former postdocs receiving prestigious ERC Starting Grants. The Mann group has developed innovative clinical proteomics pipelines for analyzing archived tissue specimens and body fluids, aiming to identify protein markers for early detection of diseases such as diabetes and cancer. The Mann lab operates across two major research centers with state-of-the-art mass spectrometry facilities. Their Clinical Knowledge Graph platform integrates multi-omics data with extensive metadata, creating an ecosystem for machine learning applications in proteomics. Current research focuses on developing highly sensitive methods that can profile thousands of proteins from minimal cell samples, enabling the identification of critical disease-related proteins and supporting the development of individualized therapies.
Yan Liu is a full professor in the Thomas Lord Department of Computer Science at the University of Southern California (USC), serving as Director of the USC Machine Learning Center within the Viterbi School of Engineering. He holds courtesy appointments in the Ming Hsieh Department of Electrical Engineering and the Quantitative and Computational Biology Department. Before joining USC in 2010, he was a research staff member at IBM's T.J. Watson Research Center. He earned his M.S. and Ph.D. from Carnegie Mellon University. His research focuses on machine learning for time series, physics-informed AI, and interpretable models, with applications in healthcare, sustainability, and social media. Notable projects include developing AI for surgical training, analyzing misinformation on social platforms, and predicting cancer treatment outcomes. He has held leadership roles in top conferences like ICLR and ACM KDD, and serves as Associate Editor-in-Chief of TPAMI and Board Member of ICLR. Education: Ph.D., Carnegie Mellon University Affiliations: USC Machine Learning Center, Viterbi School of Engineering Service: General Chair (ICLR 2023, ACM KDD 2020), Program Chair roles across multiple conferences His lab, the Melady Group, emphasizes foundational ML advancements and interdisciplinary applications. Recent work includes physics-aware neural networks and time-series foundation models.
Jessica Lin is an Associate Professor in the Department of Computer Science at George Mason University, with a focus on data mining and time series analysis. She has published extensively on topics including motif discovery, anomaly detection, clustering, and symbolic representation of time series data. Ph.D., M.S., and B.S. in Computer Science from UC Riverside (2005, 2002, 1999) Her research spans efficient algorithms for mining massive time series datasets, extending to multimedia data like images and texts. She has developed tools such as GrammarViz and SAX for pattern visualization and symbolic analysis. Recent publications highlight advancements in variable-length motif discovery, interpretable classification frameworks, and anomaly detection. Her work appears in top conferences like AAAI, ICDM, and SDM, as well as journals including Knowledge and Information Systems and Data Mining and Knowledge Discovery . Dr. Lin has advised numerous Ph.D. students, many of whom have taken academic or industry positions. She has served on editorial boards and program committees for conferences such as KDD, ICDM, and ECML-PKDD.
Song Kim is an Associate Professor of Political Science at the Massachusetts Institute of Technology (MIT) and a Faculty Affiliate at the Institute for Data, Systems, and Society (IDSS). He holds a Ph.D. in Politics from Princeton University, where he was awarded the Harold W. Dodds Fellowship (2012-2013). His research focuses on International Political Economy, Formal and Quantitative Methodology, and Big Data analysis of international trade. He is particularly known for his work on firm-level political incentives in trade liberalization, which earned him the 2015 Mancur Olson Award and the 2018 Michael Wallerstein Award for best published article in political economy. Kim develops computational methods for analyzing trade data, including dimension reduction and visualization techniques. He maintains two key databases: LobbyView (tracking firm lobbying efforts) and TradeLab (for trade policy analysis). His research has been published in top journals such as the American Political Science Review, American Journal of Political Science, and International Organization. Educations : Ph.D. in Politics (Princeton University), B.A. not explicitly stated. His research interests include the dynamical evolution of lobbying networks, strategic links between political donations and lobbying, and the political origins of trade regulations. He also contributes methodological innovations, such as two-way fixed effects models and matching methods for causal inference with panel data. Awards : Mancur Olson Award (2015) Michael Wallerstein Award (2018) Harold W. Dodds Fellowship (2012-2013) Advising & Grants : No listed advisees. His work is supported by MIT’s IDSS and institutional funding. He collaborates on software tools like the 'wfe' and 'concordance' R packages, advancing computational social science. Labs/Teams : Associated with MIT’s Political Science Department and IDSS, focusing on interdisciplinary projects in trade, lobbying, and quantitative methods.
Dr. Katerina Marcoulides is an Associate Professor in the Quantitative and Psychometric Methods Program at the University of Minnesota's Department of Psychology. She is affiliated with the Minnesota Population Center and serves as Co-Chair of the Structural Equation Modeling Special Interest Group (SEM SIG) for the American Educational Research Association. Her research focuses on advanced data mining and modeling techniques for complex longitudinal data, particularly applied to developmental processes in economically disadvantaged immigrant children. She holds a PhD in Quantitative Psychology from Arizona State University, an MA from UC Davis, and a BA from UC Santa Barbara. Education: PhD: Quantitative Psychology, Arizona State University MA: Quantitative Psychology, University of California, Davis BA: Psychology (minor in Education), University of California, Santa Barbara Research Interests: Dr. Marcoulides develops and applies statistical methods such as structural equation modeling (SEM), Bayesian synthesis, and data fusion to study developmental and educational processes. Her work emphasizes longitudinal data analysis, item response theory, and multilevel modeling. Recent projects include NIH-funded research on parenting, marginalization, and well-being during the pandemic. Awards: APS Rising Star Award (2021) NIH Grant Award Teaching & Collaboration: She teaches courses on SEM, multilevel modeling, and data analysis at the University of Minnesota. Previously at the University of Florida, she contributed to workshops on educational data mining and served as an APA Advanced Training Institute presenter. Her interdisciplinary collaborations span population studies, health inequities, and workforce research. Labs & Groups: She leads the Data Analytics and Visualization Lab and actively participates in the Minnesota Population Center, integrating computational and statistical innovations with real-world applications.
Larissa Schlegel-Pape serves as a Scientific Associate at the Department for Ornamental and Pedigree Poultry within the Farm Animal Clinic of Freie Universität Berlin's Faculty of Veterinary Medicine. Her work focuses on developing innovative methods for assessing chicken welfare through the creation of the "Stressed Chicken Scale," which aims to systematically identify stress indicators in poultry. Her research interests center on animal welfare science, specifically stress assessment in chickens using both behavioral observation and computer vision technology. She investigates how body posture, movement patterns, and other visual indicators can reliably signal discomfort or stress in poultry, with the goal of creating practical assessment tools for veterinarians and poultry farmers. Her work bridges veterinary medicine, ethology, and technological innovation, contributing to refinement research (one of the 3Rs principles) in animal husbandry. Analysis of her publications reveals a strong focus on developing and validating the Stressed Chicken Scale across multiple contexts. Her work spans methodological development, practical implementation studies, and technological integration with computer vision systems. The research demonstrates progression from conceptual framework to validation studies and practical application, with increasing sophistication in assessment techniques and broader implications for animal welfare standards in poultry farming. Schlegel-Pape actively collaborates with the Federal Institute for Risk Assessment (BfR) and participates in interdisciplinary projects involving artificial intelligence applications in agriculture. She presents her findings regularly at major German veterinary conferences including the DVG (Deutsche Veterinärmedizinische Gesellschaft) events, DACh Epidemiology conferences, and specialized poultry medicine gatherings. Her work contributes significantly to advancing animal welfare assessment methodologies and promoting refinement in poultry husbandry practices.
Changhuei Yang is the Thomas G. Myers Professor of Electrical Engineering, Bioengineering, and Medical Engineering at California Institute of Technology, serving as Executive Officer for Electrical Engineering and Investigator at Heritage Medical Research Institute. He holds a Ph.D. and three master's degrees from MIT, with appointments at Caltech since 2003. Research focuses on: Advanced microscopy techniques including Fourier Ptychography Wavefront shaping for biological tissue imaging Optical phase conjugation for deep-tissue applications Compact medical devices for cerebral monitoring Publications demonstrate leadership in computational imaging, with recent advances in stain-free embryo analysis, portable cerebral blood flow monitors, and high-resolution volumetric imaging techniques using neural representations. Honored as National Academy of Inventors member. Research applications span deep-tissue biochemical imaging, incisionless surgery, and optogenetic activation systems.
Dr. Pedro Nava is an Associate Professor and Director of Educational Leadership in the School of Education and Counseling Psychology at Santa Clara University. His scholarship centers on educational equity for Chicanx/Latinx and immigrant communities, combining critical race theory, participatory action research, and community-engaged methodologies. Education Ph.D. in Urban Schooling – University of California, Los Angeles, 2012 Ed.M. in Administration, Planning, and Social Policy – Harvard University, 2003 B.A. – California State University, Fresno, 1996 Research Interests Dr. Nava’s research interrogates systemic inequalities in urban and rural schooling, emphasizing critical pedagogy and critical race theory . He investigates how immigration status intersects with educational opportunity, and he collaborates with families and communities to co-construct transformative practices through participatory action research . His work also explores family-school-community engagement , leveraging community cultural wealth frameworks to amplify migrant and undocumented voices in educational policy and practice. Publication Trajectory Across more than two decades, Dr. Nava’s publications trace the evolution of critical scholarship in education. Early works examine institutional barriers for Chicano students, while recent studies integrate quantitative ethnography and testimonio methodologies to illuminate intersectional experiences of Latinx youth, undocumented graduate students, and farmworker families. Scientific & Community Awards No formal awards are enumerated in the provided text. Advising & Grants While specific grants and advisees are not listed, Dr. Nava’s leadership role as Director of Educational Leadership implies extensive mentoring of master’s and doctoral students, as well as oversight of grant-funded initiatives that advance equity-focused leadership preparation. Labs, Teams & Centers Dr. Nava directs the Educational Leadership programs within the School of Education and Counseling Psychology, fostering interdisciplinary collaboration and community-engaged research teams that partner with local schools, nonprofits, and migrant organizations.
Per Kragh Andersen is a Professor at the Department of Public Health, University of Copenhagen, within the Faculty of Health and Medical Sciences. He is affiliated with the Section of Biostatistics, which focuses on statistical theory, methods, and applications in biomedical research, providing advisory services and educational programs in statistics from undergraduate to PhD levels. Location: Øster Farimagsgade 5, Building CSS (2nd floor in CSS-5, CSS-10, CSS-15), Copenhagen K Contact: +45 35 32 79 08 | pka@biostat.ku.dk The Section of Biostatistics collaborates with other health sciences departments to advance scientific knowledge through rigorous statistical analysis and education. Its mission includes advising researchers on study design, conducting methodological research, and promoting quantitative methods in biomedical sciences.
Erik Willcutt is a Professor in the Department of Psychology and Neuroscience at the University of Colorado Boulder. His research focuses on the genetic and neurobehavioral underpinnings of ADHD, learning disabilities, and developmental psychopathologies. He holds positions at the Institute for Behavioral Genetics and the Center for Neuroscience. Education: PhD in Psychology from the University of Denver (1998). Research Interests: Etiology and assessment of ADHD, reading disabilities, and developmental psychopathologies. His work integrates behavioral genetics, neuroimaging, and longitudinal twin studies to understand cognitive and psychiatric disorders. Key topics include neuroanatomical correlates of ADHD, genetic influences on dyslexia, and comorbidity between learning disabilities and psychiatric conditions. Publications highlight advanced methods like genome-wide association studies (GWAS) and phenotype harmonization (e.g., Rosetta method). His work bridges molecular genetics with clinical psychology, emphasizing translational research. Lab/Affiliations: Active in the Institute for Behavioral Genetics and collaborates with interdisciplinary teams studying neurodevelopmental disorders. Office located at Muenzinger D451B.
Fiona Lee is the Arthur F. Thurnau Professor of Psychology at the University of Michigan, with affiliations in Organizational Studies and the Advisory Committee. She holds a Ph.D. from Harvard University. Her research focuses on three core areas: Identity Integration (how individuals navigate multiple identities), Power Dynamics (effects of power on behavior), and Cultural Differences (impact of cultural contexts on organizations and individuals). She teaches courses on Organizational Psychology and Research Methods in Personality/Social Contexts. Lee’s work bridges social psychology and organizational behavior, emphasizing multicultural experiences and adaptation. Her recent studies explore refugee well-being, racial discrimination coping strategies, and the role of family in resettlement processes. Notably, she investigates how cultural identity integration influences creativity, decision-making, and social tolerance. Her publications span cross-cultural communication, biculturalism, and institutional barriers faced by minorities. Lee’s research often combines qualitative and quantitative methods to address real-world challenges in workplace dynamics and global mobility.
Jeremy Dahl is a Professor of Radiology (Pediatric Radiology) at Stanford University School of Medicine. He directs the Ultrasound Imaging & Instrumentation Lab and serves as Director of Research Academic Affairs in the Department of Radiology since 2020. He holds multiple affiliations across Stanford including Bio-X, the Cardiovascular Institute, Wu Tsai Human Performance Alliance, Maternal & Child Health Research Institute, Stanford Cancer Institute, and Wu Tsai Neurosciences Institute. Dr. Dahl received his B.S. in Electrical Engineering from the University of Cincinnati (1999) and Ph.D. in Biomedical Engineering from Duke University (2004). His research focuses on developing ultrasonic beamforming and image reconstruction methods for diagnostic imaging applications, particularly techniques that generate high-quality images in difficult-to-image patients. His laboratory specializes in B-mode and Doppler imaging techniques that utilize additional information from ultrasonic wavefields to improve image quality and develop real-time imaging systems for clinical applications including cardiac, liver, and fetal imaging. Dr. Dahl's research has led to significant advancements in ultrasound molecular imaging platforms, sound speed estimation, aberration correction, and reverberation noise suppression. His work often bridges engineering innovation with clinical applications for cancer detection and other diseases. His recent publications demonstrate strong focus on machine learning applications in ultrasound, distributed aberration correction, and molecular imaging techniques. Fellow, American Institute of Ultrasound in Medicine (2021) Senior Member, Institute of Electrical and Electronics Engineers (2020) Distinguished Investigator Award, The Academy for Radiology & Biomedical Imaging Research (2018) Outstanding Paper Award, IEEE Ultrasonics, Ferroelectrics, and Frequency Control Society (2011) Dr. Dahl serves in editorial roles for major journals including IEEE Transactions on Medical Imaging (2017-2024) and IEEE Transactions on Ultrasonics, Ferroelectrics, and Frequency Control (2013-Present). His laboratory has successfully translated numerous innovations into clinical applications, with multiple patents including recent developments in pulsed focused ultrasound therapy and speed of sound quantification.
