Annette Quinto Romani is an Associate Professor at Aalborg University's Department of Sociology and Social Work within the Faculty of Social Sciences and Humanities. She leads the Share-PBL SocMap research group and focuses on social inequality in education and health using econometric methods to uncover causal effects. Her teaching emphasizes quantitative methods, regression analysis, education inequality, rational choice theories, climate/environmental sociology, and mixed methods. Education: PhD in Sociology Cand. Oecon (Candidate of Economics) Lektor in Sociology Her research integrates sociology, economics, and health studies. Recent work explores socioeconomic influences on university students' body image and attachment styles, nature access in Danish schools, and parental employment impacts on child development. Publications span Current Psychology , Journal of Adventure Education and Outdoor Learning , and edited anthologies. Scientific awards include: Årets underviser på sociologi (2024) Uglens kald – Bogpris (2018) 2017 Conference Prize for healthy snack interventions Active in public discourse, Romani contributes to media discussions on urban-rural disparities, youth behavior, and stress coping mechanisms. She coordinates internship guidance for sociology undergraduates and participates in national research networks like RECWOWE.
Professor Jeannot Trampert is a leading seismologist at Utrecht University's Faculty of Geosciences, where he serves as Professor of Seismology within the Department of Earth Sciences. His research group focuses on extracting information from ground motion recorded at global and regional seismic networks to produce three-dimensional models of the Earth's interior and seismic sources, primarily using high-performance computing approaches. With over 250 publications spanning decades, Trampert maintains an active research program with recent works published through 2025. Trampert's research expertise encompasses several critical areas of modern seismology: Theoretical and computational seismology Earthquake physics and analysis Earth's deep interior structure Inverse theory applications Machine learning in geophysical data analysis Mineral physics and Earth composition His recent publications demonstrate an increasing integration of machine learning techniques with traditional seismological methods, particularly for analyzing seismic data from the Groningen gas field in the Netherlands. Trampert's work bridges theoretical seismology with practical applications in monitoring induced seismicity, pore pressure changes, and temperature effects on seismic wave propagation. His research group actively collaborates with international partners on projects involving full-waveform inversion, mantle structure analysis, and seismic monitoring systems. Professor Trampert has supervised numerous students throughout his career, with university records indicating 29 supervised works. His research receives consistent funding through multiple grants, supporting both fundamental research and applied projects related to geohazards and resource extraction. He maintains active collaborations with institutions across Europe and participates in major research initiatives like EPOS-NL, the Dutch solid earth science infrastructure for georesources and geohazards research.
Juhee Bae is a Senior Lecturer in the Department of Information Technology at the University of Skövde, Sweden. She is affiliated with the AI research group, focusing on machine learning, visual analytics, and explainable AI. Her work bridges research and education, with courses in advanced data science and experience in teaching coordination. Ph.D. in Computer Science, North Carolina State University Merited Teaching Award (2023) Mobility Grant for Belgian University Collaboration (2020) Her research spans machine learning , visual analytics , and explainable AI , with applications in Steel industry process optimization Climate-adaptive water management Migration intention forecasting Smart production logistics Wearable biosensor analysis Recent publications highlight predictive models for weather-driven migration, causal discovery frameworks, and interactive data mining techniques. She contributes to editorial boards and conference organization. Juhee Bae serves as course coordinator for Explainable AI and has been active in international collaborations. Scientific awards include 2023 Merited Teaching Award 2020 Mobility Grant Her projects STRATUS (AI for climate adaptation), INSITE-X (steel industry AI), and Understanding Human Migration demonstrate practical AI applications. She works with interdisciplinary teams and has presented talks on predictive machine learning and explainable AI.
Tommi S. Jaakkola is the Thomas Siebel Professor of Electrical Engineering and Computer Science at MIT, with appointments in the School of Engineering and the Institute for Data, Systems, and Society. He leads a research group at the Computer Science and Artificial Intelligence Laboratory (CSAIL). His research advances machine learning for efficient, principled, and interpretable learning, prediction, and control. Key interests include: Statistical inference and estimation Generative modeling for molecular design and natural language Game-theoretic interactions and strategic modeling Applications in biomedical domains and drug discovery Recent publications demonstrate strong focus on diffusion models and flow-based methods for protein structure generation and molecular optimization, with increasing integration of physical principles and symmetries. His group actively publishes at top AI conferences (ICML, NeurIPS, ICLR) with significant biomedical applications. Jaakkola advises numerous PhD students and has mentored graduates now at companies like Boltz PBC, DE Shaw, and Xaira. His CSAIL laboratory fosters interdisciplinary collaboration between computer science, biology, and chemistry to address complex biomedical challenges.
Dr. Verena Zuber is an Associate Professor in Biostatistics at the School of Public Health within Imperial College London's Faculty of Medicine (UK). Her research focuses on causal inference methodology applied to life sciences and health data, particularly through genetic instrumental variables (Mendelian randomization). She engages with interdisciplinary collaborations in cardiovascular health, oncology, neurodegeneration, and mental health. Her research expertise lies at the intersection of causal inference and high-dimensional statistics, incorporating: Genetic instrumental variable methods Bayesian causal structure learning Causal network analysis (DAG learning) Machine learning applications in biostatistics Doubly robust estimation techniques Counterfactual modeling Contact: v.zuber@imperial.ac.uk
Dr. Simone Cenci is a Lecturer in Economics and Finance of Sustainability at the Institute for Sustainable Resources , part of the Bartlett School of Environment, Energy & Resources at University College London (UCL). His research focuses on corporate decarbonization strategies, climate risk management, and data-driven insights for sustainable investment and policy design. Education: PhD in Applied Statistics, Massachusetts Institute of Technology (2019) MSc in Physics, University of Pisa (2015) BSc in Physics and Astrophysics, Sapienza University of Rome (2012) His work bridges empirical analysis with sustainability finance, particularly in energy and energy-intensive sectors. Recent publications examine corporate environmental actions, greenhouse gas reporting gaps, and nonlinear dynamics in ecological systems. Scientific Awards: Rising Leaders Fellow, Aspen Institute UK He previously held academic positions as an Advanced Research Fellow at Imperial College Business School (2021-2024) and a postdoctoral research associate at Blackstone Credit (formerly DCI, LLC) in San Francisco (2019-2021). He welcomes collaborations with industry, government, and NGOs in climate finance and policy.
Derin Cobia is an Assistant Professor of Psychology at Brigham Young University, affiliated with the Neuroscience program in the College of Life Sciences. His research integrates neuropsychology and neuroimaging to investigate brain structure-function relationships in neuropsychiatric disorders, with a primary focus on schizophrenia heterogeneity. Dr. Cobia's academic credentials include: PhD in Clinical Psychology (Neuropsychology), St. Louis University (2008) MS in Clinical Psychology (Neuropsychology), St. Louis University (2006) BS in Psychology, Brigham Young University (2003) Postdoctoral Fellowship in Cognitive Neuroscience, Northwestern University Feinberg School of Medicine (2011) Postdoctoral Fellowship in Clinical Neuropsychology, Mesulam Cognitive Neurology and Alzheimer's Disease Center (2010) Internship in Clinical Psychology (Neuropsychology), West Virginia University School of Medicine (2008) His cognitive neuroscience research employs computational anatomy tools to dissect clinical and biological heterogeneity in schizophrenia, particularly examining how neurobiological characteristics map onto clinical dimensions like negative symptoms. This work extends to Parkinson's disease, insomnia, and traumatic brain injury, with emphasis on structural brain changes in deep nuclei, cortical thickness, and their behavioral correlates. His methodology frequently leverages large-scale collaborative neuroimaging through consortia like ENIGMA. Analysis of his recent publications reveals dominant themes in sex differences in schizophrenia neuroanatomy, structural correlates of aggression and cognitive control, and cross-disorder comparisons of brain morphology. Key methodological approaches include shape analysis of subcortical structures, mega-analyses of multi-site data, and causal modeling of brain-behavior relationships. Scientific recognition includes: NARSAD Young Investigator Award (Brain & Behavior Research Foundation, 2014-2017) NIH Loan Repayment Program (2015-2017) Dr. Cobia teaches Behavioral Neurobiology, Principles of Neuroimaging, and Neuropsychology while leading the Brain Imaging and Behavior Lab (BiB Lab). His research is supported by NIH and foundation grants focused on schizophrenia biomarkers and computational neuroanatomy. Current projects investigate thalamocortical networks in familial schizophrenia risk and structural correlates of community functioning in psychosis. The BiB Lab maintains active collaborations with the ENIGMA-Schizophrenia Working Group and Parkinson's disease research networks, emphasizing translational applications of structural neuroimaging findings to clinical phenotyping and treatment development.
Dr. Mario Castro Ponce is a Professor at the Escuela Técnica Superior de Ingeniería (ICAI) , Universidad Pontificia Comillas, where he has worked for 28 years. He holds a Ph.D. in Physics from Universidad Complutense de Madrid and serves as a Visiting Professor at the University of Leeds since 2016-2017. His research bridges Statistical Mechanics with applications in Complex Systems , Biophysics , and Epidemiology , with a focus on modeling experimental data through analytical and computational methods. Education: Ph.D. in Physics, Universidad Complutense de Madrid B.S. in Physics, Universidad Complutense de Madrid Research Themes: Statistical mechanics of complex systems Ion-beam nanopatterning Theoretical immunology Wildfire dynamics Biofluid microrheology Machine learning in social science Publications (89+ peer-reviewed) span interdisciplinary topics, including: SARS-CoV-2 geometry and T-cell receptor dynamics Non-Newtonian blood flow modeling Wildfire risk assessment via Bayesian networks Viscoelastic thermosyphon systems Complex pattern formation in biological and physical systems Funding includes 6 principal investigator projects for Spain's Ministry of Science and participation in 3 EU Horizon 2020/Marie Skłodowska-Curie actions. His work has been cited over 2000 times.
Thomas Fischer is a Professor at the School of Design, Southern University of Science and Technology (SUSTech), with over 25 years of international academic experience. He leads the Design Cybernetics Group at SUSTech and serves as Chief Design Officer of anabrid technology company. Previously, he held teaching positions at The Hong Kong Polytechnic University (Interactive Systems Design & Industrial Design, 10+ years) and Xi’an Jiaotong-Liverpool University (Architecture & Industrial Design, 10 years), along with visiting positions at National Cheng Kung University and Humboldt University. Double PhD: Education (University of Kassel), Architecture & Design (RMIT University) Certification: Graduate Certificate in Cybersecurity (Harvard University) Research Focus: Design cybernetics, computational design theory, autopoietic systems, and digital media. His work bridges cybernetics with architectural and product design, emphasizing indeterminism, circular causality, and epistemic processes. Academic Contributions: Over 100 peer-reviewed publications with notable works on non-trivial machine design, BIM adoption models, and cybernetic design epistemology. Key journals include Kybernetes , International Journal of Architectural Computing , and Frontiers of Architectural Research . Fellow, Design Research Society Fellow, Cybernetics Society Certified Talent, International Academy for Systems & Cybernetic Sciences Recipient, Warren McCulloch Award (American Society for Cybernetics) Collaborations: Active partnerships with Christiane M. Herr, Guillermo Sánchez Sotés, and other systemic design researchers. His recent publications appear in Constructivist Foundations and Materials & Design , focusing on autopoietic architecture and cross-morphological performance analysis.
Michael Bronstein is the DeepMind Professor of Artificial Intelligence at the University of Oxford and Founding Scientific Director at the Aithyra Institute. He holds affiliations with Imperial College London (previous) and institutions like Stanford, MIT, and Harvard. His research focuses on geometric deep learning, graph neural networks, protein design, and non-human species communication. Bronstein received his PhD from the Technion in 2007 and has been awarded multiple fellowships and grants, including ERC, Google, and Amazon awards. Education: PhD in Computer Science, Technion, 2007 Research Interests: His work spans geometric deep learning, graph neural networks, 3D shape analysis, and applications in protein design. Notable projects include protein interaction design using surface fingerprints and advancing graph neural network architectures. He also explores AI in non-human communication, combining machine learning with biological systems. Publications: Recent work emphasizes knowledge graph foundation models, graph homomorphism analysis, and generative models for discrete data. His research bridges theoretical foundations (e.g., graph expressivity) with practical applications in biomedicine and AI. Awards: EPSRC Turing AI Fellowship Royal Society Wolfson Research Merit Award Academia Europaea Membership IEEE/IAPR/ELLIS Fellowships Advising & Grants: Supervises students in AI and graph learning. Active in securing ERC, Google, and industry grants. His entrepreneurial ventures include founding companies like Fabula AI (acquired by Twitter). Labs/Teams: Leads Graph Learning Research at DeepMind and collaborates with interdisciplinary teams in AI, biology, and quantum systems.
Eric Auerbach is an Assistant Professor in the Department of Economics at Northwestern University since 2017. He holds a Ph.D. in Economics from the University of California, Berkeley (2017) and a B.S. in Industrial and Labor Relations from Cornell University (2010). His research focuses on econometric theory, social and economic networks, causal inference, and policy evaluation with applications to network spillovers, algorithmic fairness, and social disruption analysis. He has presented his work at numerous institutions including Stanford, MIT, and Oxford, and serves as a referee for top journals like Econometrica and the American Economic Review. His awards include the 2017 RESTud Tour. Teaching includes graduate econometric theory and undergraduate courses in econometrics for mathematical social sciences. Key research contributions include developing methods to analyze network data structures, testing for network effects in policy evaluation, and addressing fairness-accuracy trade-offs in algorithms. His work bridges econometric theory with practical applications in social networks and policy analysis.
Malte Kurz is a researcher affiliated with the University of Hamburg Business School, specifically within the Department of Statistics. His primary role was as a Researcher under the Professorship of Statistics with Applications in Business Administration. He holds a PhD in Statistics from Ludwig-Maximilians-Universität München (2018), an M.Sc. in Statistics from LMU Munich (2013), and a B.Sc. in Mathematical Finance from Universität Konstanz (2011). His research interests focus on Machine Learning , Causal Inference , High-Dimensional Statistics , Financial Econometrics , and Dependence Modelling & Copulas . He has contributed to advancements in vine copula theory, distributed machine learning frameworks, and state space models. Key publications include works on vine copula simplifications (2019), low-dimensional Kalman smoothers (2018), and distributed double machine learning architectures (2021). He has developed influential software tools like the pacotest R package for copula hypothesis testing and the DoubleML framework for Python/R. Kurz has also contributed to open-source projects such as the VineCopulaMatlab toolbox and the SSMwLS MATLAB package for state space modeling. His work bridges statistical theory and computational implementation, with applications in finance and econometrics.
Stephen R. Grimm is a Professor of Philosophy at Fordham University, specializing in epistemology, the philosophy of the humanities, and wisdom studies. He actively organizes academic initiatives such as the FUNCTION-FIRST Epistemology Workshop and the New York-China Epistemology Conference, fostering interdisciplinary dialogue. His research focuses on understanding, intellectual virtues, and the ethical dimensions of philosophical practice. Key areas of exploration include the epistemic goals of the humanities, the nature of wisdom derived from adversity, and the transmission of knowledge across disciplines. He has contributed to debates on practical philosophy, moral development, and the intersection of psychology and theology in understanding human cognition. Notable academic engagements include leadership roles in the HUMANE UNDERSTANDING CONFERENCE and the NYC WISDOM SEMINAR. His work bridges theoretical philosophy with applied ethics, emphasizing the relevance of philosophical inquiry to contemporary societal challenges.
Carroll Graham is Associate Professor of Human Resource Development in the Department of HRDPT at Indiana State University’s Scott College of Business. Since joining ISU in 2008 as a tenure-track assistant professor and advancing to associate professor, she has built an extensive portfolio of research, service, and leadership in HRD and organizational learning. Education: Ed.D. Adult Education, University of Arkansas, 2006 (emphasis Human Resource Development) Ed.S. Industrial Education, Pittsburg State University, 2003 (emphasis HRD & Career/Technical Education) M.S. Human Resource Development, Pittsburg State University, 2002 B.A. Business/Human Resource Management, Missouri Southern State University, 2001 Research Focus: Dr. Graham’s scholarship centers on human resource development , organizational learning , and workplace performance improvement . Her work explores how small and medium-sized enterprises cultivate learning cultures, the impact of technology on mature workers in Industry 4.0, training transfer mechanisms, emotional intelligence in leadership, and the intricate relationships among organizational commitment, safety, and performance—particularly in the trucking industry. Across more than 25 peer-reviewed articles, book chapters, and conference proceedings published from 2004 to 2020, a clear trajectory emerges: early work concentrated on organizational learning mechanisms and culture within small businesses, later expanding to technology’s influence on employment and the strategic inclusion of aging workers. The research consistently integrates HRD theory with practical evaluation, providing actionable insights for scholar-practitioners. Awards & Honors: 2018 & 2016 AHRD Cutting Edge Scholarship Awards SGA Outstanding Advisor Recognition 2015–2016 Featured roles as Conference Program Chair, Editor, Associate Editor, and Managing Editor for the Academy of Human Resource Development International Research Conference (2005–2018) Walton Doctoral Fellow, University of Arkansas Who’s Who Among Students in America’s Universities and Colleges Service & Leadership: Chair, University Leaves Oversight Committee (2016–present) Member, University Promotion, Tenure & Oversight Committee (2014–2016) Member, President’s Special Library Task Force (2012–2014) Secretary, Graduate Faculty Council Program Development Subcommittee (2011–2013) Dr. Graham also maintains an active consulting profile and has received multiple internal grants for professional development. Her blend of academic rigor and industry experience—spanning 15 years in agriculture and seven years as a petroleum entrepreneur—enriches her teaching, research, and service missions at Indiana State University.
Daniel Irving Bernstein is Assistant Professor in Tulane University's Department of Mathematics. His research spans combinatorics, discrete geometry, and algebraic statistics, with particular focus on matroid theory, rigidity theory, and geometric combinatorics. He teaches undergraduate and graduate courses in linear algebra, topology, and geometric combinatorics. His research explores: Combinatorial structures in algebraic statistics Geometric rigidity and reconstructibility problems Matroid representations and lifts Algebraic methods in machine learning Recent work develops connections between combinatorial geometry and statistical learning theory. His publications demonstrate: Applications of matroid theory to statistical thresholds Geometric approaches to matrix completion Combinatorial foundations of deep learning Tropical geometric methods in phylogenetics He maintains active collaboration with computational biology and machine learning researchers.
