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Stan SiebertView profile

Professor

• Labour Economics
• Industrial Relations
• Wage Inequality
• +7 more

Professor Stan Siebert is a distinguished academic in the Department of Management at the Birmingham Business School, University of Birmingham, where he has been a faculty member since 1980. He holds the position of Professor of Labour Economics and is actively engaged in research and teaching in personnel economics and industrial relations. PhD, London School of Economics Stan Siebert's research centers on labour economics, with a strong focus on wage issues, industrial relations, European labour market regulation, and vocational training. His work investigates critical topics such as the gender wage gap, minimum wages, wage inequalities, and the impact of labour policies on employment. He has conducted research on unskilled workers in OECD countries and has explored education feedback mechanisms in rural China. His recent publications span top-tier journals such as Management Science , Industrial & Labor Relations Review , and Oxford Economic Papers , covering themes like intergenerational income mobility, temporary employment regulation in Greece, and the consequences of piece-rate pay. His research often combines theoretical insights with empirical analysis using large-scale datasets. Evidence on intergenerational income transmission using complete Dutch population data Management Economics in a Large Retail Company The impact of Greek labour market regulation on temporary employment Work-life balance: promises made and promises kept The Consequences of a Piece Rate on Quantity and Quality: Evidence from a Field Experiment Professor Siebert is a member of IZA (Institute for the Study of Labour, Bonn), where his recent working papers are available. He teaches Personnel Economics at the undergraduate level and has co-authored influential texts including The Market for Labor: An Analytical Treatment and The Economics of Earnings . He has also edited Labour Markets in Europe: Issues of Harmonisation and Regulation . While no specific grants or scientific awards are listed, his sustained publication record and long-standing academic position reflect significant scholarly impact. He is involved in research projects examining the cost-effectiveness of education feedback in rural China and the employment effects of minimum wages and employment protection laws in OECD countries. His work bridges economic theory, policy analysis, and empirical research, contributing to both academic discourse and real-world labour market understanding.

Davide La VecchiaView profile

Professor

• Time series analysis
• Robust and semiparametric inference
• Financial econometrics
• +3 more

Davide La Vecchia is a Full Professor at the Research Institute for Statistics and Information Science (RISIS) at the University of Geneva, Switzerland. He holds dual PhDs in Statistics from Bocconi University (2007) and Economics from Università della Svizzera italiana (2011). His academic journey includes roles as an Assistant Professor at the University of St. Gallen and Monash University, and he has held visiting positions at Princeton University, the University of Copenhagen, and CREST (Paris). His expertise spans time series analysis, robust and semiparametric inference, financial econometrics, and spatial statistics. Key research contributions include work on saddlepoint approximations, optimal transportation methods, and latent variable models. Davide has led multiple grants, including from the Swiss National Science Foundation and the Australian Research Council. He serves as a referee for top-tier journals in statistics and econometrics. Teaching focuses on advanced statistical methods, including probability theory, time series analysis, and multivariate inference. His recent work emphasizes high-dimensional data modeling, spatio-temporal factor models, and applications to commodities trading networks. He has been honored with editorial roles and speaker invitations at global conferences, reflecting his leadership in statistical methodology.

Eyal LubetzkyView profile

Professor

• Probability Theory
• Combinatorics
• Statistical Mechanics
• +3 more

Eyal Lubetzky is a Professor of Mathematics at the Courant Institute of Mathematical Sciences, New York University. His research focuses on probability theory and combinatorics, with notable contributions to the study of Ising models, cutoff phenomena, and random graphs. He holds a Ph.D. in Mathematics from Tel-Aviv University (2007) and a B.Sc. in Mathematics and Computer Science from the same institution (2002). Education: Ph.D., Mathematics, Tel-Aviv University, Israel, 2007 B.Sc., Mathematics and Computer Science, Tel-Aviv University, Israel, 2002 His research interests span probability theory, combinatorics, and statistical mechanics, with a focus on phase transitions, stochastic processes, and the analysis of complex systems. Notable works include groundbreaking studies on the cutoff phenomenon in Ising models and the mixing times of Markov chains. He has authored numerous influential papers, including studies on the dynamics of SOS surfaces, critical wetting phenomena, and the mixing properties of spin systems. His work has been recognized in volumes like What's Happening in the Mathematical Sciences . Lubetzky serves as an Associate Editor for journals such as Probability and Mathematical Physics , Probability Theory and Related Fields , and Annals of Applied Probability . He has organized workshops on topics like Markov chain mixing times and probabilistic combinatorics.

Markus HeydenreichView profile

Professor

• Probability Theory
• Statistical Mechanics
• Random Graphs
• +7 more

Markus Heydenreich is a Professor at the Institute of Mathematics, Faculty of Mathematics, Natural Sciences and Technology, University of Augsburg. His research focuses on probability theory, particularly discrete random objects in statistical mechanics, mean-field behavior, lace expansion, and spatial random graph models. 2025: Workshop Bayrischzell 2024: Workshop Frauenchiemsee His recent publications highlight critical phenomena in random connection models, percolation, and spatial networks. He contributes to editorial boards and DFG-funded projects, including Random Geometric Systems and Mathematics of Many-Body Quantum Systems . Teaching includes courses on stochastic processes and seminars on probability and forensics. Key research themes: phase transitions, high-dimensional random walks, and mathematical physics applications. Collaborators include Remco van der Hofstad, Christian Hirsch, and Kilian Matzke. The team at Augsburg comprises doctoral researchers and postdocs.

Koenraad SchalmView profile

Professor

• String Theory
• Theoretical Physics
• Holographic Duality (AdS/CFT)
• +7 more

Koenraad Schalm is a Professor of Theoretical Physics at Leiden University, affiliated with the Leiden Institute of Physics (LION) within the Faculty of Science. He leads the Schalm Group, which focuses on string theory and its applications in condensed matter and cosmology through holographic duality (AdS/CFT). His research interests include string theory, quantum critical systems, holographic models of superconductivity, black hole physics, and early universe cosmology. He explores how string-theoretic models can explain experimental observations in quantum materials and primordial fluctuations. The recent publications highlight a strong focus on applying holography to condensed matter phenomena, including strange metals, superconductivity, and non-equilibrium transport. There is also significant work on cosmological perturbations and inflation, showing interdisciplinary reach between high-energy theory and observational cosmology. Vici Innovation Research Incentives Award, NWO (2013) Vidi Innovation Research Incentives Award, NWO (2004) FOM Program group grant 'Observing the big bang: the quantum universe and its imprint on the sky' (2014) NWO Graduate Program Award 'de Sitter Program in Cosmology' (2011) FOM Project grant with J. Zaanen (2009) FOM Program group grant 'A String Theoretic Approach to Cosmology and Quantum Matter' (2009) NWO van Gogh Grant with P.S. Corasaniti (2007) FOM Project grant with E. Verlinde and J.P. van der Schaar (2007) Schalm has advised numerous PhD students and collaborators, including Aravindh Swaminathan, Shankar Aleksandar Bukva, Ludwig Hoffmann, and Petter Säterskog. He has been principal investigator on multiple NWO and FOM grants. He co-organized workshops such as 'Black Hole Answers for Condensed Matter Questions' and 'Effective Field Theory in Inflation', and is actively involved in academic service. He leads the Schalm Group, which investigates the interface between string theory and experiment, particularly through the AdS/CMT correspondence. The group aims to uncover quantum signatures of string theory in condensed matter systems and cosmological data.

• Nanotechnology
• Metamaterials
• Plasmonics
• +7 more

Dr. Changxu Liu is a Lecturer in the Department of Electronic Engineering at the University of Exeter, United Kingdom. He is affiliated with the Nano Engineering Science and Technology (NEST) group and the Centre for Metamaterial Research and Innovation (CMRI). Prior to joining Exeter, he served as an Assistant Professor at Northumbria University, UK, and held prestigious research fellowships at Ludwig Maximilian University of Munich (Humboldt and LMU Incoming Fellow) and the University of Birmingham. Education: BSc in Electrical Engineering, Tongji University, China MSc in Electrical Engineering, University of Rochester, USA PhD in Electrical Engineering, King Abdullah University of Science and Technology, Saudi Arabia Changxu Liu's research is centered on nanophotonics, plasmonics, metamaterials, and nanotechnology , with a particular focus on light-matter interactions in disordered and hybrid systems. His work explores topological transitions, hot electron engineering, and energy applications in photocatalysis and electrocatalysis. He employs both theoretical and experimental approaches to design novel nanostructures with unique optical responses. His recent publications reveal a strong trend in leveraging disorder for functional optical materials, enabling applications in structural color, light extraction, and energy conversion. He frequently publishes in high-impact journals such as Nature Nanotechnology , Nature Photonics , and Physical Review Letters , often as corresponding author. His research has attracted international media attention and recognition, including a mention in the Guinness World Record. Scientific Awards and Recognition: Humboldt Research Fellow LMU Incoming Research Fellow Research featured in Guinness World Record Media coverage in The Wall Street Journal, Independent, Daily Mail, and Yahoo Dr. Liu has been actively involved in numerous high-impact collaborative research projects, frequently co-authoring with leading scientists such as Stefan A. Maier, S. Zhang, and Min Liu. While no formal grants are listed, his publication record in premier journals suggests sustained funding and research support. He is likely supervising PhD and postdoctoral researchers, given his role as a corresponding author on multiple studies. He is a key member of the NEST and CMRI research groups at Exeter, which focus on cutting-edge nanoscale engineering and metamaterial innovation. These teams work on both fundamental and applied aspects of light manipulation at the nanoscale, positioning Dr. Liu at the forefront of next-generation photonic technologies.

Dan SteinbergView profile

Academic

• Probabilistic Machine Learning
• Variational Inference
• Bayesian Deep Learning
• +7 more

Dan Steinberg is a senior research scientist and team leader of the Decisions & Statistical Learning team at CSIRO Data61 in Canberra, Australia. His expertise lies in probabilistic machine learning, variational inference, Bayesian deep learning, causal inference, and their application to domains spanning synthetic biology, geospatial analytics, and algorithmic fairness. Education PhD in Computer Vision / Machine Learning (2013) – University of Sydney, Australian Centre for Field Robotics Bachelor of Engineering (Mechatronics, First-Class Honours) – University of Sydney (2008) Bachelor of Commerce (Finance) – University of Sydney (2008) Research Interests Steinberg’s core research agenda revolves around building scalable probabilistic models that can learn efficiently from limited or noisy data and provide principled uncertainty estimates. Key themes include: Variational Inference & Bayesian Deep Learning: developing lightweight yet powerful algorithms for approximate posterior inference in complex models (e.g., Aboleth, Revrand). Active Learning & Experimental Design: creating methods that decide which experiments or measurements will maximise information gain, with recent focus on in-silico protein engineering via Variational Search Distributions (VSD). Causal Inference: leveraging machine-learning tools to perform robust observational causal studies for evidence-based policy, including work on youth well-being and academic outcomes. Algorithmic Fairness: translating normative notions of equity into quantifiable objectives for regression-based decision systems. Large-scale Spatial Analytics: Landshark—an open-source TensorFlow toolkit for supervised learning on massive geospatial raster datasets. Notable Software & Tools Aboleth: A minimal-overhead TensorFlow framework for Bayesian deep learning. Landshark: Command-line tools for large-scale spatial inference. Revrand: Scalable Bayesian generalised linear models with non-conjugate likelihoods. libcluster: Extensible C++ library for hierarchical Bayesian clustering. Scientific Awards Oral Presentation Award – ICML 2025 Workshop on Scaling up Intervention Models (SIMS) Oral Presentation Award – NeurIPS 2024 Workshop on Bayesian Decision-making and Uncertainty (BDU) Oral Presentation Award – NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning Spotlight Paper Award – NeurIPS 2014 (Extended and Unscented Gaussian Processes) Research Team & Collaborations As Team Leader – Decisions & Statistical Learning at CSIRO Data61, Steinberg directs a multi-disciplinary group that partners with government agencies (e.g., Jobs and Skills Australia, Australian Institute of Health and Welfare) and industry to deploy machine-learning solutions at scale. He has previously held roles as Principal Researcher at Gradient Institute (2019-2023), Senior Research Engineer at CSIRO Data61 (2016-2019), Researcher at NICTA (2013-2016), and Research Associate at the University of Sydney (2012-2013).

Rebecca Dizon RossView profile

Associate Professor

• Development Economics
• Applied Microeconomics
• Human Capital
• +5 more

Rebecca Dizon-Ross is an Associate Professor of Economics at the Booth School of Business , University of Chicago. She is also the Charles E. Merrill Faculty Scholar and a former Prize Fellow in Economics, History, and Politics at Harvard University. Her academic journey includes a Ph.D. in Economics from Stanford University and a B.A. (summa cum laude) from Harvard University. University: The University of Chicago School: Booth School of Business Department: Department of Economics Rank: Associate Professor Awards: Charles E. Merrill Faculty Scholar Research Interests : Rebecca focuses on development economics , applied microeconomics , and human capital . Her work examines: Household investments in health and education Effectiveness of interventions to increase investment Parental behavior and gender preferences Dynamic incentive design for health behaviors Policy evaluation in social safety nets Selected Publications span topics in child development , parental decision-making , and public health subsidies . Her ongoing projects include incentives for drug abstinence, the relationship between government benefits and work values, and education's impact on female labor force participation in India. Scientific Awards : Charles E. Merrill Faculty Scholar (University of Chicago) Prize Fellow in Economics, History, and Politics (Harvard University) Education : Ph.D. in Economics (Stanford University) B.A. in Economics (summa cum laude, Harvard University)

• X-ray tomography
• Computed tomography
• Ghost imaging
• +6 more

Dr. Andrew Kingston is a Postdoctoral Fellow in the Department of Materials Physics at the Australian National University, where he contributes to the X-ray tomography and applications research group. His work focuses on advancing imaging technologies with applications across materials science, geology, and potentially medical fields. Dr. Kingston's research spans X-ray tomography, computed tomography, ghost imaging, and digital image processing. He has developed novel techniques for high-fidelity X-ray micro-tomography and image reconstruction algorithms that address challenges in image quality, radiation dose reduction, and complex material analysis. His work bridges theoretical developments with practical applications in materials characterization and geological analysis. Analysis of Dr. Kingston's publication trend shows consistent innovation in X-ray imaging technologies, particularly in ghost imaging techniques and micro-tomography. His work demonstrates increasing sophistication in handling beam hardening effects, motion correction, and spectral information extraction, reflecting the evolving complexity of modern imaging challenges. Dr. Kingston actively collaborates with researchers across multiple institutions, contributing to the advancement of X-ray imaging methodologies. His work with the X-ray tomography and applications group at ANU has produced significant contributions to the field, particularly in developing methods that improve image quality while addressing practical constraints like radiation exposure and computational efficiency.

Bhaswar B. BhattacharyaView profile

Associate Professor

• nonparametric statistics
• distribution-free inference
• networks analysis
• +4 more

Bhaswar B. Bhattacharya is an Associate Professor of Statistics and Data Science at The Wharton School of the University of Pennsylvania, with a secondary appointment in the Department of Mathematics. His research spans several interconnected areas at the intersection of statistics, probability, and computational geometry. Dr. Bhattacharya received his Ph.D. in Statistics from Stanford University in 2016 under the supervision of Persi Diaconis. Prior to that, he earned both his Bachelor of Statistics (2009) and Master of Statistics (2011) from the Indian Statistical Institute in Kolkata. His research interests focus on three main pillars: nonparametric statistics (including distribution-free inference, nearest-neighbor methods, and inference on networks), combinatorial probability (covering counting problems in random graphs, random colorings, and graph limit theories), and discrete and computational geometry (including facility location problems, Voronoi games, and geometric Ramsey problems). His work often bridges theoretical developments with practical applications in network analysis, statistical learning, and geometric optimization. Recent publications demonstrate a strong trajectory in developing distribution-free methods for network analysis, with significant contributions to understanding fluctuations in graphon-based random graphs and developing optimal tests for inhomogeneous random graph models. His work also shows increasing focus on higher-order network structures through hypergraph models and applications to real-world problems like vaccination site optimization. NSF Career Award (2021-2026) Alfred P. Sloan Research Fellowship (2021) Probability Dissertation Award, Stanford University (2016) Sabyasachi Roy Memorial Gold Medal for best master's thesis, Indian Statistical Institute (2009-2011) Dr. Bhattacharya teaches advanced courses in mathematical statistics at both undergraduate and graduate levels at Wharton. His research program involves collaborations across multiple institutions and disciplines, with recent work applying statistical methods to public health challenges such as optimizing vaccination site locations. He maintains active research collaborations with colleagues in statistics, computer science, and applied mathematics departments.

Zada PajalicView profile

Professor

• Health Promotion
• Preventive Work
• Nursing
• +6 more

Zada Pajalic serves as Vice Dean for the Faculty of Health and Social Sciences at the University of South-Eastern Norway (USN), with specific responsibility for Research, Development and Innovation. Her strategic leadership focuses on advancing research activities and strengthening academic collaborations across departments, disciplines, and campuses within USN's health and social science domains. Dr. Pajalic has earned professor-competency designations across multiple specialized fields including health promotion and preventive work (2024, University of Stavanger), nursing (2019, Inland University College; 2024, Mittuniversitetet), e-health (2021, University of Agder), health sciences (2019, VID University College), and healthy aging and rehabilitation (2019, Western Norway University of Applied Sciences). Her research portfolio demonstrates significant expertise in maternal health, oxytocin physiology, and welfare technology applications. Analysis of her publication record reveals a strong focus on systematic reviews across health sciences, particularly examining oxytocin dynamics during childbirth and breastfeeding, maternal-newborn health, assistive technologies for vulnerable populations, and digital health solutions. Her work spans multiple disciplinary intersections including obstetrics, endocrinology, gerontology, and health informatics, with increasing publication output in recent years. Dr. Pajalic actively participates in national and international academic and research policy forums, contributing her expertise to advance faculty interests and promote sustainable social development through innovation. Her strategic leadership extends to overseeing the faculty's PhD program and working to increase publications while strengthening externally funded research projects.

Robert PlominView profile

Professor

• Behavioural Genetics
• Quantitative Genetics
• Molecular Genetics
• +7 more

Robert Plomin is MRC Research Professor in Behavioural Genetics at the Institute of Psychiatry, Psychology & Neuroscience (IoPPN), King's College London. He helped launch the Social, Genetic and Developmental Psychiatry Research Centre in 1994 and established the Twins Early Development Study (TEDS) in 1995, which has followed 10,000 pairs of UK twins for over 25 years. With more than 800 publications and numerous books including "Blueprint: How DNA Makes Us Who We Are," he is a leading figure in behavioral genetics. Professor Plomin earned his PhD from the University of Texas at Austin and his BA from DePaul University. His academic journey includes positions at the University of Colorado and Pennsylvania State University before joining King's College London. Plomin's research focuses on using quantitative genetic and genomic approaches to understand how genetic and environmental factors shape behavioral development. His work particularly examines cognitive development and educational achievement, investigating how DNA influences individual differences in learning abilities and disabilities. Through large-scale twin studies like TEDS, he has made significant contributions to understanding the heritability of psychological traits and the role of nonshared environmental influences. His recent publications reveal a strong emphasis on genome-wide association studies, examining connections between genetics and various psychological outcomes including mental health, educational achievement, language development, and psychopathology. His work increasingly integrates molecular genetic techniques with traditional behavioral genetics approaches, demonstrating how DNA can predict behavioral outcomes from childhood through adulthood. Professor Plomin has received numerous prestigious honors including: Lifetime Achievement Award from the British Psychological Society (2020) Youngest President of the international Behavior Genetics Association Lifetime research achievement awards from multiple major associations in his field Fellowship in the American Academy of Arts and Sciences Fellowship in the British Academy Fellowship in the American Academy of Political and Social Science Fellowship in the Academy of Medical Sciences (UK) As principal investigator of the Twins Early Development Study (TEDS), Plomin has secured continuous funding for 25 years through Medical Research Council programme grants. TEDS has grown from studying infant twins to following participants into early adulthood, providing unprecedented longitudinal data on genetic and environmental influences. He has mentored numerous researchers through this project and has collaborated extensively with institutions worldwide, significantly advancing the field of behavioral genetics. The Social, Genetic and Developmental Psychiatry Centre at King's College London, where Plomin is based, serves as a hub for integrating genetic and environmental research approaches. TEDS remains his flagship project, but his influence extends through collaborations on international consortia studying everything from dyslexia to mental health outcomes. His current work focuses on applying polygenic scoring methods to predict behavioral outcomes and understanding the mechanisms through which genes influence development.

Jessica A. GoldbergView profile

Associate Professor

• Development Economics
• Financial Inclusion
• Rural Labor Markets
• +3 more

Jessica Goldberg is an Associate Professor of Economics at the University of Maryland, where she founded and directs the Promoting Achievement and Diversity in Economics (PADE) program for undergraduates. She holds faculty affiliations with the Abdul Latif Jameel Poverty Action Lab (J-PAL), Bureau for Research and Economic Analysis of Development (BREAD), and National Bureau of Economic Research (NBER), while serving as Co-Editor of the Journal of Human Resources and Associate Editor for the Review of Economics and Statistics and Journal of Development Economics. Dr. Goldberg earned her PhD from the University of Michigan. Her research spans rural labor markets, financial inclusion barriers, and social network dynamics in developing economies, with current field experiments in Uganda, Zambia, Bangladesh, and India examining credit access, mobile money adoption, and disease treatment outreach. Her publication record reveals consistent focus on behavioral and institutional constraints in financial decision-making across Sub-Saharan Africa and South Asia, predominantly using randomized controlled trials to evaluate interventions in savings, labor supply, and health. Recurring methodological themes include leveraging social networks for program delivery and testing low-cost behavioral nudges. Scientific recognition includes: NBER Faculty Research Fellow Through PADE and teaching advanced courses like Econ 416 (Economic Development) and Econ 417 (Impact Evaluation), she mentors undergraduate researchers while leading J-PAL initiatives as a committee member and regional scholar mentor. Her field work implies substantial grant funding from development research institutions though specific awards aren't detailed. Dr. Goldberg collaborates with international research teams across multiple countries, frequently partnering with Innovations for Poverty Action and J-PAL on interdisciplinary projects addressing poverty alleviation through evidence-based policy design.

• Computational Algebra
• Finite Group Theory
• Combinatorics
• +7 more

Alice Niemeyer is a Professor at the Algebra Teaching and Research Area of RWTH Aachen University , specializing in computational algebra, finite group theory, and interdisciplinary applications in civil engineering. She has co-authored over 30 publications in journals like PNAS Nexus , Linear Algebra and Its Applications , and International Journal of Solids and Structures , with recent work bridging abstract algebra and topological interlocking concrete systems. Research Interests : Finite groups, matrix decomposition algorithms, combinatorial design, and applications to sustainable construction. Key Collaborators : Cheryl Praeger, Tomasz Popiel, Wilhelm Plesken, Daniel Robertz, Reymond Akpanya. Notable Grants : Funding from the Simons Foundation for computational algebra research. Awards : No explicit awards mentioned in the provided text.

Leung TsangView profile

Professor

• Remote Sensing
• Electromagnetic Scattering
• Wave Propagation in Random Media
• +6 more

Leung Tsang is a Professor of Electrical Engineering and Computer Science at the University of Michigan, Ann Arbor, holding the Robert J. Hiller Professorship of Engineering. He received his SB, SM, EE, and Ph.D. from MIT. His academic career includes positions at Texas A&M University (1980-1983), University of Washington (1983-2014) as Professor and Department Chair (2006-2011), and a visiting Professorship at City University of Hong Kong (2001-2004). He has served as Editor-in-Chief of IEEE Transactions on Geoscience and Remote Sensing and President of the Electromagnetics Academy and IEEE Geoscience and Remote Sensing Society. His research focuses on microwave remote sensing , multiple scattering theory , electromagnetic wave propagation in random media , photonic crystals , and signal integrity . His work spans theoretical advancements (e.g., radiative transfer theory, UV multi-level methods) and practical applications (e.g., snow parameter retrieval, ocean foam modeling, via coupling analysis). Key trends include Development of computational techniques for large-scale scattering problems Integration of neural networks for inversion in remote sensing Investigation of backscattering enhancement and polarimetric signatures Notable awards include Fellow of IEEE and Optical Society of America William T Pecora Award (2012) Van De Hulst Light Scattering Award (2018) Membership in the National Academy of Engineering (2020) His group utilizes high-performance computing resources, including the Flux HPC cluster at the University of Michigan and XSEDE allocations, with specialized nodes for electromagnetic modeling. Current students include Ruoxing Gao (electromagnetic theory), Firoz Borah (snow remote sensing), Jongwoo Jeong (forest scattering), and Zhenming Huang (snow modeling).