Find Academic Supervisors

• Archaeological prospection
• Geophysics
• Ground-penetrating radar
• +6 more

Immo Trinks is Associate Professor (Privatdozent) at the University of Vienna and Head of the Vienna Institute for Archaeological Science . He coordinates a comprehensive teaching programme in archaeological prospection, archaeometry and scientific documentation, and leads large-scale geophysical projects across Europe. Education & qualifications: Dipl.-Geophys. (Diploma in Geophysics) PhD (awarded) Habilitation (Privatdozent, Austrian post-doctoral lecturing qualification) Research interests revolve around non-destructive mapping of buried archaeological landscapes . He specialises in ground-penetrating radar , magnetometry and multi-sensor robotic platforms , developing workflows that fuse high-resolution geophysical data with 3D archaeological interpretation. Coastal wetlands, Roman towns, Viking harbours and prehistoric pile dwellings serve as key study areas. Recent publications demonstrate a clear trend toward automated, large-coverage surveys and multi-method data fusion . Papers in 2022-2025 report motorised GPR arrays on snow-covered fields, semi-autonomous driverless systems, and integrated interpretation frameworks that combine magnetics, GPR and remote sensing to reveal Roman military compounds, Etruscan cities, Swedish war camps and Neolithic monumental landscapes. Scientific awards & honours: No specific awards are listed in the supplied text; however, his continuous funding and invited keynote contributions (e.g., ArchaeoProspection conference series) indicate international recognition. Teaching & student supervision: Regular courses: Introduction to Archaeological Prospection, Archaeometry, Magnetic Prospection, GPR Practical, Underwater Prospection, Human Evolution & Archaeological Science, LaTeX for Archaeologists Excursions: 2-day field schools at Alpine pile-dwelling sites and around Vienna Thesis seminars: continuous Master Thesis Topic Search & Exposé and final thesis colloquia every semester Labs & facilities: Trinks heads the Vienna Institute for Archaeological Science (VIAS), operating several motorized multi-channel GPR systems, high-resolution magnetometer arrays, electromagnetic induction sensors, underwater sonar and multi-spectral cameras. The institute maintains dedicated GIS/visualisation labs and field vehicles for pan-European surveys within the international ArchPro and Stonehenge Hidden Landscapes initiatives.

Jignesh M. PatelView profile

Professor

• Database Systems
• Query Optimization
• Data Analytics
• +3 more

Jignesh M. Patel is a Professor in the Computer Science Department at Carnegie Mellon University, where he leads research on efficient data analysis methods. His work focuses on improving both system efficiency (e.g., high-performance data algorithms) and human efficiency (e.g., user productivity with data systems). Research Focus: Patel's group specializes in database systems, query optimization, hardware acceleration, and human-data interaction. Their interdisciplinary work spans: Transactional processing and real-time analytics Query optimization techniques Hardware-algorithm co-design Natural language interfaces for data systems Memory-efficient data processing Professional Activities: Co-founded four technology companies (Paradise, Locomatix, Quickstep, DataChat). Serves on program committees for premier conferences including SIGMOD and CIDR (as co-chair). Teaches database systems courses at CMU. Awards: Received Best Paper Award at DaMoN 2010 for work on cluster efficiency.

• Cognitive Neuroscience
• Animal Cognition
• Comparative Psychology
• +7 more

Orsola Rosa Salva is an Assistant Professor (RTDa) at the Interdepartmental Center for Mind/Brain Sciences (CIMEC) at the University of Trento since November 2020. Her research focuses on comparative cognitive neuroscience using domestic chickens as a model system to investigate innate social predispositions, cerebral asymmetry, and cognitive development. She has maintained continuous academic appointments at the University of Trento since 2010, initially as a post-doctoral researcher under Prof. Giorgio Vallortigara. Her primary research interests include: Animal cognition and comparative psychology Neuropsychology and cognitive neuroscience Cerebral asymmetry and lateralization Statistical learning mechanisms Autism spectrum disorder research through comparative models Ethology and neuroethology of social behavior Analysis of her recent publications (2022-2024) reveals a consistent focus on innate social cognition in visually naive domestic chicks. Her work demonstrates chicks' spontaneous preferences for animate motion patterns, non-predictable reciprocal movements, and agent interactions. These studies bridge comparative psychology, developmental neuroscience, and social cognition, establishing chicks as valuable models for understanding the evolutionary foundations of social perception. Her methodological approach integrates behavioral observation with neural correlates analysis. Scientific recognition includes: Best Poster Prize at the 2018 Avian Cognitive Neuroscience Forum Dr. Salva has extensive teaching experience across multiple Italian universities including Padova, Trieste, and Trento. Her instructional contributions span courses in Animal Cognition, Comparative Psychology, Neuroscience, and Psychobiology. While specific grant information isn't detailed in the provided text, her research trajectory demonstrates sustained funding through post-doctoral positions and research contracts since 2006. She actively participates in international conferences on cognitive development, neuroethology, and comparative psychology. Her work is conducted primarily through the Interdepartmental Center for Mind/Brain Sciences (CIMEC) at the University of Trento, where she collaborates with Prof. Giorgio Vallortigara's research group. This center provides the interdisciplinary environment necessary for her comparative cognitive neuroscience research, combining psychological, neurological, and ethological approaches to study fundamental cognitive processes across species.

Archer YangView profile

Associate Professor

• Statistical machine learning
• High-dimensional statistics
• Computational statistics
• +8 more

Archer Yang is an Associate Professor in the Department of Mathematics and Statistics at McGill University, with additional affiliations as an Associate Academic Member of Mila - Quebec AI Institute, Associate Member of the School of Computer Science, and Member of the Quantitative Life Science Program. His academic journey began with a PhD from the University of Minnesota under the supervision of Hui Zou, establishing his foundation in statistical methodology and machine learning. Dr. Yang's research spans three interconnected themes: statistical machine learning, applications in drug discovery, and computational genomics and healthcare. In statistical machine learning, he focuses on developing dimensionality reduction, probabilistic models, and causality-inspired methods to address complex high-dimensional data challenges. His work in drug discovery involves creating machine learning models to accelerate drug candidate identification and enhance understanding of drug efficacy and safety. In computational genomics and healthcare, he develops techniques to analyze genomic data, identify biomarkers, and explore the genetic basis of diseases, with the goal of improving precision medicine and predicting patient outcomes. His overarching objective is to bridge advanced data-driven methodologies with impactful applications in pharmacology, genomics, and healthcare. His recent publications reveal a strong trend toward applying machine learning to healthcare challenges, particularly in congenital heart disease analysis, mortality prediction, and drug discovery. His work demonstrates expertise in developing interpretable models that can handle complex, high-dimensional biomedical data while maintaining statistical rigor. The integration of causal inference methods with machine learning appears to be a growing focus in his research trajectory. ICML Spotlight Paper (top 2.6%, 313/12,107) Dr. Yang actively supervises a large research group including multiple postdoctoral fellows, PhD students, and Master's students. His lab has developed several notable software tools, including ml-mr for machine learning in Mendelian randomization. His supervision extends across statistics, computer science, and biomedical applications, reflecting the interdisciplinary nature of his work. He appears to maintain strong collaborative relationships with researchers in healthcare and genomics fields. His laboratory, the Archer Yang Lab, maintains active GitHub repositories focused on machine learning applications in healthcare and drug discovery, with particular emphasis on interpretable models and statistical methodology development. The lab appears to work at the intersection of theoretical statistics and practical biomedical applications, with projects spanning from algorithm development to clinical implementation.

• Partial differential equations
• Optimal transportation
• Geometry
• +5 more

Dr. Jiakun Liu is an Associate Professor at the School of Mathematics and Applied Statistics, Faculty of Engineering and Information Sciences, University of Wollongong, Australia. He has held this position since January 2022 and maintains an active research program in nonlinear partial differential equations with applications in geometry and optimal transportation. His work has been published in top mathematical journals including Annals of Mathematics, Inventiones Mathematicae, and Communications on Pure and Applied Mathematics. Dr. Liu received his PhD from The Australian National University, Australia. His academic journey includes prestigious fellowships such as the Simons Postdoctoral Fellowship and the Australian Research Council's Discovery Early Career Researcher Award (DECRA). Dr. Liu's primary research focuses on nonlinear elliptic and parabolic partial differential equations, with special emphasis on the regularity theory of Monge-Ampère equations, Hessian equations, and other variational problems. His work extends to related areas of geometry and physics, including the geometry of convex bodies, minimal surfaces, surfaces of prescribed curvatures, and geometric flows. His research has significant applications in optimal transportation theory, which connects mathematical analysis with practical problems in economics, physics, and computer science. Analysis of Dr. Liu's recent publications reveals a strong focus on regularity theory for Monge-Ampère equations and optimal transportation problems. His work spans pure mathematical theory (regularity of solutions, singular sets, free boundaries) while maintaining connections to geometric applications and practical problems. A notable trend is the increasing interdisciplinary nature of his research, with recent work connecting optimal transport to statistical methods and computer science applications. Dr. Liu has received several prestigious awards and fellowships: UIC International Links Scheme (University of Wollongong, 2015) Discovery Early Career Researcher Award (DECRA) from Australian Research Council Discovery Project (DP170100929) from Australian Research Council Shiing-Shen Chern Mathematical Scholarship from Zhejiang University Simons Foundation Simons Postdoctoral Fellowship Dr. Liu is actively involved in mentoring the next generation of mathematicians, having supervised numerous PhD students including Siyuan Li, Jihang Zhang, Phung Trong Thuc, Yating Wu, and Meng Ji. He has also guided honours students such as Matthew Hughes, Luke McDonald, and Brad Rice. His research is supported by multiple grants, including "Singularity and regularity for Monge-Ampere type equations" (2023-2026), "Monge-Ampere type equations and their applications" (2023-2027), and "Monge-Ampere equations and applications" (2020-2024), demonstrating sustained funding for his research program. Dr. Liu is an active participant in the mathematical community, organizing conferences such as the International Conference on Nonlinear Partial Differential Equations (honoring Professor Neil Trudinger's 80th birthday) and the 4th Australia-China Joint Conference on Geometric Analysis. He maintains collaborative relationships with researchers across Australia, China, and internationally, contributing to a vibrant research environment in geometric analysis and PDEs.

• Partial Differential Equations
• Fluid Mechanics
• Mathematical Analysis
• +3 more

Dr. Thomas Eiter is a Young Investigator in the Collaborative Research Centre CRC 1114 at Freie Universität Berlin and a member of the Partial Differential Equations research group at the Weierstrass Institute of Applied Analysis and Stochastics (WIAS). He holds a PhD from Technical University of Darmstadt (2020), focusing on existence and spatial decay of periodic Navier-Stokes flows in exterior domains. His research emphasizes mathematical analysis of PDEs motivated by fluid mechanics, including existence of solutions, time-periodicity, unbounded domains, and asymptotic behavior. Teaching highlights include courses such as 'Introduction to Mathematical Modeling with PDEs' at Freie Universität Berlin and 'Harmonic Analysis' at University of Kassel. He has organized workshops like the 2025 'Mathematical Analysis of Fluid Flows by Variational Methods' at WIAS. Current projects include the SPP 2410 initiative on energy-variational solutions for hyperbolic conservation laws. His work bridges theoretical PDE analysis with applications in continuum mechanics, with contributions to viscous flow dynamics, Navier-Stokes equations, and material models. He actively participates in academic leadership through seminar organization and conference minisymposia on fluid mechanics and nonlinear analysis.

• Financial Econometrics
• Time Series Analysis
• Volatility Modeling
• +4 more

Jeroen ROMBOUTS is a Professor at ESSEC Business School (France) and holds the Full Professor position of the Accenture Strategic Business Analytics Chair since 2017. He joined ESSEC in 2013, previously serving as Associate Professor at HEC Montreal (2004–2012). His research focuses on financial econometrics, volatility modeling, and machine learning applications in financial markets. He holds a Ph.D. in Econometrics from the Catholic University of Louvain (2004) and has held visiting professorships at numerous institutions, including the University of Melbourne, Aarhus University, and Tilburg University. Education: PhD in Econometrics (2004), Catholic University of Louvain; Master's degrees in Statistics (2001), Econometrics (2000), and Economics (1999), all from the same institution. He is also a Researcher at the Finance and Insurance Lab (CREST) since 2014 and serves on editorial boards of journals like Quantitative Finance and International Journal of Forecasting . Research Interests: His work emphasizes volatility modeling, time series analysis, and applications of machine learning to forecast financial markets. Key areas include GARCH models, structural breaks, and cross-temporal forecasting for digital platforms. He has published extensively in top journals such as Journal of Econometrics and International Journal of Forecasting . Articles Overview: Recent contributions include novel methods for cross-temporal forecast reconciliation using machine learning and sparse change-point VAR models. His work bridges econometric theory with practical applications in asset pricing and risk management. Awards: Recipient of the 2024 Risk-Shift award in France. His research has been recognized for advancing methodologies in volatility modeling and financial econometrics. Advising & Grants: While no specific grants are listed, his roles as a researcher and editor highlight significant contributions to the academic community. He advises on policy and industry applications of his models through consulting roles in financial econometrics and macroeconomic forecasting. Labs & Teams: Affiliated with the Finance and Insurance Lab (CREST) and leads the Information Systems, Data Analytics, and Operations department at ESSEC. Collaborates with global institutions on projects involving high-frequency data and platform economics.

Alain HecqView profile

Professor

• Econometrics
• Time Series Analysis
• Financial Economics
• +5 more

Alain Hecq is a Full Professor in the department of QE Econometrics at the School of Business and Economics, Maastricht University. His research focuses on econometric methodologies, particularly in time series analysis, noncausal models, and financial econometrics. He has contributed significantly to the understanding of volatility dynamics, cryptocurrency markets, and inflation targeting regimes. His work often addresses policy-relevant questions in macroeconomics and financial markets. Key research interests include mixed causal-noncausal autoregressive models, volatility modeling with MARMA-GARCH frameworks, and the application of these techniques to real-world phenomena such as oil price bubbles and cryptocurrency volatility. He has also explored the credibility of central banking policies during crises, such as the Brazilian inflation-targeting regime during the pandemic. His recent work emphasizes methodological advancements in high-dimensional time series analysis, including spectral estimation, hierarchical regularizers for mixed-frequency data, and reduced-rank matrix autoregressive models. These contributions reflect a blend of theoretical rigor and practical applicability in addressing complex economic and financial problems. While no formal awards are listed, his extensive publication record and focus on cutting-edge econometric techniques underscore his scholarly impact. Advising and grant activities are not detailed in the provided information, but his research demonstrates sustained engagement with both academic and policy-oriented audiences.

• Structural Health Monitoring
• Structural Control
• Stochastic Fatigue
• +4 more

B. F. Spencer Jr. is the Nathan M. and Anne M. Newmark Endowed Chair in Civil Engineering at the University of Illinois at Urbana-Champaign, where he directs the Multi-Axial Full-Scale Sub-Structured Testing & Simulation Facility and the Smart Structures Technology Laboratory. He joined the university in 2002 after serving as Leo E. and Patti Ruth Linbeck Professor of Engineering at the University of Notre Dame (1985-2002). Education includes: Ph.D. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1985) M.S. in Theoretical and Applied Mechanics, University of Illinois at Urbana-Champaign (1983) B.S. in Mechanical Engineering, University of Missouri-Rolla (1981) His research focuses on pioneering innovations in structural health monitoring, stochastic mechanics, and smart sensor technologies. Key areas include development of wireless sensor networks for real-time infrastructure assessment, seismic hazard mitigation strategies, and AI-driven damage detection systems. His work bridges theoretical computational mechanics with practical civil engineering applications to enhance resilience against natural disasters. Recent publications emphasize digital twins, UAV-based structural inspection, machine learning for damage identification, and advanced sensor networks. Trends show strong integration of AI, 3D visualization, and edge computing for rapid post-disaster evaluation and predictive maintenance of critical infrastructure. Major scientific honors: ASCE Housner Medal (2015) J.M. Ko Medal (2014) Foreign Member of Polish Academy of Sciences (2005) Structural Health Monitoring Person of the Year (2011) JSPS Fellowships (1999, 2000) He leads significant infrastructure projects including NSF-funded facilities and industry collaborations. Laboratory initiatives involve full-scale testing of bridges, gates, and seismic mitigation systems. Educational outreach includes K-12 STEM programs like 'Shakes and Quakes' to inspire future engineers.

Stefano MartinianiView profile

Assistant Professor

• Computational and Statistical Physics
• Materials Science
• Artificial Intelligence for Science
• +5 more

Stefano Martiniani is an Assistant Professor of Physics, Chemistry, Mathematics, and Neuroscience at New York University, affiliated with the Center for Soft Matter Research and the Simons Center for Computational Physical Chemistry. His interdisciplinary research explores computational physics of complex systems, including neural circuit theories, non-equilibrium statistical mechanics, and AI-driven materials discovery. He has pioneered methods for analyzing high-dimensional energy landscapes and received prestigious awards like the NSF CAREER Award (2024) and IUPAP Early Career Prize (2023). Education: PhD in Physics (2017), University of Cambridge MPhil in Physics (2013), University of Cambridge BSc in Physics (2012), Imperial College London Research Interests: His work bridges statistical physics and artificial intelligence, focusing on: Engineering disordered materials with tailored spectral properties Quantifying entropy production in active matter Developing open science frameworks like ColabFit for machine learning interatomic potentials Neural circuit models for cortical communication Grants & Collaborations: Funded by NSF, NIH, Chan Zuckerberg Initiative, and Simons Foundation. Leads interdisciplinary teams in computational physics, AI, and materials science. Labs/Initiatives: Core member of NYU's Center for Soft Matter Research; develops software tools like FReSCo and KLIFF-Torch for computational materials science.

• Dementia
• Alzheimer's Disease
• Cerebrovascular Disorders
• +4 more

Matthew Pase is an Associate Professor at Monash University, leading the Epidemiology of Dementia Lab, the Aging and Neurodegeneration Research Program at the School of Psychological Science, and the Aging Well Pillar at the Turner Institute. He holds an adjunct position as Associate Professor of Epidemiology at Harvard University and has conducted postdoctoral training at the Framingham Heart Study and Boston University School of Medicine. Education: PhD in Neuropsychology/Neuroscience (2014), specializing in modifiable hemodynamic predictors of cognitive aging Research focuses on modifiable risk factors for dementia, validation of non-invasive biomarkers, and sleep-dementia relationships. Notable projects include the Brain and Cognitive Health (BACH) cohort, NIH-funded Sleep and Dementia Consortium, and the Vascular Contributions to Dementia (VCD-CRE) initiative. Key themes in recent articles include sleep architecture’s impact on dementia risk, neurovascular integrity in aging, and biomarkers like YKL-40 and plasma tau. His work emphasizes translational research to reduce cognitive disorder burdens. Grants/Projects: ARC Training Centre for Optimal Ageing (2023–2028) Poor Sleep and Hypertension combined Alzheimer’s risk (2023–2025) Vascular Contributions to Dementia (2022–2027) Labs/Teams: Leads multi-institutional collaborations, including the Framingham Heart Study and International Stroke Genetics Consortium’s Cognitive Working Group.

Xin ZhouView profile

Associate Professor

• Analysis
• Geometry
• Geometric Analysis
• +3 more

Xin Zhou is an Associate Professor of Mathematics at Cornell University, affiliated with the College of Arts and Sciences. He holds a Ph.D. in Mathematics from Stanford University (2013), a M.S. from Peking University (2008), and a B.S. in Physics and Mathematics from Tsinghua University (2006). His research focuses on geometric analysis, calculus of variations, and differential geometry, particularly in the study of minimal surfaces, constant mean curvature hypersurfaces, and free boundary problems. Zhou is a co-organizer of the Cornell Analysis and Geometric Analysis Seminar and has contributed to workshops such as the Brin Center’s 'Weyl Laws Across Mathematics.' His awards include the 2024 Fellow of the American Mathematical Society, the 2024 Von Neumann Fellowship at the Institute for Advanced Study, Simons Fellowships (2023-2024), and an Alfred P. Sloan Research Fellowship (2019-2021). He delivered an invited talk at the 2022 International Congress of Mathematicians. Zhou’s work integrates advanced min-max theory, topological methods, and geometric analysis to address fundamental questions in global differential geometry. His recent research emphasizes the existence and properties of minimal hypersurfaces, multiplicity conjectures, and applications to manifold structure and curvature problems.

Moo ChungView profile

Professor

• Computational Neuroimaging
• Topological Data Analysis
• Brain Networks
• +5 more

Moo Chung is a Professor of Biostatistics and Medical Informatics at the University of Wisconsin-Madison, affiliated with the School of Medicine and Public Health. He holds additional appointments in the Department of Statistics and the College of Letters and Science. Chung earned his Ph.D. in Mathematics and Statistics from McGill University under Professors Keith J. Worsley and James O. Ramsay. His research focuses on computational neuroimaging, leveraging MRI, fMRI, and DTI to study brain dynamics through topological and geometric methods. Key areas include persistent homology, brain network analysis, and statistical modeling of high-dimensional imaging data. He has pioneered methods like hyper-network construction and exact topological inference for paired brain networks, addressing computational challenges in analyzing large-scale neuroimaging datasets. Chung’s work integrates advanced mathematical techniques such as Hodge Laplacian, spectral graph theory, and Wasserstein distances to analyze brain connectivity. He has secured NIH funding for projects like the Brain Initiative (2017-2020) and current grant MH133614 (2023), focusing on geometric data analysis and topological dynamics. His contributions include three books on brain imaging and network analysis, with ongoing research on topological data analysis applications. Awards: Vilas Associate Award (2013-2014), NIH Brain Initiative Award, Editor's Award (2011) Labs/Teams: Leads brain imaging workshops globally, including Seoul National University (2024) and POSTECH (2024), and organizes conferences like ISBI and MICCAI special sessions. Grants: NIH EB022856 (Brain Initiative), MH133614 (2023), and collaborations with institutions like Vanderbilt University and University of Chicago. Chung actively mentors students and postdocs in biomedical data science, offering fellowships through the CIBM program. His group maintains a Google mailing list for brain image analysis discussions and hosts regular seminars on methodological advancements.

• Natural Language Processing
• Text Generation
• Machine Learning
• +1 more

Zihao Fu is a Researcher at the Oxford Internet Institute (OII), University of Oxford, where he served as a Postdoctoral Researcher from March 2024 to April 2025, focusing on the Trustworthiness Auditing for AI project. Previously, he was a Research Associate (PostDoc) at the University of Cambridge's Language Technology Lab under Prof. Nigel Collier. His research emphasizes Natural Language Processing, Text Generation, Machine Learning, and Biomedical Applications. Education includes a Ph.D. from The Chinese University of Hong Kong (supervised by Prof. Wai Lam) and a visiting student period at Tsinghua University's NLP Lab. He has substantial experience with large-scale distributed algorithms in Alibaba Cloud's PAI platform. Research interests span NLP, biomedical applications, and AI ethics, with notable contributions to datasets like BAND and frameworks like OxonFair. His work addresses challenges in text generation repetition, parameter-efficient fine-tuning, and algorithmic fairness.

• Mathematical Modeling
• Kinetic Theory
• Socio-Economic Dynamics
• +3 more

Giuseppe Toscani is a Full Professor at the Department of Mathematics of the University of Pavia. His research focuses on mathematical modeling of socio-economic systems, epidemiological dynamics, and kinetic theory. He explores topics such as wealth distribution, opinion formation, and epidemic spread using advanced methods in partial differential equations and statistical mechanics. Key research areas include analysis of inequality indices (Gini Index extensions), multi-agent systems, and nonlinear diffusion processes. His work intersects with applications in public health, economic policy, and social behavior modeling, emphasizing the interplay between theoretical frameworks and real-world phenomena. Education: Not explicitly detailed in provided texts, but his academic role implies a doctoral degree in Mathematics or related field. Labs/Teams: Affiliated with the Methods and Models for Applied Sciences research group. Grants/Awards: No specific grants or awards listed in the text. Recent publications address condensation patterns in opinion dynamics, multidimensional inequality metrics, and pandemic modeling through compartmental systems. His kinetic models provide foundational tools for understanding complex social and biological systems.