جست‌وجوی استاد و پژوهشگر

• Statistical Theory
• Probability
• Mathematical Statistics
• +۴ مورد دیگر

Afendras Giorgos is an Associate Professor in the Department of Statistics and Operations Research at Aristotle University of Thessaloniki since January 2023. Previously, he served as an Assistant Professor at the same institution (2018–2023) and held roles at the University at Buffalo (USA), including Adjunct Assistant Professor (2018–2022) and Research Associate (2015–2018). His research focuses on statistical theory, probability, and machine learning, with a strong emphasis on variance bounds, covariance identities, and cross-validation techniques. Giorgos holds a PhD in Mathematics from the National and Kapodistrian University of Athens (2008), a Master's in Statistics and Operations Research (2004), and a degree in Mathematics (2001). His professional experience includes visiting roles at the University of Cyprus and external associate positions at Athens University of Economics and Business. His research interests span distribution families, orthogonal polynomials, asymptotic statistics, dependency measures, and statistical machine learning. Recent work includes advancements in cross-validation methodologies and model selection criteria, with applications to resampling effectiveness and training/test size optimization. No scientific awards are explicitly mentioned in the text. His professional trajectory reflects extensive contributions to academic research and teaching in statistical theory and applied mathematics.

Minzhang Zhengمشاهده پروفایل

استادیار

• Complex Systems
• Computational Genomics
• Artificial Intelligence
• +۳ مورد دیگر

Minzhang Zheng serves as an Assistant Research Professor of Physics at George Washington University and Senior Bioinformatics Research Scientist at St. Jude Children’s Research Hospital. His interdisciplinary work bridges physics, computational biology, and artificial intelligence to address complex biomedical challenges through data-driven methodologies. Dr. Zheng's research centers on the dynamics of complex systems with applications in functional genomics and clinical decision support. He develops AI-enabled approaches to elucidate spatial-temporal regulation of cis-regulatory elements in the human genome, focusing on their role in disease mechanisms. His methodology integrates network science, temporal data analysis, and multiomics profiling to decode biological complexity in contexts ranging from astronaut health to diabetic pathophysiology. Analysis of his recent publications reveals a cohesive research trajectory applying temporal network analysis to biological and social systems. Key themes include visibility-graph community detection for time-series data, longitudinal omics profiling in spaceflight and metabolic disorders, and modeling of online hate networks. His work demonstrates consistent innovation in transforming complex temporal datasets into actionable insights for precision medicine and public health interventions.

Zuzana Brokešováمشاهده پروفایل

استادیار

• Risk Management
• Insurance Theory
• Behavioral Insurance
• +۳ مورد دیگر

Zuzana Brokešová is an Assistant Professor and Deputy Head of the Department for Pedagogical Activities and Foreign Relations at the Department of Insurance, Faculty of Economics and Finance, Bratislava University of Economics and Business. She specializes in risk management, insurance theory, and behavioral insurance. Her research focuses on risk assessment in insurance markets, new risk types, and behavioral aspects of financial decision-making. She has conducted significant research through projects such as the Horizon FAME initiative and APVV-19-0573, exploring evidence-based policy design and disaster preparedness. Her work integrates behavioral economics with practical insurance applications, including experimental studies comparing field and lab risk-taking behaviors. Key Research Projects: - Federated Data Marketplace (Horizon FAME) - Evidence-Based Policy System for Voluntary Donations (APVV-19-0573) - Risk Management Education (KEGA projects) She has held international visiting positions at institutions like the University of Arkansas, University of Strasbourg, and the University of Hong Kong, enhancing her global academic network. Her pedagogical contributions include curriculum development in risk management and has been recognized with the Dean's Award for Teaching Excellence (2016). Her publications span journals like Manufacturing & Service Operations Management , Journal of Economic Psychology , and The Geneva Papers on Risk and Insurance , addressing topics from behavioral biases in risk decisions to socioeconomic determinants of insurance adoption.

• Bayesian Statistics
• Mixture Models
• Computational Statistics
• +۳ مورد دیگر

Gertraud Malsiner-Walli is an Associate Professor at the Institute of Statistics and Mathematics at WU Vienna University of Business and Economics. Her research focuses on Bayesian mixture models , model-based clustering , and computational statistics , with applications in medical imaging, neurology, and psychiatry. She has contributed to advancements in sparse finite Gaussian mixtures and telescoping sampling algorithms , enabling efficient Bayesian inference in mixture models. Her work bridges theoretical developments and practical applications, addressing challenges in identifying data clusters and handling high-dimensional datasets. Notable contributions include methodologies for effect fusion in regression models and multivariate regression analysis of mixed-type panel data. She has published in top journals such as Statistics and Computing , Advances in Data Analysis and Classification , and Bayesian Analysis . Her research also extends to medical applications, including studies on Alzheimer’s disease biomarkers and glioma diagnosis using combined imaging techniques. She has received funding from the Austrian Science Fund (FWF) and WU Projects grants. Collaborations with experts like Sylvia Frühwirth-Schnatter and Bettina Grün highlight her active role in advancing Bayesian statistical methodologies globally.

• Diabetes Research
• Pregnancy Outcomes
• Endocrinology
• +۳ مورد دیگر

Trine Tang Christensen is a Clinical Professor at Aalborg University Hospital's Department of Endocrinology under the Faculty of Medicine. She specializes in diabetes research with a focus on pregnancy-related outcomes, gestational diabetes management, and maternal-fetal health. Her work integrates clinical practice with population-based cohort studies to improve understanding of diabetes complications during pregnancy and fetal development. Her research interests include glycemic control trajectories, fetal biometrics prediction, and the epidemiology of adverse pregnancy outcomes in diabetic populations. Key contributions include leading the Danish Diabetes Birth Registry 2 cohort study and analyzing hemoglobin A1c dynamics in Type 2 diabetes during pregnancy. Education: Medical degree from Aalborg University (inferred from affiliation) Dr. Christensen has authored/co-authored 6 peer-reviewed journal articles between 2011-2024, with recent emphasis on prospective cohort studies and population-based analyses. Her work has been cited 30+ times, highlighting contributions to clinical guidelines for gestational diabetes management. 41 professional activities including conference presentations, academic teaching, and committee memberships Key collaborator in international diabetes research networks (e.g., European Association for the Study of Diabetes) She maintains active involvement in clinical education through workshops for healthcare professionals and serves as a trusted advisor in diabetes care protocols.

• Genetic Epidemiology
• Migraine Genetics
• Genome-wide Association Studies
• +۷ مورد دیگر

Dale R. Nyholt is a Professor of Genetic Epidemiology at Queensland University of Technology's School of Biomedical Sciences. He leads the Genetic Epidemiology research group with a primary focus on understanding the genetic basis of migraine and its comorbidities. His research spans multiple disciplines including neurology, genetics, and epidemiology, with particular expertise in genome-wide association studies (GWAS) and Mendelian randomization approaches. University: Queensland University of Technology School: School of Biomedical Sciences Research Focus: Genetic basis of migraine and comorbid conditions Methodologies: GWAS, Mendelian randomization, epigenetic analyses Professor Nyholt's research interests primarily center on the genetic epidemiology of migraine, with significant contributions to understanding its relationship with psychiatric disorders, endometriosis, thyroid dysfunction, and other neurological conditions. His work often involves large-scale international collaborations examining the shared genetic architecture between migraine and various comorbid conditions through cross-trait analyses. His research has expanded into epigenetic mechanisms underlying migraine, including DNA methylation changes associated with treatment response. Nyholt has also made important contributions to understanding the genetic basis of endometriosis and its comorbidity with pain conditions. Analysis of Professor Nyholt's recent publications reveals a strong focus on migraine genetics and comorbidity studies. His work consistently employs advanced genetic methodologies to explore causal relationships between migraine and various conditions including psychiatric disorders, endocrine abnormalities, and gastrointestinal diseases. A significant portion of his research investigates the shared genetic architecture between migraine and other conditions through cross-trait analyses, Mendelian randomization, and integration of multiple 'omics' data types. His work spans both neurological and non-neurological conditions, demonstrating the broad applicability of genetic epidemiological approaches to understanding disease comorbidity. Extensive publication record with over 296 items documented Multiple high-impact publications in journals like Nature Genetics, Nature Communications, and American Journal of Human Genetics Significant contributions to migraine genetics consortia Professor Nyholt's research has important implications for understanding the biological mechanisms underlying migraine and its comorbid conditions, potentially informing new therapeutic approaches. His work bridges the gap between basic genetic research and clinical applications, with findings that could lead to improved diagnosis and treatment strategies for migraine patients with comorbid conditions. His research group maintains active collaborations with numerous international institutions, contributing to large-scale genetic studies across multiple disease domains.

• Machine Learning
• Artificial Intelligence
• Causal Discovery
• +۵ مورد دیگر

Dr Erdun Gao is a Grant-Funded Researcher at the Australian Institute for Machine Learning (AIML), part of the Faculty of Sciences, Engineering and Technology at the University of Adelaide. His research focuses on machine learning, causal discovery, multimodal systems, and data hiding techniques. He is eligible to supervise Masters and PhD students as a co-supervisor. Key research interests include cross-modal learning, domain generalization, federated learning, and scalable causal inference methods. His work addresses challenges such as missing data imputation, adversarial model behavior, and secure information embedding in images. Notable contributions span technical areas like DAG structure learning, diffusion model bias mitigation, and high-order score estimation in incomplete datasets. Publications from 2019-2025 showcase advancements in both foundational AI theory and applied data science solutions. His team develops algorithms for real-world applications in healthcare, image processing, and secure data transmission. Dr Gao’s current projects involve: Exploring multimodal LLM vulnerabilities through adversarial 'distraction' techniques Developing scalable causal discovery frameworks for mixed data types Innovating reversible data hiding methods for medical imaging applications

Max Bonomiمشاهده پروفایل

پژوهشگر

• Integrative and computational structural biology
• Biophysics
• Molecular dynamics
• +۱۳ مورد دیگر

Max Bonomi is a Researcher and Head of the Brain-immune communication group at the Institut Pasteur, Paris, within the Department of Computational Biology. He leads a research team focused on integrative computational approaches to model the structure and dynamics of complex biological systems, particularly using cryo-EM data and molecular simulations. Institution: Institut Pasteur Department: Department of Computational Biology Role: Group Leader and Principal Investigator His research lies at the intersection of biophysics, machine learning, and structural biology, with a strong emphasis on developing and applying methods for integrative modeling of proteins and RNA. He is a key contributor to the PLUMED open-source software and leads projects such as HARIBOSS and SHAMAN, which aim to advance RNA-targeted drug discovery. The recent publications of Dr. Bonomi span topics including RNA-small molecule interactions, conformational dynamics of proteins, cryo-EM integration, and AI in structural biology. His work shows a clear trend toward computational tools that enhance reproducibility, integrate diverse data sources, and enable rational drug design, particularly for challenging targets like RNA and dynamic membrane proteins. He has received no explicitly listed scientific awards in the provided text, but his publications in high-impact journals and leadership in open-source scientific software indicate significant recognition in the field. Dr. Bonomi advises PhD students and postdoctoral researchers, including Carla Cabal Fernandez and Matteo Marengo. His group is supported by ongoing projects in integrative modeling and software development. He actively promotes scientific reproducibility through the PLUMED Consortium and develops public resources like PLUMED-NEST for data sharing. His research group manages several tools and databases: PLUMED : Open-source library for free-energy calculations in molecular systems. HARIBOSS : Curated database of RNA-small molecule structures. PLUMED-NEST : Public repository for reproducing PLUMED simulation results. SHAMAN : Computational technique for identifying small-molecule binding sites in RNA ensembles.

Michael Nilgesمشاهده پروفایل

استاد پژوهشی

• Structural Biology
• Computational Biology
• Nuclear Magnetic Resonance (NMR)
• +۹ مورد دیگر

Michael Nilges is a leading research scientist and Principal Investigator at the Institut Pasteur in Paris, France, affiliated with the Department of Structural Biology and Chemistry. He leads multiple high-impact research projects including the ERC-funded BAYCELLS, the Marie Curie ITN AEGIS, and the ViBrANT training network, focusing on integrative structural biology, computational modeling, and NMR-based structure determination. His research interests span Structural Biology , Computational Biology , NMR methodology , protein-protein interactions , and drug discovery . He develops advanced software tools such as ARIA and ARIAweb for automated NMR structure calculation and promotes open science through platforms like InDeepNet and PIE. The recent articles highlight a strong trend in computational and integrative structural biology, with applications in infectious diseases such as malaria and rabies, and a focus on protein function prediction, ligand binding, and macromolecular dynamics. His work bridges physics, chemistry, and biology to understand cellular processes at the molecular level. His scientific awards include the prestigious ERC-2011-AdG BAYCELLS grant and the Marie Skłodowska-Curie AEGIS ITN, recognizing his leadership in training and innovation. He actively mentors PhD students and postdoctoral researchers, including Borja Rodríguez de Francisco, Laura Ortega-Varga, Alexandra Moine-Franel, and Luis Checa Ruano. His research is supported by significant grants and collaborative networks across Europe. He also leads the development of key software and databases for the structural biology community. Michael Nilges heads the Structural Biology facility and is deeply involved in organizing scientific events such as the 'NMR: a tool for biology' conference series, fostering collaboration and dissemination of cutting-edge methodologies in structural biology.

Marco Guarnieriمشاهده پروفایل

دانشیار

• Security and Privacy
• Program Verification
• Programming Languages
• +۳ مورد دیگر

Marco Guarnieri is an Associate Professor at the IMDEA Software Institute in Spain. His research focuses on the design, analysis, and implementation of secure systems, with particular emphasis on security at the hardware-software boundary. His work addresses microarchitectural attacks (e.g., Spectre), secure compilation, hardware verification, and database security. Education PhD in Computer Science, ETH Zurich (2017) MSc in Computer Engineering, Università degli Studi di Bergamo (2012) BSc in Computer Engineering, Università degli Studi di Bergamo (2010) Research Focus Guarnieri's research spans several interconnected areas: Hardware-Software Security: Developing foundations for reasoning about security at the hardware-software boundary Microarchitectural Attacks: Analysis and mitigation of speculative execution attacks Formal Methods: Application of verification techniques to security problems Secure Compilation: Ensuring compiler-generated code maintains security guarantees Database Security: Information flow control and access mechanisms Research Trends His recent publications demonstrate a strong focus on hardware-software co-design for security, with increasing emphasis on formal verification of processor security (especially RISC-V), automated testing of speculation countermeasures, and leakage contract frameworks. His work consistently bridges theoretical foundations with practical implementations. Awards and Honors Best Paper Award at CCS 2024 Distinguished Paper Award at CCS 2023 Distinguished Paper Award at CCS 2022 Best Paper Award at S&P 2021 Research Leadership Guarnieri leads research projects on secure speculation and hardware-software contracts. He actively recruits research interns, PhD students, and postdocs to work on security at the hardware-software interface. His group develops tools like SPECTECTOR for detecting speculative information flows and LeaVe for verifying processor security.

Michael Greenbergمشاهده پروفایل

پژوهشگر

• Programming Languages
• Static Analysis
• Type Systems
• +۴ مورد دیگر

Michael Greenberg is a researcher at Stevens Institute of Technology. He actively contributes to programming language design, static analysis, and formal methods, with a focus on Datalog, SMT solvers, and type systems.

Dale Millerمشاهده پروفایل

استاد پژوهشی

• Computational Logic
• Proof Theory
• Automated Reasoning
• +۵ مورد دیگر

Dale Miller is a Research Professor at Inria Saclay , with affiliations at the Laboratoire d'Informatique (LIX) and Institut Polytechnique de Paris . He previously served as a Professor at the University of Pennsylvania, École Polytechnique (France) , and as Department Head at Pennsylvania State University. He holds a Ph.D. in Mathematics from Carnegie Mellon University (1983) , supervised by Peter Andrews. Research interests span computational logic , proof theory , automated reasoning , logic programming , unification , operational semantics , and proof certificates . His work unifies logical foundations with computational applications, particularly in higher-order logic programming and theorem proving. Key contributions include the Abella and Bedwyr theorem provers, λProlog language, and foundational research on proof certificates . He has received the ERC Advanced Grant (2012-2016) , Dov Gabbay Prize (2023) , and LICS Test-of-Time Awards (2011, 2014) . Students advised include Farah Al Wardani , Jui-Hsuan Wu , Matteo Manighetti , and Zakaria Chihani , among others. He leads the Partout team at Inria, focusing on logical frameworks and verification.

Lionel Parreauxمشاهده پروفایل

استادیار

• Programming Languages
• Type Systems
• Functional Programming
• +۳ مورد دیگر

Lionel Parreaux is an Assistant Professor at The Hong Kong University of Science and Technology (HKUST) in the Department of Computer Science and Engineering since February 2021. He is actively recruiting students for his research group focusing on programming languages, type systems, and compiler optimization. He earned his PhD in 2020 from École polytechnique fédérale de Lausanne (EPFL) under the Data Analysis Theory and Applications Laboratory (DATA), where he developed the Squid type-safe metaprogramming library for Scala. His research spans Programming Languages Type Systems Functional Programming Generative Programming Compiler Optimization Language Design Recent publications (2024) cover type inference, metaprogramming, and compiler optimization. Notable works include Scope-safe metaprogramming Deforestation techniques Conditional syntax design Subtyping constraints Lazy memory management These reflect his focus on theoretical and practical language implementation challenges. Current research projects include "A Lightweight Type System for Scope and Effect Safety" (2024, Huawei Technologies) "Optimizing Functional Programs by Building on Optimal Graph Reduction Techniques" (2022-2025, RGC Early Career Scheme) He supervises 8 PhD/MPhil students and serves on program committees for POPL, APLAS, GPCE, and SPLASH symposia.

• Artificial Intelligence
• Machine Learning
• Fuzzy Logic
• +۴ مورد دیگر

Martin Tabakow, PhD, is a researcher at the Department of Artificial Intelligence within the Faculty of Information and Communication Technology at Wrocław University of Science and Technology. His work focuses on artificial intelligence applications in medical imaging and computational biology. University: Wrocław University of Science and Technology School: Faculty of Information and Communication Technology Department: Artificial Intelligence Academic Rank: Researcher His research interests include: Deep learning for histopathology and retinal disease detection Fuzzy logic systems and type-2 fuzzy inference Data bias mitigation and class imbalance solutions Medical image segmentation and computational healthcare Publications (2019–2024) demonstrate expertise in using AI for biomedical applications. Notable trends include: Advancements in histopathological image analysis Development of type-2 fuzzy models for improved classification Focus on synthetic data generation and bias reduction Integration of AI with clinical diagnostic systems Current laboratory affiliations include the Artificial Intelligence Department's research teams focused on medical applications.

• Information Visualisation
• Bioinformatics
• Bayesian Networks
• +۳ مورد دیگر

Thanasis Vogogias is a Researcher at the School of Computing , Edinburgh Napier University . His work focuses on the intersection of information visualisation , bioinformatics , and computational biology , with a particular emphasis on developing tools for analyzing complex biological networks and datasets. Research Interests include: Network analysis with multiple edge types Bayesian network structure learning Hierarchical clustering and dendrogram visualization Genomic data processing and QTL mapping Integration of computational methods with biological applications Recent publications highlight his work in: Visual encodings for network representation Bayesian inference in biological systems Multi-height branch-cut techniques for gene expression data Software development for autotetraploid population analysis The BayesPiles tool exemplifies his contributions to visual analytics for consensus network construction.