جستجوی استاد راهنمای دانشگاهی

• Statistical Modeling
• Biostatistics
• Biomedicine
• +۷ مورد دیگر

Dae-Jin Lee is an Assistant Professor at IE University’s School of Science and Technology, specializing in statistical modeling and data science. Previously, he served as a Research Line Leader at the Basque Centre for Applied Mathematics (BCAM) and coordinated the Knowledge Transfer Unit in Data Science/AI. His academic background includes a Ph.D. in Mathematical Engineering (2010) from Universidad Carlos III de Madrid and postdoctoral research at CSIRO (Australia). His research focuses on statistical methods for complex data, including penalized splines, tensor product smooths, and applications in biomedicine, epidemiology, environmental science, and sports analytics. He has led multidisciplinary projects funded by public and industry grants, collaborating globally with experts across fields like engineering, medicine, and biology. Key research themes include predictive modeling for health outcomes (e.g., SARS-CoV-2 pneumonia severity), sports injury prevention, and AI in healthcare. His work integrates machine learning with traditional statistical techniques, addressing real-world challenges like pedestrian dynamics simulations and automated medical diagnostics. He is actively involved in scientific organizations, including the Spanish Biostatistics Society and the Statistical Modelling Society. His recent publications highlight innovations in growth curve modeling, AI ethics, and spatiotemporal data analysis, reflecting his commitment to advancing both theoretical and applied statistics.

Yuan Zhongمشاهده پروفایل

دانشیار

• Applied Probability
• Stochastic Systems
• Operations Management
• +۷ مورد دیگر

Yuan Zhong is an Associate Professor of Operations Management at the University of Chicago Booth School of Business . He previously held positions as an Assistant Professor at Columbia University’s Department of Industrial Engineering and Operations Research and was a Postdoctoral Scholar at UC Berkeley’s Computer Science Department. Education: PhD in Operations Research, MIT (2012) MA in Mathematics, Caltech (2008) BA in Mathematics, University of Cambridge (2006) His research focuses on applied probability and stochastic system design , with applications in cloud computing , supply chain management , and e-commerce logistics . Recent work explores multi-period production systems and dynamic resource allocation in data centers and healthcare operations . Recent publications analyze cloud value chains , sparse graph design for delivery networks, and process flexibility in manufacturing. He has contributed to journals like Operations Research , Annals of Applied Probability , and Stochastic Systems . Scientific Awards: 2012 Kenneth C. Sevcik Outstanding Student Paper Award Best Student Paper Award at ACM Sigmetrics (2012) He teaches courses in business process fundamentals and queueing theory , with a future schedule including Operations Management: Business Process Fundamentals (2025–2026). No explicit student advising list was provided.

Marta Casanellasمشاهده پروفایل

استاد

• Algebraic Geometry
• Computational Biology
• Phylogenetics
• +۴ مورد دیگر

Marta Casanellas is a full professor in the Department of Mathematics at the Polytechnic University of Catalonia (UPC) and a researcher at the Centre de Recerca Matemàtica. She teaches at the Faculty of Mathematics and Statistics, ETSEIB, and FIB. She earned her PhD in mathematics from the University of Barcelona under RM Miró-Roig, focusing on algebraic geometry and liaison theory. After completing a postdoc at UC Berkeley with a Fulbright Scholarship, she shifted her research to applications of algebraic geometry in computational biology, particularly phylogenetics. Her educational background includes a PhD from the University of Barcelona (2002), followed by a postdoctoral fellowship at UC Berkeley (2002-2003) supported by a Fulbright Scholarship. She obtained a prestigious Ramón y Cajal contract at UPC in 2003, which marked her transition to interdisciplinary research at the intersection of mathematics and biology. Casanellas' research focuses on applying algebraic and geometric techniques to phylogenetics, with particular emphasis on evolutionary models, phylogenetic invariants, and computational methods for genomic data analysis. Her work bridges pure mathematics (particularly algebraic geometry) with biological applications, developing mathematical frameworks to reconstruct evolutionary histories and understand genomic relationships. She has published extensively in both mathematics journals like Advances in Mathematics and biology journals like Molecular Biology and Evolution. Her recent publications demonstrate a consistent focus on developing algebraic methods for phylogenetic analysis, with increasing attention to heterogeneous evolutionary processes across lineages, time-reversible models, and computational implementations of theoretical results. The trend shows progression from theoretical foundations in algebraic geometry toward increasingly sophisticated and applicable computational methods for biological data. Fulbright Scholarship for postdoctoral research at UC Berkeley Ramón y Cajal contract (2003) Casanellas has supervised PhD students including A. Kedzierska (co-supervised with R. Guigó of the CRG). She has served as principal investigator for three competitive Spanish government projects involving fifteen researchers each. She has held significant academic leadership roles including Deputy Director of Research of the Department of Mathematics at UPC (2015-2018), head of studies for the Degree in Data Science and Engineering at UPC (2018-2022), and currently coordinates UPC's PhD in Bioinformatics program and Bachelor's Degree in Bioinformatics. She leads the BIO-GEOMAP research group focused on applying mathematical techniques to biological problems.

• Probabilistic Combinatorics
• Extremal Combinatorics
• Random Combinatorial Structures
• +۳ مورد دیگر

Guillem Perarnau is an Associate Professor in the Department of Applied Mathematics at Universitat Politècnica de Catalunya (UPC), affiliated with CRM, IMTech, and BGSMath. He holds a PhD from UPC (advised by Oriol Serra) and was a CARP Postdoc Fellow at McGill University (2013–2015). From 2016 to 2019, he was a Lecturer at the University of Birmingham. His research focuses on Probabilistic and Extremal Combinatorics, Random Combinatorial Structures, and Discrete Stochastic Processes. Education: Bachelor, Master, and PhD in Mathematics at UPC. His academic journey includes postdoctoral research at McGill University and a Lecturer role at the University of Birmingham. Research interests include Percolation Theory, Random Graphs, Stochastic Processes, and Algorithmic Combinatorics. His recent work explores topics like percolation on dense graphs, random walk mixing times, and synchronization in automata. He is co-PI of the COCOA grant and coordinates the Spanish Discrete and Algorithmic Mathematics Network. He participates in the RandNET MSCA Exchange Programme. Key contributions include studies on giant components in directed graphs, phase transitions in Glauber dynamics, and extremal problems in graph colorings. His publications span top journals like Annals of Applied Probability , SIAM Journal on Discrete Mathematics , and Random Structures & Algorithms .

• Numerical Methods
• Partial Differential Equations
• Computational Mathematics
• +۳ مورد دیگر

Francisco Manuel Bernal Martínez is an Associate Professor in the Department of Mathematics at Carlos III University of Madrid. His research focuses on numerical methods, partial differential equations, and computational mathematics, with applications in industrial engineering and materials science. He leads projects such as 'Financiación adicional 5º año (2022)' and collaborates on initiatives like 'Clustering Automático de Comportamientos de Invertebrados en Libertad mediante Imagen 3D.' His work emphasizes domain decomposition algorithms, radial basis functions, and stochastic control problems. He has advised at least one PhD thesis and holds grants from regional and national funding bodies. Key research interests include meshless methods, probabilistic domain decomposition, and uncertainty quantification in energy systems. Recent publications highlight advancements in hybrid algorithms for large-scale PDEs and volatility modeling. Bernal Martínez actively participates in academic networks and has presented at international conferences on computational methods and industrial mathematics.

Ramon Ferrer Canchoمشاهده پروفایل

دانشیار

• Quantitative Linguistics
• Information Theory
• Network Theory
• +۳ مورد دیگر

Ramon Ferrer Cancho is an Associate Professor at the Polytechnic University of Catalonia's Department of Computer Science, affiliated with the Barcelona School of Informatics and the LQMC research group (Quantitative, Mathematical, and Computational Linguistics). His work focuses on quantitative linguistics, information theory, and network theory, with applications in language structure, evolution, and animal communication. Education: Doctor in Computer Science, Advanced Studies Diploma in Applied Physics and Simulation. Research Interests: Dependency syntax, linguistic laws (Zipf's law, Menzerath-Altmann law), computational models of language, and cross-disciplinary studies in biology and cognitive science. He leads projects on language optimization, complexity, and data analysis. Recent work includes studies on syntactic dependency distances, ape gesture patterns, and bottlenose dolphin communication parallels. His research bridges computer science, linguistics, and biology, emphasizing universal principles in communication systems. Notable contributions include theoretical frameworks for dependency distance minimization, optimization models of language structure, and empirical analyses of primate vocal sequences.

Amedeo Piolattoمشاهده پروفایل

دانشیار

• Industrial Organisation
• Microeconomic Theory
• Political Economy
• +۱۲ مورد دیگر

Amedeo Piolatto is an Associate Professor (with tenure) in the Department of Economics and Economic History at the Autonomous University of Barcelona (UAB), where he also serves as Co-director of Graduate Studies (IDEA) and Chair of the Hiring Committee. He is an Affiliated Professor at the Barcelona School of Economics (BSE) and a Research Affiliate at the Barcelona Institute of Economics (IEB), maintaining a strong presence in the European economics research community. Research Interests: Industrial Organisation Microeconomic Theory Political Economy Public Economics Federalism and Fiscal Federalism Media, Information, and Piracy Taxation and Tax Evasion Political Centralization and Accountability Education Economics His research employs applied theoretical models to study interactions between firms, governments, and citizens. The 15 most recent publications reflect a consistent focus on institutional design, regulatory policy, and strategic behavior in political and market environments. Key themes include the economics of digital platforms, political business cycles in federations, partisan bias in disaster relief, and the role of information in media and electoral systems. His work frequently appears in top journals such as the Quarterly Journal of Economics , Journal of Public Economics , and Regional Science and Urban Economics . Scientific Awards and Recognition: Ramón y Cajal Fellowship (2018–2023) BBVA Foundation Grant for Researchers and Cultural Creators (2016–2018) Advising and Grants: Piolatto has advised PhD students, including Francisco de Lima Cavalcanti. He has secured significant competitive funding as Principal Investigator, including grants from the Spanish Government (Generación de conocimiento, Consolidación investigadora), the Ramón y Cajal program, and BSE Seeds. He has also been a team member on multiple national and regional research projects. He actively organizes academic events, serves on editorial boards (including as Co-editor of Economics ), and participates in PhD juries and institutional governance at UAB, BSE, and other universities. Labs and Teams: He is affiliated with the Barcelona Institute of Economics (IEB) and the Barcelona School of Economics (BSE), where he contributes to research workshops and collaborative initiatives in public economics and political economy.

• Composite Materials
• Mechanical Engineering
• Structural Analysis
• +۳ مورد دیگر

Professor Albert Turon Travesa is a faculty member in the Department of Mechanical and Industrial Construction Engineering at the University of Girona (Spain), where he leads the Mechanics of Continuum Media and Theory of Structures research area. He serves as Head of Department and is affiliated with the AMADE research group. His research focuses on characterizing mechanical behavior of composite materials, developing non-deterministic modeling strategies for structural prediction, and advancing fatigue life estimation techniques. Key contributions include cohesive zone modeling, delamination growth analysis, and machine learning applications in materials science. He has authored over 100 top-tier journal papers, appears in Stanford's World Top 2% Scientists, and Research.com's Mechanical & Aerospace Engineering ranking. Awards include the 2022 ICREA Acadèmia grant and 2024 Air and Space Academy Medal for composite materials advancements in European aeronautics. His 15 most recent publications span fatigue modeling, delamination mechanics, and computational strategies, primarily applied to aerospace composites. Current work emphasizes probabilistic approaches, ply orientation effects, and residual strength prediction after damage accumulation. ICREA Acadèmia Award (2022) Air and Space Academy Medal (2024) Active collaborations with aeronautical industries include technology transfer projects on composite material implementation. His research combines experimental validation with numerical simulation to bridge theoretical modeling and industrial application.

• Applied Analytics
• Statistical Inference
• Time Series Analysis
• +۷ مورد دیگر

Dr. Laleh Tafakori is a Senior Lecturer in the Department of Statistics and Analytics at RMIT University's School of Science. Her research focuses on the intersection of statistical modeling, machine learning, and their applications in healthcare, finance, and environmental science. She is affiliated with the university's City Campus and can be contacted at laleh.tafakori@rmit.edu.au . Her teaching interests include Applied Analytics , Statistical Inference , Time Series Analysis , Mathematical Statistics , Stochastic Processes , and Probability Theory . She actively supervises research projects in areas such as: Healthcare modeling (e.g., diabetes onset prediction, maternal risk assessment) Environmental data analysis (e.g., extreme precipitation estimation via satellite data) Financial risk analysis (e.g., Value-at-Risk forecasting, credit portfolio management) Machine learning applications in complex networks and smart grids Her recent research trends emphasize predictive modeling for public health challenges, leveraging advanced statistical techniques (copula models, functional volatility) and machine learning (CNNs, random forests). She collaborates with institutions worldwide, addressing issues like Saudi Arabia's healthcare indicators and European financial systemic risk. While no formal awards are listed, her work demonstrates impactful contributions to interdisciplinary data science. Dr. Tafakori has advised over 16 students on topics ranging from diabetes epidemiology to edge computing optimization. Her research outputs include 28+ peer-reviewed articles, with a focus on methodological innovation and real-world problem-solving. She maintains active engagement in collaborative projects without explicitly listed grants.

José E. Chacónمشاهده پروفایل

استاد

• Nonparametric Kernel Smoothing
• Cluster Analysis
• Mathematical Statistics
• +۳ مورد دیگر

José E. Chacón is a Professor of Statistics at the Department of Mathematics, University of Extremadura, Spain. He is also a member of the Institute of Mathematics at the same university. His research focuses on nonparametric kernel smoothing, cluster analysis, and mathematical statistics. He earned his PhD in Statistics from the University of Extremadura in 2004. Chacón’s work emphasizes methodological advancements in density estimation, clustering algorithms, and statistical theory. His recent publications address topics like geodesic distributions, Bayesian taut splines for mode estimation, and bump detection via density curvature. He has contributed to applied areas such as animal home range estimation and data science for pandemic analysis. His articles often explore cross-validation techniques, bandwidth selection, and mixture model clustering. He co-authored the textbook Multivariate Kernel Smoothing and Its Applications (2018), consolidating his expertise in kernel-based methods. Chacón’s research bridges theoretical statistics with practical applications, influencing both academic and applied domains.

Toni Susinمشاهده پروفایل

دانشیار

• Numerical Methods
• Physically-Based Simulation
• Computer Graphics
• +۴ مورد دیگر

Toni Susin is an Associate Professor of Applied Mathematics at UPC-BarcelonaTech. He leads the Dynamic Simulation Lab, part of the ViRVIG research group in Barcelona. His research focuses on numerical methods, physically-based simulation, and applications in computer graphics and biomechanics. Key research areas include Physically-Based Animation Surgical Simulation Biomechanical Applications Image-Based Modeling Fluid Animation Techniques His recent work spans microbiome data analysis, sports performance tracking, and biomedical simulations. While no scientific awards are listed, his career includes founding three tech companies and mentoring numerous PhD/Master's students in computational methods and simulation technologies.

Ignacio Cascos Fernandezمشاهده پروفایل

دانشیار

• Multivariate Analysis
• Probability Theory
• Data Depth
• +۷ مورد دیگر

Ignacio Cascos Fernandez is an Associate Professor at the Department of Statistics, School of Engineering, Carlos III University of Madrid. His research focuses on Multivariate Analysis (depth functions, multivariate quantiles) and Probability Theory (stochastic orders, random sets, financial risk measures). Key Research Areas Multivariate analysis with emphasis on data depth and quantiles Probability theory involving stochastic orders and random sets Applications to financial risk modeling and statistical process control Notable Awards IASC ERS Young Researchers Award (2006) Elected member of the International Statistical Institute (2008) PhD Supervision Advised José Manuel Cueto (2021) on multifactor models for equity returns Advised Maicol Ochoa (2022) on expectiles and multivariate analysis Recent Projects Developed data depth methodologies for biological age prediction Created statistical tools for complex data in finance and real estate analysis

Rocio Álvarez Arandaمشاهده پروفایل

استادیار

• Time Series Analysis
• Econometric Modeling
• Forecasting
• +۳ مورد دیگر

Dr. Rocio Álvarez Aranda is an Assistant Professor in the Department of Applied Economics at the University of the Balearic Islands' College of Economics and Business. With a PhD in Economics (University of Alicante, 2012) and a BSc in Mathematics (University of Malaga, 2004), she has extensive teaching experience across multiple institutions including Universidad Autónoma de Barcelona, Universidad de Santiago de Chile, and Universidad Adolfo Ibáñez. PhD in Economics, University of Alicante (2012) BSc in Mathematics, University of Malaga (2004) Her research focuses on econometric analysis of time series, unobservable factor modeling, and forecasting methodologies. She has published in top-tier journals like International Journal of Forecasting , Journal of Business & Economic Statistics , and Economics and Human Biology , with methodological work on Markov-switching models and empirical studies examining seismic activity impacts on fetal development. Recent publications reveal trends in dynamic factor models, Markov-switching autoregressive frameworks, and interdisciplinary applications of econometrics to public health and macroeconomic forecasting. She contributes to the Econometría y Ciencia de Datos (ECD) research group while maintaining expertise in MATLAB, Stata, Eviews, and LaTeX. Fondecyt Iniciación Fellowship (2018-2021) CIP16013 Research Fellowship (2016-2018) FPI Scholarship (2008-2012) Doctoral Fellowship (2006-2008) She supervises final degree projects across economics, tourism, and business programs while teaching courses like Introduction to Econometrics and Massive Data Econometrics. Her academic collaborations span institutions in Spain, Chile, and the UK.

Michael Raskinمشاهده پروفایل

مدرس

• Theoretical Computer Science
• Distributed Systems
• Algorithms
• +۶ مورد دیگر

Michael Raskin is a Lecturer (maître de conferences) at LaBRI, University of Bordeaux, where he conducts research in theoretical computer science with a focus on distributed systems and formal methods. His work bridges theoretical foundations with practical implementations, particularly in population protocols, temporal graphs, and Petri nets. His research interests span Theoretical Computer Science , Distributed Systems , Algorithms , and Formal Methods , with specific expertise in population protocols, temporal graph analysis, vector addition systems, and verification techniques for parameterized systems. His publications demonstrate a consistent focus on computational complexity, state complexity, and threshold phenomena in distributed models. Recent publication trends show a strong emphasis on temporal graph theory (with multiple papers on random temporal graphs and giant components), population protocol analysis (including modular protocols and leaderless rendez-vous systems), and verification techniques for parameterized systems. His work often combines mathematical rigor with practical implementation insights. Raskin has developed significant software projects including: Agnostic Lizard - A portable code walker for Common Lisp QueryFS - A filesystem defined through compiled queries His technical contributions span both theoretical advances and practical systems implementation, with a recurring theme of applying programming language techniques to systems problems. His work on Common Lisp tools demonstrates a commitment to practical language implementation alongside theoretical research.

• Statistics
• Industrial Statistics
• Statistical Quality Control
• +۱۴ مورد دیگر

Javier Tarrío Saavedra is a faculty member in the Department of Mathematics at the University of A Coruña, Spain, where he contributes to the College of Engineering. His academic role centers on statistics and operational research, with extensive teaching and research engagement across multiple engineering and sustainability-focused programs. His research interests span a wide interdisciplinary spectrum, including Statistical Quality Control, Industrial Statistics, Statistical Learning, Materials Science, Thermal Analysis, Rheology, Naval and Ocean Engineering, Energy Efficiency, and Epidemiology . He applies advanced statistical methods to real-world problems in industrial processes, environmental monitoring, and marine technology. His work often integrates machine learning and data science techniques for predictive modeling and process optimization. The recent publications highlight strong trends in applying statistical models to marine engineering, material degradation, energy efficiency, and environmental sustainability . Many studies involve collaboration with interdisciplinary teams, focusing on data-driven solutions for industrial and ecological challenges. His work frequently appears in journals related to chemometrics, thermal analysis, maritime technology, and environmental science. He has directed or co-directed numerous master’s and final-year theses, indicating active involvement in student mentorship. His collaborative research projects are funded by national (Ministerio de Ciencia), regional (Xunta de Galicia), and international (CNRS, Sweden) agencies, reflecting broad academic impact. He is a member of the research group Modelización, Optimización e Inferencia Estadística , contributing to applied statistical modeling in science and engineering.