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Danfeng ZhangView profile

Professor

• Programming Languages
• Security
• Differential Privacy
• +5 more

Danfeng Zhang is a faculty member at Duke University whose research sits at the intersection of programming languages and security. Active across the premier PL conferences since 2015, Zhang has served on more than two-dozen program committees and currently co-chairs the POPL Student Research Competition. Education & Affiliation: Home page: users.cs.duke.edu/~dz132 Affiliation: Duke University, United States Research Interests: Zhang’s work spans programming-language design, static and dynamic analysis, formal verification, and security. A recurring theme is developing language-based techniques that guarantee strong security and privacy properties—ranging from side-channel resistance and constant-time execution to differential-privacy proofs—while preserving performance and usability. His recent projects combine type systems, program logics, and automated reasoning to build practical verification tools for concurrent, speculative, and approximate software. Publication Trends: Across nine representative papers (2015-2024) Zhang has advanced static detection of cache side channels, automated proofs of differential privacy, and relaxed concurrency models. The trajectory shows deepening integration of security concerns into language infrastructure, with tool-building (CtChecker, SpecSafe, LightDP) that bridge formal guarantees and real-world systems. Service & Leadership: 2024 POPL Student Research Competition Co-Chair 2025 POPL Program Committee member Repeated reviewer/PC member: PLDI, SPLASH/OOPSLA, ISSTA, ECOOP, APLAS, PriSC, PASS Zhang regularly mentors student researchers through SRC sessions and workshop panels, fostering diversity and early-career participation in the programming-languages community.

Luo MaiView profile

Assistant Professor

• Computer Systems
• Machine Learning Systems
• Data Management
• +3 more

Luo Mai is an Assistant Professor at the University of Edinburgh's School of Informatics , with an upcoming promotion to Associate Professor (UK Reader) in August 2025. He leads the Large-Scale Machine Learning Systems Group and co-leads the UK EPSRC Centre for Doctoral Training in Machine Learning Systems and an ARIA Project on Scaling AI Compute by 1000X . PhD in Computer Science (Imperial College London, 2018) MRes in Advanced Computing (Imperial College London, 2012) His research focuses on the intersection of computer systems , machine learning , and data management . Key contributions include award-winning systems like WaferLLM (wafer-scale LLM inference), Tenplex (elastic ML), and ServerlessLLM (serverless LLM serving), published at top venues (OSDI, SOSP, ICML, NeurIPS, JMLR). Recent publications demonstrate trends in GPU-based distributed systems , LLM optimization , and adaptive machine learning . His team has developed groundbreaking open-source projects including TensorLayer , TorchOpt , and ServerlessLLM . Awarded Microsoft Research StarTrack Scholar (2024) , secured ARIA grant (2024) with Imperial College & Cambridge University, and received Google Fellowship during PhD (2012-2016). As an educator, he designed Edinburgh's popular Machine Learning Systems course (150+ students). His group supervises multiple PhD students including Yao Fu (recognized as 2024 Rising Star in ML & Systems) and Leyang Xue .

Frank NIELSENView profile

Professor

• Data science
• Geometric science of information
• Information geometry
• +4 more

Frank NIELSEN is a Professor at École Polytechnique with expertise in information geometry, data science, and machine learning. He holds a PhD (1996) and HDR (2006) in computer science and has established himself as a leading researcher in geometric approaches to information science. His educational background includes a PhD in computer science (1996) followed by a Habilitation à Diriger des Recherches (HDR) in 2006, the highest academic qualification in France that qualifies one to supervise doctoral candidates. Dr. NIELSEN's research focuses on the Geometric Science of Information , where he develops theoretical frameworks for understanding data through geometric and information-theoretic lenses. His work bridges Computational information geometry Statistical manifold theory Bregman divergences and their applications Machine learning with geometric foundations High-dimensional data analysis He aims to address the challenge of inappropriate data representation in current Data Science by building a theory of Computational Information Geometry to enable Intrinsic Data Science with principled distances. His extensive publication record shows a clear trend toward developing geometric frameworks for understanding statistical divergences, with recent work focusing on Bregman geometry, Fisher-Rao metrics, and their applications in machine learning. His research spans theoretical developments in information geometry to practical implementations like the pyBregMan Python library, demonstrating both theoretical depth and practical relevance. Dr. NIELSEN has made significant contributions through his teaching and publications. He has taught courses at École Polytechnique including INF442, INF517, and INF591. His authored textbooks include Introduction to HPC with MPI for Data Science (2016), A Concise and Practical Introduction to Programming Algorithms in Java (2009), and Visual Computing: Geometry, Graphics, and Vision (2005). He has also edited influential volumes such as Computational Information Geometry for Image and Signal Processing (2016) and Geometric Theory of Information (2014). He actively organizes and participates in academic events, serving on program committees for major conferences including GSI (Geometric Science of Information), CVPR, and ICCV. His work has established him as a key figure in the growing field of geometric approaches to information science.

Pedro GaleanoView profile

Associate Professor

• Outlier Detection
• Time Series Analysis
• Bayesian Inference
• +3 more

Pedro Galeano is an Associate Professor in the Department of Statistics at Universidad Carlos III de Madrid (UC3M) since 2009. He holds a PhD in Statistics (2004) under Prof. Daniel Peña, focusing on multiple time series. Previously, he served as Visiting Assistant Professor of Statistics and Econometrics at the University of Chicago’s Graduate School of Business and as a Postdoctoral Fellow at the Department of Statistics and Operations Research at Universidade de Santiago de Compostela. His research focuses on time series analysis, outlier detection, Bayesian inference in financial models, and functional data analysis with applications to missing data. He is an Associate Editor of the Journal of Time Series Analysis and advises the Heliyon journal. Key contributions include developing methodologies for detecting structural breaks, modeling systemic risk via copula approaches, and advancing robust statistical techniques for high-dimensional data. Active in academic leadership, Galeano co-organized the NICDA Workshop 2025 and has published extensively on topics like dynamic factor models, sequential parameter change detection, and functional data applications in energy markets. His work bridges theoretical statistics with practical applications in finance, economics, and environmental science.

• Data Mining
• Bioinformatics
• Machine Learning
• +3 more

Domingo Savio Rodríguez Baena is a Professor at Pablo de Olavide University, affiliated with the Department of Computer Languages and Systems. His research focuses on data mining, bioinformatics, and computational biology, with a particular emphasis on biclustering algorithms, gene co-expression networks, and high-performance computing applications. PhD in Engineering, Data Science, and Bioinformatics (2012) from Pablo de Olavide University His work spans interdisciplinary domains, including recommender systems , livestock behavior analysis , and biological data interpretation . Recent articles highlight his contributions to multi-GPU optimization , ensemble learning , and historical database construction . Key collaborations include the DATAi Intelligent Data Analysis and DASE Data Analytics Science & Engineering research groups. He has developed tools like the CyEnGNet–App for gene network visualization and BIGO for gene enrichment analysis. Contact: dsrodbae@upo.es

Marta AriasView profile

Associate Professor

• Machine Learning
• Data Mining
• Design and Analysis of Algorithms
• +3 more

Marta Arias is an Associate Professor in the Computer Science Department at Universitat Politècnica de Catalunya (UPC), Barcelona, and a member of the LARCA research group. She has held academic positions since 2007, including roles at Columbia University and the University of Edinburgh. Her research focuses on Machine Learning, Data Mining, Algorithm Design, and Logic in Computer Science. She teaches advanced courses in Machine Learning, Information Retrieval, and Complex Networks at undergraduate and master's levels, including the Erasmus Mundus program in Data Mining and Knowledge Management. Educations: PhD in Computer Science from Tufts University (2000-2004), Postgraduate Studies at University of Edinburgh (1999-2000), B.Sc. in Computer Science from UPC (1992-1997). Professional experience includes software engineering and research roles in New York and Barcelona. Research highlights include developing algorithms for real-time ranking systems, Twitter-based financial forecasting, and causal network analysis. Notable contributions include the 'Best Paper Award' at AIPESW@ECAI 2020 and a 3rd place award in football performance research. Her work spans theoretical foundations (e.g., Horn clause learning) and applied domains like cybersecurity, healthcare analytics, and energy systems. Teaching responsibilities include labs and lectures on Machine Learning, Information Retrieval, and Programming, with active involvement in curriculum design and pedagogical innovation. Ongoing projects include enterprise risk analysis, synthetic data generation, and network modeling.

• Bayesian Statistics
• Statistical Modeling
• Operations Research
• +7 more

David Valentín Conesa Guillén is a Full Professor in the Department of Statistics and Operations Research within the Faculty of Mathematics at the University of Valencia. He is an active researcher and a key member of the Valencia Bayesian Research Group (VABAR), focusing on advanced statistical methodologies and their applications. His primary research interests encompass Bayesian Statistics , Statistical Modeling , Operations Research , Epidemiological Modeling , Spatial Statistics , and Ecological Statistics . His work bridges theoretical development with practical applications in public health, environmental science, and financial systems. The trends in his recent publications reveal a strong emphasis on developing and applying Bayesian hierarchical models to complex real-world problems. Key areas include dynamic forecasting of influenza outbreaks, modeling spatial distributions of ecological and bioclimatic data, correcting biases in ecological survival estimates, and analyzing efficiency in sectors like banking and higher education. His methodology frequently involves advanced computational techniques like the Integrated Nested Laplace Approximation (INLA). PhD in Statistics, University of Valencia (2000) Thesis: "Inferencia y predicción en colas con ingresos o servicios en grupos" Supervised by Dr. Carmen Armero Cervera David Conesa has supervised academic work, as indicated by his role as a thesis advisor. His research has been supported by collaborative projects, leading to publications in top-tier journals such as Bayesian Analysis , European Journal of Operational Research , and Journal of Agricultural, Biological, and Environmental Statistics . He has extensive collaborations with researchers across Spain and internationally. His research is conducted within the VABAR (Valencia Bayesian Research Group) , a collaborative team dedicated to Bayesian methodology and its applications. This group provides a platform for interdisciplinary projects and the training of future statisticians.

• Statistics
• Spatial Statistics
• Bayesian Inference
• +10 more

Antonio Manuel López Quilez is a Professor in the Department of Statistics and Operations Research at the Faculty of Mathematics, Universitat de València, Spain. He is affiliated with several research groups, including POpE (Public Opinion and Elections), VABAR (Valencia Bayesian Research Group), and VIO-STRAT (Advanced research strategies in family and gender violence). His email is antonio.lopez@uv.es. Doctor by the Universitat de València (1997) Thesis: Modelos lineales generalizados espaciales Supervisor: Dr. Juan Ferrándiz Ferragud His research focuses on statistical methodology with applications in spatial statistics, Bayesian inference, public health, environmental science, and social sciences. Key areas include spatial modeling, disease mapping, electoral analysis, and Bayesian hierarchical models. He frequently applies his methods to public health surveillance, such as influenza outbreak detection, and environmental modeling, including acoustic mapping with barriers. His recent publications highlight a strong trend in Bayesian modeling, particularly using integrated nested Laplace approximations (INLA), spatial smoothing, and Dirichlet regression for compositional data. He has contributed to dynamic forecasting of infectious diseases and the statistical analysis of bioclimatic indices. His work bridges theoretical statistical development with practical applications in epidemiology and social policy. No scientific awards are mentioned in the provided text. There is no information available about student advisement or research grants. However, his extensive collaborative work suggests active participation in research projects. He is a member of multiple interdisciplinary research teams, including those focused on public opinion, Bayesian methods, and gender violence research, indicating a strong team-based research approach. He leads or contributes to research in advanced statistical strategies for social and health issues, particularly through the VIO-STRAT group, which focuses on family and gender violence. His methodological expertise supports applied research in public policy and epidemiological decision-making.

Bruno EbnerView profile

Research Fellow

• Statistical Methodologies
• Goodness-of-Fit Tests
• Multivariate Statistics
• +6 more

Bruno Ebner is a Senior Research Fellow at the Karlsruhe Institute of Technology (KIT), specializing in statistical methodologies and their applications in applied mathematics and mathematical statistics. He obtained his PhD in Mathematics from KIT in 2010 and has been a permanent faculty member since 2012. His research focuses on developing statistical methods for uni-, multi-, and infinite-dimensional data, including non-Euclidean and metric spaces. Key research interests include goodness-of-fit testing, multivariate statistics, directional statistics, functional data analysis, and Stein's method. Notable contributions include the development of Sobolev tests on hyperspheres and the analysis of eigenvalues in covariance operators. Ebner has secured grants such as the STENED project, supporting PhD students like Paul Axmann and Vinícius Litvinoff. He has authored over 50 peer-reviewed articles in journals like TEST and the Journal of Nonparametric Statistics, with a focus on multivariate normality testing, exponentiality tests, and distributional characterization. His work emphasizes affine-invariant tests and neighborhood model validation. Ebner’s research bridges theoretical statistics with practical applications in data analysis and computational methods.

• Computer Architecture
• Compilers
• GPUs
• +4 more

Antonio González Colás is a Full Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona (FIB) and the Department of Computer Architecture. He leads the ARCO research group (Microarchitecture and Compilers) and focuses on advanced computer architecture, including GPUs, compilers, and embedded systems. In 2024, he received the ICREA Acadèmia distinction for his research on future computer systems integrating cognitive tasks and autonomous decision-making. His work emphasizes energy-efficient architectures, memory optimization, and hardware acceleration for AI and real-time applications. He holds a Doctorate in Computer Science and has over 1000+ publications, including top-tier journals and conferences. His research spans topics like GPU microarchitecture, compiler-assisted caching, DNN acceleration, and autonomous driving hardware. Major awards include the HiPEAC 2024 Paper Award and EU-funded projects under Horizon 2020. He advises numerous PhD students on topics such as point cloud processing for autonomous systems, GPU rendering optimizations, and neural network efficiency. His contributions bridge theoretical computer architecture with practical hardware-software co-design, addressing challenges in modern computing systems.

Jan GraffelmanView profile

Professor

• Compositional Data Analysis
• Multivariate Analysis
• Applied Statistics
• +1 more

Jan Graffelman is a Professor at the Universitat Politècnica de Catalunya (UPC), affiliated with the Facultat d'Informàtica de Barcelona and the Departament d'Estadística i Investigació Operativa. He leads the COSDA-UPC research group (COmpositional and Spatial Data Analysis) and coordinates the Doctoral Program in Statistics and Operations Research. His expertise spans compositional data analysis, multivariate analysis, applied statistics, and statistical genetics. Research focuses include genetic equilibrium testing (Hardy-Weinberg law), biplot visualization techniques, and applications in genomics and ecology. Notable contributions include methodologies for analyzing genetic markers, compositional data in precision medicine, and multidimensional scaling. Graffelman has collaborated with institutions like Barcelona Supercomputing Center and co-authored numerous high-impact publications in journals such as Heredity , Journal of Computational and Graphical Statistics , and Ecosphere . He has supervised multiple PhD students, including Iván Galván Femenía and Jordi Morales. His work integrates statistical theory with practical applications in genetics, ecology, and agriculture. Key projects include compositional methodology for family relationship inference and RNA-seq data analysis. Graffelman's academic network spans institutions globally, and he actively contributes to research projects funded by the Spanish State Research Agency and European grants. His lab develops tools for statistical analysis, including the HardyWeinberg and ToolsForCoDA R packages.

Paulo Félix LamasView profile

Professor

• Machine Learning
• Biomedical Signal Processing
• Probabilistic Reasoning
• +3 more

Paulo Félix Lamas is a Full Professor at the University of Santiago de Compostela (USC), specializing in Computer Science and Artificial Intelligence. He obtained his PhD in Physics from USC in 1999 and became an Associate Professor in 2002. As a foundational leader of CiTIUS (Research Centre in Intelligent Technologies), he served as Head (2010–2019) and Deputy Director (2019–2020). His research focuses on probabilistic learning , temporal abductive reasoning , and biomedical signal interpretation . Key applications include ECG analysis, glucose monitoring, and AI-driven healthcare solutions. His work bridges machine learning with clinical diagnostics, emphasizing interpretability and real-time systems. Recent publications (2015–2024) demonstrate interdisciplinary innovation, spanning: Healthcare AI : Personalized drug dosing, arrhythmia detection, and continuous glucose monitoring. Computational methods : Stochastic embeddings, kernel-based missing data handling, and adaptive clustering. Theoretical advances : Abductive reasoning frameworks for time-series interpretation. He leads major projects like: SOSFood (2024–2028): AI for sustainable food systems. XAI4SOC (2022–2025): Explainable AI for healthy aging. INSIDE (2022–2024): Predictive cardiac rehabilitation. At CiTIUS, he directs research on intelligent healthcare technologies, integrating sensor data with machine learning for chronic disease management.

• Statistics
• Operational Research
• Nonparametric Regression
• +2 more

César Andrés Sánchez Sellero is a Professor in the Department of Statistics, Mathematical Analysis and Optimization at the Faculty of Mathematics, University of Santiago de Compostela. He is affiliated with the Galician Mathematical Research and Technology Center (CITMAga) and leads research in the MODESTYA group (Optimization, Decision, Statistical Models and Applications). His academic career spans over 25 years, with a focus on statistical inference for censored/truncated data, nonparametric regression, and quantile modeling. PhD in Statistics (2001) from University of Santiago de Compostela Research Interests: Specializes in advanced statistical methodologies for complex data structures, including censored and truncated datasets. Key contributions in goodness-of-fit testing, bandwidth selection algorithms, and empirical likelihood frameworks for regression models. Publication Trends: His work bridges theoretical statistics with practical applications in data analysis, particularly in nonparametric methods and quantile regression. Collaborations with researchers like Wenceslao González-Manteiga and Valentin Patilea have produced significant results in error distribution estimation and model diagnostics.

• Multi-Label Learning
• Probabilistic Inference
• Explainable AI
• +7 more

Jesse Read is a Professor at École Polytechnique (Institut Polytechnique de Paris) affiliated with the ORAILIX team within the Data Analytics and Machine Learning pôle of the Computer Science Laboratory (LIX). His work focuses on multi-label and probabilistic inference , explainable and robust methods , and learning from data streams and sequential data with applications in medicine, energy, and transportation. He obtained his PhD from the University of Waikato (2010) and held postdoc positions at INFRES Télécom Paris, Aalto University, and Universidad Carlos III de Madrid before becoming Assistant Professor (2017) and Professor (2019) at École Polytechnique. Research Trends His recent publications emphasize multi-label learning (Classifier Chains, Regressor Chains) and data stream analysis with applications in degradation modeling and medical diagnostics. Key methodological contributions include probabilistic inference in chained models, scalable ensemble learning , and Monte Carlo optimization for sequential tasks. Application domains span healthcare (ECG analysis, insomnia detection), energy systems (wind turbine control), and transportation (route prediction). Scientific Awards Test of Time Award at ECML-PKDD 2019 for foundational work on Classifier Chains Habilitation à Diriger des Recherches (HDR) in Computer Science (2017) Advising & Grants Read has supervised PhD theses from Ekaterina Antonenko (Multi-Target Learning) and Olivier Pallanca (Paradoxical Insomnia Analysis). He secured an ANR grant for Dynamic Graph Signal Processing with Dr. Johannes Lutzeyer and Dr. Luca Martino.

• Probabilistic Machine Learning
• Bayesian Statistics
• Approximate Inference
• +4 more

Dr. Simón Rodríguez Santana is an Assistant Professor at the Higher Technical School of Engineering (ICAI) of Comillas Pontifical University, where he teaches in the Mathematical Engineering and Artificial Intelligence program. He holds a Physics degree from the Autonomous University of Madrid, a Master's in Theoretical Physics, and a PhD in Mathematical Engineering from Complutense University of Madrid. His research focuses on developing probabilistic machine learning and statistical techniques, particularly Bayesian methods applied to drug discovery, adversarial risk analysis, and time series forecasting. Professional experience includes a postdoctoral position at the Institute of Mathematical Sciences (ICMAT-CSIC) and visiting scholar roles at Aalto University (Finland). He has led two industrial research projects and contributed to national/international initiatives. Technical skills include Python (TensorFlow/PyTorch), R, LaTeX, and Slurm. Key research areas span probabilistic ML, Bayesian statistics, approximate inference, and operations research. Recent work emphasizes applications in personalized pricing strategies and AI-driven drug design. He has reviewed for top conferences (ICML, NeurIPS) and presented invited seminars on probabilistic ML applications. Awarded PAD accreditation from ANECA, he actively engages in academic dissemination through media appearances like 'Casting the Future' podcast and external training programs such as Generative AI for Education. His current teaching includes tenure-track positions and thesis supervision at undergraduate and master's levels.