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Zahraa S AbdallahView profile

Senior Lecturer

• Data Science
• Machine Learning
• Stream Mining
• +6 more

Dr Zahraa S Abdallah is a Senior Lecturer at the University of Bristol's School of Engineering Mathematics and Technology, specializing in data science and machine learning applications for healthcare, climate change, and smart metering systems. Her research bridges theoretical algorithm development with real-world implementation challenges in evolving data environments. Education: PhD BSc Research Focus: Dr Abdallah's work centers on time series and streaming data analysis using adaptive machine learning techniques. She develops novel frameworks for concept drift handling and real-time pattern recognition, with particular emphasis on healthcare monitoring systems and environmental data processing. Her fingerprint analysis reveals strong activity in Activity Recognition (100%), Data Streams (59%), and Time Series Data (37%) within computer science. Publication Trends: Her recent work demonstrates consistent innovation in streaming data applications, evolving from mobile activity recognition systems to sophisticated time series classifiers. The research trajectory shows increasing focus on healthcare integration, particularly through diabetes management applications using physiological data streams. Scientific Recognition: Australian Postgraduate Award (2010) for doctoral studies Mollie Holman Award (2016) for outstanding thesis contribution Research Leadership: As Co-Investigator on the UK T1D closed-loop systems project (2023-2024), she led AI components for physiological data integration. Her dataset contributions include the BrisT1D-Restricted Dataset (2025) for diabetes research and ML surrogates for genome design (2024). Collaborative Networks: Actively engaged with the Intelligent Systems Laboratory and Infection and Immunity research theme, maintaining international collaborations evidenced by multi-country research networks and 106+ Mendeley readers across publications.

Stefano FavaroView profile

Professor

• Nonparametric Bayesian Methods
• Empirical Bayes
• Statistical Machine Learning
• +5 more

Stefano Favaro is a Professor of Statistics at the University of Torino and holds the Carlo Alberto Chair of Statistics and Machine Learning at Collegio Carlo Alberto. His research focuses on Bayesian nonparametric methods, statistical machine learning, and applications to data confidentiality and deep learning theory. PhD in Statistics, Bocconi University, 2009 His research interests include: Nonparametric Bayes and Empirical Bayes methods Statistical Machine Learning with emphasis on learning-augmented algorithms Data confidentiality and fairness in machine learning Mathematical foundations of deep neural networks Recent work explores Bayesian nonparametric models for frequency estimation, deep learning asymptotics, and privacy-preserving statistical methods. He leads DataLab Algorithms at Collegio Carlo Alberto and serves as Associate Editor for Bernoulli and Statistical Science .

• Data Science
• Machine Learning
• Privacy-Preserving Technologies
• +3 more

Valentin Hartmann is a Researcher at the Data Science Laboratory (DLAB) within the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL). His work focuses on advanced topics in data science, including differential privacy, optimal transport theory, and privacy-preserving machine learning. He holds a postdoctoral position and contributes to cutting-edge research in computational statistics and secure distributed learning. His research interests span multiple domains: (1) Development of privacy-preserving techniques for machine learning and data analysis, (2) Optimization of transport problems with applications in statistics and geometry, (3) Design of secure algorithms for distributed systems, and (4) Implementation of robust statistical methods through R packages like 'transport'. Valentin actively publishes in top-tier venues, with recent contributions addressing neural network behavior analysis, privacy risks in data distribution inference, and novel cryptographic approaches for collaborative learning. His work bridges theoretical foundations with practical software solutions, as evidenced by his development of the 'transport' R package for optimal transport computations. He is affiliated with the DLAB research group at EPFL's INN building (Office INN 315), contributing to interdisciplinary projects at the intersection of computer science, mathematics, and privacy engineering.

• Machine Learning
• Biomedical Data Analytics
• Deep Learning
• +3 more

Dr. Amir Joudaki is a Researcher affiliated with the Department of Biomedical Informatics at ETH Zürich. His role is part of the Professorship for Data Analytics, focusing on interdisciplinary research at the intersection of machine learning and biomedicine. He is stationed at CAB F 53.1, Universitätstrasse 6, Zurich. His research interests span machine learning, biomedical data analytics, neural network theory, genomics, and bioinformatics algorithms. Notable contributions include work on deep neural network dynamics, batch normalization techniques, and graph-based genomic sequence analysis. Though not explicitly listed in the provided texts, his work suggests involvement in projects related to alignment-free bioinformatics methods, optimization in deep learning, and theoretical aspects of neural networks. Lab/Team Affiliation: Part of the Biomedical Informatics group at ETH Zürich, likely contributing to interdisciplinary data science initiatives in healthcare and genomics.

• Statistical Estimation
• High-Dimensional Data Analysis
• Convex Regression
• +3 more

Dr. Gil Kur is a Lecturer in the Department of Mathematics at ETH Zürich. His research focuses on statistical estimation, high-dimensional data analysis, convex regression, machine learning theory, optimization, and probability theory. He has contributed to areas such as nonparametric estimation, convex body approximation, and differential privacy mechanisms. His work bridges theoretical foundations with applications in computational statistics and optimization. Key research themes include analyzing convergence rates of estimators, developing optimal algorithms for convex regression, and studying geometric properties of high-dimensional spaces. His recent articles explore topics like debiased LASSO methods, log-concave maximum likelihood estimation, and the performance of empirical risk minimization under various constraints. Kur’s publications demonstrate a strong focus on rigorous mathematical analysis, often combining tools from probability, functional analysis, and convex geometry. While no specific awards or grants are listed, his active publication record reflects sustained contributions to statistical theory and machine learning fundamentals.

Rico ZenklusenView profile

Professor

• Combinatorial Optimization
• Theoretical Computer Science
• Network Optimization
• +3 more

Prof. Rico Zenklusen is a Full Professor at the Department of Mathematics and Deputy Head of the Institute for Operations Research at ETH Zurich. He previously held positions at Johns Hopkins University and conducted postdoctoral research at MIT and EPFL. His research focuses on Combinatorial Optimization, including algorithm design for complex optimization problems using structures like matroids, submodular functions, and polyhedral methods, with applications in Theoretical Computer Science and Graph Theory. Education: PhD in Mathematics from ETH Zurich Master's degree in Mathematics from EPFL Research Interests: Combinatorial Optimization, Network Design, Submodular Maximization, Matroid Theory, and Applications in Operations Research. His work emphasizes efficient algorithms for optimization problems with real-world applications, such as train engine scheduling, operating room management, and rolling stock scheduling for railways. Grants & Awards: ERC Consolidator Grant (ICOPT) Swiss National Science Foundation Support Students & Advising: Advises PhD and master’s students in optimization and related fields. Current and past students include Adam Kurpisz, Etienne Bamas, and Vera Traub. Visit the group page for project opportunities. Labs & Collaborations: Leads the Zenklusen Group, collaborating on interdisciplinary projects with industries like BLS Cargo (train engine optimization) and the SBB (rolling stock scheduling). Active in ETH Zurich's AI Center for complex system research.

• Computational Geometry
• Algorithm Design and Analysis
• VLSI Computer-Aided Design
• +3 more

Evanthia Papadopoulou is a Full Professor of Computer Science at the University of Lugano (Università della Svizzera italiana) since 2016. Previously, she was an Associate Professor at USI (2008–2016), a Research Staff Member at IBM T.J. Watson Research Center (1996–2008), and an Assistant Professor at Athens University of Economics and Business (2004–2008). She holds a BS in Mathematics from the University of Athens, an MS in Computer Science from the University of Illinois at Chicago, and a PhD in Computer Science from Northwestern University (1995). Her research focuses on computational geometry, algorithm design, and their applications in VLSI CAD and geometric computing. Key areas include Voronoi diagrams, proximity algorithms, triangulations, and geometric optimization. She has received the IBM Outstanding Innovation Award (2006) and the Technical Accomplishment for IBM Research (2006) for her work on VLSI critical area analysis using Voronoi diagrams. As part of the Dalle Molle Institute for Artificial Intelligence (IDSIA USI-SUPSI), her work integrates theoretical and applied aspects of computational geometry. Her contributions span algorithmic frameworks, geometric data structures, and practical applications in VLSI layout analysis and manufacturing. Her recent research emphasizes high-order Voronoi diagrams, subdivision methods for geometric optimization, and certified approximation algorithms. These advancements address challenges in geometric computing, such as farthest-color Voronoi diagrams and dynamic geometric problems.

• Reservoir Computing
• Machine Learning
• Neural Networks
• +4 more

Lyudmila Grigoryeva is a Professor at the University of St. Gallen. Her research focuses on reservoir computing, machine learning, and nonlinear dynamical systems. She has contributed to advancing theoretical frameworks for recurrent neural networks, including memory capacity analysis, approximation bounds, and time-series forecasting. Key collaborations include work with Juan-Pablo Ortega Lahuerta and Lukas Gonon on reservoir systems and stochastic processes. Research Themes: Infinite-dimensional reservoirs, geometric invariant learning, and parallel-in-time solvers. Applications: Macroeconomic forecasting, chaotic attractor learning, and PDE solving. Her recent work addresses foundational questions in reservoir computing, such as memory capacity evaluation and universal approximation properties. She has also explored interdisciplinary applications in econometrics and computational neuroscience.

Gilles VilmartView profile

Senior Lecturer

• Numerical Analysis
• Geometric Numerical Integration
• Multiscale Methods
• +3 more

Gilles Vilmart is a Senior Lecturer (Maître d'enseignement et de recherche) at the University of Geneva's Department of Mathematics since 2013. His research focuses on numerical analysis of geometric and multiscale methods for deterministic and stochastic differential equations, emphasizing geometric numerical integration and numerical homogenization. He holds a PhD (2008, double doctorate from University of Rennes 1 and Geneva) and a Habilitation (2013, ENS Rennes). Former affiliations include the Swiss Federal Institute of Technology Lausanne (EPFL), University of Rennes 1, and INRIA Rennes. He organizes the numerical analysis seminar with Martin Gander and Bart Vandereycken, and co-founded the CAMAS Consulting platform. He is a member of Swiss mathematical society, Swiccomas, and SMAI (France). Key achievements include the Young Researcher First Prize 2013 from Brittany Region for Mathematics. His work includes developing numerical methods for stochastic and multiscale systems, with software contributions like PIROCK and multirevolution integrators. He advises PhD students and organizes workshops on numerical analysis and complex systems.

Rie Shimizu-InatsugiView profile

Researcher

• Evolutionary biology
• Ecological genomics
• Plant adaptation
• +4 more

Rie Shimizu-Inatsugi is a Research Associate at the Department of Evolutionary Biology and Environmental Studies, University of Zurich, Switzerland. Her work focuses on plant evolutionary biology, particularly genome duplication (polyploidization) and its role in adaptation and speciation. Current Position: Research Associate (2011-present) Prior Positions: SNF Marie Heim-Vögtlin Programme Research Associate (2011-2014), Postdoctoral Fellow at The University of Tokyo (2004-2006) Key research areas include: Environmental tolerance of plants Speciation through polyploidization Evolution of mating systems after genome duplication Evolutionary and ecological genomics Recent work explores polyploid transcriptome plasticity, homeolog expression patterns, and neighbor genotypic effects in plant communities. She has contributed to developing genomic tools for polyploid species analysis (e.g., ARPEGGIO workflow, low-coverage RNA-seq methods). Her publications span high-impact journals like Nature and Molecular Ecology . Scientific Awards SNF Marie Heim-Vögtlin Programme Research Associate

• Quantum Computing
• Quantum Error Correction
• Superconductivity
• +3 more

Dr. Christoph Hellings is a Lecturer at the Department of Physics at ETH Zürich, specializing in quantum technologies and superconducting circuits. His research focuses on quantum error correction, quantum hardware implementation, and the development of scalable quantum systems. He contributes to advancements in superconducting qubit architectures, leakage reduction mechanisms, and fault-tolerant quantum computing protocols. His work bridges theoretical models with experimental validation, particularly in areas like stabilizer code decoding, lattice surgery, and device-independent randomness amplification. Hellings collaborates on projects involving multi-module quantum processors, photonic cluster states, and real-time feedforward systems. His experiments frequently address practical challenges such as magnetic flux control, inter-chip coupling optimization, and mitigation of loss mechanisms in superconducting circuits. Key research themes include improving qubit coherence times, enhancing gate fidelity through dynamic control, and advancing quantum error correction techniques for surface codes. Recent studies explore the integration of leakage reduction units and the characterization of quantum processor performance under realistic conditions.

• Analog Circuits Design
• Mixed-Mode Electronics
• Low Power Electronics
• +5 more

Maher Kayal is an Honorary Professor at the École Polytechnique Fédérale de Lausanne (EPFL), Switzerland, affiliated with the School of Engineering (STI) and the Department of Mechanical and Process Engineering (STI-SMT). His primary role involves academic leadership in electronics and energy management research. He holds a Master's and PhD in Electrical Engineering from EPFL (1983 and 1989 respectively), and has been with the Electronics Laboratory (ELab) since 1990, directing the 'Energy Management and Sustainability' section. His work focuses on analog/mixed-signal IC design, energy harvesting, smart grid technologies, and sensor systems. Education: M.S. (1983), Ph.D. (1989) in Electrical Engineering, EPFL. Affiliations: EPFL’s Electronics Laboratory (ELab), School of Engineering. Research interests include ultra-low-power sensor interfaces, energy-efficient electronics for smart buildings, and real-time power grid emulation. He has authored/co-authored three textbooks on mixed-mode CMOS design and holds 11 patents. His work bridges semiconductor physics, circuit design, and energy systems. Recent articles focus on wearable biosensors (ANTIGONE project), blockchain-based smart-building energy management, and high-speed power system emulators. Notable awards include the Credit Suisse Teaching Award (2009) and multiple best-paper prizes at IEEE conferences. He has advised over 30 PhD students, with notable alumni working in semiconductor design and energy systems. His labs pioneer innovations in analog emulation for power systems and IoT-enabled smart infrastructure.

• Algorithms
• Computational Geometry
• Optimization
• +3 more

Prof. Bernd Gärtner is a Lecturer at the Department of Computer Science of ETH Zürich. His research focuses on algorithms, computational geometry, optimization, and theoretical computer science. He has contributed significantly to the study of unique sink orientations, combinatorial algorithms, and algorithm design. Gärtner teaches courses such as 'Algorithms, Probability, and Computing' and 'Geometry: Combinatorics and Algorithms,' reflecting his expertise in foundational computer science topics. His work bridges discrete mathematics and algorithmic theory, addressing challenges in linear programming, combinatorial optimization, and geometric algorithms. His recent research explores the realizability of structures in unique sink orientations, optimization techniques for symbolic visibility, and the analysis of opinion dynamics in networks. He has published extensively on topics including ARRIVAL game complexity, sampling algorithms, and high-dimensional learning models. His contributions also extend to the development of efficient algorithms for geometric problems and the study of cellular automata systems. Teaching: Courses include Algorithms, Probability, and Computing (252-0209-00L), Linear Algebra (401-0131-00L), and Geometry: Combinatorics and Algorithms. Research Interests: Algorithms, computational geometry, optimization, combinatorics, and theoretical computer science. Labs/Teams: Affiliated with the Institute of Theoretical Computer Science at ETH Zürich.

Maximilian ProbstView profile

Lecturer

• Graph Algorithms
• Convex Optimization
• Dynamic Algorithms
• +3 more

Maximilian Probst is a Lecturer at ETH Zürich's Department of Computer Science, affiliated with Rasmus Kyng's Algorithms and Optimization Group. He holds a Ph.D. from the University of Copenhagen (2020) and has conducted research at MIT. His research focuses on graph algorithms, dynamic networks, and optimization, with notable contributions to expander decomposition and network flow algorithms. Probst has received prestigious awards including the 2023 Frontiers of Science Award and the 2022 FOCS Best Paper Award. Education: Ph.D. in Computer Science (2020), University of Copenhagen; M.Sc. in Computer Science, University of Copenhagen. Supervisors: Christian Wulff-Nilsen and Mikkel Thorup. Research Interests: Graph Algorithms (Dynamic, Expander-based), Network Flows, Convex Optimization, and Algorithm Design. His work emphasizes theoretical guarantees for real-world graph problems, particularly in dynamic environments. Publications: Probst's work spans foundational algorithmic breakthroughs, including nearly-linear-time maximum flow algorithms and dynamic graph sparsification techniques. His research bridges theory and practice, with applications in routing, congestion balancing, and expander decomposition. Awards: Frontiers of Science Award (2023) FOCS Best Paper (2022) ESA Best Student Paper (2018) Teaching: Co-instructor for courses like 'Advanced Graph Algorithms and Optimization' and 'Advanced Algorithms' at ETH Zurich. Lab/Team: Member of the Algorithms and Optimization Group, collaborating closely with Rasmus Kyng and leading Ph.D. students in theoretical computer science.

• Theoretical Computer Science
• Combinatorics
• Discrete Mathematics
• +5 more

Dr. Heidi Gebauer is a researcher at the ZHAW School of Engineering, specializing in Scientific Computing & Algorithmics. She is based at the Winterthur campus and actively contributes to theoretical computer science and discrete mathematics research. Her research interests span Theoretical Computer Science , Combinatorics , Random Graphs , Algorithm Design , Advice Complexity , and Graph Theory . Her work combines mathematical rigor with computational applications, particularly in algorithmic game theory and combinatorial structures. The recent publications reflect a strong focus on theoretical analysis of algorithms and combinatorial models, with applications in computational complexity and discrete optimization. Her work often appears in high-quality peer-reviewed venues in combinatorics and computing. Dr. Gebauer has contributed to completed research projects including the development of an intelligent scheduling system for a 14-axis laser welding machine and a new end-point controller. These projects suggest applied algorithmic work alongside theoretical contributions. No scientific awards listed in the provided text. She has collaborated with researchers such as Dennis Clemens, Anita Liebenau, Dennis Komm, and others. There is no mention of student supervision or grant leadership in the available information. Dr. Gebauer is part of the research team in the Research Focus Scientific Computing & Algorithmics at ZHAW, which likely involves collaboration on algorithm development, mathematical modeling, and computational problem-solving.