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Paolo Tonella is a Full Professor and Director of the Software Institute at the Faculty of Informatics, Università della Svizzera italiana (USI) in Lugano, Switzerland. He also holds an Honorary Professorship at University College London (UK) and previously led the Software Engineering group at Fondazione Bruno Kessler (Italy). His research focuses on software testing, analysis, and AI-driven systems. He has authored over 200 peer-reviewed papers and 100 journal articles, with an H-index of 72. He teaches courses in Data and Software Engineering and Informatics, including Information Modeling, Probability & Statistics, and Knowledge Search. Key contributions include foundational work on web application testing (ICSE MIP award), evolutionary testing techniques (eToc/EvoSuite tools), and reverse engineering of object-oriented systems. He led the ERC-funded PRECRIME project on anticipatory testing. His recent work addresses AI dependability, autonomous systems testing, and deep learning fault analysis. Scientific awards include the ICSE MIP Award (2001) and ERC Advanced Grant (2018). He has served on editorial boards for major journals like IEEE Transactions on Software Engineering and ACM TOSEM. Current roles include leadership in the Software Institute and organizing the SIESTA summer school.

• Software Engineering
• Software Testing and Analysis
• Web Engineering
• +4 more

Prof. Dr. Raphael Sznitman serves as Director of the ARTORG Center for Biomedical Engineering Research and Head of the Artificial Intelligence in Medical Imaging group at the University of Bern, Switzerland, holding a Full Professor position in AI for Medical Imaging since 2015. Education: PhD in Computer Science, Johns Hopkins University (2011) MSc in Computer Science, Johns Hopkins University (2009) BSc in Cognitive Systems, University of British Columbia (2007) Research Interests: Sznitman's work centers on computational vision , probabilistic methods , and statistical learning applied to medical imaging challenges. His group develops AI algorithms for ophthalmic diagnostics, surgical robotics, and medical image analysis, with emphasis on OCT, surgical phase recognition, and domain adaptation techniques. Key application areas include retinal disease detection and cataract surgery automation. Publication Trends: His 2021-2025 publications reveal concentrated efforts in deep learning for medical imaging , particularly in ophthalmology (OCT analysis) and surgical video understanding. Emerging themes include LLM applications for clinical monitoring, unsupervised out-of-distribution detection for surgical safety, and physics-informed AI for multimodal medical data fusion. Research Leadership: As ARTORG Center Director, Sznitman oversees interdisciplinary research bridging computer science and clinical medicine. His group collaborates extensively with Bern University Hospital clinicians on translational projects, securing funding for AI-driven diagnostic tools and surgical assistance systems. Current initiatives focus on real-time intraoperative guidance and spaceflight ophthalmology applications. Laboratory: The Artificial Intelligence in Medical Imaging group operates within ARTORG's dedicated facilities, maintaining partnerships with surgical robotics labs and ophthalmology departments for clinical validation of AI systems. Their work integrates multimodal data streams including OCT, VR perimetry, and surgical video feeds.

• Computer Vision
• Probabilistic Methods
• Statistical Learning
• +2 more

Giancarlo Ferrari Trecate is an Adjunct Professor at the Swiss Federal Institute of Technology Lausanne (EPFL) , affiliated with the School of Engineering and the SCI-STI-GFT department. He is also involved in teaching and research through the STI-SGM and EDRS-ENS programs. Research Interests : Automatic control, state estimation, system identification, machine learning, distributed control, hybrid systems, microgrids, biochemical networks, voltage and frequency stabilization in AC/DC microgrids. Publications Trends : His recent work focuses on integrating Neural ODEs and Hamiltonian structures for stable control systems, regret minimization in distributed control, and robust state estimation under uncertainty. Applications include autonomous mobility-on-demand , power grid optimization , and secure microgrid control against cyber-attacks. Scientific Awards : No specific awards mentioned in the provided data. Teaching & Advising : He supervises PhD students in mechanical engineering and teaches courses on Multivariable control and Networked control systems . His lab, DECODE , specializes in Dependable Control and Decision systems.

• Automatic control
• State estimation
• System identification
• +7 more

Gianni Franchi is an Assistant Professor at ENSTA Paris, part of Institut Polytechnique de Paris. His research focuses on robust computer vision, uncertainty quantification, and explainable AI (XAI). He has been teaching Deep Learning, Computer Vision, and Machine Learning courses since 2020 at ENSTA Paris and Télécom Paris. PhD in Fusion of Information, Machine Learning, and Image Processing (2016) from Mines de Paris Postdoctoral experience at Paris Saclay University (2018-2020) and Seigen University (2016-2018) Current PhD students: Rémi Kazmierczak, Olivier Laurent, Adrien Lafage, Mouïn Ben Ammar Alumni: Xuanlong Yu (2020-2023) Research interests include robust computer vision, anomaly detection, uncertainty quantification, out-of-distribution detection, certifiable AI, and explainable AI. He leads the development of the PyTorch library Torch Uncertainty for uncertainty quantification in deep learning. Recent publications span uncertainty quantification in foundation models, trajectory forecasting, vision-language adaptation, and explainability benchmarks. Gianni actively collaborates on multimodal autonomous driving datasets and uncertainty-aware systems for human-agent interaction.

• Machine Learning
• Computer Vision
• Uncertainty Quantification
• +2 more

Fabio Sigrist is a Professor of Applied Statistics and Data Science at the Institute of Financial Services Zug (IFZ) , part of the Lucerne University of Applied Sciences and Arts . He also holds a Senior Scientist and Lecturer position at the Seminar for Statistics, ETH Zurich . His career spans academic research, industry consulting, and project leadership in finance and data science. PhD in Statistics (2013), ETH Zurich MSc in Mathematics with distinction (2008), ETH Zurich MEd in Mathematics Education (2008), ETH Zurich Sigrist’s research focuses on integrating Machine Learning with Spatial Statistics for applications in Financial Econometrics and Credit Risk . His work includes developing novel algorithms like GPBoost and KTBoost , advancing spatio-temporal modeling , and applying tree-based boosting to financial problems. Projects such as CreHos (credit risk in hospitality) and NISMO (interpretable real estate modeling) highlight his interdisciplinary approach. His publications address challenges in large-scale spatial data , loss given default modeling , and stock volatility prediction . He contributes to software development with tools like spate (R package) and varycoef (spatially varying coefficients).

• Machine Learning
• Spatial Statistics
• Financial Econometrics
• +3 more

Reinhard Heckel is a Tenured Associate Professor (equivalent to Professor) of Machine Learning at the Department of Computer Engineering, Technical University of Munich (TUM), and Adjunct Faculty in Electrical and Computer Engineering at Rice University. He was previously an Assistant Professor at Rice (2017–2019), a postdoc in the Berkeley Artificial Intelligence Research (BAIR) Lab at UC Berkeley, and a researcher at IBM Research Zurich. Education: PhD, 2014 – ETH Zurich Visiting PhD student – Department of Statistics, Stanford University Research Interests: His work centers on machine learning and information processing with three major thrusts: (1) developing algorithms and theoretical foundations for deep learning, especially for accelerated magnetic resonance imaging ; (2) establishing rigorous mathematical and empirical underpinnings for modern machine-learning systems; and (3) leveraging DNA as a digital information-storage medium , including error-correction coding and system design for DNA-based storage. Across more than 100 peer-reviewed papers since 2017, Heckel’s research exhibits a strong interdisciplinary blend of computational imaging , machine-learning theory , and molecular data storage . Recent 2024–2025 publications show intensive focus on robust MRI reconstruction using diffusion priors, evaluation of bias in large web-text corpora, and state-of-the-art error-correcting codes for DNA storage channels. A forthcoming book, Deep Learning for Computational Imaging (Oxford University Press), consolidates his contributions to the field. Outreach & Media: Keynote and panel talks at DLD, TUM, and major ML conferences Op-eds in Frankfurter Allgemeine on ChatGPT and DNA storage Science features on Netflix, BBC, and German television (Galileo, “Gut zu Wissen”) Research Environment: At TUM he leads a group investigating theoretical and applied aspects of deep learning, compressed sensing, and coding for DNA storage. Open-source repositories on GitHub (e.g., dna_data_storage , supplement_deep_decoder ) provide code and data supplements accompanying his publications.

• Machine Learning
• Deep Learning
• Computational Imaging
• +5 more

Jian Peng is an Assistant Professor in the Department of Computer Science at the University of Illinois at Urbana-Champaign. His research focuses on computational biology, machine learning, and their applications to protein structure prediction, drug design, and molecular modeling. He has contributed to advancements in antibody engineering, protein-ligand docking, and generative models for biological systems. Key research areas include: Machine Learning for Molecular Modeling Protein Structure Prediction Antibody and Peptide Design Genomics and Single-Cell Analysis Structure-Based Drug Discovery His work emphasizes integrating deep learning techniques with biological datasets to address challenges in precision medicine, drug development, and systems biology. Notable achievements include developing the FastFold system to accelerate AlphaFold training and pioneering flow-based methods for antibody design. Awards include the Overton Prize (2020), recognizing contributions to computational biology. His research has been published in top journals and conferences, spanning topics from protein mutation prediction to geodesic-based immune complex modeling.

• Machine Learning
• Bioinformatics
• Computational Biology
• +5 more

Hugo Duminil-Copin is a Full Professor at the University of Geneva and a permanent professor at the Institut des Hautes Études Scientifiques (IHES) since 2016. His research focuses on Mathematical Physics , Combinatorics , and Probability Theory . Education : École Normale Supérieure (ENS) Paris, University of Paris-Saclay Awards : 2022 Fields Medal for work in statistical physics Research Trends : Probabilistic aspects of lattice models Phase transitions and critical phenomena Conformal invariance and percolation theory Collaborations : Active collaborations with researchers such as R. Panis, S. Goswami, I. Manolescu, and others. Teaching : Offers courses in mathematical physics and probability at the University of Geneva. His recent publications emphasize critical models, random-cluster models, and Gaussian free fields, with applications in planar and high-dimensional systems. He supervises doctoral students including Emile Averous , Aman Markar , and Tiancheng He .

• Mathematical Physics
• Combinatorics
• Probability Theory

Xiang Yin is a Research Associate at the Department of Computing in Imperial College London , affiliated with the Computational Logic and Argumentation group (CLArg) . His work bridges Explainable AI (XAI) and Computational Argumentation (CA) , focusing on the explainability of Quantitative Bipolar Argumentation Frameworks (QBAFs) through attribution and counterfactual explanations. Research Interests: Explainable AI (XAI) Computational Argumentation Quantitative Bipolar Argumentation Frameworks Model Interpretability Human-AI Interaction Logical Reasoning for AI Publication Trends reveal a focus on argumentation-based explainability, with 2025-2024 works addressing large language models for claim verification, truth-discovery frameworks, and counterfactual explanations. Earlier works (2023-2022) explore random forest explanations, faithfulness criteria, and QBAF analysis. His 2018 publications on aircraft prediction systems demonstrate applied machine learning expertise. Education PhD in Artificial Intelligence under Prof. Francesca Toni and Dr. Nico Potyka Pre-PhD: Machine Learning R&D Engineer at Baidu Labs & Teams Xiang is part of the CLArg group at Imperial College London, focusing on integrating computational argumentation with AI explainability and contestability.

• Explainable AI
• Computational Argumentation
• Quantitative Bipolar Argumentation Frameworks
• +2 more

Sebastijan Dumancic is an Assistant Professor at Delft University of Technology, focusing on neuro-symbolic AI through program synthesis and probabilistic programming. He leads the RAIL lab and collaborates with institutions like Harvard, MIT, and CNRS. His research bridges symbolic AI and machine learning, applying program synthesis to scientific discovery, transportation, and robotics. He holds an FWO-funded PhD from KU Leuven and has participated in initiatives like ELLIS and the Symbolic Computation and Machine Learning Initiative. Program synthesis Probabilistic programming Neuro-symbolic AI Constraint-based learning His recent articles highlight advancements in program synthesis, neuro-symbolic integration, and constraint satisfaction. Projects like Find2Fix and Intelligent Greenhouse Horticulture (funded by NWO) demonstrate practical applications. ELLIS Membership University Teaching Qualification He supervises numerous MSc and PhD students in projects involving logic programming, program synthesis, and probabilistic modeling. Active in workshops and symposia, he contributes to neuro-symbolic AI and scientific discovery.

• Program Synthesis
• Probabilistic Programming
• Neuro-Symbolic AI
• +2 more

Johanna Ziegel is a Professor of Statistics at ETH Zurich, Switzerland, since 2024, and a Visiting Scientist at the Heidelberg Institute for Theoretical Studies (HITS). Previously, she held positions at the University of Bern, where she was promoted to Full Professor in 2023. Her research focuses on decision-theoretically sound methods for forecast evaluation, probabilistic forecasting, risk measures in finance, and applications in meteorology, medicine, and climate science. She is actively involved in editorial roles for journals like Bernoulli , JASA: Theory & Methods , and SIAM Journal on Financial Mathematics . Education: PhD in Stereological Analysis of Spatial Structures from ETH Zurich (2010), supervised by Paul Embrechts and Eva B. Vedel Jensen. Postdoctoral research at the University of Melbourne and Heidelberg University. Research Interests: Forecast evaluation, elicitable functionals, risk measures, isotonic regression, statistical calibration, and applications in finance, climate science, and biostatistics. Her work bridges theoretical statistics with practical challenges in uncertainty quantification and decision-making under uncertainty. Advising & Collaborations: Supervised 7 PhD students and mentored several postdocs. Collaborates with the Computational Statistics group at HITS and the Oeschger Centre for Climate Change Research. Her group explores distributional regression under order constraints and novel methods for forecast comparison. Recognition: Credit Suisse Award for Best Teaching (2022), H.I.T. Program for Academic Leadership (2021–2022). Active in professional service, including the Bernoulli Society Council and editorial boards.

• Statistical Forecasting
• Probability Theory
• Financial Risk Management
• +3 more

Yuning Jiang is a Visiting Professor at the École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Automatic Control Laboratory (LA3) within the School of Engineering (STI). He teaches the doctoral course Optimal Control for Dynamic Systems and contributes to research in distributed optimization, model predictive control (MPC), and smart grid technologies. His work bridges theoretical advancements in control systems with practical applications in power networks and autonomous systems. Current research emphasizes scalable solutions for AC optimal power flow, real-time MPC for embedded systems, and robust optimization under uncertainty. His research interests span Optimal Control , Power Systems , Smart Grids , and Federated Learning . Notable contributions include distributed algorithms for large-scale power systems and privacy-preserving co-simulation frameworks. Recent publications focus on microservice deployment in satellite-terrestrial networks and real-time pricing mechanisms for vehicle-to-grid (V2G) integration. Yuning holds a position in the EDEE-ENS unit under EPFL’s Academic Affairs division (VPA-AVP-DLE), reflecting his role in academic administration and teaching infrastructure. His lab, the Automatic Control Laboratory, focuses on cutting-edge research in control theory and its interdisciplinary applications.

• Optimal Control
• Model Predictive Control
• Power Systems
• +4 more

Dina Khaled Sayed Abdelhadi is a Doctoral Assistant at the Communication Theory Laboratory (LTHC) within the School of Computer and Communication Sciences (IC) at École Polytechnique Fédérale de Lausanne (EPFL). She is also a PhD student in the Doctoral Program in Computer and Communication Sciences (EDIC), hosted under the EDOC school at EPFL. Her work is centered in the Institute of Computer and Communication Sciences (IINFCOM), focusing on theoretical and applied aspects of communication systems. Her research interests are rooted in Information Theory , Communication Theory , and Signal Processing , with potential applications in wireless networks and data transmission. Given her affiliation with LTHC, her work likely involves advanced mathematical modeling, probabilistic methods, and algorithm design for communication systems. No scientific awards or publications were mentioned in the provided text. However, her position as a Doctoral Assistant suggests active involvement in research projects funded by EPFL or external agencies, possibly including collaborations within the laboratory or international consortia. She is advised by faculty within the LTHC group, though no advisor is explicitly named. There is no mention of students supervised by her. She is part of a structured doctoral program, indicating rigorous academic training and research progression. The Communication Theory Laboratory (LTHC) at EPFL is known for cutting-edge research in information theory, network coding, and communication algorithms. As a member of this lab, Dina contributes to ongoing projects that bridge theoretical advances with practical communication technologies.

• Information Theory
• Communication Theory
• Signal Processing
• +3 more

Dr. Maren Boese is a Senior Research Scientist at the Swiss Seismological Service (SED) and Seismology and Geodynamics (SEG) groups at ETH Zurich. Her expertise spans seismology, earthquake early warning systems, and planetary seismology. She leads projects like the CISN ShakeAlert system for California and contributed to NASA's InSight mission to Mars. She earned her Ph.D. from Karlsruhe Institute of Technology (2006) and a Master's from Kiel University (2002). Research Focus: Real-time seismic event detection, finite-fault rupture analysis, ground-motion simulation, and Marsquake seismology. Key contributions include developing the FinDer algorithm for the ShakeAlert system and advancing early warning methodologies in Central America and Switzerland. Awards: 2011: Outstanding Publication Award (Luxembourg National Research Fund) 2010: Ausgewählter Ort im Land der Ideen (BMBF Project EWS Transport) 2006: European IST Prize (Early Warning for Strong Earthquakes) Grants & Leadership: Led the CISN ShakeAlert team (2009–2014), managed the German CRC 461 project, and coordinated international collaborations like the InSight Marsquake Service. Labs/Teams: Swiss Seismological Service (ETH Zurich), NASA InSight Mission, and the CHEAP smartphone network project in Chile.

• Seismology
• Earthquake Early Warning
• Real-Time Seismic Monitoring
• +4 more

Niklas Linde is a full professor at the University of Lausanne's Faculty of Geosciences and Environment, leading the Department of Earth Sciences. He holds a PhD in Geophysics from Uppsala University (2005) and has held roles including Assistant Professor (2008), Associate Professor (2013), and Full Professor (2019). His research focuses on transforming geophysical signals into realistic hydrogeological models with rigorous uncertainty quantification. Key areas include probabilistic inversion, Bayesian methods, and geostatistical modeling applied to environmental and subsurface processes. Education: PhD in Geophysics (Uppsala University, 2005), postdoctoral positions at Lawrence Berkeley National Lab (USA), CNRS-CEREGE (France), and ETH Zurich (Switzerland). He joined UNIL in 2008 as an Assistant Professor in Environmental Geophysics. Research interests span geophysical inversion techniques, subsurface heterogeneity characterization, and the integration of geophysical and hydrological data. Current projects emphasize Bayesian approaches for model selection and rare event estimation, supported by grants from the European Commission and Swiss National Science Foundation. Collaborations involve international teams addressing challenges in hydrogeology, rock fracture dynamics, and 4D hydrogeology. Publications reflect advancements in inverse problem solving, stochastic simulation, and machine learning applications. His work bridges theory and practice, with field studies in alpine environments, fractured media, and environmental monitoring. Students under his supervision have explored topics like deep generative networks and Bayesian hydrogeological inversion. Advising: Supervised over a dozen PhD students, including recent works on variational Bayesian methods and geophysical data fusion. Grants include projects on uncertainty quantification and experimental design. Active in scientific societies and editorial roles, contributing to methodological advancements in Earth sciences.

• Geophysics
• Environmental Geophysics
• Probabilistic Inversion
• +3 more