جست‌وجوی استاد و پژوهشگر

Erol Orelمشاهده پروفایل

پژوهشگر

• Machine Learning
• Data Science
• Infectious Disease Epidemiology
• +۵ مورد دیگر

Erol Orel is an econometrician and data scientist at the University of Geneva's Institute of Global Health, part of the Faculty of Medicine. He works in the Division of Infectious Diseases and Mathematical Modelling, focusing on applying machine learning and data science to global health challenges. Erol holds a Master of Science in Quantitative Economics, a D.E.A. in Econometrics, and a PhD in Biomedical Sciences (Global Health track) from the University of Geneva. Before transitioning to global health, he spent over a decade in finance as a quantitative analyst and risk manager, specializing in portfolio risk and alternative investments. He teaches statistics and epidemiology in the MAS en santé publique program at the Faculty of Medicine. His research emphasizes HIV epidemiology, including socio-behavioral risk prediction, disease surveillance, and machine learning applications. He contributed to WHO, FOPH, and HUG collaborations on pandemic modeling and developed LiteRev, an NLP-driven tool for automating literature reviews. Key projects include analyzing HIV viral suppression in Malawi, predicting antiretroviral treatment interruptions in South Africa, and assessing SARS-CoV-2 antibody prevalence in Cameroon. His work bridges econometric methods with public health, aiming to improve data-driven decision-making in global health systems.

Sandro Pedrazziniمشاهده پروفایل

استاد مدعو

• Software Engineering
• Programming Languages
• Natural Language Processing
• +۵ مورد دیگر

Sandro Pedrazzini is an Adjunct Professor in Software Engineering and the Head of the Bachelor's Program in Computer Engineering at the Department of Innovative Technologies (DTI) of SUPSI. He holds an M.Sc. in Computer Science from ETH Zurich and a Ph.D. in Natural Sciences from the University of Basel. His research focuses on Software Engineering methodologies, Programming Languages, Natural Language Processing, and Information Retrieval. He has extensive experience in both academia and industry, co-founding companies like Canoo AG and Karakun AG. His teaching responsibilities include courses on computer graphics, programming paradigms, and software engineering practices. Research Interests: Software Engineering: Agile methodologies, continuous integration, and project-based learning Natural Language Processing: Term extraction, morphological analysis, and e-learning tools Web Technologies: 3D interfaces, enterprise systems, and middleware architectures Publications reflect a blend of theoretical and applied research, with contributions to enterprise search systems, semantic analysis, and educational technologies. His work emphasizes practical applications of academic research in real-world contexts. Professional Experience: Co-founder of Canoo AG (2000) and Karakun AG (2018) Head of Computer Science Bachelor Program since 2012 Supervisor for multiple academic projects and courses Labs/Teams: Active in SUPSI’s DTI department, leading projects like 3D MMA (3D interface design) and TicinoTurismo II (web-based tourism platform).

• Computational Chemistry
• Molecular Machine Learning
• Catalyst Design
• +۲ مورد دیگر

Clémence Corminboeuf is a Professor at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the Laboratory of Computational Molecular Design (LCMD). Her research focuses on computational chemistry, machine learning for molecular design, and catalyst optimization using physics-inspired models. Email: clemence.corminboeuf@epfl.ch Scopus Author ID: 6603320500 Researcher ID: I-5973-2018 Current research involves: Developing integer linear programming algorithms for molecular machine learning training set selection Generative inverse design pipelines for singlet fission materials discovery Automated volcano plot analysis tools (SPOCK) for catalyst design Enantiospecific synthesis methods for planar chiral organometallic complexes Neural network potential frameworks for solute-solvent reaction dynamics Recent methodological advances include: Cost-informed Bayesian optimization (CIBO) for efficient chemical experimentation 3DReact geometric deep learning architecture for reaction property prediction MARC software for automated transition state ensemble analysis

• Machine Learning
• Deep Learning
• Artificial Intelligence
• +۵ مورد دیگر

Jean Hennebert is an Ordinary Professor at the Haute école d'ingénierie et d'architecture de Fribourg (HES-SO) and serves as the Institute Head for iCoSys (Institute of Complex Systems). His research spans multiple domains including Machine Learning , Deep Learning , Artificial Intelligence , and Data Science , with applications in smart building systems , medical diagnostics , and document image analysis . PhD , Master in Architecture (HES-SO) BSc in Computer Science (HES-SO) His academic contributions focus on adaptive document image binarization (YinYang, ZigZag algorithms), time series generation (diffusion models), and smart building data platforms (BBData v2.0). His work integrates ML engineering with practical implementations in energy efficiency and medical device monitoring (e.g., contact lens sensor analytics for glaucoma detection). Key projects include the iCoSys Institute infrastructure for urban data analysis , radon concentration monitoring systems , and industrial process monitoring architectures . Publications demonstrate technical expertise in transformer-free models (SLANet-1M), recurrent neural networks , and low-cost sensor networks with active learning.

Volker Ziebartمشاهده پروفایل

پژوهشگر

• Machine Learning
• Optimal Control
• Energy Optimization
• +۳ مورد دیگر

Dr. Volker Ziebart is a Researcher at the Zurich University of Applied Sciences (ZHAW) School of Engineering, focusing on Machine Learning in Optimal Control for Industry . He has led and contributed to projects addressing energy optimization in building control, ecological and economic process optimization in cement production using reinforcement learning and physics-informed machine learning. Role: Researcher at ZHAW School of Engineering Key Projects: Reinforcement Learning for heating systems, energy optimization in buildings, physics-informed ML for cement production His research bridges machine learning with industrial applications, particularly in control systems and spectroscopy. He has published peer-reviewed articles and posters in journals and conferences such as the Journal of Magnetic Resonance and the ANNPR Workshop . While no formal awards are documented, his work reflects expertise in deep learning, predictive modeling, and sustainable manufacturing.

Luca Bosettiمشاهده پروفایل

مدرس

• Machine Learning
• Chemical Process Design
• Crystallization Technology
• +۵ مورد دیگر

Luca Bosetti is a Lecturer at the Department of Mechanical and Process Engineering at ETH Zürich , focusing on machine learning applications in chemical engineering and crystallization process design. Key research areas: Machine learning for thermodynamic predictions, sustainable chemical recycling, crystallization technology, and lifecycle assessment. Collaborations: Active partnerships with André Bardow, Marco Mazzotti, Benedikt Winter, and Johanna Lindfeld. Recent projects: Development of ML-CAMPD frameworks for solvent design, CO2 electrolyzer integration, and polyurethane waste recycling. His work combines machine learning with process engineering to address sustainability challenges in the chemical industry, including CO2 utilization , chemical recycling , and energy-efficient separations . Publications span journals like Computers & Chemical Engineering and Crystal Growth & Design , with recent contributions on hybrid crystallization processes , liquid-liquid equilibria , and automated LCA tools . His research has received funding from the CIRCULAR FOAM project (EC grant 101036854) , aiming to expand circular ecosystems for foam waste.

Tobias GOLLINGمشاهده پروفایل

استاد

• High Energy Physics
• Machine Learning in Particle Physics
• Neutrino Reconstruction
• +۳ مورد دیگر

Tobias Golling is a Professor at the University of Geneva , Department of Nuclear and Particle Physics, actively involved in the ATLAS experiment at CERN. His research spans high-energy physics, machine learning applications in particle physics, and neutrino reconstruction techniques. Role: Academic leader in ATLAS collaboration Location: Office AEM 025 Research Interests Developing diffusion models and normalizing flows for particle cloud generation Advancing anomaly detection in jet physics through machine learning Applying graph neural networks to topological reconstruction of particle processes Article Trends show a focus on integrating maximum likelihood estimation , optimal transport , and conditional flows into high-energy physics simulations. Key domains include neutrino reconstruction , event generation , and ML-driven detector optimization . Supervision includes doctoral student Bálint MÁTÉ . Collaborations frequently involve Prof. Voloshynovskiy and the ATLAS collaboration .

Alexander Heinleinمشاهده پروفایل

استادیار

• Numerical methods for partial differential equations
• Scientific computing
• Domain decomposition methods
• +۶ مورد دیگر

Dr. Alexander Heinlein is an Assistant Professor in the Numerical Analysis group at the Delft Institute of Applied Mathematics (DIAM), Faculty of Electrical Engineering, Mathematics & Computer Science (EEMCS), Delft University of Technology (TU Delft). His work bridges scientific computing and machine learning through scientific machine learning (SciML) , focusing on domain decomposition methods and multiscale approaches for solving complex partial differential equations on modern hardware like GPUs. Research interests include: Developing high-performance computing algorithms for nonlinear PDEs with applications in fluid-structure interaction and photonic crystals Advancing physics-aware machine learning techniques for groundwater heat transport and post-burn contraction prediction Creating parallel preconditioners like FROSch for challenging problems in computational mechanics Building hybrid numerical-ML frameworks with domain decomposition for multi-physics applications His recent publications highlight a 128-235x speedup in biomedical simulations through deep operator networks , and keynote presentations on geometric challenges in machine learning-based surrogate models at international conferences like CASML 2024. Scientific awards include: 2025 NWO Open Technology Programme grant for the RAPID-Wind project on offshore wind turbine foundations Students and collaborations involve: Yuhuang Meng (PhD candidate, 2024) Jing Zhao (co-supervisor) Prof. Jun Zou (Chinese University of Hong Kong collaboration, 2024) He leads software development for COMSOL and Trilinos extensions while maintaining open-source reproducibility standards.

• Graph Machine Learning
• Signal Processing
• Higher-Order Networks
• +۵ مورد دیگر

Elvin Isufi is an Associate Professor at the Delft University of Technology (TU Delft) , where he co-founded and co-directs AIdroLab , one of the 24 TU Delft AI Labs. His research focuses on fundamental and applied graph-based data processing with applications to water networks, flood modeling, infrastructure systems, and recommender systems. Elvin obtained his Ph.D. from TU Delft in graph signal processing and completed his master's and bachelor's studies at the University of Perugia in Italy. He has been mentored by renowned academics including Prof. Alejandro Ribeiro (postdoc), Prof. Geert Leus (Ph.D.), and Prof. Paolo Banelli (master's/bachelor's). His research integrates signal processing , machine learning , and mathematical modeling to develop techniques for graph signal processing, graph neural networks (GNNs), and higher-order network analysis. Key application domains include water distribution networks , flood modeling , and recommender systems . His work addresses critical challenges such as stability analysis of GNNs, dynamic graph processing , and physics-informed machine learning . Recent publications highlight advancements in multi-scale hydraulic GNNs for flood prediction, carbon footprint-aware recommender systems , and simplicial vector autoregressive models for edge flow analysis. His research group includes Ph.D. students like Bishwadeep Das and M.Sc. students exploring topics such as online edge flow prediction and topological signal processing . 2021: Audience Choice Award, IEEE Data Science and Learning Workshop 2022: Best Student Paper Award, IEEE DSLW 2023: Top 3% Recognition Award, ICASSP Elvin actively supervises students in graph machine learning projects, emphasizing requirements like Python and PyTorch/TensorFlow expertise . He provides structured thesis project themes covering dynamic graph analysis , physics-informed GNNs , and self-supervised learning for networked data.

• Natural Language Processing
• Machine Learning
• Uncertainty Estimation
• +۴ مورد دیگر

Wilker Aziz is an assistant professor at the Institute for Logic, Language and Computation (ILLC) within the Faculty of Science at the University of Amsterdam, where he leads the Probabilistic Language Learning research group. His academic journey includes a PhD from the University of Wolverhampton (2014), followed by research positions at the University of Sheffield and ILLC before joining the UvA faculty in January 2019. His research focuses on the intersection of machine learning, natural language processing, and probabilistic modeling. Aziz investigates how to design models and algorithms that learn to represent, understand, and generate language data, with particular emphasis on uncertainty estimation in language models, probabilistic inference techniques, and developing novel decoding algorithms for text generation. His work addresses fundamental problems in language modeling, machine translation, syntactic parsing, textual entailment, text classification, and question answering. Aziz's recent publications (2022-2024) demonstrate a strong focus on uncertainty in natural language generation, with multiple papers accepted at top-tier conferences including EACL and EMNLP. His research shows how language models can better align with human uncertainty patterns and how to properly evaluate model confidence when humans themselves disagree on annotations. Best paper award at Coling 2020 for 'Is MAP Decoding All You Need? The Inadequacy of the Mode in Neural Machine Translation' Outstanding Area Chair award at ICLR 2022 Active program committee member for major ML/NLP conferences including NeurIPS, ICML, ACL, EMNLP, and ICLR Aziz actively supervises PhD and MSc students, with a current cohort of five ongoing PhD candidates and numerous recent graduates. His Probabilistic Language Learning group develops theoretical frameworks and practical applications for uncertainty-aware natural language processing. He has contributed to open-source projects including PET (Post-Editing Tool), grasp (Randomised Semiring Parsing), and mosesdecoder, demonstrating his commitment to advancing the field through shared research tools.

Isabelle Guyonمشاهده پروفایل

استاد

• Machine Learning
• Artificial Intelligence
• Computer Vision
• +۳ مورد دیگر

Isabelle Guyon is a Full Professor at University Paris-Saclay and a Research Scientist at Google DeepMind since 2022. She is also a community lead for the open-source Codalab competitions project and President of ChaLearn, a non-profit organizing machine learning challenges. Her career spans academia, industry, and non-profit leadership, with affiliations at INRIA and the LISN laboratory. PhD in Paris under Gerard Dreyfus and Leon Personnaz Bell Labs researcher (1990s) Graduate of ESPCI Her research focuses on machine learning challenges and benchmarks across diverse domains including causality , computer vision , deep learning , and high energy physics . Recent projects explore power grid optimization , LLM-assisted paper reviewing , and systematic bias mitigation in AI applications. Key awards include the BBVA Frontier in Knowledge Award (2020), ICMI Ten-Year Impact Award (2023), and IEEE Best Paper Award (2023). She is also an ELLISS fellow and AMIA fellow .

• Machine Learning
• Support Vector Machines
• Evolution Strategies
• +۴ مورد دیگر

Christian Igel is a Full Professor at the Department of Computer Science (DIKU), University of Copenhagen. He serves as Director of the SCIENCE AI Centre and co-lead of the Pioneer Centre for Artificial Intelligence. Igel holds a Doctoral degree from Bielefeld University (2002) and a Habilitation from Ruhr-University Bochum (2010). Previously, he was a Juniorprofessor at Ruhr-University Bochum (2003-2010). His research focuses on fundamental and applied machine learning, including: kernel methods, evolutionary optimization, deep neural networks, PAC-Bayesian analysis, and applications supporting sustainable development goals. He maintains extensive collaborations across disciplines, particularly in environmental monitoring and medical informatics. Igel's recent publications demonstrate strong interdisciplinary focus with applications in: Environmental science (tree cover mapping, carbon stock quantification) Healthcare (sleep staging, mental health epidemiology) Core ML theory (monotonic networks, PAC-Bayesian bounds) His work consistently appears in high-impact venues including Nature, ICML, and NeurIPS. Awards and Honors: ELLIS Fellow (European Lab for Learning and Intelligent Systems) Editorial Roles: Editor, German Journal on Artificial Intelligence (KI) Associate Editor, Evolutionary Computation Journal (ECJ) Associate Editor, Artificial Intelligence Journal (AIJ)

Thomas Langerakمشاهده پروفایل

مدرس

• Human-Computer Interaction
• Reinforcement Learning
• Haptic Feedback Systems
• +۳ مورد دیگر

Thomas Langerak is a PhD student and Lecturer at ETH Zurich's Advanced Interactive Technologies lab under Prof. Otmar Hilliges. His academic journey includes a double-degree in Human-Computer Interaction and Design from Aalto University (Finland) and University of Twente (Netherlands), and a B.Sc. in Industrial Design from Eindhoven University of Technology. His research spans Human-Computer Interaction , Reinforcement Learning , and Haptic Systems , with recent work focusing on multi-agent RL for adaptive interfaces and novel electromagnetic actuation for VR/AR. Earlier contributions centered on custom haptic hardware like the Hedgehog pin array and Omni volumetric system. Publication trends reveal an evolution from hardware prototyping (2018-2020) toward intelligent interface optimization (2021-2024), with increasing emphasis on user modeling and adaptive automation. Key venues include ACM UIST, CHI, and IEEE Transactions. Honorable Mention - Best Paper Award at World Haptics Conference 2021 As an academic mentor, he supervised Mengfan Wu's thesis on electromagnetic tracking and Aline Abler's Hedgehog interface development. His service includes UIST organizing roles (Data Co-Chair 2021-2022) and session chairing. Teaching spans HCI, Computational Haptics, and ML fairness courses since 2019. Laboratory work occurs within ETH's Advanced Interactive Technologies group, specializing in computational interaction and electromagnetic systems for immersive environments.

Fabrizio Peceمشاهده پروفایل

پژوهشگر

• Human Computer Interaction
• Computer Vision
• Virtual Reality
• +۴ مورد دیگر

Fabrizio Pece is a postdoctoral researcher at ETH Zurich , affiliated with the Advanced Interactive Technologies Lab under the Institute of Intelligent Interactive Systems . Prior to this, he completed his PhD (2010-2014) and MSc (2009) at University College London in the Virtual Environment and Computer Graphics group under Prof. Jan Kautz. He holds a BSc in Computer Science from Università degli Studi di Roma Torvergata (2008) and interned at Disney Research Zurich (2010) supervised by Prof. Wojciech Matusik. His research spans Human-Computer Interaction (HCI) , Computer Vision , and Machine Learning , focusing on Virtual/Augmented Reality , Interactive Systems , and Computational UI Design . His work integrates novel hardware with data-driven methods to explore digital interaction trends. Recent publications highlight expertise in 3D motion infilling , digital ink editing , wearable haptics , and deformable input devices . He has received prestigious awards including Honorable Mentions at UIST 2017 and CHI 2013 , SNF Grant funding , and ETH Postdoctoral Fellowship (Marie Curie Co-Fund). Teaching experience across ETH Zurich (User Interface Engineering, ML for Interactive Systems) and UCL (Computer Graphics, Multimedia Computing) Supervised multiple BSc/MSc thesis students including Adrian Spurr, Ralph Aeschimann, and Manuel Kaufmann Active in academic service as a reviewer for ACM CHI, SIGGRAPH, and IEEE ISMAR

Benjamin Hoffmanمشاهده پروفایل

پژوهشگر

• Machine Learning
• Control Systems
• Networked Systems
• +۷ مورد دیگر

Benjamin Hoffman is a PhD Researcher at the Networked Systems Group (NSG) within the Department of Information Technology and Electrical Engineering at ETH Zurich. He is also affiliated with the ETH AI Center , working under the guidance of Prof. Laurent Vanbever. Benjamin holds a BSc in Mechanical Engineering and an MSc in Robotics, Systems and Control from ETH Zurich, with an academic exchange at Lund University. His research focuses on leveraging data and machine learning to model, interpret, and control complex systems, emphasizing robustness, safety, and efficient generalization across diverse contexts. His recent work includes publications in ACM SIGCOMM'25 and under-review papers addressing network model generalization, real-world ML applications in networking, and model-based reinforcement learning in robotics. He has supervised student projects at ETH Zurich and gained practical experience through internships in management consulting, data science, and robotics software engineering. Benjamin is actively involved in local politics as a member of the Nature and Heritage Protection Commission since 2025. His broader interests span diplomacy, international development, policy, and economics.