جست‌وجوی استاد و پژوهشگر

• Probabilistic Machine Learning
• Causal AI
• Imprecise Probabilities
• +۲ مورد دیگر

Marco Zaffalon is Professor and Scientific Director at IDSIA (Istituto Dalle Molle di Studi sull'Intelligenza Artificiale), affiliated with the Università della Svizzera italiana's Faculty of Informatics. He leads a 30-member research group on probabilistic machine learning and has published over 150 papers. Education: M.Sc. in Computer Science (Università degli Studi di Milano) Ph.D. in Applied Mathematics (Università degli Studi di Milano) Research spans probabilistic machine learning, causal AI, imprecise probabilities, and quantum computation. His work develops theoretical foundations for uncertainty reasoning and applies them to AI systems. Recent publications focus on causal inference with LLMs, counterfactual computation, and quantum decision models. Articles consistently explore intersections of probability theory, computational methods, and real-world applications like healthcare. Trends include advancing tractability in causal queries and bridging logical frameworks with machine learning. Administrative roles include co-founding Artificialy (as Chief Scientist) and directing IDSIA since 2019. He teaches courses in Causal AI, Uncertain Reasoning, and Probability.

Ola Svenssonمشاهده پروفایل

دانشیار

• Approximation Algorithms
• Combinatorial Optimization
• Computational Complexity
• +۳ مورد دیگر

Ola Svensson is an Associate Professor at the School of Computer and Communication Sciences, EPFL. His research focuses on approximation algorithms, combinatorial optimization, computational complexity, and scheduling. He has been supported by grants including the ERC Starting Grant "OptApprox" (2014-2019), SNF grants, and the ERC Consolidator Grant "POTCO" (2023-). He teaches courses such as Advanced Algorithms and Approximation Algorithms and Hardness of Approximation. Education: PhD from IDSIA - Universita della Svizzera italiana (2009) and Master's from Uppsala University (2005). Research Interests: Design and analysis of approximation algorithms for NP-hard problems, scheduling, and computational complexity. He explores limitations of approximation techniques through hardness results and contributes to theoretical computer science. Publications span clustering, scheduling, and graph problems like the Traveling Salesman Problem. Recent work includes learning-augmented algorithms and robust optimization. Awards: I&C teaching award and best paper awards at FOCS (2017) and STOC (2018). Over a dozen PhD students advised, many entering postdocs or industry roles. Labs/Teams: Part of the theory group at EPFL, collaborating on academic projects and course development.

Markus Renoldnerمشاهده پروفایل

پژوهشگر ارشد

• Numerical Analysis
• Partial Differential Equations
• Finite Element Methods
• +۳ مورد دیگر

Markus Renoldner is a Doctoral Assistant at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the School of Basic Sciences, Institute of Mathematics, specifically within the Chair of Numerical Modelling and Simulation (MNS) and the Picasso Group. His office is located at MA C2 622 in Building MA, Station 8, 1015 Lausanne. His educational background includes Doctoral studies in Mathematics at EPFL (2023-present), MSc in Computational Science and Engineering at TU Wien (2021-2023), Master's Thesis in Applied Mathematics at ETH Zürich (2022-2023), and an Exchange semester in Applied and Computational Mathematics at KTH Stockholm (2022). Renoldner specializes in numerical analysis of partial differential equations (PDEs) , with current research focusing on Finite Element approximation of solutions to PDEs in fusion plasma physics. His work spans PDE theory and control systems engineering, as evidenced by his GitHub repositories containing numerical implementations for Navier-Stokes equations and shallow water models. His publications demonstrate expertise in developing mass, energy, and helicity conserving discretization methods for fluid dynamics problems. His professional experience includes simulation engineering at Team Tumbleweed (2021-2024), a visiting associate position at Boston Consulting Group (2021), systems engineering at RUAG Space (2020), and data analysis at KPMG (2017). He has also held leadership roles including CEO for one month at Adecco Group (2020) and Vorstand/Consultant at icons - consulting by students (2017-2020).

Sylvain Sardyمشاهده پروفایل

دانشیار

• statistical machine learning
• sparsity
• optimization
• +۶ مورد دیگر

Sylvain Sardy is an Associate Professor in the Department of Mathematics at the University of Geneva, where he conducts research at the intersection of statistics, optimization, and machine learning. He is affiliated with the Analysis, Mathematical Physics and Probability research group and has held significant editorial positions including Associate Editor for Computational Statistics and Data Analysis since 2020. Professor Sardy's research focuses on statistical machine learning, sparsity, and optimization with applications spanning astronomy, chemometrics, finance, and tomography. His work develops innovative methods for high-dimensional data analysis, particularly using wavelet-based approaches and LASSO regularization techniques for feature selection, denoising, and model selection. His publications reveal a consistent focus on finding sparse signals in complex datasets across diverse scientific domains. Professor Sardy has mentored numerous graduate students, currently supervising PhD candidate Maxime van Cutsem and having previously guided Dr. Xiaoyu Ma, Dr. Pascaline Descloux, Prof. Jairo Diaz Rodriguez, and Dr. Caroline Giacobino. His Master's students include Jairo Diaz (now Professor at Universidad del Norte, Colombia), Jean-Luc Baeriswyl, and others who have pursued careers in academia, industry, and education. His academic service includes leadership roles as Swiss representative at the European Regional Committee of the Bernoulli Society (2014-2018), President of the Doctoral School of Applied Statistics and Probability (2010-2013), and Student Advisor for the Mathematics Section (2008-2015). His teaching portfolio includes Optimization with Applications I, Statistical Machine Learning, and Pharmaceutical Statistics and Methodology, reflecting his expertise in statistical methodology and its practical implementation.

Rui Yaoمشاهده پروفایل

مدرس

• Large-scale equilibrium modeling in multi-modal transport systems
• Individual mobility choice modeling
• Demand management strategies
• +۳ مورد دیگر

Rui Yao is a Lecturer at École Polytechnique Fédérale de Lausanne (EPFL), affiliated with the College of Engineering (ENAC) and the Department of Civil Engineering. Additionally, he works as a Scientist in the Laboratory for Human-Oriented Mobility Eco-system (HOMES) within EPFL. His research focuses on large-scale equilibrium modeling in multi-modal transport systems, individual mobility choice modeling, and demand management strategies. Education: B.Sc. in Civil Engineering, Technion – Israel Institute of Technology Direct-track Ph.D. in Transportation Engineering, Technion – Israel Institute of Technology His research spans both theoretical and applied domains, including stochastic traffic equilibrium , multi-passenger ridesharing systems , perturbed utility models , and deep learning for choice analysis . He has contributed to advancements in data-driven route choice modeling , integrated equilibrium models for electrified logistics , and stable matching frameworks for mobility platforms . Rui Yao is affiliated with the Human-Oriented Mobility Eco-system (HOMES) lab at EPFL, which focuses on innovative transportation solutions. His work bridges theoretical modeling with real-world applications in smart mobility and transportation policy.

Matthias Grossglauserمشاهده پروفایل

استاد

• Machine Learning
• Stochastic Networks
• Discrete Choice Models
• +۴ مورد دیگر

Matthias Grossglauser is a Full Professor at the School of Computer and Communication Sciences at École Polytechnique Fédérale de Lausanne (EPFL), where he co-directs the Information and Network Dynamics lab. He serves on the Federal Communications Commission (ComCom), Switzerland's telecommunications regulatory authority, and previously directed EPFL's Doctoral School in Computer and Communication Sciences (2016-2019). His career includes positions at Nokia Research Center (Internet Laboratory lead), AT&T Research, and EPFL (Assistant Professor). Education Ph.D. in Computer Science from Sorbonne Universités M.Sc. in Electrical Engineering from Georgia Institute of Technology Engineering degree in Communication Systems from EPFL Research Focus Grossglauser's research integrates machine learning, stochastic networks, and discrete choice models to address challenges in artificial intelligence, network science, computational social sciences, and recommender systems. His work emphasizes both theoretical foundations and practical applications, including political forecasting, climate communication, and network dynamics. Publication Trends Recent articles demonstrate strong focus on causal inference, optimal learning algorithms, and social network analysis. Dominant themes include reinforcement learning optimization, graph-based modeling, and NLP applications in political science. Methodological innovations in matrix factorization, Bayesian modeling, and stochastic processes recur throughout. Awards & Honors Fellow of IEEE and ELLIS Cor Baayen Award (1998) CoNEXT/SIGCOMM Rising Star Award (2006) Best Paper Awards: ACM COSN (2014), IEEE INFOCOM (2001) Nokia Mobile Data Challenge Winner (2012) Academic Leadership Has advised 16+ PhD students to completion and currently supervises 4 doctoral candidates. Secured research funding for projects including dynamic recommender systems, network alignment algorithms, and computational social science tools (e.g., Predikon.ch vote prediction platform). Leads the Information and Network Dynamics lab, focusing on AI-driven network analysis.

Marc Rennhardمشاهده پروفایل

استاد

• Information Security
• Software Security
• Security Engineering
• +۳ مورد دیگر

Marc Rennhard is a Professor and current Director of the Department of Information Technology, Electrical Engineering and Mechatronics at Zurich University of Applied Sciences (ZHAW) School of Engineering. Previously, he led the Institute of Computer Science (InIT) and the Information Security Research Group at ZHAW School of Engineering. His academic credentials include: PhD in anonymous Internet communication from ETH Zurich MSc in Electrical Engineering from ETH Zurich Professor Rennhard specializes in Information Security with concentrated expertise in Software Security, Security Engineering, and Automated Security Testing. His research bridges theoretical security frameworks with industrial applications, emphasizing vulnerability assessment in software systems and privacy-preserving network communications. This work drives practical solutions for real-world security challenges through rigorous engineering approaches. He has directed approximately 20 applied research projects primarily funded by CTI/Innosuisse and executed in partnership with industry stakeholders. These initiatives focus on developing implementable security technologies and methodologies for commercial deployment. Professor Rennhard previously established and led the Information Security Research Group at InIT, fostering collaborative cybersecurity innovation within ZHAW's engineering ecosystem.