جست‌وجوی استاد و پژوهشگر

Florian Brueckمشاهده پروفایل

پژوهشگر

• Statistical Model Comparison
• Non-Parametric Bayesian Analysis
• Lévy Processes
• +۳ مورد دیگر

Florian Brueck is a Postdoctoral Researcher at the Research Center for Statistics at the University of Geneva, under Prof. Sebastian Engelke. He earned his PhD in Mathematics (Dr. rer. nat.) from the Technical University of Munich (TUM) in 2023, with distinction, under Prof. Matthias Scherer. His research focuses on statistical model comparison, Bayesian non-parametric survival analysis, Lévy processes, extreme value theory, and the integration of machine learning into classical statistics. Education: PhD in Mathematics, TUM (2023) MSc in Financial Mathematics and Actuarial Sciences, TUM (2019) BSc in Business Mathematics, Ludwig Maximilians University Munich (2017) Research Interests: Statistical model selection via Maximum Mean Discrepancy (MMD) Exchangeability and non-parametric Bayesian methods in survival analysis Inference for Lévy and stable processes Extreme value theory applications in risk modeling Machine learning techniques for statistical inference Key Contributions: Developed distribution-free MMD tests for model selection with estimated parameters Advanced infinitely divisible priors for multivariate survival functions Applied generative neural networks to characteristic function estimation Awards & Recognition: None explicitly listed. Advising & Grants: Supervised 5 Master’s theses on topics including MMD-based model selection and clustering-based portfolio optimization Co-led industry project with WWK Versicherungen on risk assessment tools Labs/Teams: Research Center for Statistics at University of Geneva.

Marco Zaffalonمشاهده پروفایل

استاد

• Machine Learning
• Causal AI
• Probabilistic Graphical Models
• +۳ مورد دیگر

Marco Zaffalon is a Professor at the University of Applied Sciences and Arts of Southern Switzerland (SUPSI) and Scientific Director of the Dalle Molle Institute for Artificial Intelligence (IDSIA). He holds an MSc in Computer Science (cum laude) and a PhD in Applied Mathematics. His research focuses on causal AI, machine learning, and probabilistic models under uncertainty, with applications in defense, environment, medicine, engineering, and business. He leads a 30-member research group in probabilistic machine learning and has secured over 15 million Swiss Francs in competitive grants. Education: MSc in Computer Science (cum laude), PhD in Applied Mathematics. Professional milestones include roles as a software engineer at an Italian insurance group, consultant for SGS-Thomson, post-doctoral fellow at IDSIA, and tenure as a researcher since 2001. Research interests emphasize foundational and applied aspects of probability theory, particularly imprecise probability, and causal reasoning. His work integrates knowledge-based systems, probabilistic graphical models, and robust algorithms for real-world problems. Notable projects include OmniProfiler , Credal Model Averaging , and Learning under Near-Ignorance . Awards & Recognition: President of SIPTA (2019–present) Senior Area Editor for International Journal of Approximate Reasoning (IJAR) Top 2% Scientist (Stanford’s ranking since 2019) Grants & Leadership: Oversaw 15+ million CHF in competitive research funding Founded and directs a 30-member research group He collaborates on interdisciplinary projects, including environmental modeling, medical applications, and defense-related AI systems through IDSIA’s labs and partnerships.

• Robotics
• Human-Computer Interaction
• Machine Learning
• +۳ مورد دیگر

Dr. Yi-Chi Liao is a Lecturer in the Department of Computer Science at ETH Zürich, specializing in intelligent interactive systems. Their research focuses on human-robot interaction, wearable technology, and optimization techniques for user interface design. Key areas include developing datasets for naturalistic handover behaviors with robotic limbs, human-in-the-loop optimization methods, and computational workflows for designing input devices. Research Interests: Explores the intersection of robotics, machine learning, and human-centered design. Recent work emphasizes Bayesian optimization techniques, affordance theory, and tactile feedback systems. Projects like the 3HANDS dataset and ThirdHand wearable robotic arm demonstrate innovation in human augmentation and interaction design. Advising: Supervises doctoral student Peizhuo Li in the D-INFK program. Research outputs span 2015–2025, with notable contributions to haptic interfaces, multi-objective optimization, and wearable computing. Active in conferences and journals addressing HCI, robotics, and design automation.