List of selected seminars at the division of Systems and Control
For seminars before 2021, see this page.
Fall 2023
- Liam Taghavian, KTH, Health-aware fast-charging of lithium-ion batteries with unknown models
- Half-time seminar, Sofia Ek: Machine Learning for Decision-Making with Confidence
- Docentship lecture, Niklas Wahlström: How do you structure your machine learning project? Opportunities and pitfalls
- Simo Särkkä, Aalto University: Parallel methods for state-space models
- Licentiate seminar, Anh Tung Nguyen: Security Allocation in Networked Control System
- Torsten Söderström, Uppsala University: PEM and ML in an EIV setting
- Yasemin Vardar, Delft University of Technology: Digitizing Touch Sense: Unveiling the Perceptual Essence of Tactile Textures
- Anastasia Bizyaeva, University of Washington: Nonlinear dynamics of beliefs over social networks
- Licentiate seminar, Philipp Pilar: Integrating Prior Knowledge into Machine Learning Models with Applications in Physics
- Arto Klami, University of Helsinki: Better priors for everyone
- Sebin Gracy, Rice University: Spreading processes over networks
Spring 2023
- Gerben Beintema, Eindhoven University: Meta-state-space learning: A Novel Approach for the Identification of Stochastic Dynamic Systems
- PhD defence, David Widmann: Reliable Uncertainty Quantification in Statistical Learning
- Dino Sejdinovic: Generalized Variational Inference in Function Spaces and Meelis Kull: Evaluation, fitting, and guarantees in classifier calibration
- Licentiate seminar, Håkan Runvik: Modeling and Estimation of Impulsive Biomedical Systems
- Martin Danelljan, ETH Zurich: Towards Tracking and Segmenting Any Object
- Half-time seminar, Ruoqi Zhang: Towards robust and safe policies: Enhancing reinforcement learning for set-point control problems
- José Araújo and Magnus Lindhé, Ericsson Research: From Sensing & Perception to Decision & Control at Ericsson Research
- Jack Umenberger , University of Oxford: Shortest Paths in Graphs of Convex Sets, with Applications to Control and Motion Planning
- Emre Neftci, RWTH Aachen and Forschungszentrum Juelich: Enabling Learning on Neuromorphic Hardware
- Andrea Iannelli, University of Stuttgart: Online Learning for Control: Bringing Sequential Decision Making in the Loop
- Peyman Esfahani, TU Delft: The Role of Convexity in Data-Driven Decision-Making
- Subhrakanti Dey, Uppsala University: A Newton-type algorithm for federated learning based on incremental Hessian eigenvector sharing
- Half-time seminar, Maria Bånkestad: On neural networks for graph-structured data and non-parametric modeling
- Marcel van Gerven, Donders Institute for Brain, Cognition and Behaviour: Brain-inspired deep learning
- Mahyar Fazlyab, Johns Hopkins University: Reachability analysis of neural autonomous systems
Fall 2022
- Quanyan Zhu, New York University: Security and Privacy of Distributed Machine Learning
- Daniel Selvaratnam, KTH: Linear Temporal Logic for the Uninitiated
- Anastasios Tsiamis, ETH Zurich: Linear Systems can be Hard to Learn
- Dissertation, Anna Wigren: Sequential Monte Carlo methods for conjugate state-space models
- Guilherme Ramos, Instituto Superior Técnico, Lisbon: Improving security and privacy in consensus methods
- Christos Verginis, Uppsala University: Scalable and Verifiable Coordination of Adaptable Autonomous Systems
- Mohammad Pirani, University of Waterloo: Resilience and Security of Networked Control Systems (With Applications to Autonomous Multi-Agent Systems)
- Tim Martin, University of Stuttgart: A framework for data-driven system analysis and control for nonlinear systems using Taylor polynomials
- Manon Kok, Delft University of Technology: Indoor localisation using the magnetic field
- Frank Allgöwer, University of Stuttgart: Data-driven Model Predictive Control: Concepts, Algorithms and Properties
Spring 2022
- Dissertation, Muhammad Osama: Robust machine learning methods
- Dissertation, Carl Jidling: Tailoring Gaussian processes and large-scale optimisation
- Dissertation, Carl Andersson: Deep probabilistic models for sequential and hierarchical data
- Half-time seminar, Daniel Gedon: Deep models for temporal data with applications to electrocardiography
- Dissertation, Viktor Bro: Volterra Modeling and Estimation of the Human Smooth Pursuit
- Half-time seminar, Fredrik Gustafsson: Energy-Based Probabilistic Regression in Computer Vision
Fall 2021
- Licentiate seminar, Niklas Gunnarsson: On the registration and modeling of sequential medical images
- Anton Proskurnikov, Politecnico di Torino: Networks of Social Influence: Simple Models and Experimental Results