Machine Learning and Dynamical Systems Seminar

@mlds_seminar

46 tuit · page 1/2

Formal & Physical Sciences AI — Models & Research

Thibaut Germain: Geometric Dictionary Learning of Dynamical Systems with Optimal Transport

Thibaut Germain

June 18, 2026 61 min

★ ★ ★ ★ ☆ 4/5

Dictionary LearningDynamical SystemsOptimal Transport

Formal & Physical Sciences AI — Models & Research

Tolga Birdal: Generalization at the Edge of Stability

Tolga Birdal

June 11, 2026 61 min

★ ★ ★ ★ ☆ 4/5

GeneralizationEdge of StabilityFractal Dimension

Formal & Physical Sciences Mathematics

Maximilian Engel: Random Dynamical Systems in Deep Neural Networks

Maximilian Engel

June 4, 2026 51 min

★ ★ ★ ★ ☆ 4/5

Random Dynamical SystemsDeep LearningStochastic Gradient Descent

Formal & Physical Sciences AI — Models & Research

Lorenzo Livi: Toward a Dynamical Theory of Deep Learning

Lorenzo Livi

April 3, 2026 58 min

★ ★ ★ ★ ☆ 4/5

Deep LearningDynamical SystemsRecurrent Neural Networks

Formal & Physical Sciences AI — Models & Research

Yuma Nakamura: Ensemble Reservoir Computing for Physical Systems

Yuma Nakamura

March 7, 2026 27 min

★ ★ ★ ★ ☆ 4/5

Reservoir ComputingPhysical ComputingEnsemble Averaging

Formal & Physical Sciences AI — Models & Research

Yingdong Lu: Stability and geometric structure of the Gradient Flows for In-Context Learning

Yingdong Lu (IBM Research)

March 7, 2026 31 min

★ ★ ★ ★ ☆ 4/5

Gradient FlowIn-Context LearningTransformers

Formal & Physical Sciences AI — Models & Research

Yuanchao Xu:Generative Modeling through Koopman Spectral Analysis: An Operator-Theoretic Perspective

Yuanchao Xu

February 25, 2026 23 min

★ ★ ★ ★ ☆ 4/5

Koopman OperatorGenerative ModelingWasserstein Gradient Descent

Formal & Physical Sciences AI — Models & Research

Wei Huang: Trained Mamba Emulates Online Gradient Descent in In-Context Linear Regression

Wei Huang

February 25, 2026 29 min

★ ★ ★ ★ ☆ 4/5

MambaIn-Context LearningLinear Regression

Formal & Physical Sciences Mathematics

Wei Hao Tey: Towards early warning signals for critical transitions in random DS with bounded noise

Wei Hao Tey

February 25, 2026 33 min

★ ★ ★ ★ ☆ 4/5

Random Dynamical SystemsBounded NoiseCritical Transitions

Formal & Physical Sciences AI — Models & Research

Victor Churchill: Efficient Data-Driven Modeling of PDEs: Partially-Observed Flow Map Learning

Victor Churchill

February 25, 2026 23 min

★ ★ ★ ★ ☆ 4/5

PDEFlow Map LearningReduced Basis

Formal & Physical Sciences AI — Models & Research

Soon Hoe Lim: On the Potential and Pitfalls of Flow Matching for Probabilistic Forecasting

Soon Hoe Lim

February 25, 2026 34 min

★ ★ ★ ★ ☆ 4/5

Flow MatchingProbabilistic ForecastingDynamical Systems

Applied Sciences & Engineering Mathematics

Robert Szalai: Invariant foliations as a practical data-driven modelling tool

Robert Szalai

February 25, 2026 31 min

★ ★ ★ ★ ☆ 4/5

Invariant FoliationsData-Driven ModelingDynamical Systems

Formal & Physical Sciences AI — Models & Research

Renier Mendoza: Image reconstruction in electrical impedance tomography using deep neural networks

Renier Mendoza

February 25, 2026 30 min

★ ★ ★ ★ ☆ 4/5

Electrical Impedance TomographyDeep LearningDifferential Evolution

Formal & Physical Sciences AI — Models & Research

Pantelis Vlachas: Hypernetworks for Adaptive and Generalizable Forecasting in Dynamical Systems

Pantelis R. Vlachas

February 25, 2026 32 min

★ ★ ★ ★ ☆ 4/5

HypernetworksDynamical SystemsForecasting

Formal & Physical Sciences Mathematics

Massimiliano Tamborrino: A Probabilistic Numerical Parallel-in-Time Solver for Differential Eqns

Massimiliano Tamborrino

February 25, 2026 31 min

★ ★ ★ ★ ☆ 4/5

Probabilistic NumericsParallel-in-TimeGaussian Processes

Formal & Physical Sciences AI — Models & Research

Masanobu Horie: Structure-Preserving Graph Neural Networks: Enforcing Symmetry and Conservation Laws

Masanobu Horie

February 25, 2026 23 min

★ ★ ★ ★ ☆ 4/5

Graph Neural NetworksPhysics-InformedConservation Laws

Formal & Physical Sciences AI — Models & Research

Luca Galimberti: Infinite-dimensional Deep Learning methods for Finance

Luca Galimberti

February 25, 2026 31 min

★ ★ ★ ★ ☆ 4/5

Infinite-DimensionalNeural NetworksUniversal Approximation

Formal & Physical Sciences Philosophy & Ethics

Lior Horesh: Evolving the Scientific Method from (AI-) Descartes, through Hilbert to Noether

Lior Horesh

February 25, 2026 47 min

★ ★ ★ ★ ☆ 4/5

Scientific MethodSymbolic RegressionAI-Descartes

Formal & Physical Sciences AI — Models & Research

Krishnan Raghavan: The role of architecture in learning a continuum of task

Krishnan Raghavan

February 25, 2026 28 min

★ ★ ★ ★ ☆ 4/5

Continual LearningNeural ArchitectureDynamical Systems

Formal & Physical Sciences Mathematics

Jinqiao Duan: Tipping Dynamics in Stochastic Dynamical Systems

Jinqiao Duan

February 25, 2026 28 min

★ ★ ★ ★ ☆ 4/5

Stochastic Dynamical SystemsCritical TransitionsTipping Points

Formal & Physical Sciences AI — Models & Research

James Murray Louw: Stability in learning the climate of dynamical systems with state space systems

James Murray Louw

February 25, 2026 28 min

★ ★ ★ ★ ☆ 4/5

Dynamical SystemsReservoir ComputingClimate Learning

Applied Sciences & Engineering Engineering & Technology

George Stepaniants: Volterra Equations, Neural ODEs, and Constitutive Laws of Materials

George Stepaniants

February 25, 2026 28 min

★ ★ ★ ★ ☆ 4/5

Neural ODEsHomogenizationViscoelasticity

Formal & Physical Sciences AI — Models & Research

Florian Rossmannek: Echo states and fading memory in state-space systems

Florian Rossmannek

February 25, 2026 29 min

★ ★ ★ ★ ☆ 4/5

Echo State PropertyFading MemoryState-Space Systems

Formal & Physical Sciences Mathematics

Christian Uhl: Extracting Dynamical Systems-Based Signals from Noisy and Incomplete Datasets

Christian Uhl

February 25, 2026 30 min

★ ★ ★ ★ ☆ 4/5

Dynamical Component AnalysisKoopman OperatorSignal Decomposition

Formal & Physical Sciences AI — Models & Research

Boumediene Hamzi: Toward an Algorithmic Theory of Machine Learning via Kernel Methods

Boumediene Hamzi

February 25, 2026 38 min

★ ★ ★ ★ ☆ 4/5

Algorithmic Information TheoryKernel MethodsKolmogorov Complexity

Applied Sciences & Engineering Computing & Cybersecurity

Andrea Novoa: Towards real-time digital turbulence

Andrea Novoa

February 25, 2026 28 min

★ ★ ★ ★ ☆ 4/5

Digital TwinData AssimilationEnsemble Kalman Filter

Applied Sciences & Engineering AI — Models & Research

Wei Kang: Deep Learning for Data Assimilation

Wei Kang

February 24, 2026 45 min

★ ★ ★ ★ ☆ 4/5

Data AssimilationDeep LearningObservability

Formal & Physical Sciences AI — Models & Research

Tobias Sutter: Towards Optimal Offline Reinforcement Learning

Tobias Sutter

February 24, 2026 28 min

★ ★ ★ ★ ☆ 4/5

Offline Reinforcement LearningDistributionally Robust OptimizationLarge Deviations

Formal & Physical Sciences Biology

Mor Nitzan: Learning Diffeomorphisms for Dynamical Prototype Matching in Biological Data

Mor Nitzan

February 24, 2026 33 min

★ ★ ★ ★ ☆ 4/5

Dynamical SystemsNormalizing FlowsSingle-Cell

Formal & Physical Sciences AI — Models & Research

Lin Wan: Learning collective multicellular dynamics with an interacting mean field neural SDE model

Lin Wan (Chinese Academy of Sciences)

February 24, 2026 45 min

★ ★ ★ ★ ☆ 4/5

Mean Field SDECell-Cell InteractionsSingle-Cell RNA-Seq

Formal & Physical Sciences AI — Models & Research

Benjamin Charles Walker: Linear Neural Controlled Differential Equations

Benjamin Charles Walker

February 24, 2026 30 min

★ ★ ★ ★ ☆ 4/5

Neural CDEsState-Space ModelsExpressivity

Formal & Physical Sciences AI — Models & Research

Ying-Cheng Lai: Unsupervised learning for anticipating critical transitions

Ying-Cheng Lai

February 23, 2026 33 min

★ ★ ★ ★ ☆ 4/5

Critical TransitionsUnsupervised LearningReservoir Computing

Formal & Physical Sciences AI — Models & Research

Masanobu Inubushi: Reservoir computing with generalized readout based on generalized synchronization

Masanobu Inubushi

February 23, 2026 30 min

★ ★ ★ ★ ☆ 4/5

Reservoir ComputingGeneralized SynchronizationChaos Prediction

Formal & Physical Sciences Mathematics

Jeroen Lamb: Learning random dynamical systems

Jeroen S. W. Lamb

February 23, 2026 35 min

★ ★ ★ ★ ☆ 4/5

Random Dynamical SystemsIterated Function SystemsDelay Embedding

Applied Sciences & Engineering Neuroscience

Deniz Eroglu: Data-Driven Reconstruction of Brain Network Dynamics

Deniz Eroglu

February 23, 2026 30 min

★ ★ ★ ★ ☆ 4/5

Brain NetworksData-DrivenNetwork Reconstruction

Applied Sciences & Engineering AI — Models & Research

Daniel Lathrop: Using nonlinear dynamics for low-power high-speed machine learning electronics

Daniel Lathrop

February 23, 2026 27 min

★ ★ ★ ★ ☆ 4/5

Reservoir ComputingProbabilistic ComputingFPGA

Formal & Physical Sciences AI — Models & Research

Andrew Wilson: Epiplexity: A Measure of Structural Information Content for OOD Generalization

Andrew Wilson

February 22, 2026 50 min

★ ★ ★ ★ ☆ 4/5

EpiplexityOut-of-Distribution GeneralizationStructural Information

Formal & Physical Sciences Control & Robotics

Anas Barakat: Multi-Agent Online Control with Adversarial Disturbances

Anas Barakat

February 22, 2026 25 min

★ ★ ★ ★ ☆ 4/5

Multi-Agent ControlOnline LearningAdversarial Disturbances

Formal & Physical Sciences Mathematics

Aurelian Gheondea: Mercer's Theorem. An Operator Theorist's Perspective.

Aurelian Gheondea

January 29, 2026 46 min

★ ★ ★ ★ ☆ 4/5

Mercer's TheoremReproducing Kernel Hilbert SpaceIntegral Operators

Formal & Physical Sciences Mathematics

Aurelian Gheondea: Reproducing Kernel Hilbert Spaces. General Theory towards Mercer's Theorem

Aurelian Gheondea

January 25, 2026 71 min

★ ★ ★ ★ ☆ 4/5

Reproducing Kernel Hilbert SpaceMercer's TheoremPositive Semidefinite Kernel