Lectures related to Energy landscape: Symmetric Interactions

Transdisciplinarity: Integrative Research Approach

Explores transdisciplinarity in integrative research and sustainable landscape development.

Attractor Networks: Hopfield Model Generalizations

Explores attractor networks and Hopfield model generalizations in computational neuroscience, focusing on memory retrieval and dynamics convergence.

Martingale-based Methods for Stochastic Systems

Explores martingales in stochastic systems, focusing on formal analysis, termination analysis, and stability verification.

Spatial Ergodicity for SPDEs

Explores spatial ergodicity for SPDEs, covering basic formulations, initial data effects, and results on ergodicity and CLT.

Mean Field Theory: Stochastic Analysis and Applications

Explores classical mean field theory, local interactions, and examples like individual-based SIR models and raindrop formation.

Attractor Networks and Spiking Neurons

Explores attractor networks, spiking neurons, memory data, and realistic networks in neural dynamics.

Maximum Entropy Principle: Stochastic Differential Equations

Explores the application of randomness in physical models, focusing on Brownian motion and diffusion.

Fokker-Planck & KPZ Equations

Explores the Fokker-Planck and KPZ equations in stochastic processes and random growth models.

Stochastic Models for Communications: Continuous-Time Stochastic Processes Examples 4-5

Covers examples of continuous-time stochastic processes in communication systems.

Stochastic Hopfield model

Explores the Stochastic Hopfield model, noisy neurons, firing probabilities, memory retrieval, and overlap equations in attractor networks.

Attractor Networks and Generalizations

Explores attractor networks, Hopfield model generalizations, and memory dynamics in computational neuroscience.

Martingales and Brownian Motion

Discusses convergence, martingales, Brownian motion, joint laws, testing procedures, and stop times.

Discrete-Time Stochastic Processes: Wiener Filter

Explores the Wiener filter for discrete-time stochastic processes and its applications.

Stochastic Models for Communications

Covers stochastic models for communications and the estimation of correlation functions.

Stochastic Analysis & Random Fields

Covers the outcomes of a virtual seminar on stochastic analysis.

Stochastic Models for Communications: Continuous-Time Stochastic Processes - Stationarity

Explores the concept of stationarity in continuous-time stochastic processes and its implications.

Linear Prediction and Estimation

Explores linear prediction, optimal filters, random signals, stationarity, autocorrelation, power spectral density, and Fourier transform in signal processing.

Stochastic Simulation: Variance Reduction Techniques

Covers stochastic simulation and variance reduction techniques, focusing on Courra variate and auxiliary distribution generation.

Flow Equations: Singular SPDEs and Quantum Field Theory

Covers recent research on singular SPDEs and their applications in quantum field theory.

Learning from the Interconnected World with Graphs

Explores learning from interconnected data using graphs, covering challenges, GNN design, research landscapes, and democratization of Graph ML.