Seminar | TransferLab — appliedAI Institute

Seminar

The TransferLab Seminar is a platform where researchers and engineers share and discuss recent advances in AI/ML, striking a balance between accessibility and mathematical depth.

We organize comprehensive talks on ML, RL, DL and statistics, emphasizing both theoretical understanding and practical implementation. These range from introductory overviews to recent developments - and are open to all interested individuals!

Each talk, lasting about 45min followed by around 15min discussion, focuses on a specific topic related to our ongoing series topics. To stay informed, follow the appliedAI Institute on LinkedIn. If you’re interested in presenting your work, contact us. Recordings of past seminars are available on YouTube.

Upcoming Talks

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Previous Talks

Towards a statistical theory of data selection under weak supervision - Pulkit Tandon (Granica)

Oct 30, 2024

Pulkit Tandon, research engineer at Granica, will present his work on data selection, showing how using surrogate models to select subsamples of a data set for labeling can improve training efficiency and performance.

Introduction to Reduced Order Modeling - Sridhar Chellappa (MPI Magdeburg)

Oct 17, 2024

Sridhar Chellappa will introduce the concept of reduced order modeling (ROM), a technique used in the field of simulation and AI to reduce the complexity of mathematical models. The seminar will cover the basics of ROM, its applications, and a lead up to more ML-flavoured approaches.

Simulation and AI

Sridhar Chellappa

Recent Advancements in Tractable Probabilistic Inference - Antonio Vergari (University of Edinburgh)

Sep 26, 2024

Antonio Vergari will give an overview about recent advancements in tractable probabilistic inference.

Probabilistic Models

Antonio Vergari

Generalized Stability Guaranteed Quadratic Embeddings for Nonlinear Dynamical Systems - Pawan Goyal (appliedAI Initiative)

Sep 12, 2024

Pawan Goyal, Senior AI Engineer at appliedAI, will present a recent work in physics-enhanced machine learning on generalized quadratic embeddings for nonlinear dynamics.

Simulation and AI

Stochastic Optimal Control Matching - Carles Domingo-Enrich (Microsoft Research New England, previously at Meta & NYU)

Sep 5, 2024

Carles Domingo-Enrich will present his work on Stochastic Optimal Control Matching (SOCM), a novel Iterative Diffusion Optimization (IDO) technique for stochastic optimal control that stems from the same philosophy as the conditional score matching loss for diffusion models.

Probabilistic Models

Carles Domingo-Enrich

From Theory to Practice: Neural Operators Transforming Acoustics - Jakob Wagner

Jul 18, 2024

Jakob Wagner, Junior AI Researcher at the appliedAI Institute for Europe, will talk about the topic of his Master’s thesis, conducted in collaboration with TUM, on applying neural operators to real-world acoustic problems.

Simulation and AI

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Our Block Seminars

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Uncertainty quantification for neural networks

In this seminar series, we review seminal and recent papers on uncertainty quantification that didn’t make it into our training on Bayesian ML.

Oct 22, 2022