activity
20232026
most citedAI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study

1 citations · 1 across the 2 of their papers we have counts for

collaborators

5 papers

cs.CV2026

A Universal Raw-IQ Radar Encoder for Cross-Sensor Human Sensing

Jonas Leo Mueller, Dario Zanca, Sebastian Hoefler +5

Radar-based human sensing typically relies on end-to-end task-specific models trained on hand-chosen signal projections, which generalize poorly across unseen users, tasks, and har…

cs.SI2026

Not All Nodes Are Created Equal: Homophily-Aware Stratification for Stable GNN Evaluation

Naga Venkata Sai Jitin Jami, Thomas Altstidl, Sebastian Hoefler +4

Graph neural networks are widely used for transductive node classification, with accuracy typically measured on randomly drawn train/validation/test splits. Reported accuracy has b…

cs.CV2026

You Only Flow Once: Calibrated and Real-Time Radar Pose Estimation with Multi-Hypothesis Normalizing Flows

Jonas Leo Mueller, Sebastian Hoefler, Dario Zanca +3

Sparse and noisy millimeter-wave radar point cloud observations often correspond to multiple plausible human poses, making deterministic pose estimation fundamentally ill-posed. Ye…

cs.CV2025

Stratify or Die: Rethinking Data Splits in Image Segmentation

Naga Venkata Sai Jitin Jami, Thomas Altstidl, Jonas Mueller +4

Random splitting of datasets in image segmentation often leads to unrepresentative test sets, resulting in biased evaluations and poor model generalization. While stratified sampli…

cs.CE2023★ 1 cited

AI Driven Near Real-time Locational Marginal Pricing Method: A Feasibility and Robustness Study

Naga Venkata Sai Jitin Jami, Juraj Kardoš, Olaf Schenk +1

Accurate price predictions are essential for market participants in order to optimize their operational schedules and bidding strategies, especially in the current context where el…