Lennart Bastian

I am currently a PhD student at the Chair for Computer Aided Medical Procedures (CAMP) in Munich supervised by Prof. Dr. Nassir Navab. Since May '23 I have been coordinating the Surgical Data Science team.

I completed my M.Sc. in Applied Mathematics at the Technical University of Munich (TUM), with my thesis "SDP Solvers with Optimal Storage Requirements and Applications" under the supervision of Prof. Dr. Michael Ulbrich.

Previously, I obtained my Bachelor's degree in Mathematics and Computer Science from the New York University Courant Institute. I also worked as a software engineer at H2oMetrics, a water management cloud startup, and as a research assistant at the Levine Lab at Memorial Sloan Kettering Cancer Center developing computational tools for genetic analysis of patients with myeloid leukemias.

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Research

My research interests span 3D Computer Vision and Geometry in Machine Learning, including application areas such as surgical scene understanding and studying shape deformation in both medical and general problem settings.

Forecasting Continuous Non-Conservative Dynamical Systems in SO(3)

We study rotational forecasting on the manifold SO(3). This leads to the proposed Savitzky-Golay Neural Controlled Differential Equations which learn continuous data-driven priors on the manifold and excel at extrapolating rotational states.

Beyond Role-Based Surgical Domain Modeling

We achieve generalizable re-identification and tracking for surgical operating rooms (OR). This enables our proposed staff-centric surgical domain models, the first concept for personalized intelligent systems in the OR.

Beyond Complete Shapes: A Quantitative Evaluation of 3D Shape Matching Algorithms

We present BeCoS, the first comprehensive benchmark and evaluation framework for the challenging but widely applicable problem setting of partial shape correspondence.

Hybrid Functional Maps for Crease-Aware Non-Isometric Shape Matching

We propose a hybrid approach for estimating functional maps that leverage the strengths of basis functions originating from different operators overcoming limitations of previous approaches for non-isometric deformations.

S3M: Scalable Statistical Shape Modeling through Unsupervised Correspondences

We propose an unsupervised method for statistical shape model (SSM) construction that utilizes functional correspondences to learn shape structures across population anatomies, demonstrating robustness to noisy and potentially scaling to larger patient populations without manual annotations.

SegmentOR: Obtaining Efficient Operating Room Semantics through Temporal Propagation

SegmentOR is a weakly-supervised 3D semantic segmentation method for operating room environments that leverages temporal consistency in 4D point cloud sequences to significantly reduce the annotation burden. We demonstrate the utility of the resulting segmentation maps for improving downstream surgical workflow analysis.

DisguisOR: Holistic Face Anonymization for the Operating Room

DisguisOR improves the robustness of operating room video anonymization by leveraging multi-view data to accurately localize and anonymize individuals’ faces in 3D, resulting in geometrically consistent privacy protection across all camera views.

Design adapted from here