I am a first-year PhD student in the Visual Computing Group (VCG) at Harvard University, advised by Prof. Hanspeter Pfister. I hold a B.Sc. in Engineering Science and an M.Sc. in Robotics, Cognition, and Intelligence from the Technical University of Munich. Before returning to academia I spent several years in industry - at deevio, WATTx, Agile Robots and Merantix Momentum, and most recently as Senior Specialist for AI & Software at DB Systel.
PhD Student | Visual Computing Group, Harvard
University
Researching open-source tooling for scalable connectomics and 3D segmentation for emerging image modalities and annotation-scarce settings.
Research Fellow | School of Engineering and Applied
Sciences, Harvard University
Developed CySGAN for annotation-free cross-modal 3D nuclei segmentation, and started working on SynAnno, an interactive system for proofreading synaptic annotations.
M.Sc. Robotics, Cognition, Intelligence | Technical
University of Munich
Thesis: "3D Instance Segmentation of an Unlabeled Modality via Cyclic Segmentation GANs", written during my research stay with the Visual Computing Group at Harvard.
B.Sc. Engineering Science | Technical University of
Munich
Thesis: "Energy-based Condition Monitoring for Industry 4.0", written as a student researcher at fortiss.
SynAnno: Interactive Guided Proofreading of Synaptic
Annotations
Leander Lauenburg, Jakob Troidl, Adam Gohain, Zudi
Lin, Hanspeter Pfister, Donglai Wei
(IEEE VIS, 2025)
SynAnno introduces a structured and intuitive approach to
proofreading synapses in connectome datasets. I began
developing it as a Research Fellow at Harvard
University's Visual Computing Group and completed it as a
Research Affiliate, alongside my full-time industry role.
Code
Preprint
IEEE VIS
Replicability Stamp
3D Domain Adaptive Instance Segmentation via Cyclic
Segmentation GANs
Leander Lauenburg*, Zudi Lin*, Ruihan Zhang, Márcia
dos Santos, Siyu Huang, Ignacio Arganda-Carreras, Edward S.
Boyden, Hanspeter Pfister, Donglai Wei
(IEEE JBHI, 2023)
The paper '3D Domain Adaptive Instance Segmentation via
Cyclic Segmentation GANs', is available via open access in
the IEEE Journal of Biomedical and Health Informatics. The
paper is the result of my Master's thesis written during my
research stay with the Visual Computing Group at
Harvard.
Research Code
Production Code
IEEE JBHI
Master's Thesis
Reinforcement Learning for Solving Robotic Reaching Tasks
in the Neurorobotics Platform
Márton Szep, Leander Lauenburg, Kevin Farkas, Xiyan
Su, Chuanlong Zang
(HBP Student Conference, 2022)
The paper is my team's contribution to the master course
"Cloud-Based Machine Learning in Robotics" (grade 1.0). I
presented the work at the 6th Human Brain Project Student
Conference.
Code
arXiv Paper
Conference Abstract
PackLog Solutions: Estimation Tool for Streamlining
Logistic Operations
Franz Schubart, Leander Lauenburg, Lukas Pries
Marcel, Perez San Blas
(First Place in TechChallenge BEFIVE, Innovation
Challenge by
UnternehmerTUM, 2021)
In close collaboration with our industry partners, we
developed an MVP for streamlining planning and communication
processes between the various players in the logistics of
large manufacturers.
Code
Video
ELA - Object SLAM
Leander Lauenburg, Andy Chen, Ezgi Cakir
The work is my team's contribution to the master course
"Advanced Topics in 3D Computer Vision" (grade 1.0). It is
an extension of
CubeSLAM: Monocular 3D Object SLAM, IEEE Transactions on
Robotics 2019, S. Yang, S. Scherer. In addition to cleaning up, streamlining, and dockerizing
CubeSLAM, we improved the work by adding dynamic object
filtering, object class-dependent scaling, and embedding
stream enrichments.
Code
Senior Specialist for AI & Software | R&D
Project KIDispo, DB Systel
Mr. Lauenburg was an outstanding top performer whose
achievements in every respect consistently earned our
fullest recognition. He was the ideal employee. - Vera
Müller
Research Software Engineer | Research Team, Merantix
Momentum
Mr. Lauenburg is distinguished by his excellent team-oriented work ethic, outstanding communication skills, and very high flexibility. - Dr. Johannes Otterbach
Working Student | Department for Perception and
Software Development, AgileRobots
Leander has been an exceptional dedicated and driven team member who consistently demonstrated his highly efficient and independent work style. - Patrick Schulte
Entrepreneur in Residence (Tech Lead) | Stryza
(formerly Predicpro), WATTx
Leander's work laid the new foundation for Predicpro [Stryza] for which we are entirely grateful. His vision and hard-work ultimately resulted, among other things, in a pilot project with a DAX listed company." - Dr. Martin Mittermeier
Data Scientist Intern | Engineering Team, deevio
We would like to highlight Leander's versatility and independent working style, as he proactively identified and resolved issues related to his current tasks. - Dr. Tassilo Glander
Working Student | Smart Energy Team, fortiss
Mr. Lauenburg shows an extraordinary level of commitment, an exceptional willingness to perform, and a great eagerness to learn. - Dr. Markus Duchon