Leander Lauenburg

About Me

As an AI enthusiast with a solid theoretical foundation and considerable practical experience, my mission is to develop innovative AI solutions that can make a positive impact on the world. I earned both my B.Sc. in Engineering Science and my M.Sc. in Robotics, Cognition, Intelligence from the Technical University of Munich (TUM). During my Master's studies, I had the honor of being invited to Harvard's Visual Computing Group (VCG) as a research fellow. There, I was given the opportunity to lead the development of CySGAN, a 3D instance segmentation method for neurobiology. Currently, I'm continuing my work at Harvard as a Research Assistant, advancing the development of SynAnno, a proofreading tool for synaptic polarity annotations. Between my undergraduate and graduate studies, I spent a year in Berlin working as a Data Scientist at deevio and later as a Technical Project Lead and Integrated Systems Engineer at Predicpro (now Stryza), a WATTx venture. While completing my M.Sc., I gained valuable experience as a working student in the perception and software development department at Agile Robots AG, Germany's first robotics unicorn, and at the national research institute fortiss. Most recently, I held the position of Research Software Engineer at Merantix Momentum, an AI Consultancy and Research Hub. In this role, I developed MLOps tools for scalable and traceable ML training, designed CI/CD systems for safety-critical ML components, and provided MLOps training to my colleagues.

Experience

Research Assistant | School of Engineering and Applied Sciences, Harvard University
Time: Jun 2021 - Present. Advisor: Prof. Hanspeter Pfister

Research Software Engineer | Research Team, Merantix Momentum
Time: Feb 2023 - Jul 2023. Mentor: Dr. Johannes Otterbach

Mr. Lauenburg is distinguished by his excellent team-oriented work ethic, outstanding communication skills, and very high flexibility. - Dr. Johannes Otterbach

Research Fellow | School of Engineering and Applied Sciences, Harvard University
Time: Oct 2021 - Jul 2022. Advisor: Prof. Hanspeter Pfister

We would like to strongly recommend him [Leander] for any academic programs or industry positions without reservation. - Prof. Hanspeter Pfister

Working Student | Department for Perception and Software Development, AgileRobots
Time: Dec 2020 - Jul 2021. Mentor: Patrick Schulte

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
Time: May 2019 - Oct 2019. Mentor: Dr. Martin Mittermeier

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
Time: Nov 2018 - May 2019. Mentor: Dr. Tassilo Glander

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
Time: Jun 2017 - Sep 2018. Mentor: Dr. Markus Duchon

Mr. Lauenburg shows an extraordinary level of commitment, an exceptional willingness to perform, and a great eagerness to learn. - Dr. Markus Duchon

Projects

SynAnno: Proofreading Tool for Synaptic Polarity Annotations
Leander Lauenburg, Zudi Lin, Jakob Troidl, Johanna Beyer, Hanspeter Pfister, Donglai Wei


SynAnno is a tool for proofreading and correcting synaptic polarity annotations in electron microscopy volumes. The tool is developed by Harvard's Visual Computing Group and Lichtman Lab, it integrates with Seung Lab's CAVE (Connectome Annotation Versioning Engine). This project is currently being prepared for first-author submission to IEEE VIS 24.
Code

Lynx
Leander Lauenburg

Lynx is a lightweight tool that automates and schedules sequential ML subtasks, tailored for experimental ML MVPs that are not ready for a full-fledged Flyte setup. Ideal for research, testing, and debugging, Lynx can run tasks once or on a cronjob-like schedule, executing pipelines in the foreground or background.
Code

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

Awards

  • IFI Scholarship, German Academic Exchange Service (DAAD), 2022
  • First Place in TechChallenge BEFIVE, Innovation Challenge by UnternehmerTUM, 2021