Deep learning might currently be one of the few technologies that has a great transformative potential for us all. To advance deep learning, I think one of the most important aspects is to understand the technology very well. Such an understanding should be accessible to as many people as possible. Thus, I am excited whenever I have the chance to work on something that fosters this understanding or makes it available to more users.
Work
Founding Member of Technical Staff (May 2024 - Present)
AI + Biology
Postdoctoral Researcher, Carnegie Mellon University (Oct 2023 - May 2024)
Research and development of tools in the areas of AI evaluation and prompt engineering.
Research Scientist, Sigma Computing (Oct 2022 - Jun 2023)
Bringing data and analysts closer together with the help of visualization and AI.
Research Intern, Apple Machine Intelligence (Mar 2021 - Sep 2021)
Designed and developed a framework for component-based ML interfaces which can be composed in different environments such as computational notebooks and web dashboards.
Research Intern, Google TensorBoard (Jun 2020 - Sep 2020)
Designed and implemented a visualization approach for a novel bias detection algorithm. This visualization is designed to support large label spaces and multilabel classification problems.
Research Intern, Google PAIR (May 2019 - Aug 2019)
Invented and experimented with a technique similar to Feature Visualization, but for language models.
Talks
Where did my Lines go? Visualizing Missing Data in Parallel Coordinates
Symphony: Composing Interactive Interfaces for Machine Learning
Net2Vis - A Visual Grammar for Automatically Generating Publication-Tailored CNN Architecture Visualizations
Visualization for AI in Critical Domains
Classifier-Guided Visual Correction of Noisy Labels for Image Classification Tasks
Automatic Fibril-Crossover Detection in EM-Images using Deep Convolutional Networks
MedVis Workshop 2018, Ulm, Germany, (Thu Apr 12 2018)
Teaching
Lecture: Deep Learning for Graphics and Visualization (2019 - 2022)
Created and regularly held one chapter on visualization for AI at Ulm University. Talk about different explainability techniques and visualization concepts that help investigate and communicate about AI systems.
Projects: Visualization and Explainability for AI (2017 - 2022)
Supervise undergrad and grad students in regular projects. We go through the process of ideation, implementation, and writing about these projects.
Seminars: Visualization and Explainability for AI (2017 - 2022)
Held regular seminars for both undergrad and grad students.
Reviewing and Service
VGTC - Industrial Relations Chair (2023 - Present)
VISxAI - Program Committee (2022 - Present)
TVCG (2020 - 2023)
VIS (2021, 2023)
CHI (2021, 2023)
UIST (2022)
CG&A (2022)
EuroVIS (2022)
IMWUT (2022)
VCBM (2022)
MICCAI (2022, 2023)