Research Scientist · Meta AI · On-Device ML & GenAI
I am a Research Scientist at Meta AI working on on-device ML and GenAI for photo and video edits. Before that I was a Deep Learning Engineer at Apple, and completed my PhD at TU Munich in the group of Daniel Cremers. My research interests span depth estimation, RGB-D scene understanding, visual representation learning, responsible AI, and on-device machine learning.
Meta AI · 2021
A dataset of 45,186 videos featuring paid participants to help researchers evaluate AI models for fairness across age, gender, and skin tone attributes.
Meta AI · 2023
An expanded dataset with 26,467 videos covering additional fairness dimensions including perceived age, gender, physical attributes, and self-reported annotations for more robust AI evaluation.
Benchmark · 2017
Large-scale focal stack benchmark with 12 indoor scenes and dense depth ground truth — 25× larger than prior benchmarks, enabling ML-based depth-from-focus research.
TU Munich
Large-scale indoor RGB-D dataset for scene understanding and semantic segmentation research, captured across real-world environments at TU Munich.
2018
ACCV
2017
ICCV
Image-based Localization Using LSTMs for Structured Feature Correlation
2017
ICCV
Learning Proximal Operators: Using Denoising Networks for Regularizing Inverse Imaging Problems
2016
ACCV
2015
ICCV
FlowNet: Learning Optical Flow with Convolutional Networks