About
I am a postdoctoral researcher in the Photogrammetry and Remote Sensing group at ETH Zurich, broadly
interested in Computer Vision and Machine Learning research. I received my PhD in the Computer Vision
Laboratory at ETH Zurich. Even before that, I spent a decade working in the industry:
I helped NVIDIA revolutionize parallel computations with NVIDIA CUDA technology and later joined
Ubiquiti Networks to build multiple video camera products.
Publications
DiffDreamer: Consistent Single-view Perpetual View Generation with Conditional Diffusion Models
Preprint, under review
Spectral Tensor Train Parameterization of Deep Learning Layers
International Conference on Artificial Intelligence and Statistics (AISTATS) 2021
T-Basis: a Compact Representation for Neural Networks
International Conference on Machine Learning (ICML) 2020
News
- 2022.11: One paper was accepted at SlowDNN 2023 workshop: “TT-NF: Tensor Train Neural Fields.”
- 2022.11: Gave an invited talk at Huawei Zurich about our latest work, “TT-NF: Tensor Train Neural Fields.”
- 2022.11: Started as a postdoc in the Photogrammetry and Remote Sensing Group at ETH Zurich with Professor Konrad Schindler.
- 2022.09: Successfully defended my Ph.D. thesis titled “Tensor Decompositions in Deep Learning.”
- 2022.08: One paper was accepted at NeurIPS 2022: “Towards Practical Control of Singular Values of Convolutional Layers.”
- 2022.07: Attended ICVSS summer school 2022.
- 2022.06: Two patents were published based on the AISTATS 2021 paper “Spectral Tensor Train Parameterization of Deep Learning Layers.”
- 2022.03: One paper was accepted at CVPR 2022: “Pix2NeRF: Unsupervised Conditional π-GAN for Single Image to Neural Radiance Fields Translation.”
- 2021.12: One patent was published based on the ICML 2020 paper “T-Basis: a Compact Representation for Neural Networks.”
- 2021.07: One paper was accepted at ICCV 2021: “Exploring Relational Context for Multi-Task Dense Prediction.”
- 2021.06: One paper was accepted into Sparsity in Neural Networks 2021 Workshop: “Spectral Tensor Train Parameterization of Deep Learning Layers.”
- 2021.03: One paper was accepted at CVPR 2021: “Learning to Relate Depth and Semantics for Unsupervised Domain Adaptation.”
- 2021.04: torch-fidelity will be featured at PyTorch Ecosystem Day 2021.
- 2021.01: One paper was accepted at AISTATS 2021: “Spectral Tensor Train Parameterization of Deep Learning Layers.”
- 2020.07: One paper was accepted at ECCV 2020: “Reparameterizing Convolutions for Incremental Multi-task Learning without Task Interference”.
- 2020.06: One paper was accepted at ICML 2020: “T-Basis: a Compact Representation for Neural Networks.”
- 2018.05: Started as a Ph.D. candidate in the Computer Vision Lab at ETH Zurich with Professor Luc Van Gool.