Profile of TAs

Marcel Büsching
  • Brief Bio:  PhD student at RPL with focus on scene representation, scene reconstruction and neural rendering / novel view generation for dynamic scenes.
  • Deep learning software packages:
    • PyTorch
    • TensorFlow 2
    • (jax)
  • Deep learning methodologies, architectures and application fields:
    • Neural Radiance Fields (NeRF), Gaussian Splatting
    • Transformers
    • Convolutional Networks
    • Generative Models: Diffusion Models
    • Computer Vision Topics: 3D Scene Reconstruction, Neural Rendering
    • Graph Neural Networks (GNN)
Klas Wijk
  • Brief Bio:  PhD student at RPL with focus on generative models, feature selections and inverse problem with applications to fluid mechanics.
  • Deep learning software packages:
    • PyTorch
    • Some Jax
  • Deep learning methodologies, architectures and application fields:
    • Generative Models
    • Gradient Estimation (e.g. Stochastic Nodes)
    • Variational Inference
    • Geometric Deep Learning
    • Physics Constrained Learning

Li Ling
  • Brief Bio:  PhD student at RPL with focus underwater perception with sonars, 3D point cloud registration and denoising.
  • Deep learning software packages:
    • PyTorch
  • Deep learning methodologies, architecture and application fields:
    • 3D point cloud architectures
    • Convolutional networks

 

Sebastian Gerard
  • Brief Bio:  PhD student at RPL working on the segmentation of satellite images
  • Deep learning software packages:
    • Pytorch
    • Pytorch Lightning
    • wandb
  • Deep learning methodologies, architectures and application fields:
    • Self-supervised learning for images
    • Segmentation
    • Satellite images

 

Shutong Jin
  • Brief Bio:  PhD student at RPL working on learning-based methodologies for robotic manipulation with a focus on large-scale robotic systems and video as the main input.
  • Deep learning software packages:
    • PyTorch
  • Deep learning methodologies, architecture and application fields:
    • Video Transformers
    • Video Diffusion Models
    • Convolutional Neural Networks

 

Ci Li
  • Brief Bio:  PhD student at RPL working on computer vision and 3D reconstruction on animals from monocular images.
  • Deep learning software packages:
    • PyTorch
  • Deep learning methodologies, architecture and application fields:
    • Computer vision applications in pose estimation and motion pattern recognition
    • Segmentation, keypoint detections

 

Yiping Xie
  • Brief Bio:  PhD student at RPL working on underwater perception with deep learning, mainly on 3D reconstruction of the seabed.
  • Deep learning software packages:
    • PyTorch
  • Deep learning methodologies, architecture and application fields:
    • Neural radiance fields
    • Generative models: GANs, glow, VAEs
Heng Fang
  • Brief Bio: Ph.D. student at RPL working on change detection and uncertainty estimation on satellite images
  • Deep learning software packages:
    • Pytorch
    • wandb
  • Deep learning methodologies, architectures and application fields:
    • Segmentation, change detection, and uncertainty estimation
    • Transformer and Diffusion Models
    • unsupervised pre-training, disentangled representation learning

 

Lennart Van der Goten
  • Brief Bio: Ph.D. student at CST working with MRI de-identification/artifact removal
  • Deep learning software packages:
    • Pytorch
    • wandb
  • Deep learning methodologies, architectures and application fields:
    • Image-to-image translation
    • Transformer and Diffusion Models
    • Self-supervised learning
Moein Sorkhei
  • Brief Bio: Ph.D. student at CST working with transfer learning and medical images
  • Deep learning software packages:
    • PyTorch
  • Deep learning methodologies, architectures and application fields:
    • Transformers
    • Transfer learning
Alfredo Reichlin
  • Brief Bio: PhD student at RPL working mainly on Imitation Learning, Offline Reinforcement Learning and Meta-Learning for robotic manipulation
  • Deep learning software packages:
    • PyTorch
  • Deep learning methodologies, architectures and application fields:
    • Representation learning from images
    • Few-shot adaptation
    • Mostly CNN
    • Some Graph Neural Networks
    • Some generative models (VAEs, GANs)