Profile of TAs

  • Sebastian Gerard
    • Brief Bio:  PhD student at RPL working on self-supervised learning on 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
  • Yiping Xie
    • Brief Bio: PhD student in RPL and WASP, working on learning the representations of underwater perception for navigation.
    • Deep learning software packages:
      • Pytorch
    • Deep learning methodologies, architectures and application fields:
      • Generally discriminative models and generative models (VAEs, GANs)
      • Computer Visions applications: depth estimation and etc.
      • Computer Graphics: differentiable rendering, neural representation
      • Sidescan Sonar images

     

  • 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)

     

  • Heng Fang
    • Brief Bio: Ph.D. student at RPL working on change detection and uncertainty estimation on satellite images
    • Deep learning software packages:
      • Pytorch
    • Deep learning methodologies, architectures and application fields:
      • Computer vision applications in satellite imagery and biomedical scenarios
      • Segmentation, change detection, and uncertainty estimation

    I was a student of this course in 2019, feel free to contact me if you have any proposal ideas for the projects.

  • Li Ling
    • Brief Bio: PhD student in RPL, working on sonar-based perception for underwater navigation.
    • Deep learning software packages:
      • Pytorch
    • Deep learning methodologies, architectures and application fields:
      • Keypoint detection, description and matching for images
      • Sidescan Sonar images
      • Interested in contrastive representation learning for images

     

  • Lennart Alexander van der Goten
    • Brief Bio: Third year PhD student at CST (SciLifeLab) working on domain adaptation & anonymization on biomedical data (CT, MRI etc.)
    • Deep learning software packages:
      • Pytorch
      • TensorFlow
    • Deep learning methodologies, architectures and application fields:
      • Generative Adversarial Networks
      • Variational Autoencoders
      • Large-Scale & Distributed Deep Learning
      • DL on volumetric data

     

  • 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, architectures and application fields:
      • Computer vision applications in pose estimation and motion pattern recognition
      • Segmentation, keypoint detections

     

  • Marco Moletta
    • Brief Bio: PhD student working on representation learning for robotic manipulation of deformable objects, with focus on dynamics prediction and graph representations.
    • Deep learning software packages:
      • Pytorch
    • Deep learning methodologies, architectures and application fields:
      • Graph neural networks
      • Convolutional neural networks
      • Self supervised learning from image and meshes
      • Robotic manipulation

     

  • 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:
      • Transformers
      • Convolutional Networks
      • Undersupervised Learning: SimCLR, FixMatch
      • Generative Models: VAE, (Diffusion Models)
      • Computer Vision: Scene Reconstruction, Novel View Generation
      • Neural Radiance Fields (NeRF)

     

  • Shutong Jin
    • Brief Bio: PhD student at RPL with a focus on machine learning-based methodologies for robotic manipulation with a focus on large-scale cloud-based robotic systems
    • Deep learning software packages:
      • PyTorch
    • Deep learning methodologies, architectures and application fields:
      • Transformers
      • Convolutional Neural Network
      • Robotic manipulation, object detection and classification