Course Projects

Deep Learning Systems: Algorithms and Implementation

  • Built a PyTorch-like deep learning framework from the ground up, with backend support of CPU/GPU containing standard functionalities such as automatic differentiation, optimizers, data loaders, loss functions, and all required modules to employ parametrized layers.
    (Python, C++, CUDA)

  • Designed and implemented a NeuralODE layer (operators and backends, ODE numerical solver, AD) within our Pytorch-like framework and demonstrated its capability of approximating ODE dynamics with NNs. (Python, C++, CUDA)

Project Report

ML with Large Datasets

  • Conducted various analyses such as entity resolution and PCA and built ML pipelines on large datasets such as Million Song Dataset, light-sheet imaging, and Criteo 1TB click logs dataset. (Python, AWS EC2, PySpark)

  • Implemented multiple model compression techniques from scratch (network slimming, magnitude-based pruning, and …). (Python, Tensorflow)

Project Report

Quantum Integer Programming

  • Implemented a quantum computing MILP solver using D-wave quantum computer for job shop scheduling problem (Python)