Phu Pham

PhD student in Computer vision / Machine learning


I am a Ph.D. student in Computer Science at Purdue University, advised by Prof. Aniket Bera. My research focuses on computer vision and graphics, machine translation, reinforcement learning and robotics. My recent work includes 3D reconstruction from 2D images using diffusion models and neural radiance fields, DNN based predictive models for spatial and spectral data, transformer-based multimodal machine translation and multiagent path planning.

Before pursuing the Ph.D., I gained extensive experience in the industry as a full-stack software engineer in Finland. I worked for several startups and accumulated over 5 years of experience in software development.

Previously, I completed my Master’s degree in Big data and large-scale computing with a minor in Machine learning and data mining from Aalto University, advised by Dr. Jorma Laaksonen. I also did my undergrad in Information Technology at Metropolia University of Applied Sciences.


  1. cramp_thumb.png
    Crowd-Aware Multi-Agent Pathfinding With Boosted Curriculum Reinforcement Learning
    Phu Pham, and Aniket Bera
  2. dream_thumb.png
    DREAM: Decentralized Reinforcement Learning for Exploration and Efficient Energy Management in Multi-Robot Systems
    Dipam Patel, Phu Pham, Kshitij Tiwari, and 1 more author
  3. dronerf_thumb.jpg
    DroNeRF: Real-time Multi-agent Drone Pose Optimization for Computing Neural Radiance Fields
    Dipam Patel, Phu Pham, and Aniket Bera
  4. taiga_thumb.jpg
    TAIGA: A Novel Dataset for Multitask Learning of Continuous and Categorical Forest Variables From Hyperspectral Imagery
    Matti Mõttus, Phu Pham, Eelis Halme, and 3 more authors
    IEEE Transactions on Geoscience and Remote Sensing, 2022
  5. raist_thumb.jpg
    RAIST: Learning Risk Aware Traffic Interactions via Spatio-Temporal Graph Convolutional Networks
    Videsh Suman, Phu Pham, and Aniket Bera
    CoRR, 2020
  6. seeknet_thumb.jpg
    SeekNet: Improved Human Instance Segmentation via Reinforcement Learning Based Optimized Robot Relocation
    Venkatraman Narayanan, Bala Manoghar, Rama RV, and 2 more authors
    CoRR, 2020
  7. hyperspectral_thumb.jpg
    Deep learning methods for modelling forest biomass and structures from hyperspectral imagery
    Phu Pham
  8. wmt18_thumb.jpg
    The MeMAD Submission to the WMT18 Multimodal Translation Task
    Stig-Arne Grönroos, Benoit Huet, Mikko Kurimo, and 8 more authors
    In Proceedings of the Third Conference on Machine Translation: Shared Task Papers, Oct 2018