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General Information

Full Name Trung Trinh
Email trung.trinh [at] aalto [dot] fi
Github https://github.com/trungtrinh44
Twitter https://twitter.com/trungtrinh44
Research interests Robust Machine Learning, Bayesian Deep Learning, Uncertainty Quantification, Optimization

Education

  • 10/2021 - now

    Espoo, Finland

    Ph.D. in Artificial Intelligence and Machine Learning
    Aalto University, Finland
    Department of Computer Science
  • 09/2019 - 06/2021

    Espoo, Finland

    M.Sc. (Tech) in Machine Learning, Artificial Intelligence and Data Science
    Aalto University, Finland
    • GPA: 4.93/5
    • Graduating with honors.
    • Thesis: Scalable Bayesian neural networks
    • Thesis supervisor: Prof. Samuel Kaski
    • Thesis advisor: Markus Heinonen, Ph.D.
  • 09/2014 - 06/2018

    HCMC, Vietnam

    B.Eng. in Computer Science
    HCMC University of Technology, VNU-HCM, Vietnam
    • GPA: 9.09/10
    • Graduating with honors
    • Second place among overall graduates
    • Thesis: Development of an intelligent chatbot using machine learning approaches
    • Thesis advisor: Tho Quan, Ph.D

Technical skills

  • Languages: Python, Javascript, Java, C/C++
  • Libraries: Jax/Flax, Pytorch, Tensorflow, Numpy, Scipy, Scikit-learn, Pandas, Matplotlib, Seaborn.

Publications

  • A full list of my publications can be found here.

Research expericence

  • 06/2020 - 08/2020

    Espoo, Finland

    Research Assistant
    Aalto University, Finland
    Department of Computer Science
    I worked at the Probabilistic Machine Learning group for my summer internship. My work focused on developing efficient approaches to Bayesian neural networks.
    • Supervising professor: Samuel Kaski
    • Advisor: Markus Heinonen Ph.D.
  • 09/2019 - 12/2020

    Espoo, Finland

    Research Assistant
    Aalto University, Finland
    Department of Computer Science
    As a student in the Doctoral Track programme, I completed research projects at three different research groups:
    • Project 1 (09/2019 - 01/2020): Using Gaussian processes to provide better estimates for the thermodynamic variational objective, which is a tighter bound than the evidence lower bound (ELBO) for training deep generative models. Supervisor: Prof. Harri Lähdesmäki.
    • Project 2 (02/2020 - 05/2020): Applying Monte Carlo Tree Search and neural networks to Pommerman, a multi-agent game with sparse rewards and partially observable states. Supervisor: Prof. Alexander Ilin.
    • Project 3 (09/2020 - 12/2020): Developing efficient approaches to Bayesian neural networks. Supervisor: Prof. Samuel Kaski.

Work experience

  • 2021 - 2023

    Espoo, Finland

    Teaching Assistant
    Aalto University, Finland
    Department of Computer Science
    • Spring 2021, 2022, and 2023
    • Course: CS-E4890 - Deep Learning
  • 2019

    Espoo, Finland

    Teaching Assistant
    Aalto University, Finland
    Department of Computer Science
    • Autumn 2019
    • Course: CS-E4600 - Algorithmic Methods of Data Mining
  • 01/2018 - 07/2019

    HCMC, Vietnam

    R&D Engineer
    YouNet Media, Vietnam
    Proposing and implementing machine learning algorithms for the SocialHeat platform of YouNet.
    • Building a recurrent neural network for sentiment analysis of social media comments with an accuracy of 89%.
    • Building a Support Vector Machine classifier to filter out fake social influencer profiles on social media with F1-score of 0.86.
    • Using Latent Dirichlet Allocation (LDA) to categorize social media influencers based on their posts.
    • Using LDA to measure the interaction quality of a post of an influencer by checking the numbers of comments having the same topic as the post.

Academic Service

  • Reviewer for NeurIPS 2024.