cv
General Information
Full Name | Trung Trinh |
trung.trinh [at] aalto [dot] fi | |
Github | https://github.com/trungtrinh44 |
https://twitter.com/trungtrinh44 | |
Research interests | Robust Machine Learning, Bayesian Deep Learning, Uncertainty Quantification, Optimization |
Education
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10/2021 - now Espoo, Finland
Ph.D. in Artificial Intelligence and Machine Learning
Aalto University, Finland Department of Computer Science - Probabilistic Machine Learning group
- Expected graduation date: 10/2025
- Supervisor: Prof. Samuel Kaski
- Advisor: Markus Heinonen Ph.D.
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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.
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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
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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.
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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
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2021 - 2023 Espoo, Finland
Teaching Assistant
Aalto University, Finland Department of Computer Science - Spring 2021, 2022, and 2023
- Course: CS-E4890 - Deep Learning
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2019 Espoo, Finland
Teaching Assistant
Aalto University, Finland Department of Computer Science - Autumn 2019
- Course: CS-E4600 - Algorithmic Methods of Data Mining
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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.