Minh Dinh Trong

Minh Dinh Trong

MS Computer Science @ Dartmouth

About me

Hello, my name is Minh. I’m a graduate student in Computer Science at Dartmouth College 🇺🇸, where I focus on generalizable AI systems, multimodal learning, and data-efficient representation. My current work spans deepfake detection, instruction tuning, and vision-language modeling, with a particular interest in building methods that remain robust and useful across unseen domains.

I earned a Bachelor’s degree with honors in Data Science from Aalto University 🇫🇮. I have dedicated a great of my time in Professor Stephane Deny’s BRAIN Lab. For more than 1 year as a Research Assistant, I have been researching about self-supervised learning and Invariant representation. Along with that I conducted Bachelor Thesis in Scene Representation with Professor Alexander Ilin, and TA-ed in advanced courses, e.g. hosting lectures with Professor Nitin Sawhney.

In the final year, I am on a exchange study to the IC Department, EPFL (Switzerland) 🇨🇭. This exchange is selective, fully-funded, and involves both course and research work. There, I explored diffusion-based deepfake detection, submitting a method to the ELSA Deepfake Detection Challenge that evaluates residual inversion signals.

Originally I come from Hanoi, Vietnam 🇻🇳. There, I studied at HNUE High School for Gifted Students, while developed my passion in photograph 📸. Check Portfolio for my gorgeous photos 🏞️.

Research Exprience

Publications

  • EMBC 2025 (Accepted) – Impact of Noise on Deep Learning-Based Pseudo-Online Gesture Recognition with High-Density EMG. Mansour Taleshi, Dennis Yeung, Minh Dinh Trong, Francesco Negro, Stéphane Deny, Ivan Vujaklija.

Check Research

I’m broadly interested in making AI systems more efficient, generalizable, and responsible.

  • Dartmouth College (2024–): Privacy-preserving dataset design for video and multimodal models
  • Aalto University BRAIN Lab (2022–2023): Invariant self-supervised representation learning
  • EPFL IVRL (2023): Visual forensics and synthetic image detection

Teaching Exprience

Check Teaching

  • Spring 2025
    • COSC 78/278: Deep Learning (Prof. Yujun Yan)
    • COSC 89.35/189: Human-centered LLMs – Lead TA (Prof. Sarah Preum)
    • COSC 74/274: Statistical Inference and Machine Learning (Prof. Soroush Vosoughi)
  • Winter 2024
    • COSC 74/274: Statistical Inference and Machine Learning (Prof. Soroush Vosoughi)
    • COSC 70: Foundations of Applied Computer Science (Prof. Yaoqing Yang)
  • Fall 2024
    • COSC 76/276: Artificial Intelligence (Prof. Soroush Vosoughi)
    • ENGS 108: Applied Machine Learning – Lead TA (Prof. George Cybenko)
  • Spring 2023
    • CS-A1155 Databases for Data Science – Lead TA (Prof. Nitin Sawhney)
    • CS-E4800 Artificial Intelligence (Prof. Jussi Rintanen)
  • Autumn 2022
    • ELEC-A7151 Object oriented programming with C++ (Prof. Yusuf Ali)
    • CS-C3240 Machine Learning D (Prof. Pekka Marttinen & Prof. Stephan Sigg)

Outreach

  • Volunteer @2022 IEEE International Symposium on Information Theory (ISIT)
    • Active volunteer in all Plenary Sessions and in 5+ tutorial sessions.
    • Prepared stage and assisted speakers for their presentation.