I’m grateful for every opportunity to share my work. I can talk about:

  • real-time machine learning: online prediction, real-time model analytics, continual learning
  • streaming-first infrastructure for ML
  • machine learning systems design
  • machine learning in production at scale
  • machine learning interviews and recruiting pipeline
  • community building and developer relations
  • deep learning fundamentals, applications, and trends

My bios and photos can be found here. Below is the selected list of my talks (not updated).

On sharing events on social media
I avoid posting promotional content (e.g. come hear me talk). I feel very awkward when an organizer asks me to do so. Please don’t expect me to promote your event if I agree to speak. Thank you!

Jan 20, 2021: Machine learning is going real-time

Nov 19, 2020: Machine learning production

Nov 18, 2020: Machine learning production
TMLS Annual Conference & Expo

Nov 12, 2020: Machine learning production myths
MLSys Seminar Series @ Stanford

Aug 20, 2020: Machine Learning Production

Jul 22, 2020: Machine Learning Production Pipeline
Challenges in Deploying and Monitoring Machine Learning Systems @ ICML (Vienna, Austria)

Dec 9, 2019: Panel on research vs production
NewInML @ NeurIPS (Vancouver, Canada)

Nov 16-17, 2019: Machine Learning Interviews
Full Stack Deep Learning (Berkeley, USA)
Online course

Nov 2, 2019: Machine Learning Interivews (talk) | AI In Industry and Vietnam (panel)
VietAI Summit (Ho Chi Minh City, Vietnam)

Sep 12, 2019: State-of-the-art Machine Translation
SlatorCon San Francisco

Jan 25, 2018: TensorFlow Tutorial
Guest lecture for Stanford’s CS224N class (Stanford, USA)

Dec 9, 2017: Mitigating the spread of fake news by identifying and disrupting echo chambers
Spotlight talk at NIPS Workshop on Prioritising Online Content (Long Beach, USA)

Aug 5, 2012: Traveling is an equal opportunity
TEDxYouth@Hanoi (Hanoi, Vietnam)


I occasionally write papers and/or support my coworkers with their papers. My research focuses on the intersection between Natural Language Processing and Deep Learning.

For the full list of papers, see my Google Scholar profile.

NeMo: a toolkit for building AI applications using Neural Modules
Oleksii Kuchaiev, Jason Li, Huyen Nguyen, Oleksii Hrinchuk, Ryan Leary, Boris Ginsburg, Samuel Kriman, Stanislav Beliaev, Vitaly Lavrukhin, Jack Cook, Patrice Castonguay, Mariya Popova, Jocelyn Huang, Jonathan M Cohen Accepted to Machine Learning Systems workshop at NeurIPS 2019.

The Propagation of Lies: Impeding the Spread of Misinformation
H Nguyen, C Huyi, P Warren
Detect echo chambers and recommend content of different perspectives. Spotlight talk at Prioritising Online Content workshop, NIPS 2017. Americas’ regional winner for Ericsson Innovation Awards 2018.

MEWR: Automatic Machine Translation Evaluation without Reference Texts
H Nguyen, J Chang
Unsupervised translation score with strong correlation with BLEU score and human evaluation. Poster at Women in Machine Learning 2017.

Neural Networks for Automated Essay Grading
H Nguyen, L Dery
Automatically grade essays using bi-directional LSTM. Achieved a QW-Kappa score of 0.945 compared to 0.814, Kaggle winner.