Andros Tjandra
Senior Research Scientist at Meta
New York, New York, United States
Summary
🤩
Rockstar🎓
Top SchoolAndros Tjandra is a Senior Research Scientist at Meta AI with eight years of experience driving advances in speech processing, NLP, and machine learning. A PhD from Nara Institute of Science and Technology supported by a MEXT scholarship, he has contributed research and engineering across Google Brain, Facebook AI, RIKEN, and Meta’s FAIR, publishing on disentangled speech representations and transformer-based ASR. He blends deep research with practical engineering—contributing code to the widely used fairseq toolkit (including wav2vec/xlsr audio inference scripts and MMS ASR fixes) and creating a Colab tutorial to make ASR tooling more accessible. Based in New York, he repeatedly bridges academic rigor and production-ready systems, with a track record of turning complex speech models into reproducible tools.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy - PhD, Computer Science, Doctor of Philosophy - PhD, Computer Science at Nara Institute of Science and TechnologyMaster of Science - MS, Computer Science, Master of Science - MS, Computer Science at Universitas IndonesiaBachelor of Science - BS, Computer Science, Bachelor of Science - BS, Computer Science at Universitas Indonesia (UI)Science Class, Science Class at SMA Labschool Rawamangun
Github Skills (11)
pytorch10
machine-learning10
audio-processing10
python10
fairseq10
asr9
automatic-speech-recognition9
speech-recognition9
nlp9
google-colab8
google-colaboratory8
Programming languages (1)
Github contributions (5)
facebookresearch/fairseq
Oct 2022 - Oct 2022
Facebook AI Research Sequence-to-Sequence Toolkit written in Python.
Role in this project:
ML Engineer
ML EngineerContributions:2 reviews, 2 commits, 10 PRs in 1 day
Contributions summary:Andros contributed to the `facebookresearch/fairseq` repository by adding scripts for generating and evaluating embeddings and logits for a speech classification task. These scripts utilize the fairseq framework, specifically within the wav2vec and xlsr submodules, indicating work related to audio processing and machine learning model inference. Additionally, the user fixed an issue in the MMS ASR inference script by reordering the decoded output, indicating involvement in Automatic Speech Recognition (ASR) tasks. The user also created a Colab tutorial for MMS ASR inference.
pytorchnlpsequencepythontransformer-architecture
androstj/androstj.github.io
Aug 2023 - Jun 2024
Contributions:4 pushes, 1 branch in 10 months
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