Vahe Tshitoyan
Co-Founder And CTO at Sela
Mountain View, California, United States
Summary
👤
Senior🎓
Top SchoolVahe Tshitoyan is a founder-CTO and machine learning leader with eight years of experience building production AI systems, currently scaling Sela’s voice AI agents that aim to outperform human sales teams. He combines deep research roots—a PhD in Physics from Cambridge and postdoctoral NLP work at Berkeley Lab—with industry ML leadership at Google where he served as senior ML engineer, tech lead and manager. Vahe has hands-on experience taking models from research into production, including contributions to the influential mat2vec project that extracts latent knowledge from scientific literature. His background spans applied ML in healthcare sensors, materials science, and large-scale ML infrastructure, reflecting a knack for translating complex models into real-world products. Based in Mountain View, he blends academic rigor with startup execution and is actively hiring as Sela grows.
8 years of coding experience
7 years of employment as a software developer
Doctor of Philosophy (PhD) Physics, Doctor of Philosophy (PhD) Physics at University of CambridgeBachelor of Science (B.Sc.) Physics, Bachelor of Science (B.Sc.) Physics at Yerevan State UniversityMaster of Science (M.Sc.) Physics, Master of Science (M.Sc.) Physics at ETH Zürich
Stackoverflow
1reputation
0reached
0answers
0questions
Github Skills (10)
word-embeddings10
nlp10
science10
python10
machine-learning9
data-preprocessing9
modeling8
trainings8
py7
setuptools7
Programming languages (4)
Github contributions (5)
materialsintelligence/mat2vec
Jun 2019 - Aug 2019
Supplementary Materials for Tshitoyan et al. "Unsupervised word embeddings capture latent knowledge from materials science literature", Nature (2019).
Role in this project:
Data Scientist
Data ScientistContributions:28 commits, 5 PRs, 7 pushes in 2 months
Contributions summary:Vahe primarily contributed to the project by modifying the `mat2vec/processing/process.py` file, which suggests involvement in text processing tasks relevant to materials science. The commit messages indicate updates to the README file, and adjustments to the word embedding model. Additionally, the user added a script for downloading Word2vec model files, removing the need for Git LFS. These changes likely involve data preprocessing, model training, and project setup.
nlpsentencescienceldaword-embeddings
vtshitoyan/matstract
Jan 2018 - Jul 2018
Abstract extraction and analysis using natural language processing.
Contributions:101 pushes, 1 branch in 5 months
nlpextractionpythonlanguage-processingnltk
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