Dan Erez
Chief Technology Officer Email & SMS AI Ads Via Klaviyo at Inbox GeekHouse
New York, New York, United States
Summary
👤
Senior🎓
Top SchoolDan Erez is a CTO and AI leader with 8 years of hands-on R&D experience applying computer vision and speech recognition to production products, now leading AI-driven email and SMS ad automation at Mailability. He has a track record of turning research into deployed systems—from a production-grade speech-to-text and OCR at brif.ai to drone-based weed and insect detection at Taranis that cut annotation effort in half. A frequent implementer of modern ML tooling and workflows, Dan has contributed to the Keras NumPy backend, adding core ops and initializer support to a widely used deep learning library. He blends technical depth (patents and specialized architectures) with product instincts and vendor partnerships, and he routinely coaches non-technical domain experts to use Python and data analysis. Educated in mechanical engineering with an MBA in entrepreneurship, he combines systems thinking with a founder’s drive to ship impactful AI products.
9 years of coding experience
12 years of employment as a software developer
University of California, San DiegoM.B.A., Entrepreneurship/Entrepreneurial Studies, M.B.A., Entrepreneurship/Entrepreneurial Studies at Reichman University
Hebrew, English
Github Skills (10)
keras10
machine-learning10
backend10
back-end-development10
python10
numpy10
tensorflow9
neural-network9
deep-learning9
data-science8
Programming languages (8)
Github contributions (5)
keras-team/keras
Sep 2018 - Dec 2018
Deep Learning for humans
Role in this project:
Back-end Developer
Back-end DeveloperContributions:7 commits, 9 PRs, 70 comments in 2 months
Contributions summary:Dan primarily contributed to the NumPy backend of the Keras library, adding and improving functionality. Their work included implementing mathematical functions like trigonometric operations (cos, sin), adding utility functions such as `ndim`, `stack`, and variable initializers (ones, zeros, etc.), and integrating the `one_hot` and `ctc_decode` operations. The user focused on expanding the backend's capabilities to align with the core functionality of Keras.
deep-learningtensorflowneural-networksmachine-learningdata-science
danFromTelAviv/key_words_spotting
Dec 2019 - Dec 2019
implementation of "EFFICIENT KEYWORD SPOTTING USING DILATED CONVOLUTIONS AND GATING"
Contributions:13 commits, 12 pushes, 1 branch in 1 day
keyword-spotting
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