Bridger Ammar
Business Consulting Holistic Intelligence at Holistic Intelligence for Global Good
United States
Summary
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Rockstar🎓
Top SchoolBridger Ammar is a research-driven engineering leader with 14 years of experience building language and multimodal systems that remove barriers to global collaboration. His work spans foundational research (language-universal and self-supervised models, scientific literature modeling) and product-focused programs, from pioneering machine translation research at Microsoft Research and CMU to leading Semantic Scholar R&D and democratizing task-oriented dialogue at Google. As a founder and technical co-founder he has launched platforms prioritizing global impact and safety—most recently shaping wearables audio and language strategy at Meta’s smartglasses team. He has a track record of scaling ideas into cross-functional teams of 30+ and partnering with major labs and institutions including Google Research, Microsoft Research, AI2, Meta Reality Labs, and the NSF. An active contributor to open-source deep learning tooling (notably fixes to the DyNet toolkit), he combines rigorous PhD-level research with hands-on systems delivery. Currently exploring responsible, high-impact platform opportunities, he focuses on bridging cultures, modalities, and domains to accelerate meaningful human collaboration.
13 years of coding experience
3 years of employment as a software developer
Bachelor of Science, Computer Systems and Engineering, Bachelor of Science, Computer Systems and Engineering at Alexandria UniversityPhD in Computer Science, Computer Science, PhD in Computer Science, Computer Science at Carnegie Mellon University
Arabic, English
Stackoverflow
1reputation
0reached
0answers
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Github Skills (8)
machine-learning10
c-language10
deep-learning10
cprogramming-language10
dynet10
back-end-development10
n9
rnn-model9
Programming languages (6)
Github contributions (5)
clab/dynet
Sep 2015 - Oct 2015
DyNet: The Dynamic Neural Network Toolkit
Role in this project:
Back-end Developer
Back-end DeveloperContributions:9 commits, 3 PRs, 6 pushes in 29 days
Contributions summary:Bridger contributed to the DyNet deep learning toolkit by implementing and fixing features related to the `block_dropout` function, a form of regularization. Their work involved modifications to the core nodes and expression files, as well as the addition of a cubic activation function. Further contributions involved fixing the RNNBuilder to ensure the correct hidden states were accessed.
dynetdynamic-neural-networkdeep-learningneural-networksmachine-learning
clab/knowledge
Oct 2013 - Oct 2015
Contributions:41 commits, 18 pushes in 1 year 11 months
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