Akitoa Nakano
Incoming International Tax Intern
Berkeley, California, United States
Summary
👤
Senior🎓
Top SchoolAkitoa Nakano is a UC Berkeley third-year Economics student and incoming International Tax Intern at Yamada & Partners with 12 years of cumulative experience across consulting, project management, and analytics. He has led cross-functional teams for clients like Verizon and contributed strategic analyses for NASA and DoorDash, translating quantitative insights into actionable recommendations. Beyond finance and consulting, Akitoa is a back-end developer and ML engineer who refactored core components of a diffusion autoencoder implementation on GitHub, showing a knack for model architecture and configuration. Comfortable bridging technical and business domains, he has held leadership roles in Berkeley Phi Beta Lambda from marketing to consulting chair. Based in Berkeley, CA, he combines rigorous analytical training with hands-on engineering curiosity and a mission-driven ethos to “lift humans from the burden of work.”
12 years of coding experience
Bachelor of Arts - BA Economics, Bachelor of Arts - BA Economics at University of California, Berkeley
Github Skills (11)
diffusion-models10
pytorch10
custom-configuration10
configurations10
deep-learning10
yml-configuration10
system-configuration10
python10
autoencoder10
back-end-development9
deeplearning-ai9
Programming languages (11)
Github contributions (5)
phizaz/diffae
Jan 2022 - Aug 2022
Official implementation of Diffusion Autoencoders
Role in this project:
Back-end Developer & ML Engineer
Back-end Developer & ML EngineerContributions:51 commits, 3 PRs, 15 pushes in 7 months
Contributions summary:Akitoa primarily focused on refactoring and modifying the `model/blocks.py` and `model/unet.py` files, indicating significant involvement in the core architectural components of the diffusion autoencoder. Their changes involved removing gated convolution layers and adjusting condition-related parameters and operations, reflecting a focus on optimizing model structure. Furthermore, changes to the `config.py` file suggest a role in managing and configuring model parameters, particularly those related to the network architecture. This work aligns with developing or adapting the core deep learning model for the repository's focus on diffusion autoencoders.
pytorchautoencoderlsundeep-learningautoencoders
iboss-ptk/chatroomjs
Apr 2015 - Apr 2015
Contributions:64 commits, 41 pushes, 1 branch in 9 days
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