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About Me
I’m a Ph.D. student under the supervision of Prof. Eran Yahav. My research focuses on source code representations for machine learning models. We research machine learning approaches for solving code-related tasks such as code completion, edit completion, predicting method names, and automatic documentation generation. My main interest is how to represent the code in such tasks. I'm also interested in methods for processing graphs using deep neural networks.
Publications
FuseCap: Leveraging Large Language Models to Fuse Visual Data into Enriched Image Captions
Noam Rotstein, David Bensaid, Shaked Brody, Roy Ganz, Ron Kimmel
On the Expressivity Role of LayerNorm in Transformers’ Attention
Shaked Brody, Uri Alon, Eran Yahav
How Attentive are Graph Attention Networks?
Shaked Brody, Uri Alon, Eran Yahav
- Appeard in ICLR’2022
- [PDF] [Poster] [Slides] [Video] [Code] [BibTex]
- GATv2 implementations:
- [PyTorch Geometric]:
from torch_geometric.nn.conv.gatv2_conv import GATv2Conv - [DGL]:
from dgl.nn.pytorch import GATv2Conv - [TensorFlow GNN]:
from tensorflow_gnn.keras.layers import GATv2
- [PyTorch Geometric]:
A Structural Model for Contextual Code Changes
Shaked Brody, Uri Alon, Eran Yahav
Technical Reports
Patents
Service
- Program Committee: Deep Learning for Code workshop (2022, 2023), MSR’2021 Mining Challenge
- Reviewer: NeurIPS (2023), ACL (2023)
Awards
- 2023 – Department Excellence Scholarship
- 2023 – Excellent Faculty TA
- 2022 – Department Excellence Scholarship
- 2019 – Dean’s Excellence Scholarship
Contact
Feel free to reach out to me through the following platforms:
Gmail