This website is no longer maintained. My new website is at https://urialon.ml
My research focuses on machine learning and deep learning approaches for modeling, predicting, improving, and generating source code, a field we call Programming Language Processing.
In a nutshell, similarly to the way that Natural Language Processing (NLP) is concerned with computer-modeling of natural language, Programming Language Processing is concerned with modeling programming languages. Programming is difficult, time-consuming, and even expert programmers lookup for online help all the time. Can a model that was trained on millions of examples help us, humans, with these problems? I believe so, and have some ideas in mind.
Previously, I served 7 years as an officer onboard a missile ship in the Israeli Navy. Later, I completed my BSc summa cum laude at the Computer Science Department at the Technion, as an alumnus of The Rothschild-Technion Scholars Program for Excellence. Between 2014-2016 I worked at Microsoft R&D center in Haifa, developing data security services for the cloud. Between June-September of 2018, I interned at Google New-York, researching neural models for speech recognition.
In addition, I hold a B.A. in Humanities.
I am happily married to Lee and father of Gur 🙂
- How Attentive are Graph Attention Networks?
- Single-Node Attack for Fooling Graph Neural Networks
- On the Bottleneck of Graph Neural Networks and its Practical Implications
- A Structural Model for Contextual Code Changes
- Adversarial Examples for Models of Code
- Neural Reverse Engineering of Stripped Binaries using Augmented Control Flow Graphs
- Structural Language Models of Code
- Contextual Speech Recognition with Difficult Negative Training Examples
- code2seq: Generating Sequences from Structured Representations of Code
- code2vec: Learning Distributed Representations of Code
- A General Path-Based Representation for Predicting Program Properties
- Lingvo: a Modular and Scalable Framework for Sequence-to-Sequence Modeling
- Jonathan Shen, …, Uri Alon, …
- Participated in development during an internship at Google
- Program Committee: PLDI’2021, NeurIPS’2020 CAP workshop, AIDM’20, AIDM’19
- Reviewer: ICML’2021, ICLR’2021, NeurIPS’2020, ICLR’2020
- 2021-2022 – Rothschild Post-Doctoral Fellowship
- 2021-2022 – Fulbright Post-Doctoral Fellowship (declined)
- 2020 – ACM SIGPLAN Research Highlight, “code2vec: Learning Distributed Representations of Code” (POPL’2019)
- 2019 – Jacobs Excellence Scholarship
- 2019 – Faculty Funding Excellence Scholarship
- 2018 – Faculty Funding Excellence Scholarship
- 2016 – Excellent Teaching Assistant
- 2016 – Dean’s Support Excellent Scholarship
- 2016 – Alumnus of the Rothchild-Technion Program for Excellence
- 2015 – SAMBA – CS Excellent Students