Petros Maniatis
Petros Maniatis is a Senior Staff Research Scientist at Google DeepMind, in the Learning for Code Team. Prior to that, he was a Senior Research Scientist at Intel Labs, working in Intel's Berkeley Research Lab and then at the Intel Science and Technology Center on Secure Computing at UC Berkeley. He received his MSc and Ph.D. from the Computer Science Department at Stanford University. Before Stanford, he obtained his BSc with honors at the Department of Informatics of the University of Athens in Greece. His current research interests lie primarily in the confluence of machine learning and software engineering.
Authored Publications
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CodeQueries: A Dataset of Semantic Queries over Code
Surya Prakash Sahu
Madhurima Mandal
Shikhar Bharadwaj
Aditya Kanade
Shirish Shevade
Innovations in Software Engineering (ISEC), ACM, Bangalore, India (2024)
Resolving Code Review Comments with Machine Learning
Alexander Frömmgen
Peter Choy
Elena Khrapko
Marcus Revaj
2024 IEEE/ACM 46th International Conference on Software Engineering: Software Engineering in Practice (ICSE-SEIP) (to appear)
AI-assisted Assessment of Coding Practices in Industrial Code Review
Ivan Budiselic
Malgorzata (Gosia) Salawa
Juanjo Carin
Jovan Andonov
Mateusz Lewko
Marko Ivanković
Rene Just
2024
Snowcat: Efficient Kernel Concurrency Testing using a Learned Coverage Predictor
Sishuai Gong
Dinglan Peng
Pedro Fonseca
Symposium on Operating Systems Principles (SOSP) (2023)
Predicting Dynamic Properties of Heap Allocations Using Neural Networks Trained on Static Code
Christian Navasca
Guoqing Harry Xu
2023 ACM SIGPLAN International Symposium on Memory Management (ISMM 2023)
Learning to Walk over Relational Graphs of Source Code
Pardis Pashakhanloo
Aaditya Naik
Hanjun Dai
Mayur Naik
Deep Learning for Code (DL4C) Workshop @ ICLR 2022 (2022)
Graph Representations of Python Programs via Source-level Static Analysis
Daniel Johnson
Vincent Josua Hellendoorn
Arxiv (2022)
Learning to Answer Semantic Queries over Code
Surya Prakash Sahu
Madhurima Mandal
Shikhar Bharadwaj
Aditya Kanade
Shirish Shevade
Google Research (2022)
PLUR: A Unifying, Graph-Based View of Program Learning, Understanding, and Repair
Zimin Chen
Vincent J Hellendoorn
Subhodeep Moitra
Thirty-fifth Conference on Neural Information Processing Systems (NeurIPS 2021) (2021)
