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Maruth Goyal

dblp:264/2642 · DBLP profile ↗
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2ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0001-8523-3433ORCID · reported

Domains — the database's venue-derived domains; a paper can count in several

Artificial intelligence and machine learning · 1Software engineering, systems software and programming languages · 1 · 1 since 2021

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Software engineering, system software, and programming languages
2 papers
Programming languages and type systems · 54% Program synthesis and code generation · 36% Compilers and program optimization · 11%
Network and information security
1 paper
Blockchain and cryptocurrency security · 100%

Topics — the 3 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Programming languages and type systems › type inference
probabilistic type inference
0.412020
LambdaNet: Probabilistic Type Inference using Graph Neural Networks · ICLR 2020
Programming languages and type systems
type inference
0.412020
LambdaNet: Probabilistic Type Inference using Graph Neural Networks · ICLR 2020
Compilers and program optimization › memory optimization
data layout transformation
0.212022
Synthesis-powered optimization of smart contracts via data type refactoring · Proc. ACM Program. Lang. 2022

Methods — techniques the papers use, named apart from their topics

program synthesis · 1.1DSL for data type refactoring · 1.1graph neural network · 0.4
YearPublicationVenuePosition
2022 Synthesis-powered optimization of smart contracts via data type refactoring
abstract
Since executing a smart contract on the Ethereum blockchain costs money (measured in gas ), smart contract developers spend significant effort in reducing gas usage. In this paper, we propose a new technique for reducing the gas usage of smart contracts by changing the underlying data layout. Given a smart contract P and a type-level transformation, our method automatically synthesizes a new contract P ′ that is functionally equivalent to P . Our approach provides a convenient DSL for expressing data type refactorings and employs program synthesis to generate the new version of the contract. We have implemented our approach in a tool called Solidare and demonstrate its capabilities on real-world smart contracts from Etherscan and GasStation. In particular, we show that our approach is effective at automating the desired data layout transformation and that it is useful for reducing gas usage of smart contracts that use rich data structures.
Yanju Chen, Yuepeng Wang 0001, Maruth Goyal, James Dong, Yu Feng 0001, Isil Dillig
Proc. ACM Program. Lang.3
2020 LambdaNet: Probabilistic Type Inference using Graph Neural Networks
Jiayi Wei, Maruth Goyal, Greg Durrett, Isil Dillig
ICLR2