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Sumith Kulal
Researcher at Stanford University
Publications - 13
Citations - 1275
Sumith Kulal is an academic researcher from Stanford University. The author has contributed to research in topics: Computer science & Function (mathematics). The author has an hindex of 6, co-authored 10 publications receiving 760 citations. Previous affiliations of Sumith Kulal include Indian Institute of Technology Bombay.
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Journal ArticleDOI
SymPy: Symbolic Computing in Python
Aaron Meurer,Christopher Smith,Mateusz Paprocki,Ondřej Čertík,Sergey B Kirpichev,Matthew Rocklin,Amit Kumar,Sergiu Ivanov,Jason K. Moore,Sartaj Singh,Thilina Rathnayake,Sean Vig,Brian E. Granger,Richard P. Muller,Francesco Bonazzi,Harsh Gupta,Shivam Vats,Fredrik Johansson,Fabian Pedregosa,Matthew Curry,Matthew Curry,Andy R. Terrel,Štěpán Roučka,Ashutosh Saboo,Isuru Dilanka Fernando,Sumith Kulal,Robert Cimrman,Anthony Scopatz +27 more
TL;DR: The architecture of SymPy is presented, a description of its features, and a discussion of select domain specific submodules are discussed, to become the standard symbolic library for the scientific Python ecosystem.
Posted Content
SPoC: Search-based Pseudocode to Code
TL;DR: This work proposes to perform credit assignment based on signals from compilation errors, which constitute 88.7% of program failures and improves the synthesis success rate over using the top-one translation of the pseudocode from 25.6% to 44.7%.
Proceedings ArticleDOI
Contract-based resource verification for higher-order functions with memoization
TL;DR: This work presents a new approach for specifying and verifying resource utilization of higher-order functional programs that use lazy evaluation and memoization, and uses it to verify precise bounds on resources such as evaluation steps and number of heap-allocated objects on 17 challenging data structures and algorithms.
Proceedings Article
SPoC: Search-based Pseudocode to Code
TL;DR: In this paper, the task of mapping pseudocode to executable code is considered, assuming a one-to-one correspondence between lines of pseudocodes and lines of code.
Posted Content
What's hard about Boolean Functional Synthesis
TL;DR: This paper presents a two-phase algorithm for Boolean functional synthesis, where the first phase is efficient both in terms of time and sizes of synthesized functions, and solves an overwhelming majority of benchmarks.