Jordan Henkel 个人学术档案

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引用	364	359
h 指数	7	7
i10 指数	6	6

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Thomas RepsJ. Barkley Rosser Professor & Rajiv and Ritu Batra Chair Emeritus, CS Dept., Univ. of Wisconsin在 cs.wisc.edu 的电子邮件经过验证
Shuvendu LahiriSenior Principal Researcher, Microsoft Research在 microsoft.com 的电子邮件经过验证
Avrilia FloratouPrincipal Scientist at Microsoft在 microsoft.com 的电子邮件经过验证
K. Venkatesh EmaniSenior Scientist, Microsoft在 microsoft.com 的电子邮件经过验证
Carlo Aldo CurinoPartner Scientist Group Manager --- GSL Microsoft在 microsoft.com 的电子邮件经过验证
Ben LiblitAmazon Web Services在 amazon.com 的电子邮件经过验证
Christian BirdMicrosoft Research在 microsoft.com 的电子邮件经过验证
Joyce CahoonMicrosoft在 microsoft.com 的电子邮件经过验证
Fotis PsallidasPrincipal RSDE, Microsoft在 microsoft.com 的电子邮件经过验证
Brian KrothMicrosoft在 microsoft.com 的电子邮件经过验证
Markus WeimerPrincipal Scientist; Microsoft在 microsoft.com 的电子邮件经过验证
Aws AlbarghouthiUniversity of Wisconsin–Madison在 cs.wisc.edu 的电子邮件经过验证
Goutham RamakrishnanMachine Learning Engineer - II, AWS Neuron在 amazon.com 的电子邮件经过验证
Somesh JhaLubar Chair of Computer Science, University of Wisconsin在 cs.wisc.edu 的电子邮件经过验证
Zi WangPhD student, University of Wisconsin-Madison在 cs.wisc.edu 的电子邮件经过验证
Bojan KarlašHarvard University在 mgh.harvard.edu 的电子邮件经过验证
Ce ZhangTogether AI; University of Chicago在 together.xyz 的电子邮件经过验证
Wentao WuMicrosoft Research在 microsoft.com 的电子邮件经过验证
Konstantinos KaranasosData Infra at Meta在 fb.com 的电子邮件经过验证
Matteo InterlandiMicrosoft在 microsoft.com 的电子邮件经过验证

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Jordan Henkel

Senior Scientist, Microsoft

在 cs.wisc.edu 的电子邮件经过验证 - 首页

Machine Learning Software Engineering


标题按引用次数排序按年份排序按标题排序	引用次数引用次数	年份
Code vectors: understanding programs through embedded abstracted symbolic traces J Henkel, SK Lahiri, B Liblit, T Reps Proceedings of the 2018 26th ACM Joint Meeting on European Software …, 2018	98	2018
Semantic Robustness of Models of Source Code J Henkel, G Ramakrishnan, Z Wang, A Albarghouthi, S Jha, T Reps IEEE International Conference on Software Analysis, Evolution and Reengineering, 2022	88	2022
Data Science Through the Looking Glass: Analysis of Millions of GitHub Notebooks and ML. NET Pipelines F Psallidas, Y Zhu, B Karlas, J Henkel, M Interlandi, S Krishnan, B Kroth, ... ACM SIGMOD Record 51 (2), 30-37, 2022	56	2022
Learning from, Understanding, and Supporting DevOps Artifacts for Docker J Henkel, C Bird, SK Lahiri, T Reps Proceedings of the ACM/IEEE 42nd International Conference on Software …, 2020	56	2020
Shipwright: A Human-in-the-Loop System for Dockerfile Repair J Henkel, D Silva, L Teixeira, M d’Amorim, T Reps 2021 IEEE/ACM 43rd International Conference on Software Engineering (ICSE …, 2021	24	2021
A Dataset of Dockerfiles J Henkel, C Bird, SK Lahiri, T Reps Proceedings of the 17th International Conference on Mining Software …, 2020	19	2020
From Words to Code: Harnessing Data for Program Synthesis from Natural Language A Khatry, J Cahoon, J Henkel arXiv preprint arXiv:2305.01598, 0	8*
NL2SQL is a solved problem... Not! A Floratou, F Psallidas, F Zhao, S Deep, G Hagleither, W Tan, J Cahoon, ... CIDR, 2024	5	2024
Notebook for navigating code using machine learning and flow analysis BP Kroth, JJ Henkel US Patent 11,816,456, 2023	4	2023
ReAcTable: Enhancing ReAct for Table Question Answering Y Zhang, J Henkel, A Floratou, J Cahoon, S Deep, JM Patel arXiv preprint arXiv:2310.00815, 2023	2	2023
The Need for Tabular Representation Learning: An Industry Perspective J Cahoon, A Savelieva, AC Mueller, A Floratou, C Curino, H Patel, ... NeurIPS 2022 First Table Representation Workshop, 2022	2	2022
Enabling Open-World Specification Mining via Unsupervised Learning J Henkel, SK Lahiri, B Liblit, T Reps arXiv preprint arXiv:1904.12098, 2019	2	2019
Learning from Code and Non-code Artifacts J Henkel The University of Wisconsin-Madison, 2022		2022

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