ProfessorsProfessors of University of California, DavisAditya Thakur

Aditya Thakur

University of California, Davis

H-index: 19

North America-United States

About Aditya Thakur

Aditya Thakur, With an exceptional h-index of 19 and a recent h-index of 15 (since 2020), a distinguished researcher at University of California, Davis, specializes in the field of Programming Languages, Software Engineering, Security, Formal Methods, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Provable Repair of Vision Transformers: Last Layer is All You Need

Architecture-preserving provable repair of deep neural networks

Provable repair of deep neural networks

SyReNN: A Tool for Analyzing Deep Neural Networks

{DICE*}: A Formally Verified Implementation of {DICE} Measured Boot

Analogy-making as a core primitive in the software engineering toolbox

Memory-efficient fixpoint computation

A deep deterministic policy gradient based network scheduler for deadline-driven data transfers

Aditya Thakur Information

University	University of California, Davis
Position	Department of Computer Science
Citations(all)	1396
Citations(since 2020)	645
Cited By	1037
hIndex(all)	19
hIndex(since 2020)	15
i10Index(all)	27
i10Index(since 2020)	19
Email	Access Email
University Profile Page	University of California, Davis
Google Scholar	View Google Scholar Profile

Aditya Thakur Skills & Research Interests

Programming Languages

Software Engineering

Security

Formal Methods

Machine Learning

Top articles of Aditya Thakur

Title	Journal	Author(s)	Publication Date
Provable Repair of Vision Transformers: Last Layer is All You Need		Stephanie Nawas Zhe Tao Aditya Thakur	2023/10/13
Architecture-preserving provable repair of deep neural networks	Proceedings of the ACM on Programming Languages	Zhe Tao Stephanie Nawas Jacqueline Mitchell Aditya V Thakur	2023/6/6
Provable repair of deep neural networks		Matthew Sotoudeh Aditya V Thakur	2021/6/19
SyReNN: A Tool for Analyzing Deep Neural Networks	International Journal on Software Tools for Technology Transfer	Matthew Sotoudeh Zhe Tao Aditya V Thakur	2023/4
{DICE*}: A Formally Verified Implementation of {DICE} Measured Boot		Zhe Tao Aseem Rastogi Naman Gupta Kapil Vaswani Aditya V Thakur	2021
Analogy-making as a core primitive in the software engineering toolbox		Matthew Sotoudeh Aditya V Thakur	2020/11/18
Memory-efficient fixpoint computation		Sung Kook Kim Arnaud J Venet Aditya V Thakur	2020/11/18
A deep deterministic policy gradient based network scheduler for deadline-driven data transfers		Gaurav R Ghosal Dipak Ghosal Alex Sim Aditya V Thakur Kesheng Wu	2020/6/22
Abstract neural networks		Matthew Sotoudeh Aditya V Thakur	2020