Animesh Basak Chowdhury
Ph.D. Candidate, New York University
I completed my Masters of Technology (Computer Science) in 2016 from Indian Statistical Institute. From August 2016 to February 2019, I worked as R&D Systems Engineer at Tata Research Development and Design Centre, India in Embedded Software Verification & Validation Group. I was supervised by R. Venkatesh and Dr. Raveendra Kumar Medicherla.
My research interests lies around area of robust Machine Learning in EDA and threat detection in embedded systems architecture. In past, I have worked in the area of security testing and vulnerability detection in hardware design and as well as in embedded softwares. Besides that, I have keen interest in security analysis of AI based systems, particularly about their applications in VLSI-CAD.
- June 2021: Our work on "Adversarially Robust Learning via Entropic Regularization" to be presented at Adversarial Machine Learning Workshop, ICML 2021.
- Apr 2021: Invited paper on "Machine Learning for Semiconductor Test and Reliability" to be presented at VLSI Test Symposium (VTS), 2021.
- Apr 2021: "ASSURE: RTL Locking Against an Untrusted Foundry" has been accepted in IEEE Transactions on Very Large Scale Integration (VLSI) Systems (TVLSI), 2021.
- Feb 2021: Our recent work titled "Fortifying RTL Locking Against Oracle-Less(Untrusted Foundry) and Oracle-Guided Attacks" has been accepted in 58th Design Automation Conference (DAC), 2021.
- Jan 2021: Our paper titled "Robust Deep Learning for IC Test Problems" has been accepted in IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems (TCAD), 2021.
- October 2020: Our paper titled "Explaining and Interpreting Machine Learning CAD Decisions:An IC Testing Case Study" has been accepted in 2nd ACM/IEEE workshop on Machine Learning for CAD (MLCAD), 2020.
- October 2020: Check our new work on provable RTL-level logic obfuscation "ASSURE: RTL Locking Against an Untrusted Foundry". Joint work with Politecnico di Milano and submitted to IEEE Transactions in VLSI (Arxiv).
- August 2020: I have passed my Ph.D. qualifier. Officially enrolled as Ph.D. candidate!
- June 2020: Our work "Adversarially Robust Learning via Entropic Regularization" proposes a novel technique of training robust networks using data entropy guided stochaistic gradient descent" (Arxiv).
- May 2020: Our group NYU_AES participated in Hardware Capture-The-Flag (Hard-CTF) tournament HACK@DAC 2020 and secured 2nd position.
- September 2019: Our paper titled "Fault Coverage of a Test Set on Structure-Preserving Siblings of a Circuit-Under-Test" has been accepted in 28th Asian Test Symposium (ATS), 2019.
- September 2019: I have joined New York University as PhD student in the area of Secure CAD+Machine Learning.
- April 2019: Our tool RERS-Fuzz participated in 8th Rigorous Examination of Reactive Systems 2019 , as part of Toolympics, TACAS 2019.
- February 2019: Our paper titled VeriFuzz : Program-Aware Fuzzing has been accepted in Toolympics, ETAPS 2019.
- January 2019: Our tool VeriFuzz participated in 1st International Competition in Software Testing, 2019 , as part of Toolympics, TACAS 2019.