Animesh Basak Chowdhury

Ph.D. Candidate, New York University

Hi, I am second year Ph.D. candidate at NYU Centre for Cybersecurity, New York University supervised by Prof. Ramesh Karri and Prof. Siddharth Garg.

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.

I spent some quality time working as a researcher at Advanced Computing and Microelectronics Unit, ISI, Kolkata under the guidance of Prof. Susmita Sur-Kolay and Prof. Bhargab B. Bhattacharya.

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.

News

  • 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.
    • VeriFuzz topped in all the categories of TESTCOMP. Bagged 3 Gold Medals. Special thanks to my manager Dr. Raveendra Kumar Medicherla!
    • Notable tools participated :- KLEE, FairFuzz, Symbiotic.

Contact

Desk - 1033, 10th floor
370 Jay Street, New York University
Downtown Brooklyn, New York City
NY 11201, United States
+1 (347) 633-7828
abc586@nyu.edu
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