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Vidhya Kamakshi V

Assistant Professor, Center for Computational Engineering & Networking (CEN), Amrita School of Artificial Intelligence, Coimbatore

Qualification: B-Tech
v_vidhyakamakshi@cb.amrita.edu
Google Scholar Profile
Research Interest: Explainable AI, Machine Learning, Deep Learning, Computer Vision

Bio Publications Experience
Bio

Vidhya Kamakshi V serves as an Assistant Professor at CEN, Department of AI since August 2023. Prior to joining Amrita, She did her PhD at Indian Institute of Technology Ropar under the guidance of Dr. Narayanan C Krishnan, who is currently the HoD Data Science at Indian Institute of Technology Palakkad. My research focussed on explaining the working mechanism of accurate deep models which are black boxes through the lens of image sub-regions called concepts. AI being pervasive, I hope to expand my horizons to specific applications to tackle ground-level challenges.

Education

PhD (Year): 2017-2023(Thesis Submitted)
Specialization: Explainable AI, Computer Vision
Thesis title: Concept-based Explanations for Convolutional Neural Network Predictions
M. Tech (Year): Direct PhD after B.Tech

Publications

Journal Article

Year : 2021

Evaluation of Saliency-based Explainability Method

Cite this Research Publication : Sam Zabdiel Sunder Samuel, Vidhya Kamakshi, Namrata Lodhi, Narayanan C Krishnan "", ICML Workshop on Theoretic Foundation, Criticism, and Application Trend of Explainable AI, 2021

Year : 2021

MACE: Model agnostic concept extractor for explaining image classification networks

Cite this Research Publication : Ashish Kumar, Karan Sehgal, Prerna Garg, Vidhya Kamakshi, Narayanan Chatapuram Krishnan "MACE: Model Agnostic Concept Extractor for Explaining Image Classification Networks", IEEE Transactions on Artificial Intelligence, 2021

Year : 2021

Pace: Posthoc architecture-agnostic concept extractor for explaining cnns

Cite this Research Publication : Vidhya Kamakshi, Uday Gupta, Narayanan C Krishnan "PACE: Posthoc Architecture-Agnostic Concept Extractor for Explaining CNNs", International Joint Conference on Neural Networks (IJCNN), 2021

Conference Paper

Year : 2022

Explainable Image Classification: The Journey So Far and the Road Ahead

Cite this Research Publication : Kamakshi V, Krishnan NC. Explainable Image Classification: The Journey So Far and the Road Ahead. AI. 2023; 4(3):620-651.

Publisher : MDPI

Conference Proceedings

Year : 2023

Explainable Supervised Domain Adaptation

Cite this Research Publication : V. Kamakshi and N. C. Krishnan, "Explainable Supervised Domain Adaptation," 2022 International Joint Conference on Neural Networks (IJCNN), Padua, Italy, 2022, pp. 1-8, doi: 10.1109/IJCNN55064.2022.9892273.

Year : 2021

MAIRE-A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers

Cite this Research Publication : Rajat Sharma, Nikhil Reddy, Vidhya Kamakshi, Narayanan C. Krishnan & Shweta Jain "MAIRE - A Model-Agnostic Interpretable Rule Extraction Procedure for Explaining Classifiers", International Cross-Domain Conference for Machine Learning and Knowledge Extraction, 2021

Experience

Positions

Assistant Professor, CEN, Department of AI, Aug 2023 – present

Courses taught (Name, Year, UG/PG)

Problem Solving & C Programming, 2023, UG

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