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Dr. Abhishek Thakur

Dr. Abhishek Thakur

Associate Professor — Machine Learning, Deep Learning, Image Processing

Himachal Pradesh, India
edu
Joined April 2026
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Summary

Researcher focused on image and video forensics and applied machine learning — publishes in peer-reviewed venues and maintains an active Google Scholar profile documenting work on forgery detection, semantic segmentation, and deep-learning methods. theiet+1
Experienced educator with long-standing academic roles — over a decade of teaching experience across multiple Indian engineering colleges and currently serving as Associate Professor at Chitkara University, with international teaching engagements as a visiting professor at UPAEP. scribd+2
Active in translating research into applied outputs and intellectual property — involvement in hardware/IoT projects (e.g., Arduino weather station), industry-facing roles (upGrad deputation), and at least one patent application listed in institutional patent records. google+2
Academic organiser and editor — co-editor of the ICMLA 2024 proceedings and active in organising/hosting conferences, FDPs and workshops reflected in conference proceedings and institutional activity listings. springer+1

Work

Education

Projects

Writing

Machine Learning Algorithms (ICMLA 2024 Proceedings) — Editor (co-editor)

January 1, 2024

Editor (co-editor) of the refereed proceedings of the First International Conference on Machine Learning Algorithms (ICMLA 2024); the volume collects peer-reviewed papers on machine learning, image processing and deep learning.

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Cooling System for Electronic Components (Patent application 202111010723)

March 1, 2021

Patent application for a cooling system for electronic components (listed in patent records and CV).

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Copy Move and Splicing Forgery Detection using Deep Convolution Neural Network, and Semantic Segmentation

January 1, 2020

A research paper on deep-learning-based methods for detecting copy-move and splicing forgeries in images, including semantic segmentation approaches for localization.

Favicon imagescholar.google.com

Hybrid deep learning and machine learning approach for passive image forensic

January 1, 2019

A study combining deep learning and machine learning methods for passive image forgery detection, reporting high accuracy across multiple public datasets and describing localization via colour illumination techniques.

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Hobbies

Creates and shares educational content online (Udemy courses) and posts about academic events on social media. udemy+1