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Academic Experts

Abhishek Chaudhary

Biography

Mr. Abhishek Chaudhary is an Assistant Professor (Grade I) in the Department of Computer Science and Engineering and Information 
Technology at Jaypee Institute of Information Technology (JIIT), Noida. He has over 1 year of teaching experience in Computer Science and 
Engineering, along with industry exposure in database engineering, ETL, SQL and digital communication. He completed M. Tech in Computer 
Science and Engineering from JIIT Noida and B. Tech in Information Technology from KIET Group of Institutions, AKTU. He has taught 
Database Management Systems (DBMS) to several undergraduate sections, conducted labs for 200+ students in Programming in C, DBMS and 
Web Technology, and delivered AI/ML domain training using Python, TensorFlow and Scikit-learn. His teaching and mentoring approach 
integrates ICT-enabled pedagogy, hands-on labs, Jupyter Notebook based practice, project guidance and hackathon culture. His core interests 
include DBMS, machine learning, deep learning, natural language processing, computer vision and programming. 

Research Highlights

His research work spans machine learning, deep learning, natural language processing, computer vision, explainable AI and database-driven 
intelligent systems. He has 2 international journal papers, including a SCIE/Scopus/WoS indexed Wiley publication on enhanced spatial
temporal transformer networks for micro-expression temporal localization and recognition, and a Springer publication on hybrid embeddings for 
text classification. His M.Tech dissertation, titled "Region-Wise Enhanced Micro-Expression Spotting via Riesz-Based Motion Magnification 
and Multimodal CNN Fusion," developed a region-wise micro-expression spotting system using Riesz-based motion magnification and CNN 
fusion, and received the Best Dissertation Poster Award at Research & PG Day, JIIT Noida. His major research projects include hybrid 
embedding-based text classification, explainable fake news detection using BERT and TF-IDF with SHAP, quantum-inspired spam detection, 
ethical donor prediction for NGOs, and deep hybrid movie recommendation. His work demonstrates strong alignment with applied AI, robust 
classification, temporal localization, explainability and human-centered intelligent systems. 

Areas of Interest
  • Micro-Expression Spotting and Computer Vision
  • Machine Learning and Deep
  • Natural Language Processing
  • Explainable Artificial Intelligence
  • Database Management Systems and Data Engineering
Projects

Project Title: Hybrid Embedding-Based Multi-Domain Text Classification System 

Project Title: HyBERT-FiD - Explainable Hybrid Model for Fake News Detection 

Project Title: QUANTUM-FUSION - Quantum-Inspired Embedding Framework for Spam Detection 

Project Title: SocioNet-XAI - Ethical and Explainable Donor Prediction System for NGOs 

Project Title: Deep Hybrid Recommender System for Movie Personalization