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Academic Experts

Gargi Singh

Biography

Gargi Singh is an M.Tech graduate in Computer Science Engineering from Indira Gandhi Delhi 
Technical University for Women (IGDTUW), Delhi, with a specialization in Artificial Intelligence and 
Data Science. She currently serves as an Intel Instructor at CHRIST (Deemed to be University), 
Delhi NCR Campus, where she delivers industry-oriented training in Machine Learning, Deep 
Learning, Natural Language Processing, Generative AI, Transformers, and AI model deployment 
through Intel-certified programs.

She has over three years of teaching and research experience in higher education. Previously, she 
worked as an Assistant Professor at KIET Group of Institutions, where she taught undergraduate 
engineering courses including Artificial Intelligence, Machine Learning, Natural Language Processing, 
Object-Oriented Programming with Java, and Design and Analysis of Algorithms. She has also 
mentored students under the Desh Ke Mentors initiative by the Government of Delhi and was honored 
with the Best Mentor Award for her contribution to student guidance and career development.

Her research interests include Artificial Intelligence, Machine Learning, Deep Learning, Computer 
Vision, Natural Language Processing, Generative AI, and Explainable AI. She has authored five peerreviewed research publications in reputed journals and conferences, including Springer, IEEE, 
Scopus-indexed proceedings, Degres Journal, and IRJET. Her research has focused on intelligent 
systems for financial forecasting, retail security, reliability engineering, and AI-driven decision support.
Gargi is passionate about integrating research with practical teaching, mentoring future AI 
professionals, and developing innovative, industry-aligned learning experiences.

Educational Qualifications

B.Tech CSE from Jamia Hamdard University M.Tech CSE-AI from Indira Gandhi Delhi Technical University for Women

Research Highlights

AI-Powered Corporate Treasury Intelligence and Risk Prediction System — COMSIA 2026 (Scopus / 
Springer), 2026 
Presented an AI-driven framework for corporate treasury risk prediction; accepted at UNIVERSITY OF DELHI AND SHAHEED RAJGURU COLLEGE OF APPLIED SCIENCES, (Scopus & Springer LNNS indexed).

A Machine Learning-Guided Framework for Defect-Aware Reliability Allocation — COMSIA 2026 (Scopus / 
Springer), 2026
Communicated an ML-guided reliability allocation model integrating defect-awareness; accepted at UNIVERSITY OF DELHI AND SHAHEED RAJGURU COLLEGE OF APPLIED SCIENCES, (Scopus & Springer LNNS indexed).

Towards Intelligent Retail Security: ConvLSTM-Based Shoplifting Detection — Degres Journal, 2025
Proposed a ConvLSTM deep learning framework for real-time retail shoplifting detection; achieved 92% accuracy, outperforming 
CNN & LSTM baselines.

Predicting Earnings Per Share using Feature-Engineered XGBoost & Alpha Trading Strategies — Springer, 
2023 Applied XGBoost with feature engineering on S&P 500 data to predict EPS and construct alpha trading strategies; outperformed 
analyst estimates.

Bitcoin Price Prediction using Deep Learning & Twitter Sentiment Analysis — IRJET, Jan 2023
Developed a DL-based Bitcoin price prediction model integrating Twitter sentiment signals for financial forecasting.

Areas of Interest
  • Machine Learning
  • Deep Learning
  • CNN
Professional Activities

Session chair in Springer INTERNATIONAL CONFERENCE ON SMART CYBER PHYSICAL SYSTEM ( ICSCPS - 2026 ) Organized by SCHOOL OF SCIENCES, CHRIST (Deemed to be University) Delhi NCR Campus- India on 23-24 January 2026.

Session chair in IEEE INTERNATIONAL CONFERENCE ON NEXTGEN DATA SCIENCE AND 
ANALYTICS ( ICNDSA - 2026 ) Organized by SCHOOL OF SCIENCES, CHRIST (Deemed to be 
University) Delhi NCR Campus- India on 10-11 April 2026.