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Contact Information

Name Ekaagra Gupta
Professional Title Computer science undergraduate
Email ekaagrag2006@gmail.com
Location Jaipur, Rajasthan 302026

Professional Summary

Pre-final year AI/ML undergrad. I build things that retrieve, reason, and explain — from satellite anomaly detectors to hybrid RAG pipelines to a Full stack Application

Experience

  • May 2026 - July 2026

    Jaipur, Rajasthan

    Artifical Intelligence Intern
    Malaviya National Institute of Technology Jaipur (MNIT)
    Engineering a production-scale Retrieval-Augmented Generation (RAG) system integrating dense retrieval, lexical retrieval, transformer-based reranking, and GPU-optimized large language model inference. The project involves memory-efficient document indexing, metadata-aware retrieval optimization, vLLM-based inference acceleration, FlashAttention, KV-cache optimization, continuous batching, and comprehensive benchmarking of latency, throughput, memory footprint, and retrieval quality across large document corpora.
  • Feb 2026 - May 2026

    Lucknow , india

    Artifical Intelligence and Machine Learning Intern
    coreGEN
    Engineered end-to-end computer vision pipelines for satellite image analysis by developing deep learning-based super-resolution models, integrating MobileNet-SSD object detection, implementing real-time anomaly detection algorithms, and deploying interactive MERN-based dashboards for visualization, inference, and model evaluation.

Education

  • 2024 - 2028

    Rajasthan , India

    Bachelor of Technology
    Manipal university jaipur
    Computer Science and Engineering specalisation in Artificial Intelligence and Machine Learning
    • Data Structures and Algorithms - Database Management Systems - Computer Networks - Operating Systems - Agile Software Development - Artificial Intelligence - Machine Learning - Deep Learning - Natural Language Processing - Computer Vision
  • 2014 - 2024

    Rajasthan , India

    High School Diploma
    Warren academy
    Physics , chemistry and mathematics
    • Physics - Chemistry - Mathematics

Awards

  • 2025
    Student excellence award
    Manipal university jaipur

    Student Excellence Award for recognition of outstanding contributions to the ISGC’25 paper and poster presentation.

  • 2026
    Student excellence award
    Manipal university jaipur

    Student Excellence Award for my recent internship at CoreGEN.

Skills

Artificial Intelligence & Machine Learning: Machine Learning, Deep Learning, Computer Vision, Explainable AI (XAI), Agentic AI
Generative AI & Retrieval Systems: Large Language Models (LLMs), Retrieval-Augmented Generation (RAG), LangChain, LlamaIndex, Hugging Face Transformers, vLLM, FAISS, ChromaDB, BM25, ColBERT, HNSW
Geospatial AI & Remote Sensing: Satellite Image Processing, NDVI, NDWI, Remote Sensing, Geospatial Analysis, Environmental Monitoring
Programming Languages: Python, C++, C, SQL, JavaScript
Libraries & Frameworks: PyTorch, TensorFlow, Scikit-learn, OpenCV, Pandas, NumPy, Matplotlib
Full-Stack Development: MongoDB, Express.js, React.js, Node.js, REST APIs
Tools & Platforms: Git, GitHub, Docker, Linux, Jupyter Notebook, Google Colab, VS Code

Languages

Hindi : Native speaker
English : Fluent

Interests

Artifical Intelligence: Agentic AI, Explainable AI, Generative AI, Reinforcement Learning, Multi-Agent Systems

Certificates

  • Prompt Engineering & Programming with OpenAI - Columbia University (2025)
  • Essentials in Generative AI - Microsoft & LinkedIn (2025)
  • Generative AI Studio - Google Cloud (2025)

Projects

  • MemoRAG

    Engineered a memory-augmented RAG pipeline that builds a compressed KV-cache representation of long documents before retrieval, enabling globally-aware query rewriting and surrogate-question generation for multi-hop and summarization queries that stan- dard chunk-based RAG handles poorly.

    • Evaluated pipeline performance on long-context QA and summarization tasks, comparing standard RAG against the memory-augmented approach for retrieval accuracy and answer completeness
    • Integrated a dense retrieval layer using FAISS and BGE-M3 embeddings, paired with a lightweight variant (Qwen2.5-1.5B) for lower-resource deployment.
  • GETHER

    Developed a hybrid spatio-temporal air quality forecasting framework combining stacked LSTM networks, causal discovery, and explainable AI to model pollutant interactions and predict Air Quality Index (AQI). The pipeline integrates temporal feature engineering, Granger causality analysis, SHAP-based interpretability, and counterfactual policy simulation for transparent environmental decision support.

    • Built a stacked LSTM forecasting model with engineered lag and rolling-statistic features, benchmarked against Linear Regression and Random Forest baselines, while optimizing hyperparameters for improved forecasting accuracy.
    • Designed a causal inference and explainability module using Granger causality, dynamic causal graphs, SHAP feature attribution, uncertainty estimation, and counterfactual simulations to evaluate the impact of emission-control policies on future AQI.
  • SEVAS

    Engineered an AI-powered predictive environmental monitoring platform for illegal sand mining and land encroachment detection using multi-temporal satellite imagery, deep learning, and geospatial analysis. The system combines computer vision, spectral analysis, and temporal forecasting to enable proactive environmental enforcement.

    • Developed a hybrid vision pipeline integrating a custom U-Net segmentation network, Gemini Vision, NDVI/NDWI spectral indices, and temporal LSTM forecasting to detect environmental degradation, localize mining regions, and predict future violations.
    • Built a production-ready MERN application with RESTful APIs, automated severity scoring, cross-jurisdictional intelligence, and interactive geospatial visualizations for large-scale environmental monitoring and decision support.