SHIVANG GAUR

Master's Student, Department of Electrical Engineering, IIT Delhi

I am a master's student with a keen interest in machine learning. My goal is to build impactful solutions that integrate the principles of machine learning and advanced computation. You can connect with me on LinkedIn.

Educational Background

M.Tech (Computer Technology), IIT Delhi – CGPA 7.26 (GATE CS AIR 554)

B.Tech (CSE), DCRUST Murthal – CGPA 7.87/10

Schooling, DAVCPS – Class XII: 86.2%, Class X: CGPA 9.2

Major Project (Thesis)

Title: Multi-Person Identification from Geophone Signals via Spectrogram Analysis and Deep Learning

Supervisor: Prof. Subrat Kar

• Designed framework combining spectrogram-based geophone signal processing, metadata integration, and deep learning with cross-entropy & triplet loss (~90% accuracy).
• Implemented real-time inference on Raspberry Pi for human presence detection using geophones.
• Enabled result streaming to local & cloud servers for remote monitoring.

Publications

Gaur, S., Karunik, A. & Naik, S. (2024). "A Metric-Driven Comparative Study of Text Summarization Models: Insights from State-of-the-Art LLMs." International Conference on AI & Speech Technology. DOI

Projects

• Kernel-Level Gang Scheduler for HPC Workloads – Custom Linux scheduling class (SCHED_GANG) for synchronized multi-core execution.
• Resource Usage Tracker & Limiter – Kernel syscalls to monitor and cap heap memory & open files; enforced limits via SIGKILL.
• Text-to-SQL System – Schema-aware T5-base model with rule-based post-processing (37.5% exact match accuracy).
• Grammatical Error Correction System – Developed using BART/T5 with LoRA achieving BLEU 90 and 58% exact match.
• CRF Model for NER – Implemented CRF with transition constraints; achieved 84% accuracy.
• Tree-Structured Neural Network for MNIST – Hierarchical NN correcting parent misclassifications, ~93% accuracy.
• Pairwise Binary Classification for Image Similarity – DL-based model for pairwise image classification (72% accuracy).
• Summarizing PDFs using LangChain + Streamlit – Interactive LLM-powered PDF summarization with FAISS search.

Internships

• NIELIT, Delhi (Feb 2024 – Jul 2024): Training on AI (ML/DL, CV, NLP); worked on comparative study of text summarization models.
• FITT, IIT Delhi (Dec 2024 – Jan 2025): Led team for gait data collection using geophones and cameras for 100+ subjects.

Technical Skills

Languages: C, C++, Python, RISC-V

Libraries: PyTorch, Hugging Face, Transformers, scikit-learn, LoRA/PEFT, ONNX

Tools: Linux, Kaggle, Colab, Jupyter, SQL Server, Raspberry Pi, Logisim, Figma, Unreal Engine

Profiles: Personal Webpage | GitHub

Contact Me

Email: shivang@example.com

LinkedIn: Profile

GitHub: Projects