Dev Nirwal

📧 devnirwal16@gmail.com

📍 Pune, India

🏛 Newton School

I am an AI/ML researcher and engineer with a strong background in machine learning theory and practical AI systems. I completed my Master’s in Computer Science from South Asian University (International SAARC University), New Delhi, graduating Rank 2 out of 22 with a GPA of 8.02/9 on a fully-funded merit scholarship.

My research focuses on hypergraph-based learning algorithms — developing novel semi-supervised and multi-label classification methods that exploit higher-order relationships in data, consistently outperforming graph-based baselines. I have co-authored 4 publications at top venues including ICPR 2024 (Kolkata) and CODS-COMAD 2024.

On the engineering side, I work at Newton School building scalable AI-powered learning platforms (serving 3,000+ students), developing agentic AI evaluation pipelines, and running workshops on LLMs, RAG, and Machine Learning. Past projects include a RAG system with LangChain + Gemini AI + FAISS, a bioinformatics toolbox, and multiple open-source tools used by hundreds of developers.

I am actively seeking AI/ML Research and Engineering roles — from theoretical foundations to production-grade intelligent systems.

news

Apr 30, 2025	Joined Newton School as SDE + ML Instructor — building AI-powered educational platforms and agentic evaluation pipelines serving 3,000+ students.
Jan 14, 2025	Paper accepted at ICMLDE 2025: “Leveraging Hypergraph-Guided Feature Relevance in Multi-Label Learning with Missing Labels” — extending hypergraph learning research to incomplete label scenarios.
Dec 04, 2024	Presented 2 papers at ICPR 2024, Kolkata on hypergraph-based semi-supervised SVMs and multi-label classification with label-specific features.

selected publications

ICMLDE
Leveraging Hypergraph-Guided Feature Relevance in Multi-Label Learning with Missing Labels

Reshma Rastogi and Dev Nirwal

In International Conference on Machine Learning and Data Engineering (ICMLDE), 2025

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This work presents a hypergraph-guided framework for feature relevance estimation in multi-label learning scenarios where labels are partially missing, enabling more robust classification under incomplete supervision.
@inproceedings{nirwal2025hypergraph, title = {Leveraging Hypergraph-Guided Feature Relevance in Multi-Label Learning with Missing Labels}, author = {Rastogi, Reshma and Nirwal, Dev}, booktitle = {International Conference on Machine Learning and Data Engineering (ICMLDE)}, year = {2025}, }
ICPR
Improved Hypergraph Laplacian Based Semi-Supervised Support Vector Machine

Reshma Rastogi and Dev Nirwal

In Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, Dec 2024

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A novel semi-supervised support vector machine using improved hypergraph Laplacian regularization, achieving superior performance on standard ML benchmarks by leveraging higher-order data relationships.
@inproceedings{nirwal2024improved, title = {Improved Hypergraph {Laplacian} Based Semi-Supervised Support Vector Machine}, author = {Rastogi, Reshma and Nirwal, Dev}, booktitle = {Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India}, year = {2024}, month = dec, publisher = {Springer}, }
ICPR
Hypergraph Regularized Semi-supervised Least Squares Twin Support Vector Machine for Multilabel Classification

Reshma Rastogi and Dev Nirwal

In Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India, Dec 2024

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A hypergraph-regularized semi-supervised least squares twin SVM for multilabel classification, demonstrating superior performance on UCI ML Repository, MNIST Fashion, and NLP benchmark datasets.
@inproceedings{nirwal2024lstsvm, title = {Hypergraph Regularized Semi-supervised Least Squares Twin Support Vector Machine for Multilabel Classification}, author = {Rastogi, Reshma and Nirwal, Dev}, booktitle = {Pattern Recognition: 27th International Conference, ICPR 2024, Kolkata, India}, year = {2024}, month = dec, publisher = {Springer}, }
CODS-COMAD
HyperGraph Based Imbalance Multi-label Classification with Label Specific Features

Reshma Rastogi and Dev Nirwal

In Proceedings of the 7th Joint International Conference on Data Science & Management of Data (CODS-COMAD 2024), 2024

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A hypergraph-based approach for imbalanced multi-label classification leveraging label-specific features to handle class imbalance and complex label correlations effectively.
@inproceedings{nirwal2024imbalance, title = {{HyperGraph} Based Imbalance Multi-label Classification with Label Specific Features}, author = {Rastogi, Reshma and Nirwal, Dev}, booktitle = {Proceedings of the 7th Joint International Conference on Data Science \& Management of Data (CODS-COMAD 2024)}, year = {2024}, publisher = {ACM}, }