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sauravsingla/README.md

Hi, I'm Saurav Singla 👋

Head of Data Science · AI & Data Science Leader · Graph AI Researcher · Generative AI · CUDA · Open Source

Building scalable AI systems across Graph AI, Generative AI, temporal machine learning, GPU-accelerated computing and production-grade MLOps.

LinkedIn Google Scholar ORCID ResearchGate Medium X

About Me

I am an AI and Data Science leader with 20+ years of experience translating advanced research into scalable, production-ready systems. My work spans Graph AI, temporal learning, Generative AI, GPU-accelerated analytics and reliable machine-learning platforms. I combine research, hands-on engineering, published work and open-source contribution to build reproducible AI systems designed for real-world scale. I have led production-scale AI and graph analytics initiatives across large, high-volume digital ecosystems, spanning fraud intelligence, anomaly detection, federated AI and responsible AI governance.

Selected Impact

  • Led AI initiatives for one of the world's largest real-time digital payments ecosystems.
  • Built production AI systems for fraud detection, mule detection, graph intelligence, federated AI and synthetic data.
  • Published peer-reviewed research in Graph AI and temporal graph analytics.

Core Expertise

Python PyTorch scikit-learn CUDA NVIDIA RAPIDS cuGraph Docker Kubernetes GitHub Actions Linux

  • AI and ML: Graph Neural Networks, temporal graph learning, Generative AI, LLMs, Agentic AI, RAG and reinforcement learning
  • Scalable computing: CUDA, NVIDIA RAPIDS, cuGraph and high-performance analytics
  • Engineering: Python, SQL, C++, Docker, Kubernetes, CI/CD, MLOps, LLMOps, observability and testing
  • Applied modelling: Time series, anomaly detection, incremental learning and knowledge distillation

External Open-Source Contribution

Featured Technical Projects

Repository Focus and Differentiation
Topology-Aware Temporal Graph Networks Temporal graph architecture combining topology with evolving node interactions
GNN Graph Classification Reproducible graph-level learning implementations with practical modelling workflows
Cross-Modal Knowledge Distillation Efficient representation learning through teacher-student knowledge transfer across modalities
Time Series Forecasting, feature engineering and predictive modelling with reusable implementations
Outlier Detection Tutorials Practical anomaly and outlier detection methods presented through reproducible tutorials

Research & Publications

My research covers graph machine learning, temporal graphs, scalable AI, incremental learning, knowledge distillation, anomaly detection, reinforcement learning and high-performance computing. My published work includes research presented through IEEE and Springer venues.

Selected Publications

Towards Scalable Mining of Temporal Graph Motifs over Large-Scale Transaction Networks

Scalable temporal motif mining for large-scale transaction networks and high-performance graph analytics.

Adaptive Fraud Detection Via Meta-Learning with KAN and Ensemble Strategies

Adaptive fraud detection using meta-learning, Kolmogorov-Arnold Networks and ensemble strategies for evolving fraud patterns.

More research: Google Scholar · ORCID · ResearchGate

Book, Course & Technical Writing

Writing & Community Profiles

HackerNoon Quora Hugging Face

Current Work

  • Scalable Graph AI and temporal graph learning
  • GPU-accelerated graph analytics using CUDA and NVIDIA RAPIDS
  • Production AI systems for fraud intelligence, anomaly detection and trustworthy AI

Pinned Loading

  1. tgn-topology-aware tgn-topology-aware Public

    A topology-aware extension of Temporal Graph Networks (TGN) for modeling evolving graph structures with fine-grained temporal and structural dependencies. Optimized for dynamic link prediction, fra…

    Python 1

  2. gnn-graph-classification gnn-graph-classification Public

    A Graph Neural Network (GNN)-based framework for supervised graph classification tasks. Supports various GNN architectures (GCN, GAT, GraphSAGE) and is adaptable to applications like fraud pattern …

    Python

  3. Cross-Modal-Knowledge-Distillation-Framework Cross-Modal-Knowledge-Distillation-Framework Public

    A flexible cross-modal knowledge distillation framework for transferring supervision between heterogeneous modalities to improve model generalization and efficiency on low-resource tasks.

    Python

  4. Time_Series Time_Series Public

    Reproducible Python time-series forecasting with walk-forward backtesting, leakage-safe validation, statistical and machine-learning baselines, open datasets, tests and benchmark leaderboards.

    Jupyter Notebook 6

  5. Outlier_Detection_Tutorials Outlier_Detection_Tutorials Public

    Practical Python tutorials for outlier and anomaly detection using statistical methods, Isolation Forest, LOF, DBSCAN, One-Class SVM, PyOD, and autoencoders, with notebooks, benchmarks, exercises, …

    Jupyter Notebook 8 5