Head of Data Science | AI & Data Science Leader | Graph AI, Fraud Intelligence & Scalable AI Systems
Building production AI for digital payments, transaction-graph analytics, temporal machine learning, Generative AI and GPU-accelerated computing.
I am an AI and Data Science leader with 20+ years of experience translating research into scalable, production-ready systems. I specialise in Graph AI, temporal graph learning, fraud detection, money-mule detection and transaction-graph analytics for large digital ecosystems. My work combines research, hands-on engineering, published contributions and open-source participation across production machine learning, Generative AI, federated AI, synthetic data and responsible AI governance.
- 20+ years across AI, data science, machine learning and analytics leadership.
- Led AI initiatives for one of the world's largest real-time digital payments ecosystems.
- Built production AI systems for fraud intelligence, money-mule detection, anomaly detection, graph analytics, federated AI and synthetic data.
- Published peer-reviewed research in Graph AI, temporal transaction graphs, adaptive fraud detection and high-performance analytics.
- Author of Machine Learning for Finance and educator to 21,000+ learners.
- Global Fintech Fest 2025 AI Report — Independent industry recognition. See page 11 for the referenced mention.
- Graph AI and financial crime: graph machine learning, graph neural networks, temporal graphs, transaction-network analysis, fraud detection and money-mule detection
- Scalable production AI: production machine learning, real-time analytics, MLOps, LLMOps, observability, testing and responsible AI governance
- GPU-accelerated analytics: CUDA, NVIDIA RAPIDS, cuGraph and high-performance graph computing
- Generative and agentic AI: LLMs, retrieval-augmented generation, agentic workflows and enterprise GenAI
- Applied machine learning: anomaly detection, time-series forecasting, incremental learning, reinforcement learning and knowledge distillation
| Repository | Focus and Differentiation |
|---|---|
| Topology-Aware Temporal Graph Learning | Reference implementation for temporal node classification using topology-aware features, chronological evaluation and deterministic synthetic transaction graphs. |
| Cross-Modal Knowledge Distillation | ANN-to-SNN knowledge distillation for imbalanced tabular classification using spike encoding and hybrid distillation losses. |
| Time-Series Forecasting | Reproducible forecasting benchmark with walk-forward validation, leakage-safe backtesting, classical models, lag-based machine learning, tests and CI. |
| Outlier and Anomaly Detection | Reproducible tutorials and benchmarks covering statistical, distance-based, density-based, isolation, kernel, ensemble and autoencoder methods. |
- RAPIDS cuGraph PR #5584 — open contribution proposing multi-seed
ego_graphoffset handling in the Python API, with regression tests and backward-compatibility validation.
My research focuses on graph machine learning, temporal transaction graphs, fraud intelligence, scalable AI, incremental learning, knowledge distillation, anomaly detection, reinforcement learning and high-performance computing.
IEEE International Conference on Big Data (IEEE BigData 2025) · IEEE
DOI: 10.1109/BigData66926.2025.11402449
Proposes an adaptive fraud detection framework combining meta-learning, Kolmogorov–Arnold Networks (KAN) and ensemble learning to improve generalization against evolving fraud patterns in financial transaction systems.
Research Areas: Fraud Detection · Financial AI · Meta-Learning · KAN · Ensemble Learning
IEEE 32nd International Conference on High Performance Computing, Data and Analytics Workshops (HiPCW 2025) · IEEE
DOI: 10.1109/HiPCW66559.2025.00053
Presents a scalable framework for temporal graph motif mining over large-scale financial transaction networks, enabling efficient discovery of transaction patterns for anti-money laundering (AML) investigations and graph intelligence. Evaluated on public benchmark graphs and billion-scale UPI transaction data.
Research Areas: Graph AI · Temporal Graphs · AML · Transaction Intelligence · High-Performance Computing
Selected examples of how my published work has been independently reviewed, cited and extended by international researchers across healthcare simulation and natural-language processing.
View detailed research-impact evidence →
More research: Google Scholar · ORCID · ResearchGate · OpenReview · DBLP · Semantic Scholar · ACM Digital Library
- Book: Machine Learning for Finance: Beginner's Guide to Explore Machine Learning in Banking and Finance — BPB Publications, 2021
- Course: Data Analysis for Business and Finance — statistics, regression, time series and applied analytics
- Technical articles: Explore selected articles published on Towards Data Science, HackerNoon and KDnuggets →
- Medium profile: View all Medium articles