In e-commerce logistics, accurate geospatial clustering is essential for optimizing resource allocation, manpower planning, and delivery network design. However, existing density-based clustering approaches, particularly their reliance on heuristic parameter tuning, have been underexplored in datasets with significant density variations, limiting robustness and scalability. This study presents an unsupervised framework that extends DBSCAN by leveraging Gaussian Mixture Models (GMM). First, we propose a method that systematically identifies suitable clustering scales through statistical modeling. Second, the approach iteratively applies DBSCAN to extract clusters from dense to sparse regions, overcoming single-parameter limitations. Finally, we validate the method through large-scale offline experiments using data from over 200 last-mile dispatch centers (LMDC). The results demonstrate the framework’s effectiveness in identifying heterogeneous geographic demand patterns and supporting workforce planning and operational benchmarking. This framework provides a scalable solution to a critical challenge in e-commerce logistics, offering a valuable reference for strategic and operational decision-making.

Abuse detection in e-commerce platforms is critical for preventing operational losses, particularly for transaction types vulnerable to abuse such as Return-to-Origin (RTO) in Cash-on-Delivery (COD) workflows. Detecting such abuse accurate, real-time decisions to intercept malicious orders before placement, imposing stringent sub-second latency requirements on deployed systems. In this work, we present TRUST, a deployed, production-scale abuse detection system based on a unified architecture of heterogeneous Graph Neural Networks (GNNs) and Transformer-based sequence encoders. This design enables joint reasoning over multi-relational entity interactions and temporal behavioural signals, allowing the model to combine complementary information for effective abuse detection when either modality is sparse or absent. TRUST processes millions of transactions daily with an average inference latency of ~25 ms, achieving a ~9.6% absolute precision improvement over a strong XGBoost baseline in live RTO detection. We report systematic ablation studies across both graph and sequence stages, evaluating GNN variants, sampling strategies, sequence lengths, and positional encoding schemes to guide architectural choices. Deployed end-to-end in a high-throughput environment, TRUST demonstrates that GNN–Transformer cascades can deliver state-of-the-art accuracy, scalability, and operational reliability in real-world abuse detection, offering a reproducible blueprint for similar industry-scale applications.

Geocoding in India presents unique challenges due to the unstructured, multilingual and diverse nature of its address systems. While recent advances in geospatial AI have explored the combination of spatial and semantic cues, existing methods often fall short in effectively integrating both dimensions for robust address resolution. In this work, we propose GeoIndia-V2, an enhanced version of GeoIndia [21], that unifies geospatial and semantic modeling through a novel fusion framework. Our unified model combines the Graphormer architecture [27] and a Pre-trained Transformer based Language Model (PTLM) that is trained from scratch on proprietary Indian address data, using our proposed Key Modulated Cross-Attention (KMCA) mechanism. KMCA enables deep cross-modal interaction between geospatial topology and linguistic structure and allows the model to reason contextually across both geographic and textual dimention, effectively handling the semantic intricacies of Indian addresses-including colloquial usage, inconsistent formatting, and multilinguality. We leverage last-mile e-commerce delivery data to construct a fine-grained graph of neighbourhood connectivity, enabling Graphormer to capture rich spatial relationships. Unlike prior methods that rely on self-loops, we generate graphs dynamically at inference time to exploit Graphormer's topological strength. Additionally, we introduce a generative decoding strategy for predicting hierarchical H3 cells. https://www.uber.com/en-IN/blog/h3/, moving beyond conventional bit-wise classification approaches. To the best of our knowledge, this is the first method to explicitly fuse graph-based geospatial learning with language-driven semantic modeling via cross-attention in the Indian geocoding context. Our approach significantly outperforms existing solutions and marks a substantial advancement toward building scalable real-world geocoding systems for complex address ecosystems like India.

Message passing on hypergraphs has been a standard framework for learning higher-order correlations between hypernodes.Recentlyproposed hypergraph neural networks (HGNNs) can be categorized into spatial and spectral methods based on their design choices.In this work, we analyze the impact of change in hypergraph topology on the suboptimal performance of HGNNs and propose DPHGNN, a novel dual-perspective HGNN that introduces equivariant operator learning to capture lower-order semantics by inducing topologyaware spatial and spectral inductive biases.DPHGNN employs a unified framework to dynamically fuse lower-order explicit feature representations from the underlying graph into the super-imposed hypergraph structure.We benchmark DPHGNN over eight benchmark hypergraph datasets for the semi-supervised hypernode classification task and obtain superior performance compared to seven state-of-the-art baselines.We also provide a theoretical framework and a synthetic hypergraph isomorphism test to express the power of spatial HGNNs and quantify the expressivity of DPHGNN beyond the Generalized Weisfeiler Leman (1-GWL) test.Finally, DPHGNN was deployed by our partner e-commerce company, Meesho for the Return-to-Origin (RTO) prediction task, which shows 7% higher macro F1-Score than the best baseline.

Software is a crucial component of many safety-critical applications, such as nuclear power plant, fly-by-wire application, medical instruments, space applications etc. They need to satisfy safety standards such as safety integrity level (SIL), IEEE series (IEEE 1012, 1074), IEC series (IEC 60880, IEC 62138). A requirement of all these safety standards is thorough testing. Thorough testing is measured using a test coverage metric. However, as we show every existing metric suffers from several shortcomings. In this context, we propose a novel test coverage metric based on dependency of data and control in code of the components for safety-critical software.