Customer-obsessed science


Research areas
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June 25, 2025With large datasets, directly generating data ID codes from query embeddings is much more efficient than performing pairwise comparisons between queries and candidate responses.
Featured news
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2024Bin-packing is an important problem in the robotic warehouse domain. Traditionally, this problem has been studied only for rigid packages (e.g., boxes or rigid objects). In this work, we tackle the problem of bin-packing with deformable packages that have become a popular choice for fulfillment needs. We present a system that incorporates a dual robot arm bimanual setup, uniquely combining suction and sweeping
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AI-ML Systems 20242024We present Video-to-Product Ads curation system for MiniTV to identify visually relevant products ads corresponding to objects of interest in video. This retrieval task is significantly challenging due to domain gap and peculiarity in images extracted from videos. Traditionally, images to product retrieval problems are solved using contrastive models with extensive labelled image data. In this paper, we
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ACM SIGSPATIAL 20242024A de-duplicated and complete address catalog is essential for any application or business which needs to manage large volumes of address data such as delivery logistics, first-responder services and government databases. For catalog creation, address data is usually procured from disparate sources, which often vary in quality, coverage, and introduce duplicates or variations of the same physical address
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ACM SIGSPATIAL 20242024Determining the precise location of customers is important for an efficient and reliable delivery experience, both for customers and delivery associates. Address text is a primary source of information provided by customers about their location. In this paper, we study the important and challenging task of matching free-form customer address text to determine if two addresses represent the same physical
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Nature Communications2024While multiple factors impact disease, artificial intelligence (AI) studies in medicine often use small, non-diverse patient cohorts due to data sharing and privacy issues. Federated learning (FL) has emerged as a solution, enabling training across hospitals without direct data sharing. Here, we present FL-PedBrain, an FL platform for pediatric posterior fossa brain tumors, and evaluate its performance
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