Customer-obsessed science
Research areas
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September 26, 2025To transform scientific domains, foundation models will require physical-constraint satisfaction, uncertainty quantification, and specialized forecasting techniques that overcome data scarcity while maintaining scientific rigor.
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Featured news
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2024As embodied agents learn to interact, it is crucial for them to understand when, what, and to whom they should respond. While advances in natural-language processing and speech technologies have enabled conversational agents to focus on what to respond, they still struggle to determine when and to whom they should respond. In this paper, we address the addressee detection (Talking-To-Me, TTM) problem under
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IAAE 2023, Research Methods and Applications on Macroeconomic Forecasting2024We propose a simple yet robust framework to nowcast recession risk at a monthly frequency in both the United States and the Euro Area. Our nowcast leverages both macroeconomic and financial conditions, and is available the first business day after the reference month closes. In particular, we argue that financial conditions are not only useful to predict future downturns–as emphasized by the existing literature–but
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ICIPACV 20242024Anomaly detection, also referred to as one-class classification, plays a crucial role in identifying product images that deviate from the expected distribution. This study introduces Data-centric Anomaly Detection with Diffusion Models (DCADDM), presenting a systematic strategy for data collection and further diversifying the data with image generation via diffusion models. The algorithm addresses data
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EACL 20242024We present ParrotTTS, a modularized text-to-speech synthesis model leveraging disentangled self-supervised speech representations. It can train a multi-speaker variant effectively using transcripts from a single speaker. ParrotTTS adapts to a new language in low resource setup and generalizes to languages not seen while training the self-supervised back-bone. Moreover, without training on bilingual or parallel
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ECIR 20242024Recommender systems play a crucial role in the e-commerce stores, enabling customers to explore products and facilitating the discovery of relevant items. Typical recommender systems are built using n most recent user interactions, where value of n is chosen based on trade-off between incremental gains in performance and compute/memory costs associated with processing long sequences. State-of-the-art recommendation
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