February 2026

The Non-equilibrium Green’s function (NEGF) formalism is a particularly powerful method to simulate the quantum transport properties of nanoscale devices such as transistors, photo-diodes, or memory cells, in the ballistic limit of transport or in the presence of various scattering sources such as electronphonon, electron-photon, or even electron-electron interactions. The inclusion of all these mechanisms has been first demonstrated in small systems, composed of a few atoms, before being scaled up to larger structures made of thousands of […]

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So I compiled a fairly long list of reinforcement learning researchers and notable practitioners. Could you suggest any star researchers I might have missed? My goal is not to miss any new breakthroughs in RL algorithms, so I’m mostly interested in people who work on them now or have done so recently. Meaning pure RL methods, not LLM related. Stefano Albrecht — UK researcher. Wrote a book on Multi-Agent RL. Nowadays mostly gives talks and occasionally updates the […]

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Unsupervised object-centric learning models, particularly slot-based architectures, have shown great promise in decomposing complex scenes. However, their reliance on reconstruction-based training creates a fundamental conflict between the sharp, high-frequency attention maps of the encoder and the spatially consistent but blurry reconstruction maps of the decoder. We identify that this discrepancy gives rise to a vicious cycle: the noisy feature map from the encoder forces the decoder to average over possibilities and produce even blurrier outputs, while the gradient […]

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Decentralized learning (DL) enables a set of nodes to train a model collaboratively without central coordination, offering benefits for privacy and scalability. However, DL struggles to train a high accuracy model when the data distribution is non-independent and identically distributed (non-IID) and when the communication topology is static. To address these issues, we propose Morph, a topology optimization algorithm for DL. In Morph, nodes adaptively choose peers for model exchange based on maximum model dissimilarity. Morph maintains a […]

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How ShareChat engineers rebuilt a low-latency ML feature store on ScyllaDB after an initial scalability failure — and what they learned along the way The demand for low-latency machine learning feature stores is higher than ever, but actually implementing one at scale remains a challenge. That became clear when ShareChat engineers Ivan Burmistrov and Andrei Manakov took the P99 CONF 23 stage to share how they built a low-latency ML feature store based on ScyllaDB. This isn’t a […]

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In the fast-evolving hospitality landscape of 2026, a new specialist is quietly driving outsized results: the Sales IT Data Analyst. This hybrid professional bridges sales strategy, robust IT infrastructure, and advanced data analytics to optimize revenue streams across hotels, resorts, restaurants, and event venues. Unlike traditional revenue managers or general data analysts, these experts specialize in integrating real-time sales data flows with enterprise IT systems such as property management systems (PMS), CRM platforms, and point-of-sale (POS) tools to […]

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This paper proposes FedNMap, a normal map-based method for composite federated learning, where the objective consists of a smooth loss and a possibly nonsmooth regularizer. FedNMap leverages a normal map-based update scheme to handle the nonsmooth term and incorporates a local correction strategy to mitigate the impact of data heterogeneity across clients. Under standard assumptions, including smooth local losses, weak convexity of the regularizer, and bounded stochastic gradient variance, FedNMap achieves linear speedup with respect to both the […]

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Audio is a fundamental modality for analyzing speech, music, and environmental sounds. Although pretrained audio models have significantly advanced audio understanding, they remain fragile in real-world settings where data distributions shift over time. In this work, we present the first systematic benchmark for audio continual learning (CL) with pretrained models (PTMs), together with a comprehensive analysis of its unique challenges. Unlike in vision, where parameter-efficient fine-tuning (PEFT) has proven effective for CL, directly transferring such strategies to audio […]

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