Congratulations to the Amazon Last Mile team on winning the 2026 @INFORMS Daniel H. Wagner Prize. Their paper details a planning system that accounts for how temperature and terrain change a delivery EV's energy needs. amzn.to/4iHcbQU ⚡
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The SGLang workflow you already know now runs on AWS Trainium.
SGLang-Neuron: serve models and generate RL rollouts on Trainium — same server, same APIs, native PyTorch through TorchNeuron. @sgl_project #SGLangSummit aws.amazon.com/blogs/machine-…
SGLang-Neuron: serve models and generate RL rollouts on Trainium — same server, same APIs, native PyTorch through TorchNeuron. @sgl_project #SGLangSummit aws.amazon.com/blogs/machine-…
aws.amazon.com Deploy frontier AI models on AWS Trainium with SGLang-Neuron | Amazon Web Services SGLang-Neuron is now publicly available through an AWS Deep Learning Container. With SGLang-Neuron, you can serve models and generate reinforcement learning rollouts with SGLang on AWS Trainium using... Introducing Mitra-v2, a tabular foundation model for both classification and regression. Pretrained entirely on synthetic tables, it leads the single-model field on the TabArena benchmark. amazon.science/blog/mitra-v2-…
www.amazon.science Mitra-v2: Scaling synthetic pretraining for tabular foundation models Expanding the mix of synthetic data used to train a tabular foundation model enables it to adapt to new tables without new models and lead the single-model field on the TabArena benchmark. 📣 Last call: Amazon Research Awards Fall 2026 proposals are due November 11. Successful applicants receive unrestricted funds, AWS promotional credits, and training resources. amazon.science/research-award…
www.amazon.science Amazon Research Awards opens Fall 2026 call for proposals Submissions open October 1 and close on November 11, 2026 Amazon senior applied scientist Hamed Karimi received the 2026 @ECMLPKDD Test-of-Time Award for his 2016 paper on the Polyak–Łojasiewicz condition. #ECML2026 amazon.science/blog/hamed-kar…
www.amazon.science Hamed Karimi wins ECML PKDD Test-of-Time Award Amazon senior applied scientist and coauthors showed that an overlooked mathematical condition from 1963 proves why frequently used machine-learning optimization methods work well.