Pastrana, Li, Mehta, Vyas, Pakazad, Ohlsson, Paparrizos: READ-Bench: Benchmarking Historical Instance Retrieval for Time-Series Diagnosis https://arxiv.org/abs/2609.32123 https://arxiv.org/pdf/2609.32123 https://arxiv.org/html/2609.32123

OpenHands names Apple, Google, Amazon and Netflix engineers as users of its AI coding agent, raised $18.8M in a Series A on 18 Nov 2025, but its own site lists exactly one paying customer: C3.ai, quoted via VP Data Science Sina Pakazad. Logos aren't customers.

Anoushka Vyas, Aarushi Dhanuka, Sina Khoshfetrat Pakazad, Henrik Ohlsson: Data Intelligence Agents: Interpreting, Modeling, and Querying Enterprise Data via Autonomous Coding Agents https://arxiv.org/abs/2606.19319 https://arxiv.org/pdf/2606.19319 https://arxiv.org/html/2606.19319

Ian Wu, Patrick Fernandes, Amanda Bertsch, Seungone Kim, Sina Pakazad, Graham Neubig
Better Instruction-Following Through Minimum Bayes Risk
https://arxiv.org/abs/2410.02902

Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig
Synthetic Multimodal Question Generation
https://arxiv.org/abs/2407.02233

Ian Wu, Patrick Fernandes, Amanda Bertsch, Seungone Kim, Sina Pakazad, Graham Neubig
Better Instruction-Following Through Minimum Bayes Risk
https://arxiv.org/abs/2410.02902

Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig
Synthetic Multimodal Question Generation
https://arxiv.org/abs/2407.02233

Yang Song, Anoushka Vyas, Zirui Wei, Sina Khoshfetrat Pakazad, Henrik Ohlsson, Graham Neubig: NEMO: Execution-Aware Optimization Modeling via Autonomous Coding Agents https://arxiv.org/abs/2601.21372 https://arxiv.org/pdf/2601.21372 https://arxiv.org/html/2601.21372

PANAS! Pak Azad Kena Gantung Kerja Sebab Kontroversi? Ini Jawapan 'Padu' Dari ERA!

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“Dah Kena Gantung Ke?” – Pak Azad Akhirnya Buka Mulut Isu Hilang Dari Era FM, Jawapannya Buat Netizen Tergamam!

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“Anak Aku Bukan Punching Bag Korang!” – Azad Jasmin ‘Mengamuk’, Bagi Amaran Keras Buat Netizen Mulut Puaka!
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Not sure I understand figure3 in @c3.ai blog on time series modeling to classify it as a breakthrough but use of LLMs to overcome problems this article identifies with current workflows would be impactful, if true. #MachineLearning

Utsav Dutta, Gerardo Pastrana, Sina Khoshfetrat Pakazad, Henrik Ohlsson: Giving Sensors a Voice: Multimodal JEPA for Semantic Time-Series Embeddings https://arxiv.org/abs/2605.31580 https://arxiv.org/pdf/2605.31580 https://arxiv.org/html/2605.31580

Utsav Dutta, Sina Khoshfetrat Pakazad, Henrik Ohlsson: Time to Embed: Unlocking Foundation Models for Time Series with Channel Descriptions https://arxiv.org/abs/2505.14543 https://arxiv.org/pdf/2505.14543 https://arxiv.org/html/2505.14543