#PakAzad
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
September 29, 2026 at 6:39 AM
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.
September 13, 2026 at 2:36 PM
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
June 18, 2026 at 6:42 AM
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
October 29, 2024 at 7:01 PM
Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig
Synthetic Multimodal Question Generation
https://arxiv.org/abs/2407.02233
October 7, 2024 at 9:01 PM
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
October 7, 2024 at 4:02 AM
Ian Wu, Sravan Jayanthi, Vijay Viswanathan, Simon Rosenberg, Sina Pakazad, Tongshuang Wu, Graham Neubig
Synthetic Multimodal Question Generation
https://arxiv.org/abs/2407.02233
July 4, 2024 at 1:32 AM
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
January 30, 2026 at 6:29 AM
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
Time Series Modeling Redefined: A Breakthrough Approach
Decoding the language of time: AI that understands time series as seamlessly as text By Sina Pakazad, Vice President, Data Science, C3 AI, Utsav Dutta, Data Scientist, Data Science, C3 AI, and Henrik ...
c3.ai
March 22, 2025 at 12:16 PM
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
June 1, 2026 at 6:45 AM
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
May 21, 2025 at 6:20 AM