Time-MMD: A New Multi-Domain Multimodal Dataset for Time Series Analysis ⏰: Friday, December 13 This is in collaboration with @HessianLiu, Shangqing Xu, @leozhao_zhiyuan, @Harsha_64, Aditya B. Sasanur, Megha Sharma, @jiamingcui1997, @qingsongedu, @chaozhangcs, @badityap

🎉The coolest #CSE school in the world is hiring multiple faculty members! Application link below👇

What do datasets on influenza-like illnesses, stock, and weather share in common? All three were used to validate LSTPrompt's ability to break down forecasting into short-term and long-term tasks! Check out LSTPrompt this week at #ACL2024NLP! #WeCanDoThat #TogetherWeSwarm 🐝

Many thanks to my collaborators @HessianLiu (co-first, 1st place), @jd92wang, @Harsha_64, and my advisor @badityap!

We're presenting our poster at #ACL2024: What: LSTPrompt: Large Language Models as Zero-Shot Time Series Forecasters by Long-Short-Term Prompting When: Aug 14, 12:15-13:15 ICT Where: Convention Center A1 Paper: https://t.co/EjQwqcW50Y Code:

I am at #ICML24 all week. Catch me at my poster session: What: Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning When: Wed 24 Jul 11:30 a.m. — 1 p.m. CEST Where: Hall C 4-9 #2200 Paper: https://t.co/dQRufCBgIh Code:

1/3 I am excited to share that I will join @virginia_tech CS @VT_CS as a tenure-track assistant professor starting from Spring 2025. I will continue my research in machine learning and modeling, aiming to bridge public health and clinical decisions! @GTCSE @mlatgt @gtcomputing

2/2 In this paper, we develop FOIL a new framework to alleviate the inherent out-of-distribution problem in time-series forecasting via invariant learning. Paper: https://t.co/JcUKXJ7IzE Repo: https://t.co/yKCsUggRZU

1/2 Thrilled to share that our work "Time-Series Forecasting for Out-of-Distribution Generalization Using Invariant Learning" will appear in #ICML2024. Thanks to my advisor @badityap and collaborators @chaozhangcs @Harsha_64 @leozhao_zhiyuan. Let us meet in Vienna! @GTCSE

Looking forward to seeing you soon today at #ICLR2024! Thanks for the help from my advisor B. Aditya Prakash @badityap and collaborators Xueying Ding

Catch me at my poster session at #ICLR2024 : What: PINNsFormer: A Transformer-Based Framework For Physics-Informed Neural Networks When: May 10:45 am — 12:45 pm CEST Where: Halle B #31, Messe Wien Paper: https://t.co/9lkil6AwIL Code:

🎓 Excited to attend and present our paper at #ICLR2024! Looking forward to meeting you in Vienna! Open to any discussions and interested in AI for time series and AI for physics. Let's connect and explore new ideas and insights together! @GTCSE @mlatgt