Excited to announce that our inaugural "AI for Supply Chain: Today and Future" Workshop (AI4SupplyChain) has been accepted to #KDD 2025! 🎉
Please see Call For Papers and other key dates on our workshop website: https://t.co/sU574tzmTv
Submission site: https://t.co/K8fZRav3BA
My lab (https://t.co/W2r7eFGkhw) at MBZUAI is recruiting PhD students, Postdocs, and Visiting Scholars starting Fall 2026. Interested candidates can email their CV, transcript, and research plan to [email protected] with the subject: '[Name] [Position] - MBZUAI Application'.
Our 500+ page AI4Science paper is finally published:
Artificial Intelligence for Science in Quantum, Atomistic, and Continuum Systems. Foundations and Trends® in Machine Learning, Vol. 18, No. 4, 385–912, 2025
https://t.co/RzxYTDOJwx
📢 We are excited to announce "#FMSD: 1st Workshop on Foundation Models for Structured Data" has been accepted to #ICML 2025! Call for Papers: https://t.co/Zj2e9pXpQJ
Excited to announce that our inaugural "AI for Supply Chain: Today and Future" Workshop (AI4SupplyChain) has been accepted to #KDD 2025! 🎉
Please see Call For Papers and other key dates on our workshop website: https://t.co/sU574tzmTv
Submission site: https://t.co/K8fZRav3BA
We welcome submissions that apply established methods to novel supply chain problems, innovate new methods for supply chain challenges, propose solutions via position papers, share professional insights, and provide algorithmic tutorials for practitioners.
Supply chains face ongoing challenges from manual processes, external disruptions, and real-time operational shifts. Our workshop promotes AI solutions to streamline supply chain operations, leveraging recent advances in DL, Gen AI, and Agentic AI to address these challenges.
🚀Join us at #IROS2024 for the "Equivariant Robotics: The Role of Symmetry Across Perception, Estimation, and Control" workshop!
✨We welcome the contribution of short papers / extended abstracts.
🌐Check out our website: https://t.co/Fr6NONRBGx
#Robotics#AI#symmetry
After almost a year, our review paper on #Physics-Guided #DeepLearning finally appears at @PNASNews https://t.co/rQ1wBoQLvJ!
It is part of the special issue on #Physics Meet #MachineLearning https://t.co/oIVGk5JI04
(1/3)
Modeling symmetry breaking is key to understanding the fundamental changes in physical systems. We explore how relaxed group convolution can discover various symmetry-breaking factors in our new #ICML2024 paper. (1/5)
Paper: https://t.co/Nu2FbvQqy2
Code: https://t.co/BO3qkHJ8yB
We also show superior performance of relaxed group convolution compared to baselines with no symmetry and with strict symmetry biases on the task of fluid super-resolution for 3D channel flow and isotropic flow. (4/5)
Charge density is the core attribute of atomic systems in DFT. When representing and predicting charge density with ML, it is challenging to balance accuracy and efficiency. We propose a recipe that achieves SOTA on both: https://t.co/mxKQczuKzF 1/5