Deep Flow Networks
2026
Authored by
Center for Applied AI's Faculty Research
Center for Applied AI's Faculty Research
The Center for Applied AI is proud to share research from our talented faculty members and affiliates.
A Theoretical Framework for Auxiliary-Loss-Free Load Balancing of Sparse Mixture-of-Experts in Large-Scale AI Models
2026
Authored by X. Y. Han,Yuan Zhong,
From Unstructured Data to Demand Counterfactuals: Theory and Practice
2026
Authored by Giovanni Compiani,
A Comparative Study in Surgical AI: Datasets, Foundation Models, and Barriers to Med-AGI
2026
Authored by X. Y. Han,Yegor Baranovski,Eric Fithian,Kirill Skobelev,
Non-Exclusive Notifications for Ride-Hailing at Lyft II: Simulations and Marketplace Analysis
2026
Authored by Rad Niazadeh,
Still Waters, Rapid Currents: Early Labor Market Transformation under Generative AI
2026
Authored by Anders Humlum,Emilie Vestergaard,
Demand Estimation with Text and Image Data
2026
Authored by Giovanni Compiani,
Non-Exclusive Notifications for Ride-Hailing at Lyft I: Single-Cycle Approximation Algorithms
2026
Authored by Rad Niazadeh,
Agentic Interactions
2026
Authored by Alex Imas,Sanjog Misra,
Recurrent Neural Networks for Nonlinear Time Series
2026
Authored by Dacheng Xiu,Zhouyu Shen,