Title: Universal Parameter-Free Methods for Convex, Nonconvex, and Stochastic Optimization Abstract:  First-order methods are widely used to tackle data science and machine learning problems with complex structures, such as nonconvexity, nonsmoothness, and stochasticity. However, in many real-world scenarios, the problem structure … Read more

Title: Towards a Sustainable Content Creation Ecosystem: From Theory to Practice Abstract:  Modern content recommendation platforms, such as Instagram and YouTube, have raised societal concerns including the proliferation of clickbait, popularity biases, and echo chambers. While much existing research focuses … Read more

Title: Building Trustworthy AI: From AI Regulation to Computational Methods Abstract:  The widespread adoption of AI in high-stakes decisions has made trustworthy deployment crucial for preventing algorithmic harm. This is manifested in modern AI regulations like the EU's GDPR and … Read more

Title: Achieving Global Optimality for Nonconvex Optimization in Operations, Learning, and Causality Abstract:  Nonconvex optimization arises in diverse domains such as operations, machine learning, and causality, where it often encapsulates the complexity of real-world systems. While nonconvexity poses significant challenges … Read more

Title: Interpretable Causal Inference for Advancing Healthcare and Public Health Abstract:  Causal inference methods are essential across healthcare, public health, and social sciences, helping understand complex systems and inform decision-making. While integrating machine learning (ML) and statistical techniques has improved … Read more