I am a research scientist at Google Brain. I graduated from SCS of CMU, with a PhD in Machine Learning and an MSc in Lanugage Technologies, advised by Jaime Carbonell and Alex Smola.

The high penetration of distributed energy resources (DERs) exacerbates net load fluctuations in distribution networks. Existing dynamic pricing research often ignores complex physical constraints, ...

D-PDLP (Distributed PDLP) is a high-performance, distributed implementation of the Primal-Dual Hybrid Gradient (PDHG) algorithm designed for solving massive-scale Linear Programming (LP) problems on ...

Abstract: This paper proposes Triangularly Preconditioned Primal- Dual algorithm, a new primal-dual algorithm for minimizing the sum of a Lipschitz-differentiable convex function and two possibly ...

This course teaches an overview of modern mathematical optimization methods, for applications in machine learning and data science. In particular, scalability of algorithms to large datasets will be ...

As one of the important statistical methods, quantile regression (QR) extends traditional regression analysis. In QR, various quantiles of the response variable are modeled as linear functions of the ...

As AI systems scale, the limitations of cloud-based architectures, including latency, bandwidth, and privacy concerns, demand decentralized alternatives. Federated learning (FL) and Edge AI provide a ...

In this paper, aiming to achieve the target of carbon emission orientation, a multi-objective optimization model of the multi-energy flow coupling system is proposed, in which all the environmental ...

Abstract: This article considers distributed optimization by a group of agents over an undirected network. The objective is to minimize the sum of a twice differentiable convex function and two ...