Caleb Ju

Overview

I study optimization algorithms for data-driven decision making under a dynamically changing environment. My most recent works have studied this through the lens of reinforcement learning and their application to energy. You can watch a high-level overview of my most recent works and some future directions via the link below.
🎬 DOE CSGF talk

Papers

*Equal contribution

(α) Alphabetical order

[arXiv]

Preconditioning via Randomized Range Deflation (RandRAND)

Oleg Balabanov, Caleb Ju, Kaiwen He, Aryaman Jeendgar, Michael Mahoney
Pre-print
[arXiv] [code]

Strongly-polynomial time and validation analysis of policy gradient methods

Caleb Ju and Guanghui Lan (α)
Under minor revision
📌 The Alice and John Jarvis Best Paper Award (2025)
[SIAM] [arXiv] [code]

A model-free first-order method for linear quadratic regulator with Õ(1/ε) sampling complexity

Caleb Ju, Georgios Kotsalis, Guanghui Lan
SIAM Journal on Control and Optimization
[arXiv] [code]

Policy optimization over general state and action spaces

Caleb Ju and Guanghui Lan (α)
Under revision
[arXiv] [code]

Dual dynamic programming for stochastic programs over an infinite horizon

Caleb Ju and Guanghui Lan
Submitted
📌 MOPTA Best Poster Award (2023)
[arXiv] [code]

Learning a Local Trading Strategy: Deep Reinforcement Learning for Grid-scale Renewable Energy Integration

Caleb Ju and Constance Crozier
Hawaii International Conference on System Sciences 2025
📰 Featured in DEIXIS
[CE] [RS]

Reinforcement Learning-Based Control for Waste Biorefining Processes Under Uncertainty

Ji Gao, Abigael Whalen, Caleb Ju, Yongsheng Chen, Guanghui Lan, Zhaohui Tong
Communications Engineering
[arXiv]

Efficient parallel implementation of the multiplicative weight update method for graph-based linear programs

Caleb Ju, Serif Yesil, Mengyuan Sun, Chandra Chekuri, Edgar Solomonik
Pre-print
[arXiv]

Implicit regularization of Bregman proximal point algorithm and mirror descent on separable data

Yan Li, Caleb Ju, Ethan X. Fang, Tuo Zhao
Pre-print
[FoCM] [arXiv]

Communication lower bounds for nested bilinear algorithms via rank expansion of Kronecker products

Caleb Ju*, Yifan Zhang*, Edgar Solomonik
Foundations of Computational Mathematics
[SIAM] [arXiv] [code]

Derivation and analysis of fast bilinear algorithms for convolution

Caleb Ju and Edgar Solomonik
SIAM Review