Jay Lee

CH Cho Chair Professor

University of Southern California

United States

Research Interests

Explore related searches

Professors in United States Professors in Energy Engineering Energy Engineering professors in United States

Contact this professor

Get a free first email built on one of their papers, ready for you to edit and send yourself.

About

Jay Lee is the CH Cho Chair Professor at the University of Southern California in the United States. His research focuses on areas including Bayesian optimization, machine learning in process systems engineering, and stochastic optimization methods. He has published recent articles related to the optimization of energy systems, battery performance prediction, and carbon capture technologies.

Articles (10)

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2024

Year:

2023

Year:

2023

Year:

2023

Year:

2023

Collaborators (1)

Bhushan Gopaluni

The University of British Columbia

CANADA

View details

ApplyKite TwinLearns as you apply

Make your next move.

Prepare your application

Review drafts before you send them.

Jay Lee

Research Interests

Explore related searches

Contact this professor

About

Articles (10)

Non-myopic Bayesian optimization using model-free reinforcement learning and its application to optimization in electrochemistry

Machine learning in process systems engineering: Challenges and opportunities

Multi-period, multi-timescale stochastic optimization model for simultaneous capacity investment and energy management decisions for hybrid Micro-Grids with green hydrogen production under uncertainty

Simultaneous extraction of intra- and inter-cycle features for predicting lithium-ion battery's knees using convolutional and recurrent neural networks

Techno-economic analysis for design and management of international green hydrogen supply chain under uncertainty: An integrated temporal planning approach

Techno-economic analysis of solar powered green hydrogen system based on multi-objective optimization of economics and productivity

Statistical Kinetic Modeling Procedure to Predict Exo-Olefin Content in Cationic Polymerization and Its Application to Polyisobutylene

Flexible operation of Post-combustion CO2 capture process enabled by NARX-MPC using neural network

Identification of sustainable carbon capture and utilization (CCU) pathways using state-task network representation

A 3D CFD study on the effects of feed spacer designs on membrane performance for high-permeance RO membranes

Collaborators (1)

Bhushan Gopaluni

Make your next move.