Research Fellow (Stochastic Optimization)

Job Description

Having witnessed the achievements of the large models in 2023, there has been a new surge of machine learning and AI models in recent days, which again puts up the public concerns about the explosively increasing energy consumption during the model training phase.  According to Dr. Sajjad Moazeni, an AI researcher at University of Washington, the training of GPT-3 roughly consumes 10 gigawatt-hour of electricity ^([1]), approximately equivalent to the daily electricity consumption of 440,000 average Singapore households in 2021 ^([2]).

Significant efforts have been devoted to improving the efficiency of the model training phase, in terms of hardware, model architecture, networking, and algorithm design, etc. The PI (Dr. Zhang Junyu) of this project would like to tackle this problem from the algorithmic perspective, by developing theoretical understanding of the open questions on the training algorithms, in terms of the convergence, complexity, local landscape properties, and potential algorithmic improvement based on the new theoretical findings.

[1] Data from UW News.   [2] Data from Statista

Qualifications

More Information

Location: Kent Ridge Campus

Organization: College of Design and Engineering

Department : Industrial Systems Engineering and Management

Employee Referral Eligible: No

Job requisition ID : 23720