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National University Of Singapore Hiring! Full Time Research Assistant (Relative smooth optimization theory) in - Ricebowl
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Job Title: Research Assistant (Relative smooth optimization theory)
University-Level Unit: College of Design and Engineering
Faculty/Department-Level Unit: Industrial Systems Engineering and Management
Employee Category: Research Staff
Location_ONB: Kent Ridge Campus
Posting Start Date: 18/06/2026
Job Description
In recent decades, we have witnessed significant progresses in the convergence and complexity theory of the first-order optimization methods, with gradient global Lipschitz continuity (GGLC) assumption playing a central role, in many classical results. However, a large class of important problems arising in modern optimization and machine learning do not satisfy this assumption. As a result, there remains a substantial gap between the theory and practical behavior of many widely used algorithms.
This project, led by Dr. Zhang, aims to strengthen the theoretical foundation of relative smooth optimization, an emerging framework developed to go beyond the classical GGLC setting. In particular, the project will study first-order methods under relative smoothness, with a focus on nonconvex problems, more appropriate optimality measures, and new non-Euclidean Lipschitz tools that better capture the underlying problem geometry. The goal is to establish sharper convergence and complexity results, clarify several widely adopted but potentially misleading arguments in the current literature, and develop a more reliable and powerful new analysis framework for the relative smooth problem class.
Job Requirements
Interested applicants are required to possess a BSc. in 2026. He/she should have a good understanding in
As we plan to apply our methodologies to the training and tuning of language models, it will be an advantage if the applicant has related experience.
Req ID: 33396
University-Level Unit: College of Design and Engineering
Faculty/Department-Level Unit: Industrial Systems Engineering and Management
Employee Category: Research Staff
Location_ONB: Kent Ridge Campus
Posting Start Date: 18/06/2026
Job Description
In recent decades, we have witnessed significant progresses in the convergence and complexity theory of the first-order optimization methods, with gradient global Lipschitz continuity (GGLC) assumption playing a central role, in many classical results. However, a large class of important problems arising in modern optimization and machine learning do not satisfy this assumption. As a result, there remains a substantial gap between the theory and practical behavior of many widely used algorithms.
This project, led by Dr. Zhang, aims to strengthen the theoretical foundation of relative smooth optimization, an emerging framework developed to go beyond the classical GGLC setting. In particular, the project will study first-order methods under relative smoothness, with a focus on nonconvex problems, more appropriate optimality measures, and new non-Euclidean Lipschitz tools that better capture the underlying problem geometry. The goal is to establish sharper convergence and complexity results, clarify several widely adopted but potentially misleading arguments in the current literature, and develop a more reliable and powerful new analysis framework for the relative smooth problem class.
Job Requirements
Interested applicants are required to possess a BSc. in 2026. He/she should have a good understanding in
- convergence and complexity analysis for (nonconvex) optimization algorithms
- stochastic process and stochastic approximation methods
As we plan to apply our methodologies to the training and tuning of language models, it will be an advantage if the applicant has related experience.
Req ID: 33396
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