Broadly speaking: Continous optimization, machine learning and data science.

• Convex and nonconvex optimization: theories, algorithms and applications. Special topics about error bounds.
• Machine learning theories, algorithms and applications, especially sparse learning, support vector machine, kernel-based learning and neural networks, etc.

1. Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço, Ting Kei Pong. Tight error bounds for log-determinant cones without constraint qualifications. Submitted March 2024.
2. Ying Lin, Scott B. Lindstrom, Bruno F. Lourenço, Ting Kei Pong. Generalized power cones: optimal error bounds and automorphisms. To appear in SIAM Journal on Optimization.
3. Ying Lin, Yimin Wei, Qi Ye*. A Homotopy method for multikernel-based approximation. Journal of Nonlinear and Variational Analysis, 2022, 6(2):139-154.
4. Ying Lin, Qi Ye*. Support vector machine classifiers by non-Euclidean margins. Mathematical Foundations of Computing, 2020, 3(4):2-5.
5. Ying Lin, Rongrong Lin*, Qi Ye. Sparse regularized learning in the reproducing kernel Banach spaces with the ℓ¹ norm. Mathematical Foundations of Computing, 2020, 3(3):205-218.
6. Qi Ye*, Ying Lin. Application of machine learning methods based on LAZE priors in cancer data. Journal of South China Normal University (Natural Science Edition), 2018, 50(04):115-120.

1. SIAM Conference of Optimization (OP23) (May 31 - Jun 3, 2023), Error bounds for the generalized power cone and applications in algebraic structure, Seattle, Washington, U.S.
2. CAS AMSS-PolyU SIAM Student Chapter Workshop (Dec 24, 2022), Error bounds for the generalized power cone and applications in algebraic structure, online.
3. CSIAM Students Forum 2020 (Nov, 2020), Non-Euclidean support vector classifiers for sparse learning, online.
4. CSIAM 2020 (Oct 29 - Nov 1, 2020), Non-Euclidean support vector classifiers for sparse learning, Changsha, Hunan, China.
5. 2019 Optimization Frontier Progress Seminar (Oct 17 - 19, 2019), Support vector classifier by maximum margin of arbitrary norm, China West Normal University, Nanchong, Sichuan, China.
6. Information Science Young Scientist Forum (Oct 20, 2018), The sparse regression model based on LAZE prior -- The application in prostate cancer detection, Jinan University, Guangzhou, Guangdong, China.
7. ICSA 2018 (Jul 2 - 5, 2018), The sparse regression model based on LAZE prior -- The application in prostate cancer detection, Qingdao, Shandong, China.

1. 2024.01 - 2024.05, Institute of Applied Mathematics, The University of British Columbia, hosted by Professor Michael P. Friedlander.
2. 2023.03, The Institute of Statistical Mathematics, hosted by Professor Bruno F. Lourenço.