Biography

Meixia Lin is an Assistant Professor in the Engineering Systems and Design Pillar at the Singapore University of Technology and Design. She received her Ph.D. in Optimization from National University of Singapore in 2020 under the supervision of Professors Kim-Chuan Toh and Chao Zhou. Prior to joining SUTD in summer 2022, she held Research Fellow positions in Department of Mathematics and Institute of Operations Research and Analytics at National University of Singapore, hosted by Professors Kim-Chuan Toh and Subhroshekhar Ghosh. She obtained her B.S. in Information and Computing Science from Nanjing University. Her research interests involve algorithm design for large-scale optimization problems in data science, signal processing and stochastic optimization.

Education

  • Ph.D. in Optimization, National University of Singapore, Singapore (2016 – 2020)
  • B.S. in Information and Computing Science, Nanjing University, China (2012 – 2016)

Research Interests

  • Algorithm design for large-scale optimization problems in data science
  • Signal processing
  • Stochastic optimization

Her research interests broadly lie in developing models and algorithms in data science with emphasis on large-scale application problems arising in machine learning, statistical estimations and operations research. She is also interested in both theoretical analysis and algorithmic design in signal analysis and stochastic optimization.

Selected Publications

  • S. Ghosh, M. Lin, and D. Sun, Signal analysis via the stochastic geometry of spectrogram level sets, IEEE Transactions on Signal Processing, 70 (2022), pp. 1104–1117.
  • R. Bardenet, S. Ghosh, and M. Lin, Determinantal point processes based on orthogonal polynomials for sampling minibatches in SGD, in Conference on Neural Information Processing Systems (NeurIPS), 2021. (Spotlight paper, <3% acceptance rate).
  • M. Lin, D.F. Sun, and K.-C. Toh, An augmented Lagrangian method with constraint generation for shape-constrained convex regression problems, Mathematical Programming Computation, 2021, pp. 1–48.
  • M. Lin, Y.-J. Liu, D.F. Sun, and K.-C. Toh, Efficient sparse semismooth Newton methods for the clustered lasso problem, SIAM Journal on Optimization, 29 (2019), pp. 2026–2052.