Li Xudong won the Young Researcher Prize at the 6th International Conference on Continuous Optimization


Associate Professor Li Xudong from the School of Data Science won the Best Paper Prize for Young Researchers in Continuous Optimization at the Sixth International Conference on Continuous Optimization (ICCOPT 2019) which was held during August 3-8 in Berlin.

Dr. Li(L) and the conference organizer Prof. Michael Hintermüller(R)

Dr. Li gave a speech entitled Exploiting Second Order Sparsity in Big Data Optimization

As a flagship conference of the Mathematical Optimization Society held every three years, ICCOPT covers all theoretical, computational, and practical aspects of continuous optimization. Over 1000 participants from about 70 countries all over the world participated in the conference this year.

Dr. Li (M) with his co-authors Prof. Kim-Chuan Toh (L) from the National University of Singapore and Prof. Defeng Sun (R) from the Hong Kong Polytechnic University

Regarded as one of the highest honors for young researchers in the field of continuous optimization, competition for the Young Researchers Prize has always been highly keen. This year, four papers were selected into the final from over 40 qualified submissions. Dr. Li finally stood out for his paper A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems.

Award Certificate

Paper reference: “X. D. Li, D. F. Sun, K.-C. Toh, A highly efficient semismooth Newton augmented Lagrangian method for solving Lasso problems, SIAM Journal on Optimization, 28 (2018), pp., 433-458.’’

Short bio for Dr. Li:

Dr. Li is a tenure-track associate professor at the School of Data Science, Fudan University and the Shanghai Center for Mathematical Sciences. He obtained his Ph.D. degree in Optimization from the National University of Singapore and was a research fellow in the Department of Mathematics at the National University of Singapore. Before joining FDU, he was a postdoctoral scholar in the Department of Operations Research and Financial Engineering at Princeton University. He received his Bachelor’s degree in Mathematics from University of Science and Technology of China. His research focuses on designing efficient algorithms for solving large-scale optimization problems arising in data science and machine learning applications. Particularly, he has published a number of papers on large-scale linear and quadratic programming, semidefinite programming, and high-dimensional statistical optimization problems. The software programs, SuiteLasso and QSDPNAL, developed by him and his co-authors, are well-recognized internationally. He is currently serving as an associate editor for Mathematical Programming Computation.