Guanghui Lan is affiliated with the Georgia Institute of Technology in the United States. The scientist's research primarily spans fields such as Computer Science and Engineering, with a focus on subfields including Artificial Intelligence, Computational Mechanics, Management Science and Operations Research, Numerical Analysis, and Computational Theory and Mathematics.
The main topics of their work include Stochastic Gradient Optimization Techniques, Sparse and Compressive Sensing Techniques, Advanced Optimization Algorithms Research, Reinforcement Learning in Robotics, Optimization and Variational Analysis, Risk and Portfolio Optimization, and Machine Learning and Algorithms.
Guanghui Lan has authored numerous papers, contributing especially to venues like arXiv (Cornell University), Mathematical Programming, SIAM Journal on Optimization, Computational Optimization and Applications, and SIAM Journal on Control and Optimization. Notable recent papers include:
The scientist has collaborated frequently with a core group of coauthors, including Tianjiao Li, Yan Li, Georgios Kotsalis, Digvijay Boob, and Yuyuan Ouyang.
In addition to journal publications, Guanghui Lan has contributed to book literature. A notable book titled First-order and Stochastic Optimization Methods for Machine Learning was published by Springer International Publishing in 2020.
A. Nemirovski;A. Juditsky;G. Lan;A. Shapiro
Anatoli Juditsky;Guanghui Lan;Arkadii S. Nemirovski;Alexander Shapiro
Saeed Ghadimi;Guanghui Lan
Saeed Ghadimi;Guanghui Lan
Guanghui Lan
Saeed Ghadimi;Guanghui Lan;Hongchao Zhang
Saeed Ghadimi;Guanghui Lan
Guanghui Lan
Yunmei Chen;Guanghui Lan;Yuyuan Ouyang
Yuyuan Ouyang;Yunmei Chen;Guanghui Lan;Eduardo Pasiliao
Guanghui Lan;Soomin Lee;Yi Zhou
Guanghui Lan;Yi Zhou
Guanghui Lan;Gail W. DePuy;Gary E. Whitehouse
Guanghui Lan;Zhaosong Lu;Renato D. C. Monteiro
Saeed Ghadimi;Guanghui Lan
Guanghui Lan;Arkadi Nemirovski;Alexander Shapiro
Guanghui Lan;Yi Zhou
Guanghui Lan
Guanghui Lan;Renato D. Monteiro
Cong D. Dang;Guanghui Lan
Yunmei Chen;Guanghui Lan;Yuyuan Ouyang
If you think any of the details on this page are incorrect, let us know.
For students interested in Mathematics, exploring related online degrees can open diverse career opportunities. Many students consider advanced business education, such as an MBA, to complement their analytical skills. If you’re looking for flexibility, can you transfer MBA programs offers valuable insights into credits transferability, making it easier to continue your education without losing previous coursework.
Data analytics is another thriving field that heavily relies on mathematical foundations. Pursuing one of the data analysis programs can sharpen your skills in interpreting complex data sets, preparing you for roles in technology, finance, healthcare, and more.
For those worried about admission challenges, several schools offer easiest MBA specialization paths, allowing students to smoothly transition into graduate studies. Additionally, identifying the easiest MBA programs can help reduce barriers to entry while providing valuable credentials.
Understanding these options allows Mathematics students to diversify their qualifications and enhance career prospects through flexible, accessible online programs.
Nanyang Technological University
Nature Research Centre
Delft University of Technology
University of South Carolina
Columbia University
Chinese Academy of Sciences
University of New Orleans
Sungkyunkwan University
Miguel Hernandez University
University of Wisconsin–Madison
Bielefeld University
Ludwig-Maximilians-Universität München
Renmin University of China
Beijing University of Chemical Technology
Beth Israel Deaconess Medical Center
Université Paris Cité
