Research

Dr. Lan’s research interests lie in theory, algorithms and applications of stochastic optimization and nonlinear programming.  Most of his current research concerns the design of efficient algorithms with strong theoretical performance guarantees and superior practical performance for solving challenging optimization problems. He is actively pursuing the applications of stochastic and nonlinear optimization, machine learning, and reinforcement learning in healthcare and sustainability areas.

RESEARCH GRANTS

  • National Science Foundation (CMMI-1000347), “Theory and Applications of Stochastic First-order Methods for Large-Scale Stochastic Convex Optimization”,  May 2010 – April 2014.
  • Office of Naval Research (ONR N00014-13-1-0036), “Dynamic and Adaptive Sensor Operations Under Uncertainty”,  Jan. 2013 – Dec. 2015. (with Cole Smith).
  • National Science Foundation (CMMI-1254446), “CAREER: Reduced-order Methods for Big-Data Challenges in Nonlinear and Stochastic Optimization”,  Jan 2013 – Dec. 2017.
  • National Science Foundation (DMS-1319050), “Accelerated Algorithms for a Class of Saddle Point problems and Variational Inequalities”, Sep. 2013 – Aug. 2016. (with Yunmei Chen).
  • National Science Foundation (CMMI-1537414), “Gradient Sliding Schemes for Large-scale Optimization and Data Analysis”, Oct. 2015 – Sep. 2018.
  • SF Express, “Data-driven Courier Scheduling and Management for Express Services”, Aug. 2017-Dec. 2018.
  • Amy Research Office (W911NF-18-1-0223), “Optimization for Distributed Machine Learning”, June 2018-May 2020.
  • National Science Foundation (CISE-1909298), “CIF: Small: Collaborative Research: Acceleration Algorithms for Large-scale Nonconvex Optimization”, Oct. 2019-Sep. 2022.
  • National Science Foundation / US Department of Agriculture (NIFA 2020-67021-31526), “CPS: Collaborative Research: Robust and Intelligent Optimization of Controlled-environment Agriculture Systems for Food Productivity and Nutritional Security”, March 2020 – Feb. 2023.
  • National Science Foundation (DMS 1953199), “DMS: Collaborative Research: Algorithms for Optimal Adaptive Enrichment Design in Randomized Trials”, June 2020 – May 2023.

Former/Current Group Members (including students, Postdocs, and visitors involved in Dr. Lan’s research, not necessarily having him as an official advisor)

  • Andew Romich, obtained Ph.D. degree in Summer 2013, Sandia National Lab.
  • Yuyuan Ouyang, obtained Ph.D. degree in Summer 2013, School of Mathematical and Statistical Sciences at Clemson University.
  • Saeed Ghadimi, obtained Ph.D. degree in Summer 2014, Department of Management Sciences at the University of Waterloo.
  • Cong D. Dang, obtained Ph.D. degree in Spring 2015, School of Applied Mathematics and Informatics at Hanoi University of Science and Technology, Vietnam.
  • Qi Deng, obtained Ph.D. degree in Summer 2015, Interdisciplinary Sciences at Shanghai University of Finance and Economics.
  • Wei Zhang, obtained Ph.D. degree in Summer 2016, Google Research.
  • Soomin Lee, post-doc from 2016-2017, Yahoo Labs.
  • Yi Zhou, obtained Ph.D. degree in Summer 2018, IBM Almaden Research Center.
  • Hanxi Bao, obtained Ph.D. degree in Summer 2019, NBC Universal Media LLC.
  • Zhize Li, visiting Ph.D. student from Tsinghua University in 2019.
  • Digvijay Boob, obtained Ph.D. degree in Summer 2020, Department of Engineering Management, Information, and Systems at Southern Methodist University.
  • Zhiqiang Zhou, obtained Ph.D. degree in Summer 2020, Damo Institute, Alibaba.
  • Georgios Kotsalis, obtained Ph.D. degree in Summer 2022, Amazon Core AI.
  • Yi (Starry) Cheng, obtained Ph.D. degree in Fall 2022, Argonne National Laboratory.
  • Zhe (Jimmy) Zhang, expect to graduate in Summer 2023, Department of Industrial Engineering at Purdue University.
  • Yan Li, current Ph.D. student.
  • Tianjiao Li, current Ph.D. student.
  • Caleb Ju, current Ph.D. student.