Zhichao Lu (陆智超)

Name: Zhichao Lu (陆智超)
Position: Postdoctoral Fellow
Institution: Department of Computer Science and Engineering, SUSTech
Research Area: Automated machine learning, 

Multi-objective optimization, 

Neural architecture search.

Contact: luzc@sustech.edu.cn
www.luzhichao.com

 

Zhichao Lu is currently a post-doctoral research fellow with the Dept. of Computer Science and Engineering at the Southern University of Science and Technology, Shenzhen, China. He received the B.Sc. and Ph.D degrees in Electrical and Computer Engineering from Michigan State University, USA in 2014 and 2020. He was a member in the COIN Laboratory, under the supervision of Prof.Kalyanmoy Deb. His research interests are in the field of evolutionary machine learning, notably machine learning assisted evolutionary algorithms, automated machine learning, and in particular evolutionary neural architecture search.

Eduction Experiences

  • 2014 – 2020: Ph.D, Michigan State University, MSU.
  • 2009 – 2013: B.Sc, Michigan State University, MSU.

Work Experiences

  • 2020/10 – present: Post-doc Research Fellow, Southern University of Science and Technology, China.
  • 2018/05 – 2018/08: Research Scientist (Intern), Siemens PLM Software, USA.
  • 2013/12 – 2014/08: Research Assistant, Michigan State University, USA.

Honors and Awards

  • 2021: SUSTech the 8th Presidential Outstanding Postdoctoral Award, China. 
  • 2020: College of Engineering GOF Fellowship, MSU, USA. 
  • 2019: GECCO Best Paper Award (Evolutionary Machine Learning Track), ACM, USA.
  • 2019: Graduate School Dissertation Completion Fellowship, MSU, USA. 
  • 2016: GECCO Best Paper Award Runner-up (Real-World Application Track), ACM, USA.

Publications

   Journal Papers

  1. Zhichao Lu, Gautam Sreekumar, Erik Goodman, Wolfgang Banzhaf, Kalyanmoy Deb, and Vishnu Naresh Boddeti, Neural Architecture Transfer, IEEE Transactions on Pattern Analysis and Machine Intelligence (TPAMI), 2021. (forthcoming)
  2. Shuwei Zhu, Lihong Xu, Erik Goodman, and Zhichao Lu, A New Many-Objective Evolutionary Algorithm based on Generalized Pareto Dominance, IEEE Transactions on Cybernetics (TCYB), 2021. (forthcoming)
  3. Zhichao Lu, Ian Whalen, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, and Vishnu Naresh Boddeti, Multi-Objective Evolutionary Design of Deep Convolutional Neural Networks for Image Classification, IEEE Transactions on Evolutionary Computation (TEVC), 2020.
  4. Ankur Sinha, Zhichao Lu, Kalyanmoy Deb, and Pekka Malo, Bilevel Optimization based on Iterative Approximation of Multiple Mappings, Journal of Heuristics, 2020.
  5. Zhichao Lu, Kalyanmoy Deb, and Ankur Sinha, Uncertainty Handling in Bilevel Optimization for Robust and Reliable Solutions, International Journal of Uncertainty, Fuzziness and Knowledge-Based Systems, 2018.

   Conference Papers

  1. Shengran Hu, Ran Cheng, Cheng He, and Zhichao Lu, Multi-objective Neural Architecture Search with Almost No Training, Evolutionary Multi-Criterion Optimization (EMO), 2021.
  2. Shuwei Zhu, Lihong Xu, Erik Goodman, Kalyanmod Deb, and Zhichao Lu, The (M-1)+1 framework of relaxed Pareto dominance for evolutionary many-objective optimization, Evolutionary Multi-Criterion Optimization (EMO), 2021.
  3. Zhichao Lu, Kalyanmoy Deb, Erik Goodman, Wolfgang Banzhaf, and Vishnu Naresh Boddeti, NSGANetV2: Evolutionary Multi-Objective Surrogate-Assisted Neural Architecture Search, European Conference on Computer Vision (ECCV), 2020. (Oral)
  4. Zhichao Lu, Ian Whalen, Vishnu Naresh Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf, NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm (Extended Abstract), International Joint Conference on Artificial Intelligence (IJCAI), 2020.
  5. Zhichao Lu, Kalyanmoy Deb, and Vishnu Naresh Boddeti, MUXConv: Information Multiplexing in Convolutional Neural Networks, IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 2020.
  6. Zhichao Lu, Ian Whalen, Vishnu Naresh Boddeti, Yashesh Dhebar, Kalyanmoy Deb, Erik Goodman, and Wolfgang Banzhaf, NSGA-Net: Neural Architecture Search using Multi-Objective Genetic Algorithm, Genetic and Evolutionary Computation Conference (GECCO), 2019. (Best Paper Award)
  7. Zhichao Lu, Kalyanmoy Deb, and Hemant Singh, Balancing Survival of Feasible and Infeasible Solutions in Constraint Evolutionary Optimization Algorithms, IEEE Congress on Evolutionary Computation (CEC), 2018.
  8. Zhichao Lu, Kalyanmoy Deb, Erik Goodman, and John Wassick, Solving a Supply-chain Management Problem using a Bilevel Approach, Genetic and Evolutionary Computation Conference (GECCO), 2017.
  9. Brad Barnhart, Zhichao Lu, Moriah Bostian, Ankur Sinha, Kalyanmoy Deb, Lyubov A. kurkalova, Manoj Jha, and Gerald W. Whittaker, Handling Practicalities in Agricultural Policy Optimization for Water Quality Improvements, Genetic and Evolutionary Computation Conference (GECCO), 2017.
  10. Zhichao Lu, Kalyanmoy Deb, and Ankur Sinha, Finding Reliable Solutions in Bilevel Optimization Problems under Uncertainties, Genetic and Evolutionary Computation Conference (GECCO), 2016. (Best Paper Award Runner-up)
  11. Zhichao Lu, Kalyanmoy Deb, and Ankur Sinha, Handling Decision Variable Uncertainty in Bilevel Optimization Problems, IEEE Congress on Evolutionary Computation (CEC), 2015.
  12. Kalyanmoy Deb, Zhichao Lu, Chris B. McKesson Cherie C. Trumbach and Larry DeCan, Towards optimal ship design and valuable knowledge discovery under uncertain conditions, IEEE Congress on Evolutionary Computation (CEC), 2015.