Feng Liu

Assistant Professor

School: School of Systems and Enterprises

Email: fliu22@stevens.edu

Website

Education
  • Other (2020) Harvard Medical School (Brain Imaging/Computational Neuroscience)
  • PhD (2018) University of Texas at Arlington (Industrial Engineering)
  • MS (2013) Huazhong University of Science and Technology (Control Science and Engineering)
  • BE (2010) Qingdao University (Automatic Control)
Research

Research interest: Machine Learning, Manifold Learning, Brain Imaging, Computational Neuroscience, Health Informatics.

My research involves the areas of machine learning, optimization, signal processing, and control theory with applications to the healthcare and the renewable energy field. I am particularly interested in using machine learning and data analytics to understand the brain mechanism and provide solutions for brain disorders.

General Information

Dr. Feng Liu is an Assistant Professor at the School of Systems and Enterprises at Stevens Institute of Technology. Dr. Liu was a Postdoctoral Research Fellow at Harvard Medical School from 2018 to 2020. He was a research affiliate at Picower Institute for Learning and Memory at MIT and Martinos Center for Biomedical Imaging at MGH from 2018 to 2020. Dr. Liu received his Ph.D. degree from the University of Texas at Arlington in Industrial Engineering in 2018. His research interests include brain imaging, inverse problem, health informatics, machine learning, and dynamic system. Prof. Liu is the winner of the Best Paper Award at 11th International Conference of Brain Informatics in 2018, and the winner of the Best Paper Award of INFORMS Data Analytics Society in 2019.

Google Scholar: https://scholar.google.com/citations?user=HVZdbX0AAAAJ&hl=en

Experience

Data Science/Operations Research Intern, CSX Transportation, Jacksonville, FL, 2015-2016

Institutional Service
  • EM/ISE Academic Committee Member
Professional Service
  • Frontiers in Physics Special Issue Guest Editor
  • Frontiers in Neuroscience Topic Associate Editor
  • Energies Guest Editor
Appointments

Research Affiliate, Picower Institute for Learning and Memory, MIT, 06/2018-08/2020
Postdoc Fellow, MGH/Harvard Medical School, 06/2018-08/2020
Editor team, OR Tomorrow, 2018-2020

Honors and Awards

Best Paper Award of INFORMS Data Science, INFORMS, 2019
Best Paper Award, 11th International Conference of Brain Informatics, 2018
Travel Awards, AAAI, UC Berkeley Neuroscience Data Analytics Summer School, ICERM at Brown Univerisity, IBBM at SCI U of Utah, IPAM at UCLA etc.
Dean Fellowship, UT Arlington, 2015
Graduate Studnet Scientific Achievement Award, HUST, 2012
National Scholarship, Qingdao University, 2008

Professional Societies
  • INFORMS – Institute for Operations Research and the Management Sciences Member
  • IEEE – Institute of Electrical and Electronics Engineers Member
Selected Publications
Conference Proceeding
  1. Chao, J. Y.; Whitaker, E. E.; Yozawitz, E. G.; Legatt, A. D.; Liu, F.; Walline, M.; Holmes, G.; Purdon, P. L.; Shinnar, S.; Williams, R. K. (2019). Electroencephalographic Assessment of Sedation after Infant Spinal Anesthesia: A Multi-center Pilot Study.
  2. Ju, X.; Chen, V.; Rosenberger, J.; Liu, F. (2019). Knot Optimization for Multivariate Adaptive Regression Splines. IISE Annual Conference 2019.
  3. Liu, F.; Stephen, E.; Prerau, M.; Purdon, P. (2019). Sparse Multi-task Inverse Covariance Estimation for Connectivity Analysis in EEG Source Space. 9th International IEEE EMBS Conference on Neural Engineering.
  4. Hosseini, R.; Liu, F.; Wang, S. (2018). Construction of Sparse Weighted Directed Network (SWDN) from the Multivariate Time-Series. International Conference on Brain Informatics (pp. 270--281).
  5. Liu, F.; Wang, S.; Qin, J.; Lou, Y.; Rosenberger, J. (2018). Estimating Latent Brain Sources with Low-Rank Representation and Graph Regularization. International Conference on Brain Informatics (pp. 304--316).
  6. Liu, F.; Wang, Z. (2017). A novel adaptive genetic algorithm for wine farm layout optimization. 2017 North American Power Symposium (NAPS) (pp. 1--6).
  7. Qin, J.; Liu, F.; Wang, S.; Rosenberger, J. (2017). EEG source imaging based on spatial and temporal graph structures. 2017 Seventh International Conference on Image Processing Theory, Tools and Applications (IPTA) (pp. 1--6).
  8. Liu, F.; Qin, J.; Wang, S.; Rosenberger, J.; Su, J. (2017). Supervised EEG Source Imaging with Graph Regularization in Transformed Domain. International Conference on Brain Informatics (pp. 59--71).
  9. Liu, F.; Wang, S.; Rosenberger, J.; Su, J.; Liu, H. (2017). A sparse dictionary learning framework to discover discriminative source activations in EEG brain mapping. Proceedings of the AAAI Conference on Artificial Intelligence (1 ed., vol. 31).
  10. Liu, F.; Xiang, W.; Wang, S.; Lega, B. (2016). Prediction of seizure spread network via sparse representations of overcomplete dictionaries. International Conference on Brain Informatics (pp. 262--273).
  11. Liu, F.; Wang, Z. (2013). Electric load forecasting using parallel RBF neural network. 2013 IEEE Global Conference on Signal and Information Processing (pp. 531--534).
Journal Article
  1. He, M.; Liu, F.; Nummenmaa, A.; H\"am\"al\"ainen, Matti; Dickerson, B. C.; Purdon, P. L. (2021). Age-Related EEG Power Reductions Cannot Be Explained by Changes of the Conductivity Distribution in the Head Due to Brain Atrophy. Frontiers in Aging Neuroscience (vol. 13, pp. 26). Frontiers.
  2. Yang, J.; Liu, F.; Wang, B.; Chen, C.; Church, T.; Dukes, L.; Smith, J. (2021). Blood Pressure States Transition Inference Based on Multi-state Markov Model. IEEE Journal of Biomedical and Health Informatics. IEEE.
  3. Ju, X.; Chen, V. C.; Rosenberger, J. M.; Liu, F. (2021). Fast knot optimization for multivariate adaptive regression splines using hill climbing methods. Expert Systems with Applications (pp. 114565). Pergamon.
  4. Ding, L.; Nie, S.; Li, W.; Hu, P.; Liu, F. (2021). Multiple Line Outage Detection in Power Systems by Sparse Recovery Using Transient Data. IEEE Transactions on Smart Grid. IEEE.
  5. Liu, F.; Wang, L.; Lou, Y.; Li, R.; Purdon, P. (2021). Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Priors. IEEE Transactions on Medical Imaging. IEEE.
  6. Wang, B.; Wong, C. M.; Kang, Z.; Liu, F.; Shui, C.; Wan, F.; Chen, C. P. (2021). Common Spatial Pattern Reformulated for Regularizations in Brain-Computer Interfaces. IEEE Transactions on Cybernetics. IEEE.
  7. Liang, C.; Ge, M.; Liu, Z.; Ling, G.; Liu, F. (2020). Predefined-Time Formation Tracking Control of Networked Marine Surface Vehicles. Control Engineering Practice. Elsevier.
  8. Chen, Y.; Liu, F.; Rosenberger, J.; Chen, V.; Kulvanitchaiyanuntf, A.; Zhou, Y. (2020). Efficient Approximate Dynamic Programming Based on Design and Analysis of Computer Experiments for Infinite-Horizon Optimization. Computers & Operations Research.
  9. Liu, F.; Ju, X.; Wang, N.; Wang, L.; Lee, W. (2020). Wind farm macro-siting optimization with insightful bi-criteria identification and relocation mechanism in genetic algorithm. Energy Conversion and Management (vol. 217, pp. 112964). Elsevier.
  10. Chen, C.; Liu, F.; Wu, L.; Yan, H.; Gui, W.; Stanley, E. (2020). Tracking Performance Limitations of Networked Control Systems with Repeated Zeros and Poles. IEEE Transactions on Automatic Control.
  11. Lai, Q.; Didier Kamdem Kuate, Paul; Liu, F.; Lu, H. H. (2019). An Extremely Simple Chaotic System with Infinitely Many Coexisting Attractors. IEEE Transactions on Circuits and Systems II: Express Briefs.
  12. Ju, X.; Liu, F.; Wang, L.; Lee, W. (2019). Wind farm layout optimization based on support vector regression guided genetic algorithm with consideration of participation among landowners. Energy Conversion and Management (vol. 196, pp. 1267--1281). Elsevier.
  13. Lai, Q.; Norouzi, B.; Liu, F. (2018). Dynamic analysis, circuit realization, control design and image encryption application of an extended L\"u system with coexisting attractors. Chaos, Solitons \& Fractals (vol. 114, pp. 230--245).
  14. Zhang, S.; Wang, D.; Liu, F. (2018). Separate block based parameter estimation method for Hammerstein systems. Royal Society Open Science (vol. 5, pp. 172194).
  15. Han, Z.; Wang, D.; Liu, F.; Zhao, Z. (2017). Multi-AGV path planning with double-path constraints by using an improved genetic algorithm. PloS one (7 ed., vol. 12, pp. e0181747). Public Library of Science San Francisco, CA USA.
  16. Liu, F.; Rosenberger, J.; Lou, Y.; Hosseini, R.; Su, J.; Wang, S. (2017). Graph regularized EEG source imaging with in-class consistency and out-class discrimination. IEEE Transactions on Big Data (4 ed., vol. 3, pp. 378--391). IEEE.
  17. Liu, F.; Guan, Z. (2014). Multistability, multiperiodicity, multichaos: in a unified framework. arXiv preprint arXiv:1403.1657.
  18. Li, J.; Liu, F.; Guan, Z.; Li, T. (2013). A new chaotic Hopfield neural network and its synthesis via parameter switchings. Neurocomputing (vol. 117, pp. 33--39). Elsevier.
  19. Guan, Z.; Liu, F.; Li, J.; Wang, Y. (2012). Chaotification of complex networks with impulsive control. Chaos: An Interdisciplinary Journal of Nonlinear Science (2 ed., vol. 22, pp. 023137). AIP.
Courses

Fall 2020, EM 612 Project Management of Complex Systems
Spring 2021, EM 600 Engineering Economics and Cost Analysis
Summer 2021, EM 612 Project Management of Complex Systems
Fall 2021, EM 612 Project Management of Complex Systems