Feng Liu

Assistant Professor

School: School of Systems and Enterprises

Building: North Building

Room: 206

Phone: (201) 216-8009

Email: fliu22@stevens.edu

Website

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 Patrick Purdon's lab at MGH 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 Intern, CSX Transportation, Jacksonville, FL, 2015-2016

Institutional Service
  • Faculty Committee, First General College Student Living and Learning Community Member
  • EM/ISE Academic Committee Member
Professional Service
  • the 16th International Conference on Brain Informatics Chair of Organization Committee
  • Machine Learning with Applications Associate Editor
  • The 17th INFORMS Workshop on Data Mining and Decision Analytics Co-Chair
  • Frontiers in Physics Special Issue Guest Editor
  • Frontiers in Neuroscience - Brain Imaging Method Topic Associate Editor
  • Energies Guest Editor
  • NIH Panel Reviewer
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
  • SfN – Society for Neuroscience (C-025158 for your endorsement) Member
  • MICCAI Member
  • INFORMS – Institute for Operations Research and the Management Sciences Member
  • IEEE – Institute of Electrical and Electronics Engineers Member
Selected Publications
Conference Proceeding
  1. Wan, G.; DeSimone, M.; Liu, F.; Nguyen, N.; Leung, B.; Choi, M.; Bruce, A.; Stagner, A.; Lian, C.; Russell-Goldman, E.; others (2022). 649 CNN-based histopathology image analysis for early-stage melanoma recurrence. Journal of Investigative Dermatology (8 ed., vol. 142, pp. S112). Elsevier.
  2. Jiao, M.; Liu, F.; Asan, O.; Nilchiani, R.; Ju, X.; Xiang, J. (2022). Brain Source Reconstruction Solution Quality Assessment with Spatial Graph Frequency Features. 15th International Conference on Brain Informatics. Springer.
  3. Guo, Y.; Jiao, M.; Wan, G.; Wang, S.; Xiang, J.; Liu, F. (2022). EEG Source Imaging using GANs with Deep Image Prior. 44th Annual International Conference of the IEEE EMBC.
  4. 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.
  5. 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.
  6. Ju, X.; Chen, V.; Rosenberger, J.; Liu, F. (2019). Knot Optimization for Multivariate Adaptive Regression Splines. IISE Annual Conference 2019.
  7. 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).
  8. 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).
  9. Liu, F.; Wang, Z. (2017). A novel adaptive genetic algorithm for wine farm layout optimization. 2017 North American Power Symposium (NAPS) (pp. 1--6).
  10. 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. Thirty-First AAAI Conference on Artificial Intelligence.
  11. 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).
  12. 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).
  13. 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).
  14. 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. Wang, Q.; Liu, F.; Wan, G.; Chen, Y. (2022). Inference of Brain States under Anesthesia with Meta Learning Based Deep Learning Models. IEEE Transactions on Neural Systems and Rehabilitation Engineering. IEEE.
  2. Jiao, M.; Wan, G.; Guo, Y.; Wang, D.; Liu, H.; Xiang, J.; Liu, F. (2022). A Graph Fourier Transform Based Bidirectional Long Short-Term Memory Neural Network for Electrophysiological Source Imaging. Frontiers in Neuroscience (vol. 16). Frontiers Media SA.
  3. Bai, F.; Ju, X.; Wang, S.; Zhou, W.; Liu, F. (2022). Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search reinforcement learning. Energy Conversion and Management (vol. 252, pp. 115047). Pergamon.
  4. Chen, V. C.; Zhou, Y.; Fallahi, A.; Viswanatha, A.; Yang, J.; Liu, F.; Ohol, N. S.; Ghasemi, Y.; Farahani, A. A.; Rosenberger, J. M.; others (2021). An Optimization Framework to Study the Balance Between Expected Fatalities due to COVID-19 and the Reopening of US Communities. IEEE Transactions on Automation Science and Engineering. IEEE.
  5. Jiao, M.; Wang, D.; Yang, Y.; Liu, F. (2021). More intelligent and robust estimation of battery state-of-charge with an improved regularized extreme learning machine. Engineering Applications of Artificial Intelligence (vol. 104, pp. 104407). Elsevier.
  6. Liang, C.; Ge, M.; Xu, J.; Liu, Z.; Liu, F. (2021). Secure and privacy-preserving formation control for networked marine surface vehicles with sampled-data interactions. IEEE Transactions on Vehicular Technology (2 ed., vol. 71, pp. 1307--1318). IEEE.
  7. 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 (vol. 171, pp. 114565). Pergamon.
  8. Ju, X.; Rosenberger, J. M.; Chen, V. C.; Liu, F. (2021). Global optimization on non-convex two-way interaction truncated linear multivariate adaptive regression splines using mixed integer quadratic programming. Information Sciences (vol. 597, pp. 38--52). Elsevier.
  9. Xu, J.; Ge, M.; Ling, G.; Liu, F.; Park, J. H. (2021). Hierarchical predefined-time control of teleoperation systems with state and communication constraints. International Journal of Robust and Nonlinear Control (18 ed., vol. 31, pp. 9652--9675). Wiley Online Library.
  10. Liang, C.; Ge, M.; Liu, Z.; Ling, G.; Liu, F. (2021). Predefined-time formation tracking control of networked marine surface vehicles. Control Engineering Practice (vol. 107, pp. 104682). Elsevier.
  11. 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 (pp. 26). Frontiers.
  12. Yang, J.; Liu, F.; Wang, B.; Chen, C.; Church, T.; Dukes, L.; Smith, J. O. (2021). Blood Pressure States Transition Inference Based on Multi-State Markov Model. IEEE Journal of Biomedical and Health Informatics (1 ed., vol. 25, pp. 237--246). IEEE.
  13. Liu, F.; Wang, L.; Lou, Y.; Li, R.; Purdon, P. L. (2021). Probabilistic Structure Learning for EEG/MEG Source Imaging With Hierarchical Graph Priors. IEEE Transactions on Medical Imaging (1 ed., vol. 40, pp. 321--334). IEEE.
  14. 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 (4 ed., vol. 12, pp. 3448--3457). IEEE.
  15. Wang, B.; Wong, C. M.; Kang, Z.; Liu, F.; Shui, C.; Wan, F.; Chen, C. P. (2020). Common spatial pattern reformulated for regularizations in brain-computer interfaces. IEEE Transactions on Cybernetics. IEEE.
  16. Chen, Y.; Liu, F.; Rosenberger, J. M.; Chen, V. C.; Kulvanitchaiyanunt, A.; Zhou, Y. (2020). Efficient approximate dynamic programming based on design and analysis of computer experiments for infinite-horizon optimization. Computers \& Operations Research (vol. 124, pp. 105032). Elsevier.
  17. Chen, C.; Liu, F.; Wu, L.; Yan, H.; Gui, W.; Stanley, H. E. (2020). Tracking performance limitations of networked control systems with repeated zeros and poles. IEEE Transactions on Automatic Control (4 ed., vol. 66, pp. 1902--1909). IEEE.
  18. 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.
  19. Lai, Q.; Kuate, P. D.; Liu, F.; Iu, H. H. (2019). An extremely simple chaotic system with infinitely many coexisting attractors. IEEE Transactions on Circuits and Systems II: Express Briefs (6 ed., vol. 67, pp. 1129--1133). IEEE.
  20. 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.
  21. 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).
  22. Zhang, S.; Wang, D.; Liu, F. (2018). Separate block based parameter estimation method for Hammerstein systems. Royal Society Open Science (vol. 5, pp. 172194).
  23. 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.
  24. 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.
  25. Liu, F.; Wang, Z. (2014). Offshore wind farm layout optimization using adapted genetic algorithm: A different perspective. arXiv preprint arXiv:1403.7178.
  26. 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.
  27. 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.
Technical Report
  1. Jiao, M.; Liu, F. (2022). Extended Brain Sources Estimation via Unrolled Optimization Neural Network. bioRxiv. Cold Spring Harbor Laboratory.
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
Spring 2022, EM 623, Data Science and Knowledge Discovery
Fall 2022, EM 623, Data Science and Knowledge Discovery