Feng Liu (fliu22)

Feng Liu

Assistant Professor

School of Systems and Enterprises

Systems & Software

Research

Research interest: Machine Learning, Brain Imaging, Computational Neuroscience, AI for healthcare.

My research involves the areas of machine learning, optimization, signal processing, and control theory with applications to the healthcare and 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.

Currently, our group has two projects 1) brain source imaging utilizing graph structures in the data. 2) deep learning framework for melanoma recurrence prediction based on H&E whole slide images.

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, MICCAI, 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

Journal publication:

AI for Medical Imaging and Healthcare:
Meng Jiao#, Guihong Wan, Yaxin Guo#, Dongqing Wang, Hang Liu, Jing Xiang, Feng Liu*, A Graph Fourier Transform Based Bidirectional LSTM Neural Network for EEG Source Imaging, Frontiers in Neuroscience, 2022 (Corresponding author)

Qihang Wang*, Feng Liu*, Guihong Wan, Ying Chen, Inference of Brain States under Anesthesia with Meta-Learning Based Deep Learning Models, IEEE Transactions on Neural Systems and Rehabilitation Engineering, 2021 (Equal contribution)

Feng Liu, Li Wang, Yifei Lou, Ren-Cang Li, Patrick Purdon, Probabilistic Structure Learning for EEG/MEG Source Imaging with Hierarchical Graph Prior, IEEE Transactions on Medical Imaging, 2021 (Best Paper Award for INFORMS Data Mining Society, 2019, IF: 10.1)

Alex He, Feng Liu, Patrick Purdon, Signal Attenuation Analysis of Brain Shrinkage with Real and Realistic Head Model, Frontiers in Aging Neuroscience, 2021 (IF: 4.5)

Boyu Wang, Chi Man Wong, Zhao Kang, Feng Liu, and Feng Wan, A New Formulation of Regularized Common Spatial Pattern for Brain-Computer Interfaces, 2020, IEEE Transactions on Cybernetics (IF: 11.5)

Feng Liu, Jay Rosenberger, Yifei Lou, Rahil Hosseini, Shouyi Wang, Jianzhong Su, Graph Regularized EEG Source Mapping with in-Class Consistency and out-Class Discrimination, IEEE Transactions on Big Data, Vol. 3 Issue:4, 2017, pages 378 – 391 (IF: 3.3)

Jingmei Yang, Feng Liu*, Boyu Wang, Chaoyang Chen, Jeff Smith, Blood Pressure State Transition Inference Based on Multi-state Markov Model, IEEE Journal of Biomedical and Health Informatics, 2021, (corresponding author, IF: 5.22)

Jingmei Yang, Xinglong Ju, Feng Liu*, Onur Asan, Timothy S. Church, Jeff Smith, Risk Prediction for Multiple Chronic Conditions among Working Population in the United States with Machine Learning Models, IEEE Open Journal of Engineering in Medicine and Biology, 2021 (corresponding author)

Victoria Chen*, Yuan Zhou, Alireza Fallahi, Amith Viswanatha, Jingmei Yang, Feng Liu*, Nilabh Ohol, Yasaman, Ghasemi, Ashkan Farahani, Jay Rosenberger, Jeffrey Guild, An Optimization Framework to Study the Balance Between Expected Fatalities due to COVID-19 and the Re-opening of US Communities, IEEE Transactions on Automation Science and Engineering, 2021 (co-corresponding author, IF: 5.0)

Energy System Optimization:
Fangyun Bai, Xinglong Ju, Shouyi Wang, Feng Liu*, Wind farm layout optimization using adaptive evolutionary algorithm with Monte Carlo Tree Search Reinforcement Learning, Energy Conversion and Management (Accepted, IF: 9.7, Corresponding author)

Feng Liu, Xinglong Ju, Li Wang, Ning Wang, Wei-Jen Lee, Wind farm macro-siting optimization with insightful bi-criteria identification and relocation mechanism in genetic algorithm. Energy Conversion and Management, (2020) (IF: 9.7)

Li Ding, Shihao Nie, Wenqu Li, Ping Hu, Feng Liu, Multiple Line Outage Detection in Power Systems by Sparse Recovery Using Transient Data, IEEE Transactions on Smart Grid, 2021(IF: 9.0)

Xinglong Ju, Feng Liu*, Wind Farm Layout Optimization using Self-Informed Genetic Algorithm with Information Guided Exploitation, Applied Energy 248 (2019): 429-445. (*corresponding author, IF: 9.7)

Xinglong Ju, Feng Liu*, Li Wang, Wei-Jen Lee, Wind Farm Layout Optimization based on Support Vector Regression Guided Genetic Algorithm with Consideration of Participation among Landowners, Energy Conversion and Management, 196 (2019): 1267-1281 (*corresponding author, IF: 9.7)

Meng Jiao, Dongqing Wang, Yan Yang, and Feng Liu. More intelligent and robust estimation of battery state-of-charge with an improved regularized extreme learning machine. Engineering Applications of Artificial Intelligence 104 (2021): 104407. (IF: 6.2)

Dynamical Programming, Statistical Modelling and Applications
Xinglong Ju, Victoria Chen, Jay Rosenberger, Feng Liu, Global optimization using mixed integer quadratic programming on non-convex two-way interaction truncated linear multivariate adaptive regression splines, Information Science, 2022 (IF: 5.0)

Ying Chen, Feng Liu*, Jay Rosenberger, Victoria Chen, Yuan Zhou, Efficient Approximate Dynamic Programming with Design and Analysis of Computer Experiments for Infinite-Horizon Optimization, Computers & Operations Research, 2020 (*corresponding author, IF:4.0)

Xinglong Ju, Victoria CP Chen, Jay M. Rosenberger, and Feng Liu. Fast knot optimization for multivariate adaptive regression splines using hill climbing methods. Expert Systems with Applications 171 (2021): 114565. (IF: 7.0)

Dongqing Wang, Zengliang Han, Feng Liu, Zhiyong Zhao, Multiple automated guided vehicle path planning with double-path constraints by using an improved genetic algorithm, PloS One, 2017, 12(7): e0181747.


Dynamic and Complex Systems
Chang-Duo Liang, Ming-Feng Ge, Jing-Zhe Xu, Zhi-Wei Liu, and Feng Liu, Secure and Privacy-Preserving Formation Control for Networked Marine Surface Vehicles with Sampled-Data Interactions, IEEE Transactions on Vehicular Technology, Accepted, 2022 (IF: 5.978)

Chaoyang Chen, Feng Liu, Huacheng Yan, Weihua Gui, H. Eugene Stanley, Tracking Performance Limitations of Networked Control Systems with Repeated Zeros and Poles, IEEE Transactions on Automatic Control, 2021 (IF:5.6)Chang-Duo Liang, Ming-Feng Ge, Zhi-Wei Liu, Guang Ling, and Feng Liu. Predefined-time formation tracking control of networked marine surface vehicles. Control Engineering Practice 107 (2021): 104682. (IF: 3.5)

Xu, Jing‐Zhe, Ming‐Feng Ge, Guang Ling, Feng Liu, and Ju H. Park. Hierarchical predefined‐time control of teleoperation systems with state and communication constraints. International Journal of Robust and Nonlinear Control (2021). (IF: 4.4, Featured article on the cover)

Qiang Lai, Kamdem Didier, Feng Liu and Herbert Ho-Ching Iu, An Extremely Simple Chaotic System with Infinitely Many Coexisting Attractors, IEEE Transactions on Circuits and Systems II: Express Briefs, 2019 (ESI highly cited paper, IF: 3.3)

Qiang Lai, Benyamin Norouzi, and Feng Liu. Dynamic Analysis, Circuit Realization, Control Design and Image Encryption Application of an Extended Lü System with Coexisting Attractors, Chaos, Solitons & Fractals 114 (2018): 230-245. (ESI highly cited paper, IF: 5.9)

Shuo Zhang, Dongqing Wang, and Feng Liu, Separate block-based parameter estimation method for Hammerstein systems, Royal Society Open Science 5.6 (2018): 172194.

Juan Li, Feng Liu, Zhihong Guan, Tao Li, A New Chaotic Hopfield Neural Networks and Its Synthesis via Parameter Switchings, Neurocomputing, 117, 2013, 33-39 (IF: 5.7)

Zhi-Hong Guan, Feng Liu, Li Juan, Yan-Wu Wang, Chaotification in Complex Network with Impulsive Control, Chaos, 22, 023137, (2012) (IF: 3.6)


Conference:

Feng Liu, Guihong Wan, Yevgeniy R. Semenov, and Patrick L. Purdon. "Extended electrophysiological source imaging with spatial graph filters." MICCAI, pp. 99-109. Springe4, 2022. (oral presentation, 3%)

Yaxin Guo#, Meng Jiao#, Guihong Wan, Jing Xiang, Shouyi Wang, Feng Liu*, IEEE EMBC 2022

Feng Liu, Emily P. Stephen, Michael J. Prerau, Patrick L. Purdon. Sparse Multi-task Inverse Covariance Estimation for Connectivity Analysis in EEG Source Space. In 2019 9th International IEEE/EMBS Conference on Neural Engineering (NER), pp. 299-302. IEEE, 2019

Feng Liu, Shouyi Wang, Jing Qin, Yifei Lou, Jay Rosenberger, Estimating Latent Brain Sources with Low-Rank Representation and Graph Regularization, 2018 International Conference on Brain Informatics, Springer LNCS, pp. 304-316 (Best Paper Award).

Rahil Hosseini, Feng Liu, Shouyi Wang, Construction of Sparse Weighted Directed Network (SWDN) from the Multivariate Time-series, 2018 International Conference on Brain Informatics, LNCS, pp. 270-281.


Xinglong Ju, Victoria C. P. Chen, Jay M. Rosenberger, Feng Liu, Knot Optimization for Multivariate Adaptive Regression Splines, IISE Annual Conference, Orlando, FL, May, 2019

Feng Liu, Jing Qin, Shouyi Wang, Jay Rosenberger, Jianzhong Su, Supervised EEG Source Imaging with Graph Regularization in Transformed Domain, 2017 International Conference on Brain Informatics, Springer LNCS, pp. 59-71.

Feng Liu, Shouyi Wang, Jay Rosenberger, Jianzhong Su, Hanli Liu, A Sparse Dictionary Learning Framework to Discover Discriminative Source Activations in EEG Brain Mapping, Conference of American Association of Artificial Intelligence (AAAI) San Francisco, CA 2017

Feng Liu, Rahilsadat Hosseini, Shouyi Wang, Jay Rosenberger, Jianzhong Su, Supervised Discriminative EEG Brain Source Imaging with Graph Regularization, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2017, pp. 495-504

Jing Qin, Feng Liu, Shouyi Wang, Jay Rosenberger, EEG Source Imaging based on Spatial and Temporal Graph Structures, International Conference on Image Processing Theory, Tools and Applications, pp. 1-6 (IPTA 2017)

Feng Liu, Wei Xiang, Shouyi Wang, Bradley Lega, Prediction of Seizure Spread Network via Sparse Representations of Overcomplete Dictionaries, 2016 International Conference on Brain Informatics & Health, Springer LNCS, pp. 262-273 (BIH, 2016)

Feng Liu, Zhifang Wang, A novel adaptive genetic algorithm for wine farm layout optimization, 2017 North American Power Symposium (NAPS). IEEE, 2017 (3rd Prize of Best Student Poster)

Feng Liu, Zhifang Wang, Electrical load forecasting using RBF neural network, IEEE Global Conference on Signal and Information Processing (GlobalSIP), Austin, Texas, Dec. 6, 2013

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