Feng Liu

Research Interests: AI for Healthcare, Brain Imaging, Brain Networks, Epilepsy, Addiction, Aging Brain, Optimization, Chaotic Theory.

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

Postdoctoral Researcher, MGH/Harvard Medical School, Boston, MA, 2018-2020
Research Affiliate, MIT, Cambridge, MA,, 2018-2020
Data Science/Operations Intern, CSX Transportation, Jacksonville, FL, 2015-2016

Best Poster Award, AI for Epilepsy and Neurological Disorder Conference, Park City, Utah, 2024
Inaugural Best Paper Award, CHI Next-Gen Healthcare Innovators Symposium
Student Best Paper Award, Brain Informatics, 2023
Best Paper Award Finalist, INFORMS DMDA Workshop, 2023
Defense Data Grand Prix 3rd place, 2023
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 University, IBBM at SCI U of Utah, IPAM at UCLA etc.
Dean Fellowship, UT Arlington, 2015
Graduate Student Scientific Achievement Award, HUST, 2012
National Scholarship, Qingdao University, 2008

PI: Prediction of Melanoma Recurrence using Graph Neural Networks, NJ Health Foundation, $35k, 2023
PI: DoD AIRC Subtask: Dynamic Knowledge Graph Learning Using Federated Aggregation for DoD, $75k, 2023
MPI: NIH R21: Deep Learning for Sleep Apnea, 2022-2025 ($208k)
PI: NIH R21: Epileptic Network Modeling with Partially Obersable Brain Regions, 2024-2026. ($438k)
PI: Internal: Closed Loop Brain State Monitoring and Control for Cognitive Capability and Mental Disorders Improvement Based on EEG-tDCS Platform ($40k)
PI: Internal: Brain State Inference and Brain Disorder Biomarkers Study Using Multimodal Neural Imaging EEG Platform”; ($40k)
PI: Defense Logistics Agency Grand Data Prix 3^rd Place ($20k)

Computational Neuroscience, Brain Networks, EEG signature
[J1] Meng Jiao^#, Xiaochen Xian, Boyu Wang, Yu Zhang, Shihao Yang^#, Spencer Chen, Hai Sun, and Feng Liu^*, XDL-ESI: Electrophysiological Sources Imaging via explainable deep learning framework with validation on simultaneous EEG and iEEG, NeuroImage (2024): 120802. (IF: 4.7)
[J2] Jun-En Ding^#, Shihao Yang^#, Anna Zilverstand, Kaustubh R. Kulkarni, Xiaosi Gu, and Feng Liu^*, Spatial Craving Patterns in Marijuana Users: Insights from fMRI Brain Connectivity Analysis with High-Order Graph Attention Neural Networks, IEEE Journal of Biomedical and Health Informatics (2024). (IF: 6.7)
[J3] Meng Jiao^#, Shihao Yang^#, Xiaochen Xian, Neel Fotedar, and Feng Liu^*, Multi-Modal Electrophysiological Source Imaging with Attention Neural Networks Based on Deep Fusion of EEG and MEG. IEEE Transactions on Neural Systems and Rehabilitation Engineering (2024). (IF: 4.8, Won the Inaugural Best Paper Award, CHI Next-Gen Healthcare Innovators Symposium)
[J4] Shihao Yang^#, Meng Jiao^#, Jing Xiang, Neel Fotedar, Hai Sun, Feng Liu^*, Rejuvenating Classical Brain Electrophysiology Source Localization Methods with Spatial Graph Fourier Filters for Source Extents Estimation, Brain Informatics, 2024 (Citescore: 9.8, no IF)
[J5] Sepehr Asgarian, Ze Wang, Feng Wan, Chi Man Wong, Feng Liu, Yalda Mohsenzadeh, Boyu Wang, and Charles X. Ling. Multi-view Contrastive Learning for Unsupervised Domain Adaptation in Brain-Computer Interfaces. IEEE Transactions on Instrumentation and Measurement (2024). (IF: 5.6)
[J6] Yongqiang Zheng, Jie Ding, Feng Liu, and Dongqing Wang, Adaptive Neural Decision Tree for EEG Based Emotion Recognition. Information Sciences (2023): 119160. (IF: 8.1)
[J7] 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 (IF: 4.3)
[J8] Yuxiang Li, Dongqing Wang, and Feng Liu. The auto-correlation function aided sparse support matrix machine for EEG-based fatigue detection. IEEE Transactions on Circuits and Systems II: Express Briefs, 2022 (IF: 3.7)
[J9] 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(IF: 4.52, *denotes equal contribution.)
[J10] 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 (won the Best Paper Award for INFORMS Data Mining Society Competition, 2019, IF: 11.04)
[J11] Mingjian He, Feng Liu, Aapo Nummenmaa, Matti Hämäläinen, Bradford C. Dickerson, and Patrick L. Purdon. 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 13 (2021): 632310. (IF: 5.4)
[J12] Boyu Wang, Chi Man Wong, Zhao Kang, Feng Liu, Changjian Shui, Feng Wan, and CL Philip Chen. "Common spatial pattern reformulated for regularizations in brain–computer interfaces." IEEE Transactions on Cybernetics 51, no. 10 (2020): 5008-5020. (IF: 11.5)
[J13] 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: 7.5)

Conferences (peer reviewed full length conference papers)
[C1] Jun-En Ding^#, Chien-Chin Hsu, and Feng Liu^*. "Parkinson Disease classification Using Contrastive Graph Cross-View Learning with Multimodal Fusion of SPECT Images and Clinical Features." ISBI Conference (2024). (Top conference on medical image analysis, Oral Presentation: 10%)
[C2] Zeyu Gu^#, Shihao Yang, Zhongyuan Yu, Feng Liu^*, Detection of High-Frequency Oscillations from Intracranial EEG Data with Switching State Space Model, IEEE EMBC Conference (2024)
[C3] Shulin Wen^#, Shihao Yang, Xinglong Ju, Ting Liao, and Feng Liu^*. "Prediction of cannabis addictive patients with graph neural networks." International Conference on Brain Informatics, pp. 297-307. 2023.
[C4] Shihao Yang^#, Meng Jiao^#, Jing Xiang, Daphne Kalkanis, Hai Sun, and Feng Liu*. Rejuvenating classical source localization methods with spatial graph filters. In International Conference on Brain Informatics, pp. 286-296. Cham: Springer Nature Switzerland, 2023.
[C5] Meng Jiao^#, Shihao Yang^#, Boyu Wang, Xiaochen Xian, Yevgeniy R. Semenov, Guihong Wan, and Feng Liu^*. "MMDF-ESI: multi-modal deep fusion of EEG and meg for brain source imaging." In International Conference on Brain Informatics, pp. 273-285. Cham: Springer Nature Switzerland, 2023. (Best Student Paper Award)
[C6] SongWon Bae, Feng Liu, Preference Detection Harnessing Low-Cost Portable Electroencephalography and Facial Behavior Marker, 9th International Engineering Systems Symposium (CESUN 2023)
[C7] Guihong Wan, Meng Jiao^#, Xinglong Ju, Yu Zhang, Haim Schweitzer, Feng Liu*, Electrophysiological Brain Source Imaging via Combinatorial Search with Provable Optimality, AAAI, 2023 (Acceptance rate: 20%)
[C8] Feng Liu, Guihong Wan, Yevgeniy Semenov, Patrick Purdon, Extended electrophysiological source imaging with spatial graph filters, International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) 2022 (Oral presentation, 3%)
[C9] Yaxin Guo^#, Meng Jiao^#, Guihong Wan, Jing Xiang, Shouyi Wang, Feng Liu*, EEG Source Imaging using GANs with Deep Image Prior, IEEE EMBC 2022
[C10] 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
[C11] 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 (the Best Paper Award).
[C12] 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.
[C13] 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.
[C14] 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 (Top AI conference)
[C15] 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 (Top medical image analysis conference)
[C16] Jing Qin, Feng Liu, Shouyi Wang, Jay Rosenberger, EEG Source Imaging based on Spatial and Temporal Graph Structures, IEEE International Conference on Image Processing Theory, Tools and Applications, pp. 1-6 (IPTA 2017)
[C17] 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)

Health Informatics and Epidemiology
[J14] Junde Chen, Qing Li, Feng Liu; Yuxin Wen, M3T-LM: A Multi-Modal Multi-Task Learning Model for Jointly Predicting Patient Length of Stay and Mortality, Computers in Biology and Medicine, 2024 (IF: 7.2)
[J15] Jun-En Ding^#, Phan Nguyen Minh Thao, Wen-Chih Peng, Jian-Zhe Wang, et al., Feng Liu, Fang-Ming Hung, Large Language Multimodal Models for 5-Year Chronic Disease Cohort Prediction Using EHR Data, Scientific Report, 2024 (IF: 3.8)
[J16] Meng Jiao^#, Changyue Song, Xiaochen Xian, Shihao Yang, and Feng Liu^*. Deep Attention Networks with Multi-Temporal Information Fusion for Sleep Apnea Detection. IEEE Open Journal of Engineering in Medicine and Biology (2024). (IF: 2.7)
[J17] Guihong Wan, Bonnie W. Leung, Mia S. DeSimone, Nga Nguyen, Ahmad Rajeh, Michael R. Collier, Hannah Rashdan, Feng Liu, et al., "Development and validation of time-to-event models to predict metastatic recurrence of localized cutaneous melanoma." Journal of the American Academy of Dermatology 90, no. 2 (2024): 288-298. (IF: 13.8)
[J18] Nga Nguyen, Guihong Wan, Pearl Ugwu-Dike, Nora A. Alexander, Neel Raval, Shijia Zhang, Ruple Jairath, Feng Liu, et al. "Influence of melanoma type on incidence and downstream implications of cutaneous immune-related adverse events in the setting of immune checkpoint inhibitor therapy." Journal of the American Academy of Dermatology 88, no. 6 (2023): 1308-1316. (IF: 13.8)
[J19] Wei Ding, Li Ding, Zhengmin Kong, Feng Liu, The SAITS epidemic spreading model and its combinational optimal suppression control, Mathematical Biosciences and Engineering, 2023, pp 3342-3354.
[J20] Wenhui Tu, Guang Ling, Feng Liu, Fuyan Hu, and Xiangxiang Song, GCSTI: A Single-Cell Pseudotemporal Trajectory Inference Method Based on Graph Compression. IEEE/ACM Transactions on Computational Biology and Bioinformatics. 2023
[J21] Guihong Wan*, Nga Nguyen*, Feng Liu*, Mia S DeSimone, etc. Peter K Sorger, Kun-Hsing Yu, Yevgeniy R Semenov, Prediction of early-stage melanoma recurrence using clinical and histopathologic features, Nature NPJ Precise Oncology, 2023 (*denotes equal contribution, IF: 10.1, >20 media reports)
[J22] Guihong Wan, Bonnie Leung, Nga Nguyen, Mia S. DeSimone, Feng Liu, Min Seok Choi, Diane Ho et al. "The impact of stage-related features in melanoma recurrence prediction: A machine learning approach." JAAD international 10 (2023): 28-30.
[J23] 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)
[J24] 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) (IF: 5.8)
[J25] 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)

Conferences (peer reviewed conference papers)
[C18] Liao, Chun-Chieh, Wei-Ting Kuo, I. Hu, Yen-Chen Shih, Jun-En Ding, Feng Liu, and Fang-Ming Hung. EHR-Based Mobile and Web Platform for Chronic Disease Risk Prediction Using Large Language Multimodal Models. Conference on Information and Knowledge Management, (2024). (Demo paper)
[C19] Phan, Thao Minh Nguyen, Cong-Tinh Dao, Chenwei Wu, Jian-Zhe Wang, Shun Liu, Jun-En Ding, David Restrepo, Feng Liu, Fang-Ming Hung, and Wen-Chih Peng. MEDFuse: Multimodal EHR Data Fusion with Masked Lab-Test Modeling and Large Language Models. Conference on Information and Knowledge Management (2024). (Shot paper, acceptance rate: 30%)

Energy System and Renewable Energy
[J26] 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, 2022 (IF: 9.7, *Corresponding author)
[J27] 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)
[J28] 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)
[J29] 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).
[J30] 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)
[J31] 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)

Conferences (peer reviewed conference papers)
[C20] Feng Liu, Zhifang Wang, A novel adaptive genetic algorithm for wine farm layout optimization, 2017 North American Power Symposium (NAPS). IEEE, 2017 (3^rd Prize of Best Student Paper)
[C21] 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

Dynamical Programming, Statistical Modelling and Applications
[J32] Lianyu Cheng, Guang Ling, Feng Liu, and Ming-Feng Ge. "Application of uniform experimental design theory to multi-strategy improved sparrow search algorithm for UAV path planning." Expert Systems with Applications 255 (2024): 124849. (IF: 7.5)
[J33] 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)
[J34] 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)
[J35] Qiang Lai, Xiao-Wen Zhao, Feng Liu, and Leimin Wang. Advances in chaotification and chaos-based applications. Frontiers in Physics 10 (2022): 996825. (IF: 1.9)
[J36] 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: 8.7)
[J37] 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.
[C22] 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

Dynamical System and Complex Networks
[J38] Jing Chen, Yawen Mao, Dongqing Wang, Min Gan, Quanmin Zhu, Feng Liu, Reduced-Order Identification Methods: Hierarchical Algorithm or Variable Elimination Algorithm, Automatica, 2024
[J39] Lai, Qiang, Zhijie Chen, Guanghui Xu, and Feng Liu. "Analysis and realization of new memristive chaotic system with line equilibria and coexisting attractors." Journal of Vibration Engineering & Technologies 11, no. 7 (2023): 3493-3505.
[J40] 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, 2022 (IF: 5.978)
[J41] 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)
[J42] 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)
[J43] 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!)
[J44] 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)
[J45] 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)
[J46] Shuo Zhang, Dongqing Wang, and Feng Liu, Separate block-based parameter estimation method for Hammerstein systems, Royal Society Open Science 5.6 (2018): 172194.
[J47] 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)
[J48] Zhi-Hong Guan, Feng Liu, Li Juan, Yan-Wu Wang, Chaotification in Complex Network with Impulsive Control, Chaos 22, 023137, (2012) (top journal in chaos theory, published in my first year as a master student, Z. Guan was my supervisor, IF: 3.6)

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