Lumos Alpha
Research
Lumos Alpha’s Algorithmic Research Team is dedicated to advancing state-of-the-art machine learning and AI algorithms and applying these algorithms to equity trading, portfolio construction, and risk management. Our researchers also participate in algorithm competitions at leading international conferences and on Kaggle, consistently earning top-tier placements.
Beyond finance, we investigate signal processing techniques across the biomedical engineering, communications, speech processing, and computer vision domains. We believe algorithms from these areas—especially for weak-signal extraction, denoising, and data mining—will largely benefit algorithmic trading and risk control.
Selected Publications by the Algorithmic Research Team
[2017] A Robust Random Forest-Based Approach for Heart Rate Monitoring Using Photoplethysmography Signal Contaminated by Intense Motion Artifacts,
Y. Ye, W. He, Y. Cheng, W. Huang, Z. Zhang
Sensors 17 (2), 385
[2016] Quantized Compressive Sensing for Low-Power Data Compression and Wireless Telemonitoring,
B. Liu, Z. Zhang
IEEE Sensors Journal 16 (23), 8206 - 8213
[2016] Combining Nonlinear Adaptive Filtering and Signal Decomposition for Motion Artifact Removal in Wearable Photoplethysmography,
Y. Ye, Y. Cheng, W. He, M. Hou, Z. Zhang
IEEE Sensors Journal 16 (19), 7133 - 7141
[2016] A New Approach for Heart Rate Monitoring using Photoplethysmography Signals Contaminated by Motion Artifacts,
B. Sun, H. Feng, Z. Zhang
ICASSP 2016
[2015] Photoplethysmography-Based Heart Rate Monitoring in Physical Activities via Joint Sparse Spectrum Reconstruction,
Z. Zhang
IEEE Transactions on Biomedical Engineering 62 (8), 1902 - 1910
[2015] TROIKA: A General Framework for Heart Rate Monitoring Using Wrist-Type Photoplethysmographic Signals During Intensive Physical Exercise,
Z. Zhang, Z. Pi, B. Liu
IEEE Transactions on Biomedical Engineering 62 (2), 522-531
[2015] Photoplethysmography-Based Heart Rate Monitoring Using Asymmetric Least Squares Spectrum Subtraction and Bayesian Decision Theory,
B. Sun, Z. Zhang
IEEE Sensors Journal 15 (12), 7161 - 7168
[2015] Combining ensemble empirical mode decomposition with spectrum subtraction technique for heart rate monitoring using wrist-type photoplethysmography,
Y. Zhang, B. Liu, Z. Zhang
Biomedical Signal Processing and Control 21, 119-125
[2014] Training-Free Non-Intrusive Load Monitoring of Electric Vehicle Charging with Low Sampling Rate,
Z. Zhang, J.H. Son, Y. Li, et al
The 40th Annual Conference of the IEEE Industrial Electronics Society (IECON 2014)
[2014] Spatiotemporal Sparse Bayesian Learning with Applications to Compressed Sensing of Multichannel Physiological Signals,
Z. Zhang, T.P. Jung, S. Makeig, Z. Pi, B.D. Rao
IEEE Transactions on Neural Systems and Rehabilitation Engineering 22 (6), 1186 - 1197
[2014] Identifying the Neuroanatomical Basis of Cognitive Impairment in Alzheimer's Disease by Correlation- and Nonlinearity-Aware Sparse Bayesian Learning,
J. Wan, Z. Zhang, B.D. Rao, S. Fang, J. Yan, A. Saykin, L. Shen
IEEE Transactions on Medical Imaging 33 (7), 1475-1487
[2013] Compressed Sensing for Energy-Efficient Wireless Telemonitoring: Challenges and Opportunities,
Z. Zhang, B.D. Rao, T.P. Jung
Asilomar 2013
[2013] Extension of SBL algorithms for the recovery of block sparse signals with intra-block correlation,
Z. Zhang, B.D. Rao
IEEE Transactions on Signal Processing 61 (8), 2009-2015
[2013] Compressed Sensing for Energy-Efficient Wireless Telemonitoring of Noninvasive Fetal ECG via Block Sparse Bayesian Learning,
Z. Zhang, T.P. Jung, S. Makeig, B.D. Rao
IEEE Transactions on Biomedical Engineering 60 (2), 300-309
[2013] Compressed Sensing of EEG for Wireless Telemonitoring with Low Energy Consumption and Inexpensive Hardware,
Z. Zhang, T.P. Jung, S. Makeig, B.D. Rao
IEEE Transactions on Biomedical Engineering 60 (1), 221-224
[2012] Sparse Bayesian Multi-Task Learning for Predicting Cognitive Outcomes from Neuroimaging Measures in Alzheimer's Disease,
J. Wan, Z. Zhang, J. Yan, T. Li, B.D. Rao, S. Fang, S. Kim, S. Risacher, A. Saykin, L. Shen
CVPR 2012
[2012] Evolving Signal Processing for Brain-Computer Interfaces,
S. Makeig, C. Kothe, T. Mullen, N. Bigdely-Shamlo, Z. Zhang, K. Kreutz-Delgado
Proceedings of the IEEE 100 (Special Centennial Issue), 1567-1584
[2012] Recovery of Block Sparse Signals Using the Framework of Block Sparse Bayesian Learning,
Z. Zhang, B.D. Rao
ICASSP 2012
[2011] Sparse signal recovery with temporally correlated source vectors using sparse Bayesian learning,
Z. Zhang, B.D. Rao
IEEE Journal of Selected Topics in Signal Processing 5 (5), 912 - 926
[2011] Exploiting Correlation in Sparse Signal Recovery Problems: Multiple Measurement Vectors, Block Sparsity, and Time-Varying Sparsity,
Z. Zhang, B.D. Rao
ICML 2011
[2010] Sparse signal recovery in the presence of correlated multiple measurement vectors,
Z. Zhang, B.D. Rao
ICASSP 2010
[2008] Morphologically constrained ICA for extracting weak temporally correlated signals,
Z.L. Zhang
Neurocomputing 71 (7-9), 1669-1679