Machine learning is the activity of learning from data to find patterns. NYU WIRELESS is a leader in this field, as our colleague Yann LeCun is the primary inventor of convolutional neural networks. The focus on machine learning among the NYU WIRELESS faculty has to do with image understanding including video, information theoretic approaches to privacy, and methods to improve the accuracy of general machine learning methods through selective refusal of predictions.

Machine learning for visual analytics and compression

Machine Learning

One of the research projects at NYU WIRELESS is joint optimization of video coding and delivery in networked video applications. We are also looking at vehicle tracking at busy intersections of urban streets. We developed a deep learning network that can simultaneously detect and track an object.

At NYU WIRELESS, we’re trying to develop a framework that can detect the video object, which consists on multiple frames simultaneously. Our work is leveraged on a concept in deep learning called Region-CNN. The Region-CNN is a deep network that essentially can put the boxes around objects. This network was trained to recognize over 100 objects, so it can put the box on those objects.

The main kind of architecture is called a region proposal network. It considers all possibilities and decided whether or not the object is a good candidate. Then, on the candidate they choose, they do additional kind of classification to see whether this actually is an object. In our future research, we plan to extend this to detect a video object, not on a individual frame.


Does Massive MIMO represent the ultimate wireless physical layer technology, or is there something potentially much better? Our research is addressing this question through a close fusion of electromagnetic theory and communication theory.

Holographic Massive MIMO
Analogous to optical holography, the idea of Holographic Massive MIMO is to replace a large array of discrete antennas with a spatial continuum of antennas, either linear, planar, or volumetric. The spatially continuous transmit/receive aperture is a logical successor to the Massive MIMO array. In addition, Holographic Massive MIMO constitutes a new theoretical tool for analyzing the limit behavior of MIMO systems when the number of service antennas grows without bound. So far, our research has produced stochastic models for small-scale fading that rigourously account for wave propagation physics, and that are particularly attractive from a computational standpoint.

Super-Directive Antenna Arrays
Conventional phased-array antenna theory and practice dictate a beamforming gain that grows linearly with the number of antennas. Super-directivity can, in principle, yield a beamforming gain that grows quadratically with the number of antennas. The central idea is to place antennas closer together than the usual half-wavelength spacing, deliberately creating strong mutual coupling among the antennas, and then to exploit this coupling to yield super-directive gain. Our research is searching for “sweet spots” for super-directivity with respect to deployment scenarios, array configurations, and the expenditure of reactive power. In parallel with the associated numerical gain optimization, we are elucidating the physical phenomenology via the plane-wave expansion of the radiated field. Ultimately we plan for experimental validation.

Current Research

  • Vehicle Tracking
  • Communication Theory
  • Machine Learning
  • Beyond Massive MIMO Theory

Conference Papers

CitationResearch AreasDate

Yilin Song, Jonathan Viventi, and Yao Wang, Diversity encouraged learning of unsupervised LSTM ensemble for neural activity video prediction, Initial version: Nov. 2016, Last updated: July 2018.

machine learning, videoJuly 1, 2018

Yilin Song, Yao Wang, and Jonathan Viventi, Multi Resolution LSTM For Long Term Prediction In Neural Activity Video, Initial version: May 2017, Last updated: July 2018.

machine learning, Perceptual Video QualityJuly 1, 2018

A. Aparo, V. Bonnici, G. Micale, A. Ferro, D. Shasha, A. Pulvirenti, R. Giugno, S. Verlag, “Simple Pattern-only Heuristics Lead To Fast Subgraph Matching Strategies on Very Large Networks,” ISSN:2194-5357, Oral presentation at the 12th International Conference on Practical Applications of Computational Biology and Bioinformatics (PACBB’18), Toledo (Spain) 20th-22nd June, 2018.

High-speed, networking, machine learningJune 20, 2018

Shervin Minaee, Yao Wang, Alp Aygar, Sohae Chung, Xiuyuan Wang, Yvonne W. Lui, Els Fieremans, Steven Flanagan, Joseph Rath “MTBI Identification From Diffusion MR Images Using Bag of Adversarial Visual Features”, IEEE Transactions on Medical Imaging.

machine learning, Medical ImagingJune 1, 2018

Guangyu Li, Yong Liu, and Bruno Ribeiro, “On Group Popularity Prediction in Event-Based Social Networks”,
in the Proceedings of the International AAAI Conference on Web and Social Media (Poster), June

machine learning, Social SystemsJune 1, 2018

F. Porto, J. Rittmeyer, E. Ogasawara, A. Krone-Martins, P. Valduriez, D. Shasha, “Point Pattern Search in Big Data,” Scientific and Statistic Database Management, June 2018, Bolzano-Bozen, Italy.

machine learningJune 1, 2018

“Typicality Matching for Pairs of Correlated Graphs,” Information Theory (ISIT), 2018 IEEE International Symposium on. IEEE, Feb 3, 2018.

machine learning, SecurityFebruary 3, 2018

F. Shirani, G. Siddharth, E. Erkip, “Optimal Active Social Network De-anonymization Using Information Thresholds,” Information Theory (ISIT), 2018 IEEE International Symposium on. IEEE, Jan 19, 2018.

machine learning, SecurityJanuary 19, 2018

M. Heidari, F. Shirani, S. S. Pradhan, “Bounds on the Effective-length of Optimal Codes for Interference Channel with Feedback,” Information Theory (ISIT), 2018 IEEE International Symposium on. IEEE, Jan 16, 2018.

Cache-Aided Wireless Networks, High-speed, networking, machine learningJanuary 16, 2018

Yuan Wang, Yao Wang, Yvonne W Lui, Dynamic Causal Modelling with neuron firing model in generalized recurrent neural network framework, ISMRM 2018.

machine learning, network optimizationJanuary 1, 2018

Fanyi Duanmu, Xin Feng, Xiaoqing Zhu, Dan Tan, and Yao Wang, A Multi-View Pedestrian Tracking Framework Based on Graph Matching, IEEE International Conference on Multimedia Information Processing and Retrieval (MIPR), Miami, Florida, USA, 2018.

machine learning, Multimedia communicationJanuary 1, 2018

R. Wang, Y. Song, Y. Wang and J. Viventi, “Long-term prediction of μECOG signals with a spatio-temporal pyramid of adversarial convolutional networks,” 2018 IEEE 15th International Symposium on Biomedical Imaging (ISBI 2018), Washington, DC, 2018, pp. 1313-1317. doi: 10.1109/ISBI.2018.8363813.

machine learning, Medical ImagingJanuary 1, 2018

O. Levchenko, D.E. Yagoubi, R. Akbarinia, F. Masseglia, D. Shasha, B. Kolev, “Spark-parSketch: A Massively Distributed Indexing of Time Series Datasets,” CIKM 2018 demonstration.

machine learningJanuary 1, 2018

D. Yagoubi, R. Akbarinia, B. Kolev, O. Levchenko, F. Masseglia, P. Valduriez, D. Shasha, “ParCorr: Efficient Parallel Methods to Identify Similar Time Series Pairs across Sliding Windows,” Data Mining and Knowledge Discovery, 2018.

machine learningJanuary 1, 2018

F. Shirani, S. S. Pradhan, “Lattices from Linear Codes and Fine Quantization: General Continuous Sources and Channels,” Information Theory (ISIT), 2018 IEEE International Symposium on. IEEE, 2018.

High-speed, networking, machine learning, Multi-Terminal CommunicationsJanuary 1, 2018

Shervin Minaee, Yao Wang, Anna Choromanska, Sohae Chung, Xiuyuan Wang, Els Fieremans, Steven Flanagan, Joseph Rath, Yvonne W Lui, “A Deep Unsupervised Learning Approach Toward MTBI Identification Using Diffusion MRI”, International Engineering in Medicine and Biology Conference (EMBC), IEEE, 2018.

machine learning, MRIJanuary 1, 2018

Yilin Song, Yao Wang, and Johnathan Viventi, “Adversarial autoencoder analysis on human μECoG dataset“, Dec. 2017.

Bioinfomatics, machine learningDecember 1, 2017

F. Shirani, G. Siddharth, E. Erkip, “Seeded graph matching: Efficient algorithms and theoretical guarantees,” 2017 51st Asilomar Conference on Signals, Systems, and Computers, Pacific Grove, CA, Nov 28, 2017.

machine learning, SecurityNovember 28, 2017

Yilin Song, Chenge Li, Yao Wang “Pixel-wise object tracking“, Initial version: Nov. 2017, Last updated: July 2018.

Computer Vision, machine learningNovember 1, 2017

F. Shirani, G. Siddharth, E. Erkip, “An information theoretic framework for active de-anonymization in social networks based on group memberships,” 2017 55th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, Oct 11, 2017.

machine learning, SecurityOctober 11, 2017

Yuanyi Xue, Yao Wang, A Novel Video Coding Framework using Self-adaptive Dictionary, IEEE Transactions on Circuits and Systems for Video Technology, Oct. 2017.

machine learning, Video CodingOctober 1, 2017

Rangan, Sundeep, Philip Schniter, and Alyson K. Fletcher, “Vector approximate message passing,” to appear IEEE ISIT, July 2017.

AMP, machine learningJuly 1, 2017

Fanyi Duanmu, Zhan Ma, Meng Xu, and Yao Wang, “An HEVC-Compliant Fast Screen Content Transcoding Framework Based on Mode Mapping”, Submitted to IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2018.

machine learning, videoJanuary 1, 2017

Yuan Wang, Yao Wang, Yvonne W Lui, Generalized Recurrent Neural Network accommodating Dynamic Causal Modelling for functional MRI analysis, ISMRM, 2017.

machine learning, MRIJanuary 1, 2017

Shervin Minaee, Yao Wang, Palmprint Recognition Using Deep Scattering Convolutional Network, IEEE International Symposium on Circuits and Systems, 2017.

machine learning, Network DesignJanuary 1, 2017

Shervin Minaee, Yao Wang, Subspace Learning in The Presence of Sparse Structured Outliers and Noise, IEEE International Symposium on Circuits and Systems, 2017.

machine learning, Machine Learning Application in MedicineJanuary 1, 2017

An-Ti Chiang, Qi Chen, Shijie Li, Yao Wang and Mei R. Fu. Denoising of Joint Tracking Data by Kinect Sensors Using Clustered Gaussian Process Regression Proceedings of the 2nd International Workshop on Multimedia for Personal Health and Health Care. ACM, 2017.

Computer Vision, machine learningJanuary 1, 2017

Yilin Song, Yao Wang and Jonathan Viventi, Unsupervised Learning of Spike Pattern for Seizure Detection and Wavefront Estimation of High Resolution Micro Electrocorticographic (μECoG) Data, IEEE transactions on nanobioscience 16.6 (2017): 418-427.

machine learning, Machine Learning Application in MedicineJanuary 1, 2017

S Minaee, Wang Y, Chung S, Wang X, Fieremans E, Flanagan S, Rath J, Lui YW., “A Machine Learning Approach For Identifying Patients with Mild Traumatic Brain Injury Using Diffusion MRI Modeling”, ASFNR 12th Annual Meeting, 2017.

machine learning, MRIJanuary 1, 2017

S Minaee, Y Wang, “Text Extraction From Texture Images Using Masked Signal Decomposition”, Global Conference on Signal and Information Processing, IEEE, 2017.

machine learning, signal processingJanuary 1, 2017

Fanyi Duanmu, Zhan Ma, Meng Xu and Yao Wang, HEVC-Compliant Screen Content Transcoding Based on Mode Mapping and Fast Termination, IEEE Visual Communications and Image Processing (VCIP), 2017.

Image Processing, machine learningJanuary 1, 2017

Journal Articles

CitationResearch AreasDate

K. Varala, A. Marshall-Colón, J. Cirrone, M. D. Brooks, A. V. Pasquino, S. Léran, S. Mittal, T. M. Rock, M. B. Edwards, G. J. Kim, S. Ruffel, W. R. McCombie, D. Shasha, G. M. Coruzzi, “Temporal transcriptional logic of dynamic regulatory networks underlying nitrogen signaling and use in plants,” PNAS May 16, 2018. 201721487; published ahead of print May 16, 2018.

machine learningMay 1, 2018

E. Erkip, S. Panwar, S. Shahsavari, F. Fund, “Capturing Capacity and Profit Gains with Base Station Sharing in mmWave Cellular Networks” e-print in, Apr. 2018.

machine learning, MmWave cellular system design, mmWave Channel ModelingApril 1, 2018

Y. Liu, Y. Liu, Y. Shen, K. Li, “Recommendation in a Changing World: Exploiting Temporal Dynamics in Ratings and Reviews”, in ACM Transactions on the Web, Volume 12 Issue 1, February 2018.

machine learningFebruary 1, 2018

M. Sadeghi, E. Björnson, E. G. Larsson, C. Yuen and T. L. Marzetta, “Max–Min Fair Transmit Precoding for Multi-Group Multicasting in Massive MIMO,” in IEEE Transactions on Wireless Communications, vol. 17, no. 2, pp. 1358-1373, Feb. 2018.

machine learning, MIMOFebruary 1, 2018

S. Krishna, D. Shasha, T. Wies, “Go with the flow: Compositional Abstractions for Concurrent Data Structures,” Principles of Programming Languages 2018. 37:1-37:31.

machine learningJanuary 1, 2018

G. Michale, R. Giugno, A. Ferro, M. Mongiovi, D. Shasha, A. Pulvirenti, “Fast Analytical Methods for Finding Significant Labeled Graph Motifs,” Data Mining Knowledge Discovery 32(2): 504-531 (2018).

machine learningJanuary 1, 2018

S. Wesemann and T. L. Marzetta, “Channel Training for Analog FDD Repeaters: Optimal Estimators and Cramér–Rao Bounds,” in IEEE Transactions on Signal Processing, vol. 65, no. 23, pp. 6158-6170, Dec.1, 1 2017.

machine learningDecember 1, 2017

S. Rangan, A. K. Fletcher, V. K. Goyal, E. Byrne and P. Schniter, “Hybrid Approximate Message Passing,” in IEEE Transactions on Signal Processing, vol. 65, no. 17, pp. 4577-4592, Sept. 1, 2017.

AMP, machine learningSeptember 1, 2017

M. Servajean, A. Joly, D. Shasha, J. Champ, E. Pacitti, “Crowdsourcing Thousands of Specialized Labels: a Bayesian active training approach,” in IEEE Transactions on Multimedia , Volume: 19, Issue: 6, June 2017, pp. 1376-1391.

machine learningJune 1, 2017

M. Borgerding; P. Schniter; S. Rangan, “AMP-Inspired Deep Networks for Sparse Linear Inverse Problems,” in IEEE Transactions on Signal Processing , vol. 65, no. 16, pp. 4293-4308.

AMP, machine learningMay 25, 2017

X. Yang, C. Liang, M. Zhao, H. Wang, H. Ding, Y. Liu, Y. Li, J. Zhang, “Collaborative Filtering Based Recommendation of Online Social Voting”, in IEEE Transactions On Computational Social Systems, Volume 4, Issue 1, Pages 1-13, March 2017.

machine learningMarch 1, 2017

S. Rangan, A. K. Fletcher, P. Schniter and U. S. Kamilov, “Inference for Generalized Linear Models via Alternating Directions and Bethe Free Energy Minimization,” in IEEE Transactions on Information Theory, vol. 63, no. 1, pp. 676-697, Jan. 2017.

AMP, machine learningJanuary 1, 2017