2024 Brooklyn 6G Summit
2024 Brooklyn 6G Summit – October 23rd – 25th
The Pi-Radio FR3 SDR is Operational
Machine learning is the activity of learning from data to find patterns. NYU 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.
BEYOND MASSIVE MIMO
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.
Machine learning for visual analytics and compression
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.
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.
MACHINE LEARNING-SUPPORTED DEBUGGING
Large enterprises tend to have long software pipelines consisting of omponents and interconnections of high complexity. Moreover the components are sometimes black boxes whose only control points are parameter settings one can give. Failures of such pipelines can result from changes in parameter settings or bad data or software development. Sometimes failures are the result of multiple changes. We have developed a machine-learning based system called BugDoc that automatically and efficiently finds the causes of errors (including data errors) that lead to failure.
EFFICIENT HARDWARE FOR DEEP LEARNING
Training and executing deep neural networks is computationally demanding. For this reason, leading companies are designing specialized chips to accelerate deep learning workloads. Our work explores new circuit and architectural optimizations to increase the performance, reliability and energy-efficient of deep learning hardware.
Several leading companies have recently released specialized chips tailored for deep learning; Google’s Tensor Processing Unit (TPU), for instance, accelerates deep neural network (DNN) inference (and training) using a systolic array, a precisely timed array containing thousands of multiply-and-accumulate (MAC) units. At the EnSuRe group, we are pursuing cutting edge research on designing more energy-efficient and reliable hardware accelerators for ML.
SECURE MACHINE LEARNING
Deep learning deployments, especially in safety- and health-critical applications, must account for the security or else malicious attackers will be able to engineer misbehavior with potentially disastrous consequences (autonomous car crashes, for instance). How we can safely and securely deploy ML/AI technology in the real-world?
Current Research
- Beyond Massive MIMO Theory
- Communication Theory
- Machine Learning for Physical Objects
- Low Power Hardware Accelerators for Deep Learning
- Machine Learning-supported Debugging
RESEARCH PAPERS
| Citation | View Papers | Research Areas | Date |
|---|---|---|---|
O. Kanhere and T. S. Rappaport, “Calibration of NYURay, a 3D mmWave and sub-THz Ray Tracer using Indoor, Outdoor, and Factory Channel Measurements,” in ICC 2023 – 2023 IEEE International Conference on Communications, Rome, Italy, May 2023, pp. 1–6, DOI: 10.1109/ICC45041.2023.10279044 | Affiliate Access Only | Applications, Aritificial Intelligence, machine learning, mmwave rappaport, testbeds, Wireless Comm | February 24, 2023 |
S. Heidarian et al., “Covid-fact: A fully-automated capsule network-based framework for identification of covid-19 cases from chest ct scans,” in Frontiers in Artificial Intelligence, vol. 4, pp. 65, May 2021, doi: 10.3389/frai.2021.598932 | Affiliate Access Only | Applications, machine learning | May 25, 2021 |
L. Tøttrup, S. F. Atashzar, D. Farina, E. N. Kamavuako, W. Jensen, “Altered evoked low-frequency connectivity from SI to ACC following nerve injury in rats,” in Journal of Neural Engineering, vol. 18, no. 4, 2021, doi: 10.1088/1741-2552/abfeb9 | Affiliate Access Only | Applications, machine learning | May 21, 2021 |
S. Shahtalebi, S. F. Atashzar, R. V. Patel, M. S. Jog, A. Mohammadi, “A deep explainable artificial intelligent framework for neurological disorders discrimination,” in Scientific Reports, vol. 11, no. 1, pp. 1-18, May 2021, doi: 10.1038/s41598-021-88919-9 | Affiliate Access Only | Applications, machine learning | May 5, 2021 |
E. Rahimian, S. Zabihi, A. Asif, D. Farina, S. F. Atashzar, A. Mohammadi, “FS-HGR: Few-Shot Learning for Hand Gesture Recognition via Electromyography,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 1004-1015, 2021, doi: 10.1109/TNSRE.2021.3077413 | Affiliate Access Only | Applications, machine learning | May 4, 2021 |
D. Jiménez-Grande, S. F. Atashzar, E. Martinez-Valdes, A. Marco De Nunzio, D. Falla, “Kinematic biomarkers of chronic neck pain measured during gait: A data-driven classification approach,” in Journal of Biomechanics, vol. 118, 2021, 110190, doi: 10.1016/j.jbiomech.2020.110190 | Affiliate Access Only | Applications, machine learning | March 30, 2021 |
P. Gulati, Q. Hu, S. F. Atashzar, “Toward Deep Generalization of Peripheral EMG-Based Human-Robot Interfacing: A Hybrid Explainable Solution for NeuroRobotic Systems,” in IEEE Robotics and Automation Letters, vol. 6, no. 2, pp. 2650-2657, April 2021, doi: 10.1109/LRA.2021.3062320 | Affiliate Access Only | Applications, machine learning | February 25, 2021 |
A. K. Clarke et al., “Deep Learning for Robust Decomposition of High-Density Surface EMG Signals,” in IEEE Transactions on Biomedical Engineering, vol. 68, no. 2, pp. 526-534, Feb. 2021, doi: 10.1109/TBME.2020.3006508 | Affiliate Access Only | Applications, machine learning | February 1, 2021 |
C. S. M. Castillo, S. Wilson, R. Vaidyanathan, S. F. Atashzar, “Wearable MMG-Plus-One Armband: Evaluation of Normal Force on Mechanomyography (MMG) to Enhance Human-Machine Interfacing,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 29, pp. 196-205, 2021, doi: 10.1109/TNSRE.2020.3043368 | Affiliate Access Only | Applications, machine learning | December 8, 2020 |
W. Xia, S. Rangan, M. Mezzavilla, A. Lozano, G. Geraci, V. Semkin, and G. Loianno, “Millimeter wave channel modeling via generative neural networks,” IEEE Globecom 2020. Available as arXiv:2008.11006, Aug. 2020. | Affiliate Access Only | 5g and 6g apps, machine learning | December 7, 2020 |
L. Tøttrup, S. F. Atashzar, D. Farina, E. N. Kamavuako, W. Jensen, “Nerve Injury Decreases Hyperacute Resting-State Connectivity Between the Anterior Cingulate and Primary Somatosensory Cortex in Anesthetized Rats,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, vol. 28, no. 12, pp. 2691-2698, Dec. 2020, doi: 10.1109/TNSRE.2020.3039854 | Affiliate Access Only | Applications, machine learning | November 25, 2020 |
O. Levchenko, B. Kolev, D. Yagoubi, R. Akbarinia, F. Masseglia, T. Palpanas, D. Shasha, P. Valduriez, “Best Neighbor: Efficient Evaluation of kNN Queries on Large Time Series | Affiliate Access Only | machine learning | November 16, 2020 |
P. G. Sagastegui Alva, S. Muceli, S. F. Atashzar, L. William, D. Farina, “Wearable multichannel haptic device for encoding proprioception in the upper limb,” in Journal of Neural Engineering, vol. 17, no. 5, Oct. 2020, doi: 10.1088/1741-2552/aba6da | Affiliate Access Only | Applications, machine learning | October 13, 2020 |
P. Skrimponis, N. Makris, S. B. Rajguru, K. Cheng, J. Ostrometzky, E. Ford, Z. Kostic, G. Zussman, T. Korakis. 2020. COSMOS educational toolkit: using experimental wireless networking to enhance middle/high school STEM education. SIGCOMM Comput. Commun. Rev. 50, 4 (October 2020), 58–65. DOI:https://doi.org/10.1145/3431832.3431839 | Affiliate Access Only | 5g and 6g apps, machine learning, testbeds | October 1, 2020 |
M. Stachaczyk, S. F. Atashzar, S. Dupan, I. Vujaklija, D. Farina, “Toward Universal Neural Interfaces for Daily Use: Decoding the Neural Drive to Muscles Generalises Highly Accurate Finger Task Identification Across Humans,” in IEEE Access, vol. 8, pp. 149025-149035, 2020, doi: 10.1109/ACCESS.2020.3015761 | Affiliate Access Only | Applications, machine learning | August 11, 2020 |
M. Faieghi, S. F. Atashzar, M. Sharma, O. R. Tutunea-Fatan, R. Eagleson, L. M. Ferreira, “Vibration Analysis in Robot-Driven Glenoid Reaming Procedure,” in 2020 IEEE/ASME International Conference on Advanced Intelligent Mechatronics (AIM), 2020, pp. 741-746, doi: 10.1109/AIM43001.2020.9158836 | Affiliate Access Only | Applications, machine learning | August 5, 2020 |
M. Faieghi, S. F. Atashzar, O. R. Tutunea-Fatan, R. Eagleson, “Parallel Haptic Rendering for Orthopedic Surgery Simulators,” in IEEE Robotics and Automation Letters, vol. 5, no. 4, pp. 6388-6395, Oct. 2020, doi: 10.1109/LRA.2020.3013891 | Affiliate Access Only | Applications, machine learning | August 4, 2020 |
J.F. Beltran, B.M. Wahba, N. Hose, D. Shasha, R. Kline, “Inexpensive, non-invasive biomarkers predict Alzheimer transition using machine learning analysis of the Alzheimer’s Disease Neuroimaging (ADNI) database,” for the Alzheimer’s Disease Neuroimaging Initiative, PLOS ONE, July 27, 2020 | Affiliate Access Only | machine learning | July 27, 2020 |
Mohammad A. S., Kosta V., Bhargava M., “Enhancing Routing Protocol for Wireless Sensor Network to Advance Network Lifetime,” Space Flight Center, USA, June 2020 | Affiliate Access Only | 5g and 6g apps, machine learning | June 30, 2020 |
S. Dutta, A. Khalili, E. Erkip, and S. Rangan, “Capacity Bounds for Communication Systems with Quantization and Spectral Constraints,” IEEE International Symposium on Information Theory (ISIT), 21 Jun 2020. | Affiliate Access Only | machine learning | June 21, 2020 |
A. Khalili, S. Shahsavari, M. A. Khojastpour, and E. Erkip, “On Optimal Multi-user Beam Alignment in Millimeter Wave Wireless Systems,” IEEE International Symposium on Information Theory (ISIT), 21 Jun 2020. | Affiliate Access Only | machine learning | June 21, 2020 |
R. Lourenço, J. Freire, D. Shasha, “BugDoc: Algorithms to Debug Computational Processes” ACM SIGMOD, June 16, 2020 | Affiliate Access Only | machine learning | June 16, 2020 |
S. Krishna, N. Patel, D. Shasha, T. Wies, “Verifying Concurrent Search Structure Templates” ACM SIGPLAN Conference on Programming Language Design and Implementation, June 15, 2020 | Affiliate Access Only | machine learning | June 15, 2020 |
S. H. A. Shah, M. Sharma, S.Rangan, “LSTM-Based Multi-Link Prediction for mmWaveand Sub-THz Wireless Systems” accepted in 2020 IEEE International Conference on Communications (ICC), pp. 1–6, June 2020 | Affiliate Access Only | machine learning, terahertz | June 11, 2020 |
M. Stachaczyk, S. F. Atashzar and D. Farina, “Adaptive Spatial Filtering of High-Density EMG for Reducing the Influence of Noise and Artefacts in Myoelectric Control,” in IEEE Transactions on Neural Systems and Rehabilitation Engineering, doi: 10.1109/TNSRE.2020.2986099. | Affiliate Access Only | machine learning | May 14, 2020 |
J. Cirrone, M. D. Brooks, R. Bonneau, G. M. Coruzzi, D. Shasha, “OutPredict: multiple datasets can improve prediction of expression and inference of causality” Nature Scientific Reports, 2020 | Affiliate Access Only | machine learning | May 1, 2020 |
A. Khalili, S. Shahsavari, F. Shirani, E. Erkip, and Y. C. Eldar, “On throughput of millimeter-wave MIMO systems with low-resolution ADCs,” IEEE International Conference on Acoustics, Speech and signal Processing (ICASSP), 9 Apr 2020. | Affiliate Access Only | machine learning | April 9, 2020 |
E. Rahimian, S. Zabihi, S. F. Atashzar, A. Asif and A. Mohammadi, “XceptionTime: Independent Time-Window Xceptiontime Architecture for Hand Gesture Classification,” ICASSP 2020 – 2020 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP), Barcelona, Spain, 2020, pp. 1304-1308, doi: 10.1109/ICASSP40776.2020.9054586. | Affiliate Access Only | machine learning | April 5, 2020 |
S. Datta, E. Sharma and R. Budhiraja, “Power Scaling for Massive MIMO UAV Communication System,” 2020 International Conference on COMmunication Systems & NETworkS (COMSNETS), Bengaluru, India, 2020, pp. 507-510, doi: 10.1109/COMSNETS48256.2020.9027384. | Affiliate Access Only | 5g and 6g apps, machine learning | March 9, 2020 |
M. Khosravi, S. F. Atashzar, G. Gilmore, M. S. Jog and R. V. Patel, “Intraoperative Localization of STN During DBS Surgery Using a Data-Driven Model,” in IEEE Journal of Translational Engineering in Health and Medicine, vol. 8, pp. 1-9, 2020, Art no. 2500309, doi: 10.1109/JTEHM.2020.2969152. | Affiliate Access Only | 5g and 6g apps, machine learning | January 30, 2020 |
T. Marzetta, “Super-Directive Antenna Arrays: Fundamentals and New Perspectives,” 2019 53rd Asilomar Conference on Signals, Systems, and Computers, Nov 3, 2019 | Affiliate Access Only | machine learning | November 3, 2019 |
F. Carpi, C. Häger, M. Martalò, R. Raheli, H. Pfister, “Reinforcement Learning for Channel Coding: Learned Bit-Flipping Decoding” 2019 57th Annual Allerton Conference on Communication, Control, and Computing (Allerton), Monticello, IL, USA, September 24-27, 2019 | Affiliate Access Only | machine learning | September 27, 2019 |
Panagiotis Skrimponis, Emmanouil Pissadakis, Nikolaos Alachiotis, and Dionisios Pnevmatikatos, DOI: 10.3233/APC200099 | Affiliate Access Only | machine learning | September 10, 2019 |
S. Shahsavari, A. Hosseini, C. Ng, E. Erkip “On the Optimal Two-antenna Static Beamforming with Per-antenna Power Constraints,” IEEE Signal Processing Letters, July 2019 | Affiliate Access Only | machine learning | July 1, 2019 |
R. Lourenço, J. Freire, D. Shasha, “Debugging Machine Learning Pipelines,” Proceedings of the 3rd International Workshop on Data Management for End-to-End Machine Learning, June 2019 | Affiliate Access Only | machine learning | June 30, 2019 |
Q. Yang, P. Hassanzadeh, D. Gündüz, E. Erkip, “Centralized Caching and Delivery of Correlated Contents over Gaussian Broadcast Channels,” arXiv preprint arXiv:1906.09970, June 2019 | Affiliate Access Only | machine learning | June 21, 2019 |
A. Balashankar, A. Lees, C. Welty, L. Subramanian, “Pareto-Efficient Fairness for Skewed Subgroup Data,” Thirty-sixth International Conference on Machine Learning, June 2019 | Affiliate Access Only | machine learning | June 15, 2019 |
P. Hassanzadeh, A.M. Tulino, J. Llorca, E. Erkip, “Rate-Distortion-Memory Trade-offs in Heterogeneous Caching Networks,” arXiv preprint arXiv:1905.09446, May 2019 | Affiliate Access Only | machine learning | May 23, 2019 |
F. Shirani, S. Shahsavari, E. Erkip, “On the Rates of Convergence in Learning of Optimal Temporally Fair Schedulers,” arXiv preprint arXiv:1905.09843, May 2019 | Affiliate Access Only | machine learning | May 23, 2019 |
S. Shahsavari, F. Shirani, M.A. Khojastepour, E. Erkip, “Opportunistic Temporal Fair Mode Selection and User Scheduling for Full-duplex Systems,” arXiv preprint arXiv:1905.08992, May 2019 | Affiliate Access Only | machine learning | May 22, 2019 |
RW Heath, N Gonzalez-Prelcic, S Rangan, W Roh, AM Sayeed, “An overview of signal processing techniques for millimeter wave MIMO systems,” IEEE journal of selected topics in signal processing 10 (3), 436-453, April 2016 | Affiliate Access Only | machine learning, terahertz | April 15, 2019 |
S. Xu, P. Liu, R. Wang, and S. Panwar, “Realtime scheduling and power allocation using deep neural networks,” in 2019 IEEE Wireless Communications and Networking Conference (WCNC) (IEEE WCNC 2019), Marrakech, Morocco, Apr. 2019. | Affiliate Access Only | machine learning, mobile edge | April 14, 2019 |
M. D. Brooks, J. Cirrone, A. V. Pasquino, J. M. Alvarez, J. Swift, S. Mittal, C.L. Juang, K. Varala, R.A. Gutiérrez, G. Krouk, D. Shasha, G.M. Coruzzi, “Network Walking charts transcriptional dynamics of nitrogen signaling by integrating validated and predicted genome-wide interactions,” Nature communications 10 (1), 1569, April 5, 2019 | Affiliate Access Only | machine learning | April 5, 2019 |
S. Mu, S. Angel, D. Shasha, “Deferred Runtime Pipelining for contentious multicore software transactions,” Proceedings of the Fourteenth EuroSys Conference 2019, 40, March 2019 | Affiliate Access Only | machine learning | March 25, 2019 |
G. Micale, A. Pulvirenti, A. Ferro, R. Giugno, D. Shasha, “Fast methods for finding significant motifs on labelled multi-relational networks,” Journal of Complex Networks, March 3, 2019 | Affiliate Access Only | machine learning | March 13, 2019 |
S. Shahsavari, F. Shirani, E. Erkip, “A general framework for temporal fair user scheduling in NOMA systems,” IEEE Journal of Selected Topics in Signal Processing, May 2019 | Affiliate Access Only | machine learning | March 7, 2019 |
A. Aparo, V. Bonnici, G. Micale, A. Ferro, D. Shasha, A. Pulvirenti, R. Giugno, “Fast Subgraph Matching Strategies Based on Pattern-Only Heuristics,” Interdisciplinary Sciences: Computational Life Sciences 11 (1), 21-32, March 2019 | Affiliate Access Only | machine learning | March 1, 2019 |
J.S. Lu, E.M. Vitucci, V. Degli-Esposti, F. Fuschini, M. Barbiroli, J.A. Blaha and H.L. Bertoni, “A Discrete Environment-Driven GPU-Based Ray Launching Algorithm,” IEEE Transactions on Antennas and Propagation; Vol. 67, No. 2, pp. 1180-1192, February 2019. | Affiliate Access Only | machine learning, testbeds | February 1, 2019 |
S. Mu, S. Angel, D. Shasha, “Deferred runtime pipelining for contentious multicore software transactions (extended version),” Technical Report MS-CIS-19-02, University of Pennsylvania, Feb. 2019 | Affiliate Access Only | machine learning | February 1, 2019 |
E Björnson, L Sanguinetti, H Wymeersch, J Hoydis, TL Marzetta, “Massive MIMO is a Reality-What is Next? Five Promising Research Directions for Antenna Arrays,” arXiv preprint arXiv:1902.07678, Feb. 2019. | Affiliate Access Only | machine learning | February 1, 2019 |
A. Khalili, F. Shirani, E. Erkip, and Y. C. Eldar, “On multiterminal communication over MIMO channels with one-bit ADCs at the receivers,” arXiv preprint arXiv:1901.10628, 2019. | Affiliate Access Only | machine learning, mobile edge | January 30, 2019 |
A. Khalili, F. Shirani, E. Erkip, and Y. C. Eldar, “Tradeoff between delay and high SNR capacity in quantized MIMO systems,” arXiv preprintarXiv:1901.09844, 2019. | Affiliate Access Only | machine learning, mobile edge | January 28, 2019 |
S. Shahsavari, F. Shirani, E. Erkip, “On the Fundamental Limits of Multi-user Scheduling under Short-term Fairness Constraints,” arXiv preprint arXiv:1901.07719, Jan 2019 | Affiliate Access Only | machine learning | January 23, 2019 |
F. Shirani, S. Gar, E. Erkip, “A Concentration of Measure Approach to Database De-anonymization,” arXiv preprint arXiv:1901.07655, Jan 2019 | Affiliate Access Only | machine learning | January 23, 2019 |
G. Micale, S. Alaimo, A. Laganà, S. Parekh, D. Shasha, A. Pulvirenti, A. Ferro, “Machine Learning and Pathway Analysis on RNA-Seq Enables Precise Cancer Diagnosis and Prognosis,” Available at SSRN 3327353, 2019 | Affiliate Access Only | machine learning | January 1, 2019 |
D. Shasha, “Randomized anti-counterfeiting,” Communications of the ACM 62 (1), 120-ff, Dec. 2019 | Affiliate Access Only | machine learning | December 19, 2018 |
D. Dessì, J. Cirrone, D.R. Recupero, D. Shasha, “SuperNoder: a tool to discover over-represented modular structures in networks,” BMC bioinformatics 19 (1), 318, Dec. 2019 | Affiliate Access Only | machine learning | December 1, 2018 |
M Sadeghi, E Björnson, EG Larsson, C Yuen, T Marzetta, “Joint unicast and multi-group multicast transmission in massive MIMO systems,” IEEE Transactions on Wireless Communications 17 (10), 6375-6388, Oct. 2018. | Affiliate Access Only | machine learning | October 1, 2018 |
A Ashikhmin, L Li, TL Marzetta, “Interference reduction in multi-cell massive MIMO systems with large-scale fading precoding,” IEEE Transactions on Information Theory 64 (9), 6340-6361, Sept. 2018. | Affiliate Access Only | machine learning | September 1, 2018 |
EG Larsson, TL Marzetta, HQ Ngo, H Yang, “Antenna count for massive MIMO: 1.9 GHz vs. 60 GHz,” IEEE Communications Magazine 56 (9), 132-137, Sept. 2018. | Affiliate Access Only | machine learning | September 1, 2018 |
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. | Affiliate Access Only | 5g and 6g apps, machine learning | July 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. | Affiliate Access Only | 5g and 6g apps, machine learning | July 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. | Affiliate Access Only | machine learning | June 20, 2018 |
TL Marzetta, “Spatially-stationary propagating random field model for massive MIMO small-scale fading,” 2018 IEEE International Symposium on Information Theory (ISIT), 391-395, June 2018. | Affiliate Access Only | machine learning | June 17, 2018 |
H Yang, TL Marzetta, “Energy Efficiency of Massive MIMO: Cell-Free vs. Cellular,” 2018 IEEE 87th Vehicular Technology Conference (VTC Spring), 1-5, June 2018. | Affiliate Access Only | machine learning | June 3, 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. | Affiliate Access Only | 5g and 6g apps, machine learning | June 1, 2018 |
Guangyu Li, Yong Liu, and Bruno Ribeiro, “On Group Popularity Prediction in Event-Based Social Networks”, | Affiliate Access Only | machine learning | June 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. | Affiliate Access Only | machine learning | June 1, 2018 |
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. | Affiliate Access Only | machine learning | May 1, 2018 |
M Sadeghi, E Björnson, EG Larsson, C Yuen, TL Marzetta, “Mrt-Based Joint Unicast and Multigroup Multicast Transmission in Massive Mimo Systems,” 2018 IEEE International Conference on Acoustics, Speech and Signal Processing, April 2018. | Affiliate Access Only | machine learning | April 15, 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 arXiv.org:1804.0985, Apr. 2018. | Affiliate Access Only | machine learning, terahertz | April 1, 2018 |
S. Shahsavari, A. Ashikhmin, E. Erkip, and T. L. Marzetta, “Coordinated multi-point massive MIMO cellular systems with sectorized antennas,” 52nd IEEE Asilomar Conference on Signals, Systems, and Computers, 2018. | Affiliate Access Only | machine learning, mobile edge | February 20, 2018 |
“Typicality Matching for Pairs of Correlated Graphs,” Information Theory (ISIT), 2018 IEEE International Symposium on. IEEE, Feb 3, 2018. | Affiliate Access Only | machine learning, Security | February 3, 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. | Affiliate Access Only | machine learning | February 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. | Affiliate Access Only | machine learning, MIMO | February 1, 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. | Affiliate Access Only | machine learning, Security | January 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. | Affiliate Access Only | machine learning | January 16, 2018 |
Yuan Wang, Yao Wang, Yvonne W Lui, “Dynamic Causal Modelling with neuron firing model in generalized recurrent neural network framework,” ISMRM 2018. | Affiliate Access Only | 5g and 6g apps, machine learning | January 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. | Affiliate Access Only | 5g and 6g apps, machine learning | January 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. | Affiliate Access Only | 5g and 6g apps, machine learning | January 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. | Affiliate Access Only | machine learning | January 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. | Affiliate Access Only | machine learning | January 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). | Affiliate Access Only | machine learning | January 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. | Affiliate Access Only | machine learning | January 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. | Affiliate Access Only | machine learning | January 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. | Affiliate Access Only | 5g and 6g apps, machine learning | January 1, 2018 |
Yilin Song, Yao Wang, and Johnathan Viventi, “Adversarial autoencoder analysis on human μECoG dataset“, Dec. 2017. | Affiliate Access Only | 5g and 6g apps, machine learning | December 1, 2017 |
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. | Affiliate Access Only | machine learning | December 1, 2017 |
S Wesemann, TL Marzetta, “Channel Training for Analog FDD Repeaters: Optimal Estimators and Cramér–Rao Bounds,” IEEE Transactions on Signal Processing 65 (23), 6158-6170, Dec. 2017 | Affiliate Access Only | machine learning | December 1, 2017 |
H Yang, TL Marzetta, “Max-Min SINR dependence on channel correlation in line-of-sight Massive MIMO,” GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-6, Dec. 2017 | Affiliate Access Only | machine learning | December 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. | Affiliate Access Only | machine learning | November 28, 2017 |
TL Marzetta, EG Larsson, H Yang, HQ Ngo, “Fundamentals of massive MIMO,” Cambridge University Press, Nov. 2016 | Affiliate Access Only | machine learning | November 17, 2017 |
Yilin Song, Chenge Li, Yao Wang “Pixel-wise object tracking“, Initial version: Nov. 2017, Last updated: July 2018. | Affiliate Access Only | 5g and 6g apps, machine learning | November 1, 2017 |
H Yang, TL Marzetta, “Massive MIMO with max-min power control in line-of-sight propagation environment,” IEEE Transactions on Communications 65 (11), 4685-4693, Nov. 2017 | Affiliate Access Only | machine learning | November 1, 2017 |
AAI Ibrahim, A Ashikhmin, TL Marzetta, DJ Love, “Cell-free massive MIMO systems utilizing multi-antenna access points,” 2017 51st Asilomar Conference on Signals, Systems, and Computers, 1517-1521, Oct. 2017 | Affiliate Access Only | machine learning | October 29, 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. | Affiliate Access Only | machine learning | October 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. | Affiliate Access Only | 5g and 6g apps, machine learning | October 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. | Affiliate Access Only | AMP, machine learning | September 1, 2017 |
M. A. Kocak, D. Ramirez, E. Erkip, D. Shasha, “SafePredict: A Meta-Algorithm for Machine Learning That Uses Refusals to Guarantee Correctness,” Submitted to IEEE Transactions on Pattern Analysis and Machine Intelligence, August 2017 | Affiliate Access Only | machine learning | August 7, 2017 |
Rangan, Sundeep, Philip Schniter, and Alyson K. Fletcher, “Vector approximate message passing,” IEEE ISIT, July 2017. | Affiliate Access Only | machine learning, terahertz | July 1, 2017 |
E Nayebi, A Ashikhmin, TL Marzetta, H Yang, BD Rao, “Precoding and power optimization in cell-free massive MIMO systems,” IEEE Transactions on Wireless Communications 16 (7), 4445-4459, July 2017 | Affiliate Access Only | machine learning | July 1, 2017 |
M. Zhang, M. Polese, M. Mezzavilla, S. Rangan, M. Zorzi “ns-3 Implementation of the 3GPP MIMO Channel Model for Frequency Spectrum above 6 GHz,” Workshop on ns-3, June 13 – 14, 2017, Porto, Portugal. | Affiliate Access Only | machine learning, terahertz, testbeds | June 7, 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. | Affiliate Access Only | machine learning | June 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. | Affiliate Access Only | machine learning, terahertz | May 25, 2017 |
S. Sun and T. S. Rappaport, “Millimeter Wave MIMO Channel Estimation Based on Adaptive Compressed Sensing,” 2017 IEEE International Conference on Communications Workshop (ICCW), May 2017. | Affiliate Access Only | machine learning, terahertz | May 23, 2017 |
A Adhikary, A Ashikhmin, TL Marzetta, “Uplink interference reduction in large-scale antenna systems,” IEEE Transactions on Communications 65 (5), 2194-2206, May 2017 | Affiliate Access Only | machine learning | May 1, 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. | Affiliate Access Only | machine learning | March 1, 2017 |
HQ Ngo, A Ashikhmin, H Yang, EG Larsson, TL Marzetta, “Cell-free massive MIMO versus small cells,” IEEE Transactions on Wireless Communications 16 (3), 1834-1850 | Affiliate Access Only | machine learning | March 1, 2017 |
Fanyi Duanmu, Zhan Ma, Meng Xu, and Yao Wang, “An HEVC-Compliant Fast Screen Content Transcoding Framework Based on Mode Mapping”, IEEE Transactions on Circuits and Systems for Video Technology (TCSVT), 2018. | Affiliate Access Only | 5g and 6g apps, machine learning | January 1, 2017 |
Yuan Wang, Yao Wang, Yvonne W Lui, “Generalized Recurrent Neural Network accommodating Dynamic Causal Modelling for functional MRI analysis,” ISMRM, 2017. | Affiliate Access Only | 5g and 6g apps, machine learning | January 1, 2017 |
Shervin Minaee, Yao Wang, “Palmprint Recognition Using Deep Scattering Convolutional Network,” IEEE International Symposium on Circuits and Systems, 2017. | Affiliate Access Only | 5g and 6g apps, machine learning | January 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. | Affiliate Access Only | machine learning | January 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. | Affiliate Access Only | machine learning | January 1, 2017 |
S Minaee, Y Wang, “Text Extraction From Texture Images Using Masked Signal Decomposition”, Global Conference on Signal and Information Processing, IEEE, 2017. | Affiliate Access Only | machine learning | January 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. | Affiliate Access Only | 5g and 6g apps, machine learning | January 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. | Affiliate Access Only | AMP, machine learning | January 1, 2017 |
D Ying, H Yang, TL Marzetta, DJ Love, “Heterogeneous massive MIMO with small cells,” 2016 IEEE 83rd Vehicular Technology Conference (VTC Spring), 1-5, May. 2016 | Affiliate Access Only | machine learning | May 15, 2016 |
Fanyi Duanmu; Zhan Ma; Wei Wang; Meng Xu; Yao Wang, “A novel screen content fast transcoding framework based on statistical study and machine learning,” IEEE International Conference on Image Processing, 2016. | Affiliate Access Only | machine learning | January 1, 2016 |
Fanyi Duanmu, Zhan Ma, Yao Wang, “Fast Mode and Partition Decision Using Machine Learning for Intra-Frame Coding in HEVC Screen Content Coding Extension,” IEEE Journal on Emerging and Selected Topics in Circuits and Systems, 2016. | Affiliate Access Only | machine learning | January 1, 2016 |
E Nayebi, A Ashikhmin, TL Marzetta, H Yang, “Cell-free massive MIMO systems,” 2015 49th Asilomar Conference on Signals, Systems and Computers, 695-699, Nov. 2015 | Affiliate Access Only | machine learning | November 8, 2015 |
HQ Ngo, EG Larsson, TL Marzetta, “Aspects of favorable propagation in massive MIMO,” 2014 22nd European Signal Processing Conference (EUSIPCO), 76-80, Sept. 2014 | Affiliate Access Only | machine learning | September 1, 2015 |
HQ Ngo, A Ashikhmin, H Yang, EG Larsson, TL Marzetta, “Cell-free massive MIMO: Uniformly great service for everyone,” 2015 IEEE 16th international workshop on signal processing advances in wireless communications (SPAWC), June 2015 | Affiliate Access Only | machine learning | June 28, 2015 |
H Yang, TL Marzetta, “On existence of power controls for massive MIMO,” 2015 IEEE International Symposium on Information Theory (ISIT), June 2015 | Affiliate Access Only | machine learning | June 14, 2015 |
H Yang, TL Marzetta, “Energy efficient design of massive MIMO: How many antennas?,” 2015 IEEE 81st Vehicular Technology Conference (VTC Spring), 1-5, May 2015 | Affiliate Access Only | machine learning | May 11, 2015 |
H Yang, TL Marzetta, “Performance of pilot reuse in multi-cell massive MIMO,” 2015 IEEE international black sea conference on communications and networking (BlackSeaCom), May 2015 | Affiliate Access Only | machine learning | May 1, 2015 |
TL Marzetta, “Massive MIMO: an introduction,” Bell Labs Technical Journal 20, 11-22, 2015 | Affiliate Access Only | machine learning | April 9, 2015 |
E Björnson, EG Larsson, TL Marzetta, “Massive MIMO: Ten myths and one critical question,” | Affiliate Access Only | machine learning | March 23, 2015 |
H Yang, TL Marzetta, “Quantized beamforming in Massive MIMO,” 2015 49th Annual Conference on Information Sciences and Systems (CISS), 1-6, March 2015. | Affiliate Access Only | machine learning | March 18, 2015 |
Fanyi Duanmu, Zhan Ma, Yao Wang, “Fast CU Partition Decision Using Machine Learning for Screen Content Compression,” IEEE International Conference on Image Processing (ICIP), Québec, Canada, 2015. | Affiliate Access Only | machine learning | January 1, 2015 |
S. Sun, T. S. Rappaport, R. W. Heath, A. Nix, S. Rangan, “MIMO for millimeter-wave wireless communications: beamforming, spatial multiplexing, or both?” IEEE Communications Magazine, vol. 52, no. 12, pp. 110-121, December 2014. | Affiliate Access Only | machine learning, MIMO, mmwave rappaport, terahertz | November 26, 2014 |
Shervin Minaee, Yao Wang, Yvonne W Lui, “Prediction of Longterm Outcome of Neuropsychological Tests of MTBI Patients Using Imaging Features,” IEEE Signal Processing in Medicine and Biology Symposium, 2013. | Affiliate Access Only | machine learning | December 7, 2013 |
F Boccardi, RW Heath Jr, A Lozano, TL Marzetta, P Popovski, “Five disruptive technology directions for 5G,” arXiv preprint arXiv:1312.0229, Dec. 2013 | Affiliate Access Only | machine learning | December 1, 2013 |
H Yang, TL Marzetta, “Total energy efficiency of cellular large scale antenna system multiple access mobile networks,” 2013 IEEE Online Conference on Green Communications (OnlineGreenComm), 27-32, Oct. 2013 | Affiliate Access Only | machine learning | October 29, 2013 |
HQ Ngo, EG Larsson, TL Marzetta, “Massive MU-MIMO downlink TDD systems with linear precoding and downlink pilots,” 2013 51st Annual Allerton conference on communication, control, and … | Affiliate Access Only | machine learning | October 5, 2013 |
HQ Ngo, EG Larsson, TL Marzetta, “The multicell multiuser MIMO uplink with very large antenna arrays and a finite-dimensional channel,” IEEE Transactions on Communications 61 (6), 2350-2361, June 2013 | Affiliate Access Only | machine learning | June 1, 2013 |
EG Larsson, O Edfors, F Tufvesson, TL Marzetta, “Massive MIMO for next generation wireless systems,” arXiv preprint arXiv:1304.6690, April 2013 | Affiliate Access Only | machine learning | April 24, 2013 |
HQ Ngo, EG Larsson, TL Marzetta, “Energy and spectral efficiency of very large multiuser MIMO systems,” IEEE Transactions on Communications 61 (4), 1436-1449, April 2013 | Affiliate Access Only | machine learning | April 1, 2013 |
H Yang, TL Marzetta, “Performance of conjugate and zero-forcing beamforming in large-scale antenna systems,” IEEE Journal on Selected Areas in Communications 31 (2), 172-179, Feb. 2013 | Affiliate Access Only | machine learning | February 1, 2013 |
F Fernandes, A Ashikhmin, TL Marzetta, “Inter-cell interference in noncooperative TDD large scale antenna systems,” IEEE Journal on Selected Areas in Communications 31 (2), 192-201, Feb. 2013 | Affiliate Access Only | machine learning | February 1, 2013 |
C Shepard, H Yu, N Anand, E Li, T Marzetta, R Yang, L Zhong, “Argos: Practical many-antenna base stations,” Proceedings of the 18th annual international conference on Mobile computing and networking | Affiliate Access Only | machine learning | August 22, 2012 |
A Ashikhmin, T Marzetta, “Pilot contamination precoding in multi-cell large scale antenna systems,” 2012 IEEE International Symposium on Information Theory Proceedings, July 2012 | Affiliate Access Only | machine learning | July 1, 2012 |
F Rusek, D Persson, BK Lau, EG Larsson, TL Marzetta, O Edfors, “Scaling up MIMO: Opportunities and challenges with very large arrays,” arXiv preprint arXiv:1201.3210, Jan. 2012 | Affiliate Access Only | machine learning | January 16, 2012 |
HQ Ngo, TL Marzetta, EG Larsson, “Analysis of the pilot contamination effect in very large multicell multiuser MIMO systems for physical channel models,” ICASSP, 3464-3467, May 2011 | Affiliate Access Only | machine learning | May 22, 2011 |
TL Marzetta, “Noncooperative cellular wireless with unlimited numbers of base station antennas,” IEEE Transactions on Wireless Communications 9 (11), 3590 | Affiliate Access Only | machine learning | November 1, 2010 |
“A random matrix-theoretic approach to handling singular covariance estimates,” IEEE Transactions on Information Theory 57 (9), 6256-6271, Oct. 2010 | Affiliate Access Only | machine learning | October 4, 2010 |
J Jose, A Ashikhmin, TL Marzetta, S Vishwanath, “Pilot contamination and precoding in multi-cell TDD systems,” IEEE Transactions on Wireless Communications 10 (8), 2640-2651 | Affiliate Access Only | machine learning | June 30, 2010 |
K Appaiah, A Ashikhmin, TL Marzetta, “Pilot contamination reduction in multi-user TDD systems,” 2010 IEEE International Conference on Communications, 1-5, May 2010 | Affiliate Access Only | machine learning | May 23, 2010 |