SenSIP Industry Consortium

SenSIP Info in a Quad Chart

The mission of the SenSIP Industry Consortium (originally established as an NSF I/UCRC Site) is to perform use-inspired research and train students in sensor and information systems, digital signal and image processing, wireless communications, machine learning, and quantum AI.

Applications addressed by our center-affiliated faculty include information processing, sensor calibration, biomedical systems, defense and security, environmental technologies, speech/audio processing, 6G+ telephony, imaging and vision systems, smart cameras, low-power realizations, real-time implementations, AI-monitored solar energy, radar, quantum AI simulations with real-life data, and vehicular sensing.

SenSIP Consortium Membership Chart

Download SenSIP Consortium brochure

SenSIP Agreement (Small Business Industrial Membership Agreement)

SenSIP (Membership Agreement)

SenSIP Consortium bylaws

Director

Andreas Spanias

Industry Advisory Board (IAB and Project Directors):

• Matt Fox, Nitesh Shah Raytheon – Surveillance and Machine Learning
• Abhay Dias , NXP – Sensor Fusion
• Esko, Mikkola, Alphacore, Imaging
• Terje Skotheim,  Lightsense, Light Sensors, Spectroscopy
• Joe Marvin, Prime Solutions Group, radar and machine learning
• Devarajan Srinivasan, Solar Monitoring, Poundra LLC
• Lorena Costanza, AWS, Quantum Computing
• Todd Hodges, American Express, Quantum Machine Learning
• Soleh Dib, Nitesh Shah Raytheon – Surveillance and Machine Learning
• Mike Stanley,  Lightsense, Light Sensors, Spectroscopy

Members at Large (Advisors) and Industry/Lab Collaborators

• Diann Dow, On Semi, Machine Learning
• Steve Miller, Aperio DSP (Associate Membership), ML Applications
• Evgeni Gousev, Qualcomm – Computational Imaging Sensors
• Claire Jackoski, Intel
• Ruchir Sehra, Resonea (Associate Membership), Audio Breathing Analytics
• Glen Abousleman, General Dynamics
• Steve Whalley, Worldwide Ventures

The SenSIP industry consortium was established in 2009 as an NSF funded Industry University Cooperative Research Center (I/UCRC) site. Since 2023 the SenSIP consortium now operates as a graduated I/UCRC using the same agreement and bylaws templates as the I/UCRC structure.

Synopsis

• sensors and machine learning
• quantum machine learning
• detection and tracking algorithms for sensors
• source localization with microphone arrays
• motion detection with camera array sensors
• algorithms for waveform design for radar and sonar sensors
• sensor information processing for intrusion and border security
• signal processing for biological and chemical sensors
• information and decision networks for sensor arrays
• acoustic scene characterization

Presentations by the consortium director, Dr. Andreas Spanias and his colleagues and students

• Proposed project on Flexible sensors by the SenSIP Center, UTD, Richardson (Dallas), April 2017
• SenSIP Solar Power Research, KIOS center, Cyprus, Feb. 2017
• The SenSIP REU Site, Prairie View A&M University (HBCU), Dec. 2016.
• The SenSIP Consortium, Intel Vietnam, Ho Chi Minh City, Vietnam, Nov. 2016.
• SenSIP Research in Sensor Data Security,  Global Software (a Hitachi subsidiary in Vietnam), Ho Chi Minh City, Vietnam, Nov. 2016.
• The SenSIP Partnership in International Partnership in Research and Education, Ho Chi Minh University of Technology, Nov. 2016.
• SenSIP Tutorial on Machine Learning, SensMACH 2016, Hilton Scottsdale, Nov 2016.  (audience 51)
• SenSIP Consortium Projects in Machine Learning, SensMACH 2016, Hilton Scottsdale, Nov 2016.  (audience 51)
• The SenSIP Solar Array Facility, University of Cyprus, Nicosia, June 29, 2016  (audience 35).
• SenSIP Research in Audio Processing,  Toyota Institute, University of Chicago, April 2016 (audience 40)
• SenSIP Adaptive Signal Processing Tutorial, Invited Tutorial, IISA, July 14, 2016 (audience 23).
• SenSIP Research and I/UCRC, Signal GeneriX, Limassol, Cyprus, Feb 2016 (audience 10)
• SenSIP Activities in Machine Learning Algorithms, Imperial College, Nov 2015 (audience 30)
• The SenSIP Center and NSF I/UCRC, UOP, Athens, Feb 2014  (audience 25)
• SenSIP Speech Processing Algorithms, Cirrus Logic, June 2013. (audience 20)
• SenSIP Research on Loudness Estimation, Qualcomm, Feb. 2013
• Mobile Sensor Research at SenSIP , LG Communications, San Diego, May 2012 (audience 9)
• Qualcomm, “The SenSIP I/UCRC – Imaging Sensors”, Santa Barbara, Oct 15, 2019, (15 – more by telco)
• ON SEMI, Spring 2019, “The SenSIP I/UCRC Machine Learning Efforts (20)
• SenSIP NSF I//UCRC meeting, Machine Learning for Power converters, Tempe, Oct 2019 (30)
• SenSIP Summer Meeting – Project Status, July 2, 2019, Status of SenSIP Center (36)
• NSF CPS PI Meeting, The Solar CPS Project, Alexandria, Nov 2019 (200)
• SenSIP /NCSS I/UCRC presentation, Alphacore, August 2019
• SenSIP /NCSS I/UCRC presentation, PSG, August 2018
• DELFT University, April 2019, SenSIP Signal Processing project for Solar Systems – The SenSIP I/UCRC, (25)
• SenSIP /NCSS I/UCRC presentation, Virtual,  June 2020  (40)
• SensMACH 2020, Workforce Programs, Oct. 2021  (65)
• SenSIP /NCSS I/UCRC, Virtual, Covid Cough Audio Analytics, June 2021  (40)

Faculty Products and Projects

Grants

Patents

Publications (2026–2021)

1. Anjapuli, L.S.S., Patel, B., Banavar, M.K., Tepedelenlioglu, C., A. Spanias, Schuckers, S. and Achalla, M., 2026. Stability Analysis of a Modified SEIRS Compartmental Model for Infectious Diseases. IEEE Access, 14, pp.10499-10509.
2. Larson, J.S., Haywood, A.M., Marfai, F.S., Johnson, M.E., Babar, N.A., Uehara, G., Klein-Seetharaman, J., Gulick, D., Christen, J.B. and A. Spanias, 2025, November. WIP: Bringing Classical and Quantum Machine Learning in Biomedical and Environmental Applications to the Community College Setting. In 2025 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.
3. Babar, N.A., Lateef, J., Syed, S., Dietlmeier, J., O’Connor, N.E., Raupp, G.B. and A. Spanias, 2025. Brain Tumor Classification in MRI Scans Using Edge Computing and a Shallow Attention-Guided CNN. Biomedicines, 13(10), p.2571.
4. Sharma, A., Uehara, G., Wang, C., Larson, J., Barnard, W. and A. Spanias, 2025. Education Software Development for Undergraduate Exposition on the Use of Quantum Fourier Transforms in Signal Analysis. IEEE Transactions on Education.
5. Goyal, N., Uehara, G. and A. Spanias, 2025, September. Quantum-Enhanced Cancer Detection for Histopathologic Images. In 2025 IEEE International Conference on Image Processing (ICIP) (pp. 1342-1347). IEEE.
6. Nguyen, N.A., Hendricks, A., Montoya, E., Mayers, A., Rajmohan, D., Morrin, A., McCaul, M., Dunne, N., O’Connor, N., A. Spanias and Raupp, G., 2025. New Imaging Method of Mobile Phone-Based Colorimetric Sensor for Iron Quantification. Sensors, 25(15), p.4693.
7. Su, C., Vetaw, G.D., Jayasuriya, S., Uehara, G. and A. Spanias, 2025, July. Random Quantum Circuits as 3D Convolutional Kernels for 3D SAS ATR. In 2025 16th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-4). IEEE.
8. Vittal, S., Ramirez, D., Uehara, G., De Luca, G. and A. Spanias, 2025, July. Quantum Generative Neural Networks for Imaging Applications. In 2025 16th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-4). IEEE.
9. A. Spanias, A.S., 2025, July. International Research and Workforce Development in Machine Learning for Energy Applications-A Fulbright US Scholar Project. In 2025 16th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-4). IEEE.
10. Uehara, G., Vasileva, E., Chaushevska, M., Todevska, M., Kamchev, J., Ivanovski, Z., Dimitrov, D. and A. Spanias, 2025, July. International student training in quantum machine learning. In 2025 16th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-4). IEEE.
11. Ramirez, D.F., Overman, T.L., Jaskie, K., Kleine, M. and A. Spanias, 2025, May. Towards a large language-vision question answering model for MSTAR automatic target recognition. In Automatic Target Recognition XXXV (Vol. 13463, pp. 122-137). SPIE.
12. T.K. Patel, Knutson, D., Uehara, G., Marfai, F., Jazwin, C., Larson, J. and A. Spanias, 2025, May. Image Analysis-Synthesis Using the Quantum Fourier Transform. In 2025 IEEE International Symposium on Circuits and Systems (ISCAS) (pp. 1-5). IEEE.
13. A. Sharma, Uehara, G. and A. Spanias, Arizona State University ASU, 2025. Systems and methods for quantum autocorrelation computation using the qft. U.S. Patent Application 18/936,604.
14. Miller, G., Konstantinov, P., Tearney, G.J., Thrapp, A., Photiou, C., Pitris, C. and A. Spanias, 2025, March. Semantic segmentation and classification of OCT colorectal polyp images. In Optical Coherence Tomography and Coherence Domain Optical Methods in Biomedicine XXIX (Vol. 13305, pp. 127-129). SPIE.
15. Malu, M., Dow, D., Sharma, P., Cottam, A., Binggeli, M., Dasarathy, G., Pedrielli, G. and A. Spanias, 2025. High dimensional Bayesian optimization for circuit design. Intelligent Decision Technologies, 19(3), pp.1271-1282.
16. Miller, L., Patel, T.K., Uehara, G., Naik, S. and A. Spanias, 2024, October. Quantum machine learning and spectrogram fusion for speech recognition. In 2024 58th Asilomar Conference on Signals, Systems, and Computers (pp. 674-678). IEEE.
17. Miller, L., Uehara, G. and A. Spanias, 2024, July. Image Fusion and Quantum Machine Learning for Remote Sensing Applications. In 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-8). IEEE.
18. Larson, J., Pujara, D., Ramirez, D., Miller, L., Patel, T., Babar, N.A. and A. Spanias, 2024, October. WIP: Building a research experience for undergraduates in quantum machine learning. In 2024 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.
19. Naik, S., Uehara, G., Jaskie, K., Miller, L. and A. Spanias, 2024, September. Quantum Positive Unlabeled Learning Algorithms with Applications to Energy. In 2024 IEEE 34th International Workshop on Machine Learning for Signal Processing (MLSP) (pp. 1-6). IEEE.
20. Naik, S., Vaughn, N., Uehara, G.,A. Spanias and Jaskie, K., 2024, June. Quantum classification for synthetic aperture radar. In Automatic Target Recognition XXXIV (Vol. 13039, pp. 112-120). SPIE.
21. Pujara, D., Ramirez, D., Tepedelenlioglu, C., Srinivasan, D. and A. Spanias, 2024, May. Real-time pv fault detection using embedded machine learning. In 2024 IEEE 7th International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 1-5). IEEE.
22. Miller, L., Uehara, G. and A. Spanias, 2024, March. Quantum image fusion methods for remote sensing. In 2024 IEEE Aerospace Conference (pp. 1-9). IEEE.
23. Mahmood, A.U., Islam, M., Gulyuk, A.V., Briese, E., Velasco, C.A., Malu, M., Sharma, N.,A. Spanias, Yingling, Y.G. and Westerhoff, P., 2024. Multiple data imputation methods advance risk analysis and treatability of co-occurring inorganic chemicals in groundwater. Environmental Science & Technology, 58(46), pp.20513-20524.
24. Miller, L., Uehara, G. and A. Spanias, 2024, March. Quantum image fusion methods for remote sensing. In 2024 IEEE Aerospace Conference (pp. 1-9). IEEE.
25. Ramirez, D.F., Pujara, D., Tepedelenlioglu, C., Srinivasan, D. and A. Spanias, 2024, July. Infrared computer vision for utility-scale photovoltaic array inspection. In 2024 15th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-4). IEEE.
26. Malu, M., Pedrielli, G., Dasarathy, G. and A. Spanias, 2024, June. ClassBO: Bayesian Optimization for Heterogeneous Functions. In International Conference on Learning and Intelligent Optimization (pp. 249-253). Cham: Springer Nature Switzerland.
27. SA, L.S., Patel, B., Banavar, M.K., Tepedelenlioglu, C.,A. Spanias and Schuckers, S., 2023, October. Analysis of a modified SEIRS compartmental model for COVID-19. In 2023 57th Asilomar Conference on Signals, Systems, and Computers (pp. 965-969). IEEE.
28. Yarter, M., Uehara, G. and A. Spanias, 2023, August. Investigating a quantum cloud paradigm with quantum neural networks. In 2023 IEEE 66th International Midwest Symposium on Circuits and Systems (MWSCAS) (pp. 900-903). IEEE.
29. Wang, C., Sharma, A., Uehara, G., Miller, L., Pujara, D., Barnard, W., Larson, J. and A. Spanias, 2023, October. Introducing quantum computing in a sophomore signals and systems course. In 2023 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.
30. Sharma, A., Uehara, G. and A. Spanias, 2023, October. Quantum linear prediction for system identification and spectral estimation applications. In 2023 57th Asilomar Conference on Signals, Systems, and Computers (pp. 1151-1155). IEEE.
31. Billingsley, G., Dietlmeier, J., Narayanaswamy, V.,A. Spanias and O’Connor, N.E., 2023, October. AN L 2-normalized spatial attention network for accurate and fast classification of brain tumors in 2D T1-weighted CE-MRI images. In 2023 IEEE International Conference on Image Processing (ICIP) (pp. 1895-1899). IEEE.
32. Pujara, D., Ramirez, D., Tepedelenlioglu, C., Srinivasan, D. and A. Spanias, 2023, July. Design of a New Photovoltaic Intelligent Monitoring and Control Device. In 2023 14th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-4). IEEE.
33. Jaskie, K., Vaughn, N., Narayanaswamy, V., Zaare, S., Marvin, J. and A. Spanias, 2023, June. An adaptive asymmetric loss function for positive unlabeled learning. In Automatic Target Recognition XXXIII (Vol. 12521, pp. 198-205). SPIE.
34. Miller, L., Uehara, G., Sharma, A. and A. Spanias, 2023, June. Quantum machine learning for optical and sar classification. In 2023 24th International Conference on Digital Signal Processing (DSP) (pp. 1-5). IEEE.
35. Katoch, S., Iqbal, O., A. Spanias and Jayasuriya, S., 2023. Energy-efficient object tracking using adaptive ROI subsampling and deep reinforcement learning. IEEE Access, 11, pp.41995-42011.
36. Narayanaswamy, V., Ayyanar, R., Tepedelenlioglu, C., Srinivasan, D. and A. Spanias, 2023. Optimizing solar power using array topology reconfiguration with regularized deep neural networks. IEEE Access, 11, pp.7461-7470.
37. Narayanaswamy, V., Ayyanar, R., Tepedelenlioglu, C., Srinivasan, D. and A. Spanias, 2023. Optimizing solar power using array topology reconfiguration with regularized deep neural networks. IEEE Access, 11, pp.7461-7470.
38. Jayasuriya, S., Iqbal, O., Kodukula, V., Torres, V., Likamwa, R. and A. Spanias, 2023. Software-defined imaging: a survey. Proceedings of the IEEE, 111(5), pp.445-464.
39. Malu, M., Pedrielli, G., Dasarathy, G. and A. Spanias, 2023, June. Class GP: Gaussian process modeling for heterogeneous functions. In International Conference on Learning and Intelligent Optimization (pp. 408-423). Cham: Springer International Publishing.
40. A. Sharma, Uehara, G., Narayanaswamy, V., Miller, L. and A. Spanias, 2023, June. Signal analysis-synthesis using the quantum Fourier transform. In ICASSP 2023-2023 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 1-5). IEEE.
41. Rao, S., Pujara, D., A. Spanias, Tepedelenlioglu, C. and Srinivasan, D., 2023, May. Real-time Solar Array Data Acquisition and Fault Detection using Neural Networks. In 2023 IEEE 6th International Conference on Industrial Cyber-Physical Systems (ICPS) (pp. 1-5). IEEE.
42. A. Spanias, Narayanaswamy, V., Forzani, E., Raupp, G., Kellam, N., O’Donnell, M., Barnard, W., Larson, J., O’Connor, N., Dunne, N. and Daniels, S., 2022, July. The ASU-DCU International Research and Workforce Development Program on Sensors and Machine Learning. In 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-6). IEEE.
43. Narayanaswamy, V., Mubarka, Y., Anirudh, R., Rajan, D., A. Spanias and Thiagarajan, J.J., 2022. Know your space: Inlier and outlier construction for calibrating medical OOD detectors. arXiv preprint arXiv:2207.05286.
44. Esposito, M., Uehara, G. and A. Spanias, 2022, July. Quantum machine learning for audio classification with applications to healthcare. In 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-4). IEEE.
45. Yarter, M., Uehara, G. and A. Spanias, 2022, July. Implementation and analysis of quantum homomorphic encryption. In 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-5). IEEE.
46. Uehara, G.S., Narayanaswamy, V., Tepedelenlioglu, C. and A. Spanias, 2022, July. Quantum machine learning for photovoltaic topology optimization. In 2022 13th International Conference on Information, Intelligence, Systems & Applications (IISA) (pp. 1-5). IEEE.
47. Narayanaswamy, V., Mubarka, Y., Anirudh, R., Rajan, D., A. Spanias and Thiagarajan, J.J., 2022. Revisiting Inlier and Outlier Specification for Improved Out-of-Distribution Detection (No. LLNL-CONF-835509). Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States).
48. Narayanaswamy, V., Anirudh, R., Kim, I., Mubarka, Y., A. Spanias and Thiagarajan, J.J., 2022, May. Predicting the generalization gap in deep models using anchoring. In ICASSP 2022-2022 IEEE International Conference on Acoustics, Speech and Signal Processing (ICASSP) (pp. 4393-4397). IEEE.
49. Sattigeri, P., Thiagarajan, J., Ramamurthy, K., A. Spanias, Banavar, M., Dixit, A., Fan, J., Malu, M., Jaskie, K., Rao, S. and Shanthamallu, U., 2022. Instruction Tools for Signal Processing and Machine Learning for Ion-Channel Sensors. International Journal of Virtual and Personal Learning Environments (IJVPLE), 12(1), pp.1-17.
50. Fan, J., Tepedelenlioglu, C. and A. Spanias, 2022. Graph-based classification with multiple shift matrices. IEEE Transactions on Signal and Information Processing over Networks, 8, pp.160-172.
51. Jaskie, K. and A. Spanias, 2022. Positive unlabeled learning. Morgan & Claypool Publishers.
52. Uehara, G., Larson, J., Barnard, W., Esposito, M., Posta, F., Yarter, M., A. Sharma, Kyriacou, N., Dobson, M. and A. Spanias, 2022, October. Undergraduate research and education in quantum machine learning. In 2022 IEEE Frontiers in Education Conference (FIE) (pp. 1-5). IEEE.
53. Shanthamallu, U.S. and A. Spanias, 2022. Machine and deep learning applications. In Machine and Deep Learning Algorithms and Applications (pp. 59-72). Cham: Springer International Publishing.
54. Shanthamallu, U.S. and A. Spanias, 2022. Neural networks and deep learning. In Machine and Deep Learning Algorithms and Applications (pp. 43-57). Cham: Springer International Publishing.
55. Malu, M., Dasarathy, G. and A. Spanias, 2021, July. Bayesian optimization in high-dimensional spaces: A brief survey. In 2021 12th International conference on information, intelligence, systems & applications (IISA) (pp. 1-8). IEEE.
56. Shanthamallu, U.S. and A. Spanias, 2021. Machine and deep learning algorithms and applications. Morgan & Claypool Publishers.
57. Iqbal, O., Muro, V.I.T., Katoch, S., A. Spanias and Jayasuriya, S., 2022. Adaptive subsampling for ROI-based visual tracking: Algorithms and FPGA implementation. IEEE Access, 10, pp.90507-90522.
58. Thiagarajan, J., Narayanaswamy, V.S., Rajan, D., Liang, J., Chaudhari, A. and A. Spanias, 2021. Designing counterfactual generators using deep model inversion. Advances in Neural Information Processing Systems, 34, pp.16873-16884.
59. Fan, J., Rao, S., Muniraju, G., Tepedelenlioglu, C. and A. Spanias, Arizona State University ASU, 2025. Systems and methods for fault classification in photovoltaic arrays using graph signal processing. U.S. Patent 12,244,266.
60. Subramanyam, R., Narayanaswamy, V., Naufel, M., A. Spanias and Thiagarajan, J.J., 2021. Improved stylegan-v2 based inversion for out-of-distribution images (No. LLNL-CONF-829448). Lawrence Livermore National Laboratory (LLNL), Livermore, CA (United States).

Sponsored in part by NSF I/UCRC Awards 0934418 and 1035086. NSF Phase 2 I/UCRC Award 1540040.