NSF Industry/University Cooperative Research Center

Program for Virtual I/UCRC NCSS IAB Meeting

 

Date of Meeting : June 28, 2022

Time of Meeting :  9:00 am to 12:00 pm (MST) (AZ Local Time)

REGISTER here 

Zoom Link to join the Meeting:  https://asu.zoom.us/j/89782072252                                    Password: sensip2022

 

Agenda

1. Opening Remarks : A. Spanias, Director ASU Site 09:00 am
2. Status of the Center : A. Spanias 09:05 am 
3. IAB Issues: Michael Stanley, IAB Chair 9:10am
4. Tiny ML Update: Steve Whalley 09:20 am 

                    

Proposals

1. Sensors and Machine Learning for Healthcare Applications, A. Spanias 09:30 am
2. Plenary Session – Positive Unlabeled Learning for Image Classification, Kristen Jaskie 09:40 am

 

Progress Reports

                    1. Machine Learning Algorithms for Synthetic Aperture Imaging, S. Jayasuriya    10:0 am
                     2. Deep Learning Based Massive MIMO Channel Acquistion, A. Alkhateeb    10:07 am
                       3. Energy-Efficient Object Tracking using Adaptive ROI Subsampling & Reinforcement Learning, O. Iqbal     10:14 am
                       4. Bayesian Optimization for Circuit Design, M. Malu      10:21 am
                       5. Machine Learning for MEMS Sensor Validation, V. Narayanaswamy       10:28 am

                                     6.

 Implementation and Analysis of Quantum Homomorphic Encryption, M. Yarter        10:35 am

                      7.

 Virus Identification from Solutions using Spectral Unmixing Model, R. Saha        10:42 am

Break 10:50am – 11:05am

 Elevator Pitch Presentations (11:05 am onwards)

PhD Researchers:

FPGA-Based Adaptive Subsampling for Energy Efficiency of Image Sensors in Tracking Applications, O. Iqbal
Visual 3D Reconstruction in the Wild, D. Ramirez
Data Priors in Deep Learning, S. Katoch
Performance Benchmarks and Comparison of Quantum Simulators for Quantum Machine Learning, R. Kumar
RAPID project – Covid-19 hotspot density estimation, B. Patel
Design and Implementation of Smart Monitoring Device, D. Pujara

 

NSF IRES –ASU- University of Cyprus Researchers

Multi Class Model Comparisons for Solar Panel Fault Detection, K. McGuffie Click Here
Quantum Machine Learning for Solar Panel Fault Detection, T. Irvin  Click Here
Machine Learning for Solar Panel Fault Detection, S. Naik  Click Here
Solar Fault Detection Using Quantum Machine Learning, N. Kyriacou Click Here

 

IRES – ASU-Dublin City University Researchers:

Care Sensors for Iron Detection, E. Montoya Click Here
Application of a Microfluidics System for Iron Detection in Water, A. Nguyen Click Here
Application of Machine Learning for Signal Processing to Detect Iron Content on Iron Sensor, A. Mayers Click Here
Joint Classification and Segmentation of 2D T1-weighted CE-MRI Images using Deep Learning, G. Billingsley Click Here
Move Well Being Well: Predicting VO2 Max using FMS Testing of 5 to 14-Year-Olds, C. McConnell Click Here
Predicting VO2 Max with Machine Learning, M. Gunatilaka Click Here

 

REU ASU Researchers:

Machine Learning for Intracranial Tumor Treating Field Modeling, A. Bawa Click Here
Baby Boot: Devising a Multimodal Sensor for Enhanced Infant Monitoring, S. Radhakrishnan Click Here
Hybrid Quantum-classical Neural Network for Semantic Segmentation, H. Kim Click Here
Quantum Machine Learning for Audio Classification, D. McComas Click Here
Machine Learning for Medical Imaging, M. Vollkommer Click Here

 

RET Researchers:

Deep Learning-based Monocular Depth Estimation, E. Sen Click Here
Microbiology and Machine Learning: Fungal Enumeration and Classification using ML Algorithms, S. Clemens Click Here
Estimate Performance of Narrow Band Channel Using Linear Regression in Machine Learning, A. Mamun Click Here
CT Lung Segmentation of Patients with COVID-19, S. Stefan Click Here
ML for Newborn Medical Sensors, R. Diaz Click Here
Neonatal Baby Boot, K. Ernsberger
Discussion/Next Steps 11:30pm
Adjourn 12:00pm

Posters

Title of Project Authors
Space-Based Computational Imaging Systems O. Iqbal, S. Jayasuriya, A. Spanias
Nanopore Sensors and Signal Processing Algorithm for Health Monitoring

M. Malu, M. Goryll, A. Spanias, T. Thornton
Regularizing Deep Neural Networks for Quantization Robustness P. Kadambi, V. Berisha
Reconfiguring Photovoltaic Arrays for Optimizing Power Output using Neural Networks V. Narayanaswamy, A. Spanias, R. Ayyanar and C. Tepedelenlioglu
Semi-Supervised Learning and Graph Signal Processing using Attention based Graph Neural Network U. Shanthamallu, H. Song, A. Spanias, J. Thiagarajan
Machine Learning for MIMO Channel Prediction J. Booth, A. Alkhateeb, A. Ewaisha, A. Spanias
Fault Classification in PV Arrays using Machine Learning S. Rao, C. Tepedelenlioglu, D. Srinivasan, A. Spanias
Global Optimization of Graph Filters With Multiple Shift Matrices J. Fan, C. Tepedelenliogu, A. Spanias
Learning from Positive and Unlabeled Data K. Jaskie, A. Spanias
Invenio: Discovering Hidden Relationships Between Tasks/Domains Using Structured Meta Learning S. Katoch, K. Thopalli, J. Thiagarajan, P. Turaga, A. Spanias
Machine Learning for MEMS sensor validation G. Muniraju, L. Canales, T. Li, A. Spanias, S. Garre

Our Industry Membership History – Currently 10 memberships