Data Science Lab

• Use Generative Adversarial Networks (GANs) to generate high-resolution X-ray images of COVID-19 infected lungs
• Generate unseen and rare patterns, such as lung CT-scans for patients with both COVID-19 and lung cancer

Collaborators:

• Dr. Hossein Shirazi, Department of Management Information Systems, SDSU
• Department of Computer Engineering at Thapar Institute of Engineering and Technology, India

Submitting to Conference on Information and Knowledge Management (CIKM), 2022 Submitting proposal to NSF Smart Health and Biomedical Research in the Era of Artificial Intelligence and Advanced Data Science (SCH) Student:

• Sehajpreet Kaur (Undergraduate research, TIET)

• Anomaly detection from videos of underground infrastructures in Imperial Beach
• Use video processing and machine learning to detect and locate defects in the pipes

Collaborator: Dr. Hassan Davani from Department of Civil and Environmental Engineering at SDSU Student:

• Shad Fernandez (Master’s project, SDSU)

• Develop a cloud-based data management system to store and manage sensor data obtained from multiple metal printers.
• Develop machine learning models to detect anomalies in the sensor data.

Collaborator: Dr. John Kang, Mechanical Engineering Department at SDSU Submitting the proposal to NSF Future Manufacturing Program

• Use exploratory data science to analyze the impact of research projects funded by NIH
• Analyze effect of various project factors on the outcome impact
• Equity analysis by studying NIH funding allocated to different genders

Student:

• Diana Rozenshteyn (Master’s thesis, SDSU)

• Identify How effective the various machine learning algorithms are for predicting energy usage when they have been trained on pre-pandemic data
• Identify the impact of COVID-19 on energy consumption on different types of premises

Collaborator: Jerry Duggan from Energy Institute at Colorado State University Submitting to IEEE International Conference on Cognitive Machine Intelligence, 2022 Students:

• Sai Aparna Avva (Master’s project, SDSU)
• Gabriele Maurina (PhD, Department of Computer Science, Colorado State University)

Conduct deep learning analysis to identify brain reward and inhibition markers of irritability severity/trajectory Use CNN analysis to predict children’s irritability trajectory class as well as DSM diagnoses and non-overlapping symptoms at 2-YRFU using change in brain activation Collaborators: Dr. Jillian Lee Wiggins and Dr. Yukari Takarae, Translational Emotion Neuroscience & Development (TEND) Lab, SDSU Students: • Johanna Walker (PhD, Department of Psychology, SDSU) • Conner Swineford (Undergraduate research, Department of Psychology, SDSU)

• Compare existing anomaly detection approaches for sequence data
• Conduct an experimental study of the approaches using same datasets

Collaborator: Dr. Ali Balador and Dr. Sima Sinaei, RISE Research Institutes of Sweden Student:

• Alireza Dehlaghi (PhD, RISE)

• Design a statistical-based model that automatically detect temporal associations in data
• Evaluate the effectiveness of the approach using real-world sequential data

Published in British International Conference on Databases, 2021 Student:

• Joaquin Cuomo (Master’s project, Department of Computer Science, Colorado State University)

   

• Use our proposed data quality test framework to validate COVID-related data
• Validate COVID-19 patient records in the Anschutz Health Data Compass
• Validate COVID-19 records in the Johns Hopkins, New York Times, and COVID Tracking datasets

Collaborators:

• Michael G. Kahn from the University of Colorado Anschutz
• Saul Lozano from the Centers for Disease Control and Prevention (CDC)

Awarded the Google Cloud Credit Grant to support COVID-19 research Published in Springer Nature Computer Science journal special issue on AI for Healthcare, 2021