CS Colloquium

Kyle Kabasares
NASA Ames Research Center

Stevenson 1300
Monday, September 8, 2025

Accurately identifying clouds in satellite images is important for studying the Earth’s surface. Traditional methods, such as radiative transfer models and basic machine learning techniques, often struggle to process complex satellite data from the GOES-R system. In this work, I developed a deep learning-based approach to improve cloud masking by combining spatial and temporal information. I built a U-Net convolutional neural network (CNN) to analyze images pixel by pixel, classifying each pixel as CLEAR, PARTLY CLOUDY, or CLOUDY. The model was trained using a set of carefully selected satellite images and achieved nearly 88% accuracy on test and validation data. However, some challenges remain, such as distinguishing clouds from shadows and snow-covered mountains, which can lead to misclassification. Future improvements will focus on optimizing input data, refining the labeling process, and designing better methods to measure model reliability. 

Tapti Palit
UC Davis

Stevenson 1300
Monday, October 13, 2025

C and C++ remain two of the most widely used programming languages, powering everything from operating systems to critical infrastructure. However, their lack of built-in memory safety leaves applications vulnerable to exploitation, and memory corruption vulnerabilities cost the industry billions of dollars annually. To mitigate these risks, software defenses such as Control Flow Integrity (CFI) are deployed, but their effectiveness depends heavily on the precision of underlying program analysis.

In this talk, I will present my research on advancing program analysis techniques to improve software security. First, I will introduce the Invariant-Guided Pointer Analysis technique, which enhances the precision of CFI mechanisms by 59%, thus significantly improving its security guarantees. Then, I will discuss our lab's latest research on automatically transpiling C/C++ code into memory-safe languages, like Rust. Specifically, I will describe our hybrid approach, which combines Large Language Models (LLMs) with program analysis techniques to achieve high-accuracy C-to-Rust transpilation. Together, these efforts improve software security for legacy software and building a foundation for safer, more reliable software systems.

Dept Event

Overlook (Student Center, 3rd floor)
Monday, November 3, 2025

CS students, join us for Advise-a-palooza for Fall 2025 registration.

John P. Sullins III
Sonoma State University

Stevenson 1300
Monday, November 10, 2025

Sociotechnical risks are a reality of all technology design, and one that particularly matters in an organization like the Department of Defense.  We will look at a two-year project housed here at SSU where SSU faculty and students collaborated with Cal Poly SLO faculty and students to build a prototype application for helping DoD projects identify how best to utilize the responsible AI toolkit and NIST Framework as these applied to their particular projects.  We will also examine how LLMs present new problems for military AI applications.

Dept Event

Stevenson 1300
Tuesday, November 25, 2025

Short presentations of research carried out by Sonoma State Computer Science Students, and CS awards.

Dept Event

Stevenson 1300
Tuesday, December 2, 2025

Short presentations of capstone projects carried out by Sonoma State Computer Science Students