Baishakhi Ray Named VMware Early Career Faculty Award Recipient

Assistant professor Baishakhi Ray has won a VMware Early Career Faculty Award to develop machine learning tools that will improve software security. The grant program recognizes the next generation of exceptional faculty members. The gift is made in support of early-career faculty’s research and to promote excellence in teaching.

In today’s world, almost every aspect of our lives is controlled by software. Unfortunately, most software tends to be buggy, often threatening even the most safety- and security-critical software. According to a recent report, 50% of software developers’ valuable time is wasted at finding and fixing bugs costing the global economy around USD$1.1 trillion in 2016 alone.  

“The goal of my research is to address this problem and figure out how to automatically detect and fix bugs to improve software robustness, for both traditional and machine learning-based software,” said Ray, who joined the department in 2018. 

In particular, her research will address two main challenges of software robustness: (i) traditional software have numerous implicit and explicit specifications; it is often difficult to know all of them in advance. (ii) With the advent of machine learning-based systems (e.g., self-driving cars), explicitly providing such specifications involving natural inputs, like image and text, is very hard. 

Ray’s plan is a two-pronged approach. First, she and her team will build novel machine learning models to learn implicit specifications/rules from traditional programs and leverage these rules to detect and fix bugs automatically. However, such techniques are not easily extendable to machine learning-based systems as they follow different software paradigms (e.g., finite state machine vs. neural network). To improve the robustness of such systems, they will also devise new analysis techniques to examine the internal states of the models for potential violations. 

“A successful outcome of this project will produce new techniques to detect and remediate software errors and vulnerabilities with increased accuracy to make software more secure,” said Ray. 

Baishakhi Ray Receives 2019 IBM Faculty Award

IBM has selected assistant professor Baishakhi Ray for an IBM Faculty Award. The highly selective award is given to professors in leading universities worldwide to foster collaboration with IBM researchers. Ray will use the funds to continue research on artificial intelligence-driven program analysis to understand software robustness. 

Although much research has been done, there are still countless vulnerabilities that make system robustness brittle. Hidden vulnerabilities are discovered all the time – either through a system hack or monitoring system’s functionalities. Ray is working to automatically detect system weaknesses using artificial intelligence (AI) with her project, “Improving code representation for enhanced deep learning to detect and remediate security vulnerabilities”.

One of the major challenges in AI-based security vulnerability detection is finding the best source code representation that can distinguish between vulnerable versus benign code. Such representation can further be used as an input in supervised learning settings for automatic vulnerability detection and fixes. Ray is tackling this problem by building new machine-learning models for source code and applying machine learning techniques such as code embeddings. This approach could open new ways of encoding source code into feature vectors. 

“It will provide new ways to make systems secure,” said Ray, who joined the department in 2018. “The goal is to reduce the hours of manual effort spent in automatically detecting vulnerabilities and fixing them.”

A successful outcome of this project will produce a new technique to encode source code with associated trained models that will be able to detect and remediate a software vulnerability with increased accuracy.

IBM researchers Jim Laredo and Alessandro Morari will collaborate closely with Ray and her team on opportunities around design, implementation, and evaluation of this research.