CRPO Workshop by Prof Le Wei and Prof Yang Wei on AI and Security, 5th July, at Seminar Room 1-1 (Academic Building North)

05 Jul 2024 02.30 PM - 04.00 PM Current Students, Industry/Academic Partners

Date: July 5th, 2024

Time: 2:30pm-4:00pm

Location: Seminar Room 1-1 (ABN), Singapore / Academic Building North, 50 Nanyang Ave, Singapore 639798


2:30-2:40 Welcome Address by Prof Liu Yang

2:40-3:20 Le Wei, AI for vulnerability detection

3:20-4:00 Yang Wei, Enabling Efficiency Robustness and Security of Deep Learning Systems at The Edge

4:00-4:10 End of Workshop

Title : AI for vulnerability detection


Vulnerability detection has been a challenging task for program analysis and AI, as it requires an understanding of program semantics. In this talk, I will first illustrate the challenge of this problem by presenting our studies of recent models and state-of-the-art LLMs. I will then highlight our recent work on building different AI models for vulnerability detection, including (1) data-flow inspired models, (2) causality-based approaches, and (3) models built with program traces. The work will shed light on AI for other SE tasks.


Speaker Bio

Wei Le is an associate professor at Iowa State University. Her research lies at the intersection of program analysis, software engineering, and machine learning. Her work has appeared at top-tier venues including ICSE, FSE, ICML, ASE, ISSTA, KDD, TOSEM and TSE. She is a recipient of an NSF Career Award, a Google Research Award and a Distinguished Paper Award at FSE. Her presentations will be based on her recent ICSE papers, which can be found at

Title : Enabling Efficiency Robustness and Security of Deep Learning Systems at The Edge


Deep Neural Networks (DNNs) have shown potential in many applications. However, the power of using DNNs comes at substantial computational costs. The costs, especially the inference-time cost, can be a concern for deploying DNNs on resource-constrained embedded devices such as mobile phones and IoT devices. To enable deploying DNNs on resource-constrained devices, researchers propose a series of deep learning systems where the amount of inference-time computation varies for different inputs. This talk will present challenges and a series of work to deploy energy-efficient and robust deep learning systems on the Edge systems/devices. This talk first reviews some of my past research and then will discuss a few ongoing work towards enabling the wide-scale deployment of resource-constrained embedded AI systems like UAVs, autonomous vehicles, Robotics, IoT-Healthcare / Wearables, Industrial-IoT, etc.


Speaker Bio

Wei Yang is an associate professor in the Department of Computer Science at the University of Texas at Dallas. He teaches and does research on software engineering and security. He received my Ph.D. in Computer Science from the University of Illinois at Urbana-Champaign, an M.S. in Computer Science from North Carolina State University, and a B.E. in Software Engineering from Shanghai Jiao Tong University. He was a visiting researcher in University of California, Berkeley. He is a recipient of numerous awards including NSF CAREER Award and ACM SIGSOFT Distinguished Paper Award.