Bultan and Ding – NSF Grant
CS Professors Tevfik Bultan and Yufei Ding receive an NSF grant of $750,000 (4 years) and intend to develop a holistic formal-verification framework that will provide a systematic and principled approach for developing dependable and safe Neural Networks (NNs)
Tevfik Bultan and Yufei Ding | "Scalable and Quantitative Verification for Neural Network Analysis and Design"
Excerpt from the COE/CLS Convergence magazine (F/W21) article "New Grants"
Neural Networks (NNs) have been applied successfully in many areas, including computer vision, speech recognition, and natural language processing. However, the increasing adoption of NNs in safety-critical and socially sensitive domains, such as self-driving cars, robotics, computer security, criminal justice, and medical diagnosis, gives rise to a pressing need for verification techniques that can guarantee the dependability and safety of NN applications. The team intends to develop a holistic formal-verification framework that will provide a systematic and principled approach for developing dependable and safe NNs.
COE/CLS Convergence magazine (F/W21) - "New Grants" (pg. 35)
