About Me

I am a fourth-year Ph.D. student in Department of Electrical and Computer Engineering at Boston University, advised by Prof. Wenchao Li.
 

My research interests lie in the intersection of Machine Learning (ML), Formal Methods (Verification and Synthesis) and Robotics.

 

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[2020/08/13] Arxiv draft of “Adversarial Training and Provable Robustness: A Tale of Two objectives” is available.
[2020/07/07] Our paper “Divide and Slide: Layer-Wise Refinement for Output Range Analysis of Deep Neural Networks” has been accepted to EMSOFT'20.
[2020/06/23] Our new tool paper "ReachNN*: A Tool for Reachability Analysis ofNeural-Network Controlled Systems" will appear at ATVA'20.
[2019/08/9] Our new paper "Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems" will appear at ICCAD'19.
[2019/07/10] Our paper “ReachNN: Reachability Analysis of Neural-Network Controlled Systems” has been accepted to appear at EMSOFT’19.
[2019/03/20] Our Paper "Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation" has been accepted at ICLR’19 Workshop on SafeML.
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Updates

Selected Papers

Jiameng Fan and Wenchao Li

Adversarial Training and Provable Robustness: A Tale of Two Objectives [pdf]

A joint training scheme to efficiently train verifiably robust deep neural networks that achieves state-of-art verified robustness in many settings.

Jiameng Fan, Chao Huang, Wenchao Li, Xin Chen and Qi Zhu

Towards Verification-Aware Knowledge Distillation for Neural-Network Controlled Systems [pdf]

A novel verification-aware knowledge distillation framework that transfers the knowledge of a trained network to a new and easier-to-verify network.

Chao Huang, Jiameng Fan, Wenchao Li, Xin Chen and Qi Zhu

ReachNN: Reachability Analysis of Neural-Network Controlled Systems [pdf]

A new reachability analysis approach based on Bernstein polynomials that can verify neural-network controlled systems with a more general form of activation functions.

Jiameng Fan and Wenchao Li

Safety-Guided Deep Reinforcement Learning via Online Gaussian Process Estimation [pdf]

Formulate the state-action value function of a notion of trajectory-based safety as a candidate Lyapunov function and extend control-theoretic results to approximate its derivative using online Gaussian Process estimation.