Chih-Hong Cheng 鄭志弘
Consider submitting your AI safety paper to WAISE'20 (deadline May 24, 2020), your formal method paper to ATVA'20 (deadline Apr 26, 2020), or your runtime verification paper to RV'20 (deadline June 15, 2020)!
Contact:
Email: my_family_name(dot)chihhong(at)gmail(dot)com
Short bio:
Currently, Chih-Hong Cheng is a technical manager at DENSO, researching safety technologies for autonomous driving. He is also an adjunct faculty (unpaid/voluntary) at Academia Sinica, the most prestigious academic research institute in Taiwan.
Previously, Chih-Hong was a permanent researcher at fortiss - Research Institute of the Free State of Bavaria, where he developed the research topic of dependable AI for autonomous systems. Before his tenure at fortiss, he was a scientist in ABB Corporate Research Germany, where he worked on projects related to intelligent production systems (Industry 4.0), cloud-related technologies for industrial automation (PaaS, IaaS), and the analysis of complex industrial software systems. Chih-Hong received his doctoral degree with distinction in CS from the Technical University of Munich.
Papers (DBLP):
(copyrights belong to publishers)- Safety-aware hardening of 3D object detection neural network systems [SAFECOMP'20 (pdf)]
- Towards robust direct perception networks for automated driving [IV'20 (pdf)]
- Towards safety verification of direct perception neural networks [DATE'20 (pdf)]
- nn-dependability-kit: Engineering neural networks for safety-critical autonomous driving systems [ICCAD'19 (pdf)]
- Runtime monitoring neuron activation patterns [DATE'19 (pdf)]
- Towards dependability metrics for neural networks [MEMOCODE'18 (pdf)]
- Quantitative projection coverage for testing ML-enabled autonomous systems [ATVA'18 (pdf)]
- Verification of binarized neural networks via inter-neuron factoring [VSTTE'18 (pdf)]
- Neural networks for safety-critical applications - challenges, experiments and perspectives [DATE'18 (pdf)]
- Maximum resilience of artificial neural networks [ATVA'17 (pdf) (video)]
- autoCode4: Structural controller synthesis [TACAS'17 (pdf)]
- Structural synthesis for GXW specifications [CAV'16 (pdf) (video)]
- Compositional parameter synthesis [FM'16]
- Semantic degrees for Industrie 4.0 engineering [ESEC/FSE'15 (pdf)]
- Formal consistency checking over specifications in natural languages [DATE'15 (pdf) (slides)]
- G4LTL-ST: Automatic generation of PLC programs [CAV'14 (pdf)(video)]
- JBernstein: A validity checker for generalized polynomial constraints [CAV'13 (pdf)]
- MGSyn: Automatic synthesis for industrial automation [CAV'12 (pdf) (video1, video2) ]
- Algorithms for synthesizing priorities in component based systems [ATVA'11 (pdf)]
Talks (slides available for download):
- Introduction to safe machine learning, a talk given at SOTIF US conference at Austin
- When neural networks meet dependability, a talk given at various circumstances