Scholar
Hussein Sibai
Google Scholar ID: St5uCSwAAAAJ
Washington University in St. Louis
Citations & Impact
Citations
663
H-index
10
i10-index
10
Publications
20
Co-authors
8
list available
Contact
Publications
Resume (English only)
Academic Achievements
- ACM SRC Gold Medal, Graduate level, CPS-IoT Week, 2021; Ernest A. Reid fellowship, UIUC, 2021; Mavis Future Faculty fellowship, UIUC, 2020; Rambus fellowship, UIUC, 2020; Best paper award finalist, ATVA, 2019; CMU CPS V &V Workshop travel award, CMU, 2019; Fall conference travel grant, UIUC, 2019; Rambus fellowship, UIUC, 2019; Best poster award, HSCC, 2018; Best paper award finalist, HSCC, 2017; Dean’s honor list, ECE, AUB, 2010-2014; Booz and Company endowed award, ECE, AUB, 2014; First Place, Lebanese Collegiate Programming Contest (LCPC), 2013; Top 5%, IEEExtreme 5.0 Programming Contest, 2012.
Research Experience
- Postdoctoral scholar in the Electrical Engineering and Computer Sciences department at the University of California at Berkeley, co-mentored by Murat Arcak and Sanjit Seshia, between December 2021 and December 2022.
Education
- Received BE degree in Computer and Communications Engineering from the American University of Beirut (AUB) in May 2014; MS in ECE from UIUC in December 2017, advised by Sayan Mitra; Ph.D. from the Electrical and Computer Engineering (ECE) department at the University of Illinois Urbana-Champaign (UIUC) in 2021, also advised by Sayan Mitra.
Background
- I'm an assistant professor in the Computer Science and Engineering department in McKelvey School of Engineering at Washington University in St. Louis. My research group works on developing reliable robots. We address theoretical and practical research questions across the autonomy stack: perception, planning, and control. We leverage tools from deep (reinforcement) learning, formal methods for cyber-physical systems, control theory, and robotics. Current topics of interest: planning and control under uncertainty, foundation models for robotics, (neural) certified control, safe reinforcement learning, and formal methods for ML-enabled systems.
