School of Engineering
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Postdoctoral Scholar, Electrical Engineering
Current Research and Scholarly InterestsBlockchain consensus for permissionless blockchains in proof-of-work and proof-of-stake. Blockchain bootstrapping in proof-of-work using Non-Interactive Proofs of Proof-of-Work (PoPoW, NIPoPoWs) and in proof-of-stake using Proofs of Proof-of-Stake (PoPoS). Superlight clients and logspace mining. Cross-chain communication, sidechains, chain interoperability. Gradual deployment mechanisms (soft forks, velvet forks), blockchain upgradability. Optimistic and zero-knowledge rollups. Smart contracts.
Ph.D. Student in Electrical Engineering, admitted Autumn 2021
BioOrr Zohar, from Haifa, Israel, is pursuing a PhD in electrical engineering at Stanford School of Engineering. He graduated summa cum laude from the Technion with a bachelor's degree in chemical engineering and a master’s degree in electrical engineering. Orr aspires to research, develop, and translate novel machine learning methods into the open surgical domain for applications such as AI-assisted surgery and surgical skill evaluation. Currently, developing novel learning methods in open-world learning and action quality evaluation at MARVL, advised by Prof. Serena Yeung.
Before coming to Stanford, he was a machine learning and algorithms engineer at proteanTecs and a junior researcher at the Technion's LNBD, where he developed soft electronic platforms that can heal, detect damage, and serve as multifunctional electronic skins. During his undergraduate degree, Orr worked as a visiting undergraduate researcher at the de la Zerda group, Stanford University, where he developed OCT image processing algorithms for improved molecular contrast and depth-of-field. Orr is a Bazan Group scholar and was awarded the Sieden family prize for his contributions to YBCO-based photon detectors' development.
Assistant Professor of Biomedical Data Science and, by courtesy, of Computer Science and of Electrical Engineering
Current Research and Scholarly InterestsMy group works on both foundations of statistical machine learning and applications in biomedicine and healthcare. We develop new technologies that make ML more accountable to humans, more reliable/robust and reveals core scientific insights.
We want our ML to be impactful and beneficial, and as such, we are deeply motivated by transformative applications in biotech and health. We collaborate with and advise many academic and industry groups.