School of Engineering
Showing 1-50 of 127 Results
-
Christopher Hahn
Visiting Asst Prof
BioChristopher Hahn is a Visiting Assistant Professor in Computer Science at Stanford University. His research interest lies in the intersection of deep learning and formal methods. He received a Ph.D. (Dr. rer. nat) in Computer Science from Saarland University in 2021, where he was advised by Bernd Finkbeiner. He received a BSc and MSc with distinction in Computer Science from Saarland University. He currently serves as a PC member for IJCAI and ICML. His RV'17, CAV'18, and TACAS'19 papers received an invitation to journal special issues in FMSD, ACTA, and STTT, respectively. His teaching efforts at Saarland University have been awarded the Busy Beaver and BESTE awards.
-
Pat Hanrahan
Canon Professor in the School of Engineering and Professor of Electrical Engineering
BioProfessor Hanrahan's current research involves rendering algorithms, high performance graphics architectures, and systems support for graphical interaction. He also has worked on raster graphics systems, computer animation and modeling and scientific visualization, in particular, volume rendering.
-
Peter Henderson
Ph.D. Student in Computer Science, admitted Autumn 2018
Juris Doctor Student, LawBioI’m a joint JD-PhD (Computer Science) student at Stanford University where I’m lucky enough to be advised by Dan Jurafsky. I’m also an OpenPhilanthropy AI Fellow and a Graduate Student Fellow at the Regulation, Evaluation, and Governance Lab. At Stanford Law School, I help run the Domestic Violence Pro Bono Project. I’m also a Technical Advisor at the Institute for Security+Technology.
Previously, I was lucky enough to be advised by David Meger and Joelle Pineau for my M.Sc. at McGill University and the Montréal Institute for Learning Algorithms. I also spent time as a Software Engineer and Applied Scientist at Amazon AWS/Alexa.
My research focuses on creating robust decision-making systems. My goals are three-fold: (1) use AI to make governments more efficient and fair; (2) ensure that AI isn’t deployed in ways that can harm people; (3) create new ML methods for applications that are beneficial to society.
This involves an eclectic mix of research and fields including: applied and theoretical work in machine learning; investigating reproducible, ethical, sustainable, and thorough research practices and methodologies to ensure that such systems perform as expected when deployed; policy and legal work on the use of AI in government.
