Lowering the Barrier to Applying Machine Learning
Department of Computer Science and Engineering at the University of Washington
EECS/Segal Design Faculty Candidate
Data is driving the future of computation: analysis, visualization and learning algorithms power systems that help us diagnose cancer, live sustainably, and understand the universe. Yet, the data explosion has outstripped our tools to process it, leaving a gap between powerful new algorithms and what real programmers can apply in practice.
I examine how data affects the way we program. My current research focuses on machine learning algorithms. I found that the key barrier to adoption is not a poor understanding of the machine learning algorithms themselves, but rather a poor understanding of the process for applying those algorithms and poor tool support for that process. I have created new programming and analysis tools that support programmers by helping them (1) implement machine learning systems and analyze results, (2) debug data and (3) design and track experiments.
Kayur Patel is a Ph.D. student in the Department of Computer Science and Engineering at the University of Washington. Kayur received an M.S in Computer Science from Stanford and a B.S. in Computer Science and Human-Computer Interaction at Carnegie Mellon University. His work has been funded by grants from the NSF and google as well as the NDSEG and Microsoft Research fellowships. Kayur’s research interests are in human-computer interaction, software engineering, machine learning and information visualization. For his thesis, he is studying how programmers apply machine learning to solve problems and build software. Guided by this research, he creates new development tools that help programmers more effectively use machine learning algorithms.
Hosted by EECS Professor, Gokhan Memik.
Contact Debbie Labedz at 847-467-1604 or firstname.lastname@example.org for further questions.