Machine Learning Systems Lab (OxMLSys)

Overview

We investigate a variety of open problems that sit at the intersection of machine learning and various forms of computational systems (viz. embedded, cloud, mobile). The scientific contributions of our lab often take one of two forms. First, the development of novel algorithmic and theoretically principled machine learning methods — especially those with applications to the modeling of data such as image, audio, spatial and inertial information. Second, the design and architecture of system software that treat machine learning computation as a first-class citizen — this often results in transformative increases in training and inference efficiency. Our unifying aim is to invent the next-generation of device- and cloud-based systems able to perceive, reason and react to complex real-world environments and users with high levels of precision and efficiency. We seek to achieve this impact through holistic full-stack approaches that encourage lab members with skills in algorithms, hardware, statistics, mathematics and software to work closely together to solve critical challenges in this area. Our cross-discipline lab is part of the Cyber-Physical Systems theme and Department of Computer Science at the University of Oxford.

Current Members

Faculty

• Nicholas D. Lane

PhD Students

• Catherine Tong
• Milad Alizadeh
• Javier Fernandez
• Filip Svoboda
• Edgar Liberis
• Vivek Kothari
• Akhil Mathur (UCL, co-supervised with: Nadia Berthouze)
• Chongyang Wang (UCL, co-supervised with: Nadia Berthouze)

Joining and collaborating with the Lab

If you are interested in joining or collaborating with the lab please feel free to contact me. You can review the current list of open positions here