Associate Professor
Dept. of Computer Science & Tech.
University of Cambridge

Laboratory Director
Samsung AI, Cambridge

ndl32@cam.ac.uk

@niclane7
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MLSys Lab

Machine Learning Systems Lab

 

 

Overview

Our lab investigates 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 the lab often take one of two forms. First, the development of innovative 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 algorithms, system software and hardware 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 based in the department of Computer Science and Technology at the University of Cambridge.

 

Current Members

Faculty

Research Staff

Adjunct Researchers

PhD Students

 

Former Members

Graduated PhD Students

 

Joining the Lab

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

Machine Learning Systems Lab 

Overview

Our lab investigates 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 the lab often take one of two forms. First, the development of innovative 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 algorithms, system software and hardware 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 based in the department of Computer Science and Technology at the University of Cambridge.

 

Current Members

Faculty

Research Staff

Adjunct Researchers

PhD Students

 

Former Members

Graduated PhD Students

 

Joining the Lab

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