StatScale is an ambitious, 6 year programme of research, developing the next generation statistical methods for streaming data. This £2.8M EPSRC programme grant is a collaboration between both Lancaster University and the University of Cambridge, together with AstraZeneca, BT, the Office for National Statistics, Shell and the Yale PET Centre.
News
We are hosting a seminar on 3rd July, 2020
Prof. Dr. Claudia Kirch (Otto-von-Guericke University)
Title: Functional change point detection for fMRI data
See more details on our seminar page
StatScale Researcher Award Success
We are delighted to announce that Tom Berrett, former
StatScale researcher has been awarded the RSS
Research Prize 2020. See RSS website for details
StatScale Welcomes New Appointments
Welcome to Oliver Feng, Ilmun Kim and Henry Reeve
University of Cambridge from Autumn 2020
StatScale Welcomes Another New Appointment
Welcome to Hyeyoung Maeng working at
Lancaster University from October 2019
StatScale Welcomes New Appointment
Welcome to Yoav Zemel working at
University of Cambridge from September 2019
NEW StatScale PhD Studentship Open for Applications
Streaming Identification of Dependency Networks
in Complex Systems
Fully funded at Lancaster University
Mid-Term Research Forum
Successful three days for StatScale Researchers
Took place in June 2019
Recent News
StatScale Welcomes New Appointment
Welcome to Kerstin Reckrühm working at
Lancaster University from March-Aug 2019
StatScale Welcomes New Appointment
Welcome to Florian Pein working with Rajen Shah
at University of Cambridge, 1st November 2018
Statistical Scalability Workshop 2018, Windermere
This event has now finished
thank you to all those who contributed
Recent StatScale Appointments Here
We are delighted to welcome several
new members to our growing team
StatScale Workshop 2017, Lancaster
This event has now finished
Thank you to all those who contributed
StatScale Programme Announced
21st September 2016
EPSRC announces the funding of StatScale as an exciting new programme of research to improve data stream interpretation