We had a successful project meeting in Uppsala/Stockholm last month – Håkan Wieslander presented his latest research on image feature analysis, Phil Harrison his latest conformal prediction models, Ben Blamey demonstrated the prototype HASTE pipeline, Niharika Gauraha her work on SVM+. Alan Sabirsh and Johan Karlsson explained a little more about their work at Astrazeneca.
On day 2, we visited Vironova in Stockholm, and were treated to a hands-on demo of their MiniTEM electron microscope – and discussed plans for the next project phase.
Oliver’s MSc thesis will investigate intelligent ways to manage and position docker containers in a VM environment, in order to improve efficiency in physical resource usage and maintain performance. The implementation of such a controller system will be developed in coordination with the HarmonicIO streaming framework used in HASTE, which will help the automatic scaling of containers working in the system as well as evaluate the design with a real use case.
Discovering new drugs is becoming more costly. Lars Carlsson gave a presentation Machine Learning For Smarter Drug Discovery at RISE SICS Data Science & AI Day, Nov 28, 2017, where he gave some examples of how AstraZeneca is trying to improve the drug discovery phases through the use of machine learning.
We welcome Phil Harrison as new PhD Student in the Spjuth lab. Phil obtained his first PhD in marine biology in 2006 studying the population dynamics of grey seals. Between 2006-2016 he undertook several research projects modelling wildlife populations and analysing trends in biodiversity. In the HASTE project, Phil will develop machine learning methods for online, large-scale analysis of microscopy image data based on statistical earning including e.g. conformal prediction and probabilistic prediction.
We are nearing the end of an intensive recruitment period, looking for excellent established and emergent scientists to help us realize the goals of this interdisciplinary project.
This week we are very pleased to welcome Dr. Ben Blamey to the team. He will work in the Hellander lab, in close collaboration with Dr. Salman Toor, and focus on computer science challenges in designing and developing smart and efficient systems for managing scientific data, and image data in particular, in distributed computing infrastructure such as hybrid and fog cloud.
With a background on research in machine learning, natural language processing and in development of services in cloud infrastructure both in academia and in industry, Dr. Blamey brings critical experience to the team.
In the featured image Dr. Blamey (right) is busy discussing a potential design of an intelligent system to manage information hierarchies in distributed environments with Dr. Toor (left).
We are happy to welcome Håkan Wieslander to the team and to PhD education at the department of Information Technology, Uppsala University!
Håkan grew up in Lund, Sweden and moved to Uppsala 2011 to study Engineering Physics. In 2017 he obtained a masters degree in computational science. The MSc thesis was about classification of malignant cells using deep learning.
About the PhD project within HASTE:
Collection of large amounts of data often results in high-quality, highly informative data intermixed with data that is either of poor quality or of little interest in relation to the question at hand. Wieslander’s thesis work will focus on development of computationally inexpensive measurements that will identify non-informative data early on in the analysis process; either online at data collection, or off-line prior to full data analysis. The challenge is to use minimal computational time and power to extract a broad range of informative measurements from spatial-, temporal-, and multi-parametric image data, useful as input for conformal predictions and efficient enough to work well in a streaming setting.
We are happy to welcome Lovisa Lugnegård to the team! Lovisa will write her MSc thesis within the project. She plans to design and prototype a cloud-based simulator capable of streaming already generated microscopy data, varying a wide range of parameters and emulating realistic scenarios when running high-content imaging platforms. This will be a valuable tool for quick prototyping of new algorithms, and for mapping out e.g. performance requirements for feature extraction methods in real-time scenarios.