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HASTE final meeting at Uppsala University

The HASTE project is coming to an end by June 2023 and a final meeting was held on 2023-05-26 at the Ångström Laboratory, Uppsala. The participants were: Ola Spjuth (UU), Phil Harrison (UU), Ankit Gupta (UU), Ebba Bergman (UU), Andreina Francisco (UU), Tianru Zhang (UU), Carolina Wählby (UU), Salman Toor (UU), Alan Sabirsh (AstraZeneca), Andreas Hellander (UU), Ida-Maria Sintorn (Vironova).

The meeting program featured presentations reflecting on the HASTE project as a whole, but also presentations by PhD students on the completed projects and a more visionary future perspectives on the continuation of HASTE. One example is the new grants received by the HASTE PIs to carry on with parts of the projects, including an eSSENCE funded PhD project on streaming data analysis of microscopy imaging (PI: Ola Spjuth) and the award from UU Invest’s Venture Challenge 2023 (Ankit Gupta, Ida-Maria sintorn and Carolina Wählby) to continue the SimSearch application(see Gupta et al. IEEE J. Biomedical and Health informatics 26(8), 4079-4089, 2022).

It has been a great project with many successful scientific outcomes. The list of publications summarizes the scientific papers, but also highly important are the code contributions in GitHub and the datasets deposited as a result of the project.

During 2023, we look forward to two HASTE PhD students to defend their theses [updated]:

“Deep learning approaches for image cytometry: assessing cellular morphological responses to drug perturbations” by Philip Harrison PhD Defence on 2023-09-22. Full text in DIVA: http://uu.diva-portal.org/smash/record.jsf?pid=diva2%3A1785791.

“Adapting Deep Learning for Microscopy: Interaction, Application, and Validation” by Ankit Gupta planned date for PhD defence on 2023-12-01

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Dan Rosén joins the HASTE team as a Data Engineer

Dan Rosén, new Data Engineer in the HASTE project.

We are happy to present our newest member of HASTE: Dan Rosén! Dan is joining the group of Ola Spjuth to work as a Data Engineer. In his projects he will work with data pipelines and interact closely with microscopes to help reaching the goals of HASTE to act on collected image streams and make intelligent decisions and control microscopes to prioritize collecting the most interesting data.

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Data Engineer position available in HASTE

We are looking for a skilled Data Engineer to join the HASTE team!

Tasks

In collaboration with other researchers, develop, implement and test systems for AI-controlled automated microscopes. The task includes interacting directly with the microscope and establishing pipelines where models trained on previously taken images decide and control where the microscope should take images in the next step to reach a specific goal. We are looking for a candidate with a genuine interest in technology and automation, and who enjoys solving problems including both practical interaction with hardware (robots, microscopes) and different types of software. Since our microscopes generate large amounts of images, the position will also include large-scale data management and -analysis. You will work with researchers in AI modeling and biological laboratory sciences, and contribute to implementing methods and evaluating them for different types of biological problems.

This is a 2-year position that is part of the HASTE project, funded by the Swedish Foundation for Strategic Research (SSF) aiming at developing new, intelligent ways of processing and managing very large amounts of microscopy images in order to be able to leverage the imminent explosion of image data from modern experimental setups in the biosciences. Industry collaborators are Vironova AB and AstraZeneca AB.

Qualifications

A master’s degree in engineering or a university degree in a relevant field is a requirement. Good programming skills in Python and preferably more programming languages is a requirement. Experience in AI modeling, Linux systems as well as developing REST services and APIs is a requirement. Experience of AI modeling on image data, practical handling of automated microscopes and working with software containers (e.g. Docker/Singularity) is meriting.

Apply via link at the bottom of the University application: https://uu.se/en/about-uu/join-us/details/?positionId=361223

Deadline: Nov 25th, 2020

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Postdoc position: AI methods for large-scale microscopy imaging

We are currently looking for an ambitious, highly motivated Postdoc with a good background in AI and imaging to join the HASTE project.

This is a 2-year postdoc position. Assignments include development and application of methods for large-scale analysis of microscopy images using AI / Machine Learning within the framework of the HASTE project. The project focuses on AI / machine learning with quantifiable confidence or probability, based on methods such as Active Learning, Conformal Prediction, Probabilistic (Venn) Prediction, and Deep Learning. Applicants are expected to collaborate with other project members and participate in regular research visits with industry partners AstraZeneca and Vironova.

Qualifications

PhD degree or a foreign degree equivalent to a PhD degree in a relevant field. The PhD degree must have been obtained no more than three years prior to the application deadline. The three year period can be extended due to circumstances such as sick leave, parental leave, duties in labour unions, etc. Documented experience with AI / ML methods and / or computerized image analysis. Experience in programming in eg Python is a requirement. Applicants should have excellent communication skills and be keen to actively interact with other team members including biologists, systems developers and researchers in AI / ML. Furthermore, applicants should be curious and creative, take initiatives and build relationships. Applicants should have good organizational ability, be able to structure work with multiple projects and solve anticipated and unexpected problems. The applicant must be able to express themselves very well in written and oral English

Apply via link at the bottom of the University application: https://www.uu.se/en/about-uu/join-us/details/?positionId=327845 (Please note: You MUST apply to the position via the form at Uppsala University, do not send any application documents to Ola Spjuth by email.)

If you have any questions regarding the project, please contact group leader Ola Spjuth.

Deadline to apply: May 7th, 2020

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HASTE group at the conference: Phenotypic Screening, High-Content Analysis and AI: Overcoming the Challenges

Many in the HASTE team visited the conference: Phenotypic Screening, High-Content Analysis and AI: Overcoming the Challenges that was held March 2-3rd at AstraZeneca, Gothenburg, Sweden.

Ola Spjuth shared the session AI and Machine Learning where Carolina Wählby (PI of HASTE) and Phil Harrison (PhD student in HASTE) gave presentations.

Phil Harrison (PhD student in HASTE) presenting his accepted talk: Deep learning models for RNA- based drug delivery using time-lapse microscopy image data. This project is now published as a preprint:
P. Harrison, H. Wieslander, A. Sabirsh,  J. Karlsson, V. Malmsjö, A. Hellander, C. Wählby,  
O. Spjuth. Deep learning models for lipid-nanoparticle-based drug delivery.
BioRxiv 2020. DOI: https://doi.org/10.1101/2020.04.06.027672
Prof. Carolina Wählby (PI of HASTE) and Phil Harrison (PhD student in HASTE) were both part of the Expert Panel discussing “AI and Machine learning in Phenotypic Screening”.
Prof. Carolina Wählby (PI of HASTE) and Dr. Alan Shabirsh (HASTE industry partner at AstraZeneca) were both part of the Expert Panel discussing “The future of phenotypic profiling”.

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Phil Harrison joins the HASTE team to work on predictive modeling with confidence

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.