Previous seminars
Here you will find previously held COMPUTE seminars.
2 December 2024
Ruth Pöttgen, Particle and nuclear physics, Lund University: "Light dark Matter and its footprint"
15:00-15:15 Tea/coffee
15:15-16:00 Seminar
MH:309A, Mathematikcentrum
Fika will be served from 15:00 for those who registered. The seminar can be attended without registration.
Abstract
Observations tell us that dark matter makes up a large fraction of the material within galaxies. In this COMPUTE seminar Ruth Pöttgen will describe how her research is about forming a better understanding of this material. Her main research activity at the moment is to develop, in an international collaboration, a new experiment called LDMX — the Light Dark Matter eXperiment.
Pöttgen’s main focus within the LDMX collaboration are simulation studies of different detector performance aspects, background rejection capabilities and the sensitivity to dark matter signals. In this, Ruth Pöttgen and collaborators also explore the potential benefit of using different machine learning techniques, especially for cases where more than one electron hit the target at the same time. Moreover, they are starting to think ahead and develop data-driven methods to reduce their reliance on simulations and be ready when LDMX starts taking data in a few years.
16 January 2024
Andrea Beck, Stuttgart: Scientific Machine Learning for Flow Simulations – Successes, failures and a way ahead
10:30-11:30 in MH:Riesz
Fika will be served from 10:00 for those who registered. The seminar can be attended without registration.
Description
Andrea Beck is a full professor for Numerical Methods in Fluid Mechanics and deputy director of the institute for aerodynamics and gas dynamics at the University of Stuttgart, Germany.
In this talk, I will give an overview of scientific machine learning applied to fluid mechanics, in particular to the numerical simulation of flows and flow-related problems. My main focus will be the combination of scientific computing and machine learning, combining mathematical models with data-driven decision making and high performance computing. I will show some successful and also not so successful examples from my own journey into augmenting PDE solvers with ML models - from turbulence simulation to shock capturing and flow control. Along the way, I will discuss lessons learned and conclude by providing my perspective on the still missing ingredients towards true scientific machine learning for flow simulations.
30 November 2022
Göran Johansson, Chalmers: Quantum Computing - Hardware development and the search for use-cases in Sweden
Abstract
In this talk, I’ll give a brief overview of the ongoing efforts to build a superconducting quantum computer in the Wallenberg Center for Quantum Technology (WACQT). Together with our industry partners, we are also investigating possible use-cases and I will briefly describe this activity as well. In particular I will mention logistics optimisation, quantum chemistry and protein folding.
15 February 2022
Frank Krauss: Introducing JUNE - an open-source epidemiological simulation
Abstract
We constructed a detailed digital twin of the UK population, with supreme social and geographical granularity, representing 55 million residents in England and Wales and tracing their daily movements and activities. The infection is propagated through the virtual population through simulated social contacts, and the progress of the disease in infected individuals and their trajectory through the healthcare system is then simulated, based on public health data. We resolve the spatio-temporal development of the disease spread through the population in great detail and are able to find non-trivial correlations of infection and fatality rares with a variety of societal factors. Our model has been proven in the midst of the current crisis and is currently being used by NHS-England’s COVID-19 response team to inform their strategic and operational planning.
Speaker presentation
1 December 2021
Pauline Kergus - Physics-informed learning for identification of a residential building's thermal behavior
This was a joint AI Lund/COMPUTE seminar.
Speaker presentation
Pauline Kergus uses artificial intelligence to model the thermal behavior of buildings. As space heating represents a large share of total energy use, thermal networks, i.e. district cooling or heating networks, would be able to increase the efficiency of the energy system in an economic way. Thanks to the natural inertia of heat exchanges, these networks can offer flexibility. In order to explore this feature, it is important to model building's thermal behavior in order to enable the use of demand-side management control strategies. Pauline's research focuses on building such models through a physics-informed learning based approach, taking advantage of the available measurements. The goal is to use the obtained models to control power consumption and to build predictive models for production planning.
More information about Pauline Kergus at the website of Automatic Control
16 November 2021
David Sumpter: Why complexity science failed and what we can do to save it
Speaker presentation
David Sumpter is a professor of applied mathematics at Uppsala University. He studies the mathematics of collective animal behaviour, social dynamics and mathematical biology. David is the author of Soccermatics and Outnumbered, which have been translated into ten languages, and Collective Animal Behaviour, the leading text in the academic field he helped create. He has worked with a number of the world's biggest football clubs, advising on analytics,
21 October 2021
Fabian Theis: Single-cell latent space learning across labs, modalities and perturbations
This seminar was part of the Swedish Bioinformatics Workshop.
Speaker presentation
Fabian Theis uses artificial intelligence to unlock the secrets of human cells. Single cell sequencing combined with deep learning enables him to analyse and model differences between cells. Fabian is the director of the Institute of Computational Biology at the Helmholtz Center Munich and scientific director of the Helmholtz Artificial Intelligence Cooperation Unit (HelmholtzAI) which was launched in 2019. He is a full professor at the Technical University of Munich, holding the chair ‘Mathematical Modelling of Biological Systems’, associate faculty at the Wellcome Trust Sanger Institute as well as adjunct faculty at the Northwestern University.
20 October 2021
Oliver Stegle: From genotype to phenotype with single-cell resolution
This seminar was part of the Swedish Bioinformatics Workshop.
Speaker presentation
Oliver Stegle is the Head of the Computational Genomics and Systems Genetics Division at the German Cancer Research Center (DKFZ) and group leader at EMBL in Heidelberg, Germany. He uses computational methods to unravel the genotype–phenotype map on a genome-wide scale.
His research team carries out work at the interface of statistical inference, machine learning and computational biology, pioneering computational methods for integrating large and heterogeneous datasets across individual and at the single-cell level.
19 October 2021
From Robots to Rust: Artificial Intelligence and Sustainable Development
Abstract
AI is an innovative technology that will greatly influence the world's development. But how can we balance its benefits and dark sides to support sustainable development? AI is more than a model taking decisions, it includes a long chain of people, tasks and resources we must consider. As part of LU's Future Week, COMPUTE, AI Lund and the Agenda 2030 Graduate school will host a public seminar at which we will discuss the relation between sustainability and artificial intelligence. We will show examples of how AI can be a tool for sustainability projects and also discuss the importance and potential ways of making AI-based research and supercomputing more sustainable. We invite users, developers and other computing experts to discuss with us.
Programme
- 17:00-17:20: "AI as a tool and target for sustainability projects"
Sonja Aits, Associate Senior Lecturer, Faculty of Medicine, Lund University - 17:20-17:30: "Working with sustainability in supercomputing facilities"
Anders Follin, Technical Director, LUNARC, Lund University - 17:30-18:00: Panel discussion: "AI and sustainability - challenges and opportunities"
Panel members: Sonja Aits, Anders Follin, Erik Wilson (Project Manager, AI Sweden), Markus Grillitsch (Director of CIRCLE, Lund University)
Moderator: Juan Ocampo, PhD student, Agenda 2030 Graduate School
14 April 2021
Jan Malmgreen: Veberöd - the research village for a digital and sustainable society
This was a joint AI Lund/COMPUTE seminar.
Abstract
Veberöd and it surrounding villages are Sweden's first test bed for smart villages ("Smarta Byar"). This allows researchers, students, companies and other partners to conduct projects that can contribute to a sustainable and digital development of the country side.
Projects can make use of the 3D model of Veberöd, to which live data can be connected, and a platform for communication with the residents. There are also opportunities to test IoT solutions and collaborate with local companies.
Link
Home page of Smarta Byar (in Swedish).
7 April 2021
Mattias Ohlsson: Information-driven care the CAISR Health initiative
This was a joint AI Lund/COMPUTE seminar.
Abstract
The availability of data is changing rapidly in healthcare. Information-driven care will make use of all this data, together with data analytics and machine learning, with the aim improve the healthcare system. The CAISR Health research profile at Halmstad University will focus on information-driven care, understanding the whole chain from formulating and prioritising questions, to algorithms, to data collection, to engagement, to explainability, and to implementation.
Mattias Ohlsson will provide an overview of CAISR Health and briefly present some applications.
22 March 2021
Sarah Gibson - The Turing Way: Reproducible research and beyond!
Abstract
Reproducible research is necessary to ensure that scientific work can be trusted. Funders and stakeholders are beginning to require that publications and research outreach include access to the underlying data and analysis code. The goal is to ensure that all results can be independently verified and built upon in future work. This is sometimes easier said than done! Sharing these research outputs means understanding data management, library sciences, software development, and continuous integration techniques. The Turing Way is a handbook to support research professionals, stakeholders, funders, students and their supervisors in ensuring that reproducible research is "too easy not to do". It includes training material on version control, analysis testing, and open and transparent communication with future users. Beyond just the reproducibility aspect of research, The Turing Way has expanded into a series of volumes sharing insights on building data science projects that are open, collaborative, inclusive and ethical. This project is openly developed and any and all questions, comments and recommendations are welcome at our GitHub repository: https://github.com/alan-turing-institute/the-turing-way.
3 March 2021
Eran Elhaik (Molecular Cell Biology, Lund University): Answering the big WHEN and WHERE questions in paleogenomics with machine learning
This was a joint AI Lund/COMPUTE seminar.
Abstract
Ancient DNA (aDNA) has changed the studies of history, enabling us to directly analyse early genetic variation. In recent years, there has been a sharp increase in the amount of collected aDNA and high-profile studies, but insufficient information about timing and geographical origin has limited the usefulness of the collected data and resulted in many erroneous reports. To correctly interpret paleogenomic data, it is important to answer two basic questions: WHEN and WHERE does the genetic material come from?
The WHEN question is usually answered with radiometric dating (RD). But, half of all published aDNAs are missing reliable and accurate RD.
The WHEN question is usually answered by assuming that the burial site is the same as the original habitat. All in all, this results in many vague and contradictory reports and erroneous conclusions.
These difficulties hinder studies of the origins of humans and domesticated animals.
We developed a machine learning-based approach to overcome these problems. In this talk, I will share the origin of Eurasian populations in minute details as emerged from our work.